Arduino Whatsapp Notification

Security And Privacy

Gabriel K. Gegenhuber1,2, Philipp ´E. Frenzel3, Maximilian G¨unther1, Johanna Ullrich1, and Aljosha Judmayer1 Abstract—WhatsApp, with 3.5 billion active accounts as of early 2025, is the world’s largest instant messaging platform.

Given its massive user base, WhatsApp plays a critical role in global communication. To initiate conversations, users must first discover whether their contacts are registered on the platform. This is achieved by querying WhatsApp’s servers with mobile phone numbers extracted from the user’s address book (if they allowed access).

This architecture inherently enables phone number enumeration, as the service must allow legitimate users to query contact availability. While rate limiting is a standard defense against abuse, we revisit the problem and show that WhatsApp remains highly vulnerable to enumeration at scale. In our study, we were able to probe over a hundred million phone numbers per hour without encountering blocking or effective rate limiting.

Our findings demonstrate not only the persistence but the severity of this vulnerability. We further show that nearly half of the phone numbers disclosed in the 2021 Facebook data leak are still active on WhatsApp, underlining the enduring risks associated with such exposures. Moreover, we were able to perform a census of WhatsApp users, providing a glimpse on the macroscopic insights a large messaging service is able to generate even though the messages themselves are end-to-end encrypted. Using the gathered data, we also discovered the re- use of certain X25519 keys across different devices and phone numbers, indicating either insecure (custom) implementations, or fraudulent activity.

In this updated version of the paper, we also provide insights into the collaborative remediation process through which we confirmed that the underlying rate-limiting issue had been resolved.

I. Introduction

WhatsApp is the world’s largest instant messaging service with 3.5 B (billion) active accounts as of writing and plays a critical role in global communication. Holding this unique position entails a significant responsibility to safeguard its users. WhatsApp has improved security and privacy measures over the years, most notably the deployment of end-to-end encryption (E2EE) . With E2EE, messaging service op- erators can no longer access the content of user messages.

Still, the possibility to construct social graphs of their users by observing message patterns is ingrained in prevalent E2EE designs . In addition to the threat posed by curious or compromised service operators, malicious users also represent a significant risk, as they may attempt to extract information about other account holders.

WhatsApp maintains a registry of its users linked to their phone numbers to support seamless contact discovery. When users install the app, they can choose to give permission to access their local address book and upload it to WhatApp’s servers. In return, the user is informed which contacts are reg- istered on the platform. While this mechanism was designed for convenience and represented a key driver of WhatsApp in achieving widespread adoption, it can be misused to check whether a specific individual — such as a government official, former partner, or employer — is registered on WhatsApp, thereby making presence information available if you know their phone number. This basic functionality has previously been exploited at scale. In 2012, Schrittwieser et al. enu- merated 10 M (million) phone numbers and found 21,095 ac- tive WhatsApp accounts in less than 2.5 hours. Nearly a decade later, Hagen et al. expanded on this by probing 50.5 M numbers — 10% of all US phone numbers — discovering 5 M active accounts over 34 days. Privacy-preserving approaches for contact discovery have been developed, such as private set intersection (PSI) or based on trusted hardware , but their deployment is non-trivial given necessary set sizes and practical constraints .

Knowing whether a specific (mobile) phone number is linked to a messaging app is highly sensitive, especially when that number is tied to a known individual. In regions where certain messaging apps are banned (e.g., China or Myanmar), such information could carry serious consequences for the user. Moreover, large-scale databases of registered phone numbers can be misused by attackers. Since a registered number typically indicates an active device, these lists are a reliable basis for spam, phishing, or robocall attacks. This naturally raises the question of how long information, once collected, stays valid and thus can be (ab)used by criminals.

This paper reassesses the topic of phone number enumera- tion in WhatsApp, analyzes the extent of information revealed by WhatsApp to malicious users, as well as investigates the Network and Distributed System Security (NDSS) Symposium 2026

Ios

Fig. 1. WhatsApp users wrt. to continent, Android vs. iOS use, and profile picture for 3.5 B users. permanence of once-released data, and concludes that this threat is not only still present but significantly worse than expected. We developed a novel method to quickly gener- ate candidate datasets of potentially active phone numbers for 245 countries. To evaluate our approach, we leveraged available open-source WhatsApp clients to query candidate phone numbers and verify whether they correspond to active WhatsApp accounts. The combination of automatic phone number generation and direct access via a reverse-engineered WhatsApp API — instead of the app’s user interface or an automation thereof as done by previous work — enabled exhaustive enumeration of its user base, thereby revealing phone numbers associated with a substantial share of the global population. Additionally, direct access to WhatsApp’s XMPP API allows extraction of more details on its user population. Following the rationale of Hagen et al. , we argue that the only reliable way to assess the feasibility and effectiveness of phone number enumeration attacks on instant messaging platforms is through empirical evaluation — “the only way to reliably estimate the success of our attacks in the real world” .

As we assumed that our query attempts, always originating from the same IP address and the same five accounts, will get rate-limited and blocked swiftly, we initially focused our evaluation on the US to measure how many of the phone numbers leaked in 2019 are still active. To our surprise, neither our IP address nor our accounts have been blocked by WhatsApp1. Moreover, we did not experience any prohibitive rate-limiting. With our query rate of 7,000 phone numbers per second (and session), we could confirm 3.5 B phone numbers registered on WhatsApp (exceeding the “more than 2 billion people” officially stated by WhatsApp ). Beyond the existence of the phone number and its associated device 1Apart from one time-limited ban induced by an error on our side.

list, we were also able to obtain data for each discovered account consisting of: Public keys for E2EE encryption, meta information such as timestamps2 and, if available and shared by the user, profile picture, about text and business account information3.

While most data points may seem harmless, especially in isolation, their large-scale aggregation can provide meaningful and potentially revealing insights. The sheer quantity of data points allows us not only to perform a detailed census of the WhatsApp user population but also to provide a glimpse of the interesting macroscopic insights a large-scale instant messaging service is able to obtain, even without having access to message content. For example, Figure 1 shows the share of Android vs. iOS devices per continent. The figure shows the higher prevalence of iOS in economically stronger regions and diverging shares of available profile pictures, indicating regional and cultural differences regarding privacy norms.

Comparing our retrieved dataset of 3.5 B records (i.e., active accounts) to existing scraped datasets, it surpasses the dataset of the 2021 Facebook scraping incident including around 500 M records by a factor of five4. The fact that we could obtain this data unhindered allows for the possibility that others may have already done so as well. Beyond this, our

Contributions Are As Follows:

• We developed a method to generate plausible mobile phone numbers for 245 countries. Using this method, we can shrink the candidate set of potentially assigned mobile phone numbers worldwide to 63 B.

• With 3.5 B records (i.e., active accounts), we analyze a dataset that would, to our knowledge, classify as the largest data leak in history, had it not been collated as part of a responsibly-conducted research study. The dataset contains phone numbers, timestamps, about text, profile pictures, and public keys for E2EE encryption, and its release would entail adverse implications to the included users.

• We compare our dataset with the publicly available data of the 2021 Facebook data scraping incident, with 500 M entries. Half of them are still relevant and active, even six years after the original scraping. This highlights the long-lasting consequences of data incidents.

• For ethical reasons, we refrain from analyzing individual users. Instead, when analyzing this dataset, we perform a census on the WhatsApp population to show the wealth of possibilities. Among others, we provide macroscopic insights on WhatsApp accounts per country and capita, growth and churn, the availability of about text, profile picture, and companion devices, operating system (OS) shares (e.g., Android vs. iOS), account activeness, device age, and popularity. The census also emphasizes the far- reaching insight a messaging platform like WhatsApp still has despite E2EE.

2Timestamps for key updates, profile picture, and about text. 3See Section III for detailed list of the retrieved data. 4As a point of reference in terms of size, the currently largest data breach happened 2013 at Yahoo and affected around 3 B accounts.

2

• We highlight the potential consequences of phone number enumeration for users in countries where WhatsApp is banned, such as China, Myanmar and North Korea, by identifying active numbers in those countries. Coinciden- tally, we were also able to show that even before lifting the ban on WhatsApp in Iran, a significant portion of active accounts had already existed, indicating that the ban was ineffective.

• To the best of our knowledge, the dataset is also one of the largest of cryptographic (X25519) public keys generated and deployed in the field. By analyzing these keys, we were able to detect the re-occurrence of public keys across different accounts, We discovered extensive re-use (multiple hundred times) of certain public keys, as well as the repeated occurrence of one-time prekeys across different devices, both not easily explained with benign uses-cases, indicating problems in custom implementa- tions or fraudulent activity. As an example, 20 phone numbers (mainly in the US) use an all-zero private key, suggesting a broken pseudo-random number generator in this case or the use of non-standard software.

The remainder of the paper is organized as follows: Sec- tion II presents our phone number enumeration methodol- ogy, which was developed from an initial exploration with limited scope into a large-scale measurement with global coverage, and its application to the WhatsApp messaging platform, eventually collecting a dataset of 3.5 B accounts.

Section III evaluates the collected dataset to gain insight on the WhatsApp population, among others, the deployment in different countries, including those in which WhatsApp is banned, the availability of about text and profile pictures, and account churn. Section IV and Section V evaluate the E2EE keying material (i.e., public keys and key IDs) of the 3.5 B accounts, revealing further insights, including OS shares, device age, account activeness and key reusage. Comparing the collected dataset from WhatsApp with the 2021 Facebook scraping incident, Section VI clarifies how long information once leaked remains valid and could be abused by adversaries.

Section VII elaborates related work, Section VIII discusses our results, and Section IX concludes. Finally, we consider ethics and responsible disclosure.

Ii. Testing Methodology And Implementation

Reassessing the feasibility of phone number enumeration, we started with a careful initial exploration. Contrary to our expectations, we did not encounter prohibitive rate-limiting or any contact attempts by the platform operator. Consequently, we further developed our method for global coverage. Using WhatsApp’s XMPP APIs, we extracted about text and profile picture, when allowed by privacy controls, as well as pub- lic keys and timestamps used for E2EE for all discovered WhatsApp users. The measurements were conducted in mul- tiple rounds between the middle of December 2024 and the middle of April 2025. We wish to highlight that we made no effort to disguise our activity — all queries were sent from

Ts Timestamp

† Approx. queried accounts per second.

Experienced In Measurements).

details on our methodology and implementation.

A. Initial Exploration

Our initial exploration aimed to understand WhatsApp end- points for potential data collection and executed small-scale experiments on user enumeration with astonishing results.

Api-Level Whatsapp Access. Previous Work , Used

the official WhatsApp client for phone number enumeration and were consequently limited to its regular user interface. In contrast, this work leverages unofficial implementations to directly query WhatsApp’s XMPP endpoints, see Table I, returning more detailed information. For most of the crawling, we based our work on whatsmeow5, a community-driven open-source client that is based on the findings of reverse engineering WhatsApp’s official web client.

Our custom implementation allows querying whether a phone number is registered on WhatsApp. It is also possible to retrieve registered numbers’ about text and profile picture, if available, and a list of companion devices (i.e., secondary devices like tablets or WhatsApp Web registered with the same account). For all registered devices, it is possible to query the public keys used for E2EE. Batching, i.e., cumulating multiple accounts, is possible for most queries, significantly reducing their overall number. The client requires an authenticated session, which can be established by scanning a QR code to pair with an active WhatsApp account. A maximum of five concurrent sessions are allowed per WhatsApp account.

Exploratory Input and Setup. Anticipating rate limits and other protection mechanisms from WhatsApp, we began with smaller measurements, extending them in successive steps.

Initially, we enumerated all valid US phone numbers from the 2021 Facebook data leak and finished in less than an hour. Then, we enumerated all valid phone numbers in this dataset in less than a day. For phone number validation and country assignment, we relied on Google’s widely used open- source library libephonenumber. For data retrieval, we used concurrent sessions, and batched 50K accounts in one query to avoid overstressing WhatsApp’s infrastructure. Throughout

Table Ii

libphonegen YIELDS 63.17 B PHONE NUMBERS FOR ENUMERATION.

Required Post-Processing (Pp).

address for the server’s IP address. In summary, it was not only possible to enumerate all 488M phone numbers from the Facebook data leak, but it was also possible at high speed. We could infer their registration and current about text, profile picture, companion devices, and E2EE-related public keys. Table I provides the number of accounts enumerated per second. We encountered no rate limits, our accounts were not banned from the platform, and of the phone numbers included in the 2021 Facebook data leak, we refer to Section VI.

B. Global Phone Number Generation

Given the possibility of phone number enumeration, the achieved rate, and the lack of any response from WhatsApp, the idea of systematically enumerating phone numbers world- wide arose after a week of experimentation. However, the E.164 phone number format allows up to 15 digits, rendering exhaustive enumeration — even at the fastest rates — infeasible.

libphonegen. For search-space reduction, we have thus ex-

Plored And Compared Different Approaches:

• The International Telecommunication Union (ITU) de- fined numbering plans for its member countries . While significantly reducing the search space (e.g., by reducing the number of digits for individual countries), the plans primarily document allowed allocations rather than actual assignments. In practice, utilizing these plans to generate phone numbers could trigger alerts at the targeted service, as many of these numbers are unused.

Furthermore, the plans are published in non-uniform

Document Formats (Ms Word, Pdf), Requiring Manual

extraction of relevant information for each country. • By scraping public services, such as online phone books, or leaked data, similar to the rockyou.txt dictionary for passwords , a phone number ’hitlist’ could be gener- ated. On the positive side (for an attacker), these sources or employers, that could be linked with the discovered WhatsApp accounts. On the negative side, a hitlist only covers a share of the active accounts.

• Google’s libphonenumber6 is an actively maintained li- brary that is widely used for parsing, formatting, and validating phone numbers. This ensures an up-to-date view of valid phone numbers. It also provides additional metadata (e.g., operator, or landline vs. mobile number) that is helpful for the validation (or generation) of input sets.

Eventually, we developed libphonegen, a phone number generator leveraging the existing libphonenumber’s national number format data, and generated 646 B mobile phone num- bers for the 245 ISO 3166-1 countries . Table II shows all countries with more than 1 B candidate numbers.

Post-Processing (AT, IN). As apparent in Table II, Austria (AT) and Indonesia (ID) have the highest numbers of phone numbers, accounting together for 93% of the total number space. In contrast to most countries, Austria does not specify a fixed amount of numbers for mobile networks. Anything between 7 and 13 digits is possible, significantly increasing the search space. However, only specific number blocks and shorter numbers are used in practice. To refine our candidate set, we applied a hitlist-based approach to identify popular number ranges, reducing the respective amount to 480 M num- bers. For Indonesia, we reduced the search space to numbers with specific operator prefixes found on Wikipedia and posts on Indonesian web forums, effectively yielding 4.44 B phone numbers in total for enumeration. We enumerated the complete space for the other countries as proposed by libphonenumber.

Post-Processing (BR, MX). Our measurements revealed an unexpectedly low number of accounts per capita in Brazil (BR) and Mexico (MX). We found that both countries had recently modified their mobile phone number formats and libphonenumber no longer recognizes the old formats. Yet, it appears that many WhatsApp accounts are still registered with these legacy numbers and remain discoverable only when querying this old format . Consequently, we enhanced our input dataset by these numbers, see again Table II.

C. Measurement With Global Coverage

After phone number generation with libphonegen and post-processing for four countries, we ended up with 63 B phone numbers for 245 countries and enumerated them on WhatsApp. The measurement setup was equivalent to that in the exploratory phase, i.e., a single server limited to five concurrent sessions and batching 50K accounts in one query. In a first round, we generally checked for a number’s registration at WhatsApp using GetDeviceList, see Table I for an overview of the different endpoints. In a second round, we retrieved account information such as current public about text and profile picture using GetUserInfo, and in a final round, the associated public keys, key IDs and timestamps using

Whatsapp Adoption By Continent

Fig. 2. WhatsApp adoption per continent. Percentage shares are calculated by dividing the number of discovered WhatsApp accounts by the respective population size (per capita) of each continent.

GetPrekeys. The measurement rounds were conducted between mid-December 2024 and mid-April 2025. Limitations. Due to data collection in multiple rounds, the resulting dataset has sporadic blind spots, as accounts might have been deleted meanwhile and it was not possible any- more to request additional data for them. We discovered 3,456,622,387 unique phone numbers (accounts) across 235 countries in the first round, but could not collect metadata, such as profile about text or keying materials, for all of them in the later rounds. Additionally, we conducted complementary measurements to investigate WhatsApp’s account growth and churn rates, see the following paragraphs for more details.

Including also these measurements, we discovered a total of 3,546,479,731 accounts (i.e., distinct phone numbers).

And

Churn. WhatsApp’s user base is dynamic, with new accounts joining the platform and old ones being deleted. To measure the extent of account growth and churn, we repeated the first step of account discovery for four countries, namely Belgium (BE, 4x), India (IN, 3x), Iran (IR, 5x), and the US (3x).

Complementary Measurement: Profile Pictures. We relied on WhatsApp’s XMPP protocol for data collection in our measurements. However, this only provides the URL of a profile picture. The actual picture needs to be fetched out-of- band via HTTP(S) from the domain pps.whatsapp.net, resolving into IP addresses of Meta’s CDN. We collected the picture URLs for all available accounts, but restricted the actual image downloads to phone numbers within the +1 country code range. This allows us to assess the general feasibility of crawling WhatsApp profile pictures at scale and analyzing the extent of available profile pictures. For the download, we used 1,000 parallel threads on our server.

Testing for rate limits, we also experimented with more worker threads (up to 10,000), and WhatsApp’s servers did not prevent us from collecting pictures.

Iii. Results - Users And Devices

In this section, we evaluate the dataset of 3.5 B accounts collected in the first and second rounds of our measurements, i.e., accessing the endpoints GetDeviceList and GetUserInfo.

This includes the device indexes of main and companion de- vices, public profile picture, about tag line, related timestamps, and business profile information. We refer to Section IV for an analysis that also includes the keying material used for E2EE.

100 %

Fig. 3. WhatsApp Use per Capita: At 95 % in South America and 80 % in Europe, a majority of citizens have an active WhatsApp account. WhatsApp Accounts per Country. Table III provides an overview of the collected data with detailed results for the ten countries with the most accounts. A more verbose version is shown in Table X in the Appendix; the complete country overview can be inspected online7. India has the largest user population and accounts for 21% of all WhatsApp accounts.

More than 57 % of world users have a public profile picture, with regional differences from 36 % in the Philippines to 80% or more in some Western African countries (e.g., Mali, Burkina Faso, Guinea-Bissau). Similarly, we were able to retrieve the about text from at least 29 % of global users, with numbers ranging from 6 % in Algeria and Philippines to more than 45 % in several European countries (e.g., the Netherlands, Sweden, Italy, Finland and UK). The highest regional differences were discovered for the share of business accounts. Globally, about 9 % are marked as business accounts.

While in some countries, this feature seems to be active for a considerable share of regular user accounts (e.g., roughly a third of all active accounts in various African countries), other regions show more authentic values (e.g., 2 % in the US or Germany). A user can be marked as a business simply by setting up their account via the WhatsApp Business appli- cation8, as there is no formal verification or entry barrier9.

Importantly, using the business app may impact a user’s privacy, as the account’s verified name, and optional attributes such as location (latitude/longitude), website, or description are always publicly visible.

WhatsApp Accounts per Capita. We calculated the number of active WhatsApp accounts per population for ISO 3166 countries using population data from the United Nations , see Figure 3. Monaco ranks first with an exceptional 480 %, i.e., 4.8 active WhatsApp accounts per resident. The United

7Https://Github.Com/Sbaresearch/Whatsapp-Census

8https://play.google.com/store/apps/details?id=com.whatsapp.w4b 9All accounts on the business app are marked as business accounts; only verified ones receive an official checkmark.

Top 10 Countries Ranked By Number Of Whatsapp Accounts. ¸

ANDROID, IOS, PICTURE, ABOUT TEXT, BUSINESS, AND COMPANIONS REFER TO THEIR SHARE IN THE RESPECTIVE COUNTRY.

Users In Iran (Population 90.1 M) Despite Whatsapp Ban.

Arab Emirates follows with 176 %, aligning with market statis- tics and media reports . Our findings also reveal that WhatsApp is extremely popular in Latin America, with rates exceeding 98% In contrast, we identified several countries with low market penetration — Ethiopia (4 %), South Korea (4 %), and Japan (2 %). The latter numbers are consistent with the presence of dominant local competitors — KakaoTalk in South Korea and Line in Japan — that are preferred over WhatsApp. We provide further visualization per continent in Figure 2.

Countries Banning Whatsapp. As Of December 2024,

WhatsApp was officially banned in China, Iran, Myanmar, and North Korea. Despite these restrictions, we found ac- tive accounts in these countries, for example, 2.3 M Chinese phone numbers. While this represents only a small fraction of China’s total population of 1.4 B, , media reports indicate that the use of WhatsApp in China may involve considerable personal risk , . Similarly, we found 1.6 M accounts in Myanmar, equivalent to 3 % of its population.

In Iran, we identified 59 M active accounts corresponding to two-thirds of its population (90.10 M). This substantial share aligns with previous statements from Will Cathcart , the current head of WhatsApp. Coincidentally, the ban was officially lifted shortly after our first measurements , and we repeatedly queried the country’s number range to observe the development of WhatsApp in the country, see Table IV for our results. In addition to a steady increase in registered phone numbers, the proportion of users with companion sessions (e.g., WhatsApp Web) tripled in the months after the ban was lifted. The initially low share of companion devices may indicate that circumvention methods were primarily available on mobile devices, or that users avoided pairing desktop devices in public or work environments.

Lastly, a total of five active numbers were found within North Korea’s number range. About Text. While many accounts retain one of the default about tag lines provided by WhatsApp (e.g., “Hey there! I am using WhatsApp”), others publicly reveal sensitive or personal information in their custom text. For example, some users disclose their political ideology “Make Amer- ica Great Again”, indicate their sexual identity or orien- tation “LGBTQIA+”, express religious beliefs by quoting verses from the Bible or Quran, or even reference drug use “Hey there! I am using cocaine”. We found hints of criminal activity, including drug dealers listing current of- ferings and prices, as well as an actual business account named “SECRET TAXI” advertising drugs with a detailed list

Speed-Lsd-2Cb-3Mmc-4Mmc/Memphedron”.

In other cases, users shared links to accounts on external platforms (e.g., LinkedIn, Instagram, Tinder, OnlyFans), which could be used to correlate and enrich profiles with additional meta-information. Moreover, we found users posting their being part of governmental or military organizations (e.g., These practices significantly increase the risk of doxxing, identity exposure, and targeted attacks.

Profile Picture. Recent media reports revealed that high- ranking US government officials — including Pete Hegseth (Secretary of Defense), Mike Waltz (National Security Ad- viser), and Tulsi Gabbard (Director of National Intelligence) — were identified using their phone numbers as active on several messengers, including WhatsApp . According to the report, both Hegseth and Waltz had public profile pictures clearly showing their faces, which could be used to identify their accounts and correlate their phone number.

To demonstrate the scale and feasibility of this kind of

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R Random number. ∗Verified on Firefox, Chrome, Safari.

Init. Values For Key Ids As Shown By Gegenhuber Et Al. .

data extraction from the platform, we downloaded 77 M public profile pictures (overall 3.8 TB of data) for all accounts within the American +1 number range over a few hours. We then applied an automated face detection algorithm using OpenCV to a randomized sample (N = 500,000), finding that roughly two-thirds of the images (66 %) contain detectable human faces. In the hands of a malicious actor, this data could be used to construct a facial recognition–based lookup service — effectively a “reverse phone book” — where individuals and their related phone numbers and available metadata can be queried based on their face. Beyond facial features, additional elements captured in profile pictures, such as license plates, street signs, or recognizable landmarks, could enable more sophisticated profiling and leak a user’s identity, location, or daily environment.

Companion Usage. By leveraging the GetDeviceList end- point during account discovery, we were able to automat- ically identify users who utilize companion sessions (e.g., WhatsApp Web). Since companion devices are assigned auto- incrementing device indexes, this allows us to determine how many companion devices a user currently has and infer behavioral patterns. For instance, a stable set of device indexes (e.g., ) suggests a consistent setup, whereas a se-

Quence Such As May Indicate Frequent

use of ephemeral or short-lived sessions. The prevalence of companion usage varies significantly across countries. In some regions, such as the Netherlands or Israel, it is relatively common, with over 22 % of users having active companion sessions. In contrast, it is insignificant in others, with usage rates below 3 % in countries like Algeria, Congo, Iraq, Iran, Syria, Tanzania, and Yemen.

Besides the simple adoption rate of companion devices, we can also compare the longevity and stability of the sessions. For example, France and Indonesia both show a companion us- age rate of around 10 %, yet their average device indexes (i.e., the current auto-increment values) are 8 and 31, respectively.

A similar pattern emerges when comparing Germany and Colombia, with comparable companion adoption (13–14 %), but substantial differences in average auto-increment values (DE: 12, CO: 34). This suggests that Indonesian and Colom- bian users tend to create and discard companion sessions more frequently than their French and German counterparts.

Account Net Growth and Churn. Most of our crawling was conducted as a single-shot measurement, capturing a snapshot of the registered user base. We repeated our account discovery

4.34

† Normalized to average percentage rates for 30 days.

Net Growth And Churn Values For Different Target Countries.

measurements across multiple iterations for a small subset of selected target countries to gain insights into user dynamics over time. Table VI presents our longitudinal observations, reporting normalized net growth and user churn rates based on a 30-day average.

While all countries exhibited organic net user growth (rang- ing from 0 % to 1.5 % per month) across all longitudinal measurements, user churn (i.e., the share of users that leave the platform) varied significantly. For instance, Belgium main- tained a relatively stable user base, with losses of less than 1 % per month. India demonstrated a higher churn rate of 2 % per month. The highest churn rates were observed in the US, with peak losses exceeding 4 % of users in a single month.

Iv. Results - Prekey Bundle Analysis

In this section, we evaluate the collected dataset of 3.5 B accounts, considering the third round of our measurements via the GetPrekeys endpoints. The so-called prekey bundle is another data item retrievable for each device (both main and companion) regardless of any privacy settings. Therefore, the section starts with an overview of the data points available in WhatsApp’s prekey bundles and then provides various insights that can be drawn from the gathered data.

WhatsApp Public Key Types. WhatsApp adopted the Signal protocol to provide E2EE . The Signal protocol is a suite of related protocols – that have been the subject

Of Extensive Research Across Numerous Studies , –

. For detailed information, we refer to the respective specifications and papers. For this work, it is sufficient to know that the handshake protocol (X3DH ) requires three types of X25519 pub- lic keys for each communicating device (main as well as companion), namely the long-lived identity key, the medium- term signed prekey, and the ephemeral one-time prekey. The identity key is not supposed to change over the device’s

Ecdf

Fig. 4. The OS-specific characteristic initialization values (Table V) can be observed at scale. The empirical cumulative distribution functions (eCDFs) are left-skewed, reflecting the 0-based initialization patterns of the different IDs which are typical for the operating systems Android (0-initialized signed prekey ID) and iOS (0-initialized one-time prekey ID). The eCDF in the top figure corresponds to Android devices, which account for 81 % of the observed user base, while the bottom figure represents iOS devices, comprising the remaining 19 %. The spike in this figure is investigated in Section V.

lifetime, whereas a one-time prekey should be used only once, as the name suggests. The one-time prekey ensures perfect forward secrecy, also for the first messages when initiating a new conversation. Since instant messaging is asynchronous, recipients can be offline when a new communication session is initiated. Therefore, a stash of one-time prekeys is uploaded to the WhatsApp servers and replenished if needed. If no one- time prekeys are available, only identity and signed prekeys are used, which leads to imperfect forward secrecy of initial messages until the signed prekey is rotated. Therefore, these signed prekeys should be rotated periodically. Suggested inter- vals in the specifications range from a week to a month , , . WhatsApp has chosen an interval of 30 days.

OS Market Statistics. Previous work identified distinct initialization patterns in prekey IDs across different client implementations of WhatsApp, as summarized in Table V.

Building on this, we first verified these OS-specific patterns using a controlled set of test devices. We then leveraged the signed and one-time prekey IDs of bundles collected from the discovered accounts to estimate the global OS market share among WhatsApp users at scale.

Figure 4 presents the distributions of signed and one-time prekey IDs across all retrieved accounts, demonstrating that OS classification is feasible via both ID types. Our analysis across 3.5 B accounts reveals an Android share of 81 % and iOS at 19 %, as illustrated in the eCDFs in Figure 4. The numbers vary per continent, see Figure 1, and indicate higher prevalence for iOS in regions with higher purchasing power.

The information on the OS is available per phone number and could be abused by malicious actors for reconnaissance or targeted exploits for a specific OS. Apple users are often considered higher-value targets — both for commercial pro- filing and in the context of scams or ransomware — due to their alleged purchasing power.

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Fig. 5. Distribution of prekey bundle age across our retrieved data. Over 90 % of users have updated their keys within the past month. Consistent with WhatsApp’s account deletion policy, most accounts with keys older than 120 days have been automatically removed.

Verifying Account Activeness. We want to ensure that the retrieved phone numbers correspond with WhatsApp accounts that are actively used, rather than outdated or inactive ac- counts. Every prekey bundle retrieved via the GetPrekeys endpoint also contains an epoch timestamp updated every time the corresponding client device pushes new keys to the server.

The update occurs when the user updates i) their identity key (e.g., when migrating the account to a new smartphone), ii) their signed-prekey (automatically done once a month), or iii) their one-time prekey batch (necessary when the previous batch is depleted). Since the main device is expected to be consistently online, the timestamp of a prekey bundle for an active account should be younger than one month. Figure 5 shows a histogram of the bundle age of the retrieved main device prekeys. As anticipated, more than 90 % of accounts have a recent prekey bundle that was updated within the last month. Moreover, there is a noticeable dip in the histogram at 120 days, corresponding to WhatsApp’s deletion policy for inactive accounts10. Although only representing an insignifi- cant share in the overall distribution, some accounts appear to be not automatically removed after 120 days.

Estimating Device Age and Popularity. WhatsApp rotates each device’s signed prekey once per month, incrementing the signed prekey ID by one with each update. On Android, this ID starts at 0, allowing the approximation of a device’s age in months by examining its current value. Figure 6 presents a histogram of signed prekey IDs of Android devices across our dataset. Most devices appear to be relatively recent (roughly 80 % have been created within the last two years). A distinct bump occurs at 105 months. Subtracting this from the date of our measurements (February 2025) points to April 2016, the month in which WhatsApp introduced E2EE to their plat- form . We can only speculate about values above the 105- month marker. They either represent iOS devices (randomly choosing their signed prekey ID) or Android devices that

Ecdf

Fig. 6. On Android devices, auto-incrementing signed prekey IDs can be used to estimate device age.

have undergone atypical key rotations, potentially triggered by incorrect system clocks or security-related reinitialization routines.

Similarly, by analyzing the one-time prekey IDs on iOS devices, we can estimate how frequently they uploaded new prekey batches to the WhatsApp server. Since a prekey is consumed whenever a device initiates a conversation with a new contact, their depletion rate offers a valid approximation of a device’s popularity. Figure 11 in the Appendix illustrates the observed distribution across our dataset. Knowing the device age and popularity could be exploited to identify long- term or highly active users — individuals who may hold higher value from a marketing or targeting standpoint.

Identifying Longterm Users. Besides estimating a user’s device age using the prekey bundle, we can also investigate the overall account age by inspecting the timestamps when a user updated their profile picture or about text. Figures 7 and 8 illustrate the distribution of these timestamp values. These timestamps are upper and lower bounds for a user’s earliest and latest activity. Our large-scale data indicates that users update their profile pictures more frequently than their about messages. The noticeable spike in about status updates around November 2021 likely corresponds to WhatsApp’s revised privacy policy11, which followed a significant GDPR fine and received broad media attention as EU and UK users were notified about the change directly in the app.

V. Results - Public Keys Analysis

While the prekey bundle metadata consisting of timestamps and public key IDs was already analyzed in Section III, this section focuses on the actual X25519 public keys and their signatures required for the X3DH handshake protocol , which are also included in a prekey bundle: Namely the long-term identity key, the medium-term signed prekey, and an ephemeral one-time prekey. As a first step, we verified all Ed25519 signatures on the signed prekeys within these bundles to validate the integrity of the retrieved data. We encountered 11https://www.whatsapp.com/legal/privacy-policy-eea/revisions/20211122

Ecdf

Fig. 7. Distribution of profile picture timestamps. Most users have recently updated their picture.

Ecdf

Fig. 8. Distribution of about status update timestamps. About messages are updated less frequently on average compared to profile pictures. only two invalid signatures across the entire dataset, which supports a high level of confidence in the integrity of the collected data.

Missing One-time Prekeys. As mentioned in Section IV, the absence of a one-time prekey weakens perfect forward secrecy (PFS) for the initial messages in a new conversation. There- fore, we assessed the prevalence of prekey bundles without one-time prekey in our dataset. We found that 0.8 % of main devices and 13 % of companion devices did not have a one- time prekey at the scan time. One possible interpretation of this result is that companion devices, such as desktop computers, are more likely to be left turned off for a while. During this time, they are not able to replenish consumed one-time prekeys on WhatsApp servers. While main devices are smart phones that are usually always online. This finding illustrates that one-time prekey exhaustion is indeed common for companion devices in practice, even without targeted depletion attacks as described in .

Key Reusage. Ideally, no public key occurs in more than one device at the same time. Recurring public keys may indicate errors in the distribution of (one-time) prekeys, faulty implementations or configurations , or broken random number generators. To search for duplicate public keys, we

Top 10 Of Reoccurring Public Keys.

generated a Bloom filter containing all public keys to compare each public key against all other public keys in constant time and logged whether a public key was flagged as already being part of the filter. We over-provisioned the Bloom filter to minimize the theoretical possibility of false positives (false positive rate of 1e−12). Moreover, any public key flagged as being part of the filter at least twice in the log file can definitely be considered as re-used.

Using this method, we discovered 2.3 M distinct X25519 public keys that have been re-used in prekey bundles of over 2.9 M distinct devices. Table VII lists the public keys with the highest number of occurrences. As shown in the table — and further illustrated in Figure 15 — the most frequently reused public keys are predominantly associated with phone numbers from Myanmar and Nigeria. This indicates either a problem with a (custom) WhatsApp client used in these regions, or may point to fraudulent activity involving large-scale creation of accounts tied to different phone numbers.

Reusing public keys implies that the corresponding secret keys are also reused, which is particularly concerning when the devices involved belong to different entities. A compromised identity key effectively results in full account compromise with respect to the confidentiality of conversations. It enables an attacker to cryptographically sign updates to the device list and authorize the addition of new companion devices, thereby gaining the ability to intercept or participate in encrypted communications. We even identified cases in which all three public keys in a prekey bundle —namely, the identity key, signed prekey, and one-time prekey— collided with those of other devices, further highlighting the severity of key reuse.

Interestingly, key reuse is not evenly distributed across the different key types. The identity key is by far the most affected, with 2 M instances of reuse. This uneven distribution suggests that the majority of key reuses are unlikely to be caused by faulty random number generators — otherwise, we would expect similar reuse rates across all three key types.

Number Change. Despite specific keys being reused in high numbers, 97 % of distinct public keys have only been reused once or twice (see Figure 13 in the appendix). If only main devices are considered, 68 % of reuses affect identity keys, which suggests a usage pattern during regular operation as a root cause. Consequently, we looked closer at identity keys reused exactly twice in main devices. From these identity public keys re-uses, 96 % are attributed to two phone numbers within the same country, suggesting phone number change is a plausible explanation for the majority of cases. While this behavior does not constitute a security vulnerability per se, it can be a potential privacy issue, as the reuse of public keys enables linkage of users across phone number changes.

To investigate legitimate root causes for duplicate identity keys, we monitored the prekey bundle of our device while migrating a WhatsApp account to another phone number12.

We observed that both the identity and signed prekey remained unchanged throughout the migration. Additionally, we were able to reproduce scenarios in which previously installed keys — left behind after logging out of WhatsApp — were reused when setting up a new account with a different phone number on the same device. This behavior suggests poor practices in account initialization.

One-time Prekey Reuse. While the previous paragraph offers a plausible explanation for the reuse of identity keys, there is no reasonable explanation for the reuse of one-time prekeys, which, by definition, should never be reused — neither on the same device nor different devices. Their recurrence indicates a failure in the respective implementations. The top two entries in Table VII show public keys which also re-occur as one-time prekeys. Interestingly, public key number two (0df7...) only occurs in companion devices, but here as each type of public key. A possible explanation for this would be a faulty non-official implementation of the WhatsApp compan- ion protocol. Apart from these keys, we also discovered 2303 distinct one-time prekeys that are reused 3082 times. Manual inspection of some of these cases indicates that the respective phone numbers belong to the same entity, as they originate from the same country and share identical about tag lines or business names. Moreover, the ID value of the reused one- time prekeys also matches across different phone numbers.

This suggests that there is indeed a connection between the affected devices, while the root cause is still unclear. All Zero Private Key. Especially interesting are public keys which occur as different key types e.g., a identity key which 12This is supported via a built-in dialog in the official WhatsApp client.

Authors:

Peder EZ Larson 1, 2,* , Jenna ML Bernard1, James A Bankson 3, Nikolaj Bøgh 4, Robert A Bok1, Albert P. Chen 5, Charles H Cunningham 6,7, Jeremy Gordon1, Jan-Bernd Hövener 8, Christoffer Laustsen 4, Dirk Mayer 9,10, Mary A McLean11 12, Franz Schilling13, James Slater1, Jean-Luc Vanderheyden5, 14, Cornelius von Morze 15, Daniel B Vigneron1, 2, Duan Xu1, 2, and the HP 13C

94143, Usa.

Denmark. 5 GE Healthcare, Menlo Park, California, USA. 6 Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.

8 Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Medicine, Baltimore, MD, USA. Cambridge, United Kingdom.

14Jlvmi Consulting Llc, Dousman, Wi, Usa

#See Acknowledgements for a list of all HP 13C MRI Consensus Group Members This work was supported by the ISMRM Hyperpolarized Media MR Study Group, the ISMRM Hyperpolarization Methods & Equipment Study Group, and the Hyperpolarized MRI Technology Resource Center (NIH/NIBIB grant P41EB013598).

Abstract

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the “HP 13C MRI Consensus Group” as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.

Keywords: Hyperpolarized MRI, metabolic imaging, carbon-13, pyruvate, dissolution dynamic

Introduction

MRI with hyperpolarized 13C agents, also known as hyperpolarized (HP) 13C MRI, has shown great potential as a novel imaging modality, particularly for its ability to probe metabolic processes in real time. The first human studies with HP [1-13C]pyruvate were performed in 2011 in prostate cancer patients (1).

Since then, there have been over 60 papers published with imaging results of human subjects from 13 different sites, with applications including prostate cancer, brain tumors, breast cancer, kidney cancer, pancreatic cancer, metastatic disease, liver disease, ischemic heart disease, diabetes and cardiomyopathies. The vast majority of these studies used [1-13C]pyruvate (1–63), where [2-13C]pyruvate (64) and 13C-urea (56) have been demonstrated too.

As clinical HP 13C MRI advances, there is a growing need to build consensus for best practices, which are critical for comparing data across sites, performing multi-site trials,deploying methods to new sites, partnering with vendors, and potentially for obtaining broader regulatory approvals.

In March 2022, we initiated an effort to build consensus within the HP 13C MRI community with this opportunity in mind, and it was greeted with strong enthusiasm. The “HP 13C MRI Consensus Group”, containing over 55 members from 27 sites, identified the area of greatest need and opportunity for consensus building to be HP [1-13C]pyruvate human

●

Pyruvate is the most mature and widely used HP agent and has the most significant translational evidence emphasizing the potential clinical impact.

●

Clinical trials, particularly multi-site trials, have the strongest need for consensus methods to ensure that data can be combined across sites. This work is a Position Paper for which the goal is to describe current successful practices and study methods for HP [1-13C]pyruvate human studies along with justification to support those practices. This is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification (Fig. 1). The current successful practices and study methods include a literature review of published peer-reviewed journal papers showing human HP [1-13C]pyruvate study data, up to September 2022 (1–63), as well as new unpublished information from surveys of HP 13C study sites. Based on this information, we also highlight the evidence gaps, strengths, and limitations of current practices which are summarized at the end of each section.

Figure 1: Illustration of the HP 13C MRI human study process, including the 4 major areas covered in this paper: Hyperpolarized 13C-pyruvate preparation, MRI system setup and calibration, Acquisition and Reconstruction, and Data Analysis and Quantification.

Figure 2: Anatomical targets of HP [1-13C]pyruvate MRI human studies published up to September 2022.

Hyperpolarized 13C-Pyruvate Preparation

This section covers the processes for creating the HP agent, 13C pyruvate, and will include many aspects and considerations that are needed to safely and effectively prepare doses for metabolic imaging studies in human subjects. These include material, personnel, equipment and facility, fluid path preparation, quality control, and release.

It is helpful to understand that the specifications of a dose of 13C pyruvate suitable for in vivo MR HP metabolic imaging were shaped in part by early preclinical studies performed by GE HealthCare summarized in Ref. (65). In short, the safety of the two novel drug components, 13C pyruvate and the electron paramagnetic agent (EPA) AH111501, were demonstrated in those studies. The more precise formulation of the dose suitable for human use was then determined from clinical studies (66) that included two Phase 1 clinical trials in young and elderly healthy volunteers without hyperpolarization of the 13C nuclei and another Phase 1/2a dose escalation and imaging feasibility study with HP 13C pyruvate in 31 prostate cancer patients at the With the exception of the first HP 13C imaging clinical trial, which utilized a prototype device in a cleanroom (1), all HP 13C studies performed in humans to date have utilized the SPINlab polarizer (manufactured by GE HealthCare). Consequently all doses of the HP 13C pyruvate delivered by SPINlab have been produced using the “SPINlab Pharmacy Kit” that serves as the container-closure system for the various drug components (13C pyruvic acid and EPA mixture, dissolution medium, and neutralization and dilution medium) during sample polarization, dissolution and quality control (QC) processes. Thus many aspects of the HP sample preparation considerations discussed below are related to the SPINlab instrument and the consumables designed to be used with it (67).

General Considerations

While more than 860 patients or healthy subjects having been injected with HP 13C pyruvate as of January 2022 without reports of any serious adverse events (68), HP 13C pyruvate injection remains an investigational MR contrast agent and can only be administered by those with Investigational New Drug (IND) exemption from the Food and Drug Administration (FDA) in the USA, a Clinical Trial Application (CTA) in Canada, approval from National Research Ethics Committee Services in the UK, or approval from the relevant local regulatory body. Thus, methods and processes involved to produce a dose should have patient safety as the first priority. Since utilizing dissolution dynamic nuclear polarization (dissolution-DNP) for human use is still a relatively new development, there are no existing published regulatory guidelines specifically for this method.

There are two major production styles that determine how various sites approach the agent preparation. In the US, the most common approach is to rely on a sterilizing filter (“Terminal Sterilization”) to ensure sterility of the final product, akin to PET tracer production, where a starting molecule with a radioisotope is processed using various other ingredients to make the final, desired and injectable contrast agent within a necessarily short amount of time (69). For these sites, sterilization of the components and accessories upstream of this filter are not required, although many of them were manufactured and tested following Good Manufacturing Practice (GMP) or Good Laboratory Practice (GLP) requirements. The filling process is usually performed under an ISO 5 laminar flow hood, but a clean room or an isolator is not required.

This approach is typically accompanied by testing the integrity of the sterilizing filter prior to release of the dose for injection. Typically, post release endotoxin and sterility tests are performed using an aliquot reserved from each released dose.

In the UK and EU, the most common approach is to more-closely follow sterile pharmaceutical compounding guidelines (70), where all components and ingredients are required to be sterile or manufactured under GMP guidelines and are assembled and filled within a clean room environment or an isolator system (“Sterile Preparation”). Typically a batch of Pharmacy Kits for HP 13C pyruvate injection are prepared together. The sterility of the final dose is also ensured by batch validation testing, in addition to the sterility of the ingredients and the sterile compounding process. The endotoxin and sterility testing are performed for the process validation but are not performed for each injected dose.

Some institutions fill and assemble the Pharmacy Kit required for a specific study on the same day or the day prior to polarization, dissolution, and patient administration, but others have also demonstrated the feasibility of preparing a batch of kits, keeping them in a -20ºC freezer and using them over a period of a few months.

Beyond the obvious requirements that the process and the facility has to ultimately produce a dose that is safe to inject into a human, regulatory authorities will also focus on the question “Are you in control of your processes?”. To be in control of your process requires an in-depth and broad understanding of all processes involved in pre, post, and during the production process.

Personnel

It is typical and may be required to have licensed personnel involved in the production process depending on local regulations.Typically a pharmacist, radiopharmacist or other similarly qualified person (QP), in charge of the facility where the Pharmacy Kit filling and preparation is taking place, is responsible for the overall process and the release of the injectable dose.

Qualified cleanroom technicians are often involved in the Pharmacy Kit filling under the supervision of the pharmacist or QP. As is required for pharmaceutical compounding or PET tracer production, training requirements and training records for all personnel need to be maintained and available for audit by the FDA or equivalent.

Equipment And Facility

The facility and all equipment need to have standard operating procedures (SOPs) that describe how equipment is used, maintained, and calibrated to comply with relevant legislation. Currently, almost all the filling of the Pharmacy Kit takes place within a compounding laminar flow hood or isolator (typically ISO 5). At some sites, the filling is conducted within a cleanroom, while at others, it is conducted in a dedicated non-cleanroom space, reflecting differences in cleanroom approach and specifications between regulators worldwide (71). Some equipment or facilities, such as the compounding hood or cleanroom, may require external certified laboratories for testing.

Material Handling

Material handling guidelines (69,70) require SOPs detailing a system to track all of the materials involved in the HP production process for a particular patient dose, similar to current good manufacturing practice (cGMP) requirements for material handling for drug compounding. This includes acceptance standards, storage conditions, amount used in the patient dose for each ingredient and materials used in the assembly of the fluid path and Pharmacy Kit. Currently some users choose to open and inspect and sometimes modify the Pharmacy Kits upon arrival, but some users keep them in the sealed packaging until they are required for dose preparation.

Pharmacy Kit Filling And Assembling

As required by an IND or its equivalent, the preparation of the doses of HP 13C agent are detailed in the Chemistry, Manufacturing, and Control (CMC) section of an applicable regulatory submission; an example of this has been made available (72). It describes the processes of filling the Pharmacy Kit with the different components that make up the final drug product, and of assembling the final kit for either storage or immediate use in the polarizer. Special attention should be given to the laser welding process in order to satisfy installation qualification (IQ) and operational qualification (OQ). Typically, the final developed process is validated by process qualification (PQ) runs, during which 3 or more Pharmacy Kits are filled and used and the final HP 13C products are tested for endotoxin and sterility and to confirm that they meet the dose specifications for injections (usually including pyruvate concentration, residual EPA concentration, pH, liquid state polarization level and dose temperature). The data from 3 consecutive PQ runs are submitted as part of the IND submission (or its equivalent), and are often also reviewed by the Institutional Review Board (IRB) where the studies are conducted.

Quality Control And Dose Release

The quality control (QC) and dose release can be separated into two aspects: one is the QC and release of the filled Pharmacy Kit, and second is the QC and release of the HP 13C agent for injection, after polarization and dissolution. For institutions filling a batch of kits and storing them to use over a period of time, typically the batch can be released based on initial validation, environmental monitoring data from the day of kit production, and if filters are used during preparation of any of the components, filter integrity testing. But in some cases one or more kits are used for validation before the batch of kits are released for future use. For institutions that fill only the kits required for specific studies shortly before the experiment, the filled kits often do not go through separate release tests before they are used.

The quality control of the HP 13C pyruvate solution post dissolution is primarily performed to ensure that the agent meets the dose specifications (Table 1) before it is administered to the subject. These specifications target both safety (pH, residual EPA, temperature) and efficacy (pyruvate concentration, polarization, volume). Typically, the pyruvate concentration, residual EPA concentration, pH, dose temperature, dose volume, and liquid state polarization are measured by the QC accessory associated with the SPINlab polarizer. Some users perform a secondary measurement for one of the parameters, such as pH, using a different instrument or pH paper. For sites that do not go through a separate release testing process for batch filled kits, the integrity of the sterilization assurance filter, a part of the Pharmacy Kit, is typically tested as a part of the dose release. It is also common for these users to preserve an aliquot of the final HP 13C pyruvate solution for post-release endotoxin and sterility testing. This testing cannot be completed fast enough to test an individual dose prior to injection, but this is why other processes such as PQ runs and validation testing are done to minimize the chance a subject could be injected with a contaminated dose.

The Final Dose Release And Injection

should be done under the supervision of a licensed professional, based on local regulations.

Some Key Challenges

Many of the challenges associated with HP 13C pyruvate preparation can be attributed to the conditions required for the dissolution-DNP method of high magnetic field (~3-7 T) and very low temperature (~1 K) during polarization, with pressurized and superheated water necessary for the rapid dissolution event. These extreme conditions are quite challenging for the design of the container-closure and fluid path system. In particular, the cryogenic temperature in the polarizer requires special attention to any moisture or ambient (moist) air introduced into that portion of the fluid path, which can form an ice block at ~1 K. This ice can lead to flow restriction during the dissolution event and reduce the strength of the laser welded bond between the cryovial and its cap. This can ultimately produce failures in the dissolution step, including variations in final pyruvate concentration and pH that may fail to meet QC release criteria as well as fluid path ruptures that provide no available dose and result in polarizer down-time.

The polarization of the HP 13C pyruvate sample decays quickly over the span of a few minutes after dissolution, and thus the process of dissolution, QC for release, and injection should be completed as fast as possible to preserve the high polarization level achieved. Any delays in the preparation process, such as transportation time or equipment malfunction, can significantly reduce the final polarization and result in lower quality imaging data.

Current Practices

A summary of data collected from all sites performing clinical trials with HP 13C-pyruvate is shown in Fig. 3 and Table 1, including the specification of the final dose and how the quality control and release of the final dose are performed. There is a split in the Production Style, described in the General Considerations section above, with 8/13 sites using Sterile Preparation versus 5/13 using Terminal Sterilization. While many of the dose specifications show notable differences in acceptable ranges, all of these variations listed in tables have been successfully and safely been used to perform HP 13C pyruvate studies in humans. Their differences depend on the institutions’ preferences, resources and their particular regulatory situation. There is high similarity in pyruvate ranges, temperature ranges, EPA limits, and volume limits. There is modest variability in pH ranges and large variability in the endotoxin test limit. There is a 3-fold difference in acceptable polarization levels, which are measured to ensure a futile dose is not injected since the polarization is directly proportional to SNR. This reflects the decision by several sites to believe that useful data can be still be obtained with suboptimal polarizations.

Figure 3: Hyperpolarized agent preparation methods reported by sites currently performing HP

In House

Table 1: HP 13C-pyruvate preparation parameters, methods, and dose specifications used for quality control testing and release as well as validation. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. The parameters used for product release are noted in bold text, otherwise these parameters are measured for batch validation or other QC measurements. The endotoxin and sterility testing are performed during process validation of the batch and/or post-injection, and largely depends on the agent production approach.

Summary

The overall safety record of HP 13C-pyruvate has been very strong, and the SPINlab hyperpolarizer has proven to provide high polarizations at human sized doses while meeting numerous QC and release criteria. A weakness remains the failure modes of the SPINlab Phamacy Kits (e.g. ice blocks, path ruptures), which are placed under extreme requirements particularly during dissolution. The preparation process still requires a high degree of expertise.

Therefore, there is a significant need to improve the reliability, robustness, and ease of operation for generating HP 13C-pyruvate doses for human studies. Furthermore, there is a divide between manufacturing and sterile compounding style preparation as well as other site-specific practices, resulting in variations in SOPs and justification required to relevant regulatory bodies. There have also been no comparisons between these approaches. It is also unclear what release criteria and QC parameters are truly required to ensure patient safety.

However, all of the reported methods are acceptable and approved by the appropriate regulatory authorities, and have led to the rapid expansion of successful human studies in recent years.

Mri System Setup And Calibrations

This section covers the MRI system setup, including the imaging system, RF coils, phantoms, and prescan calibration methods.

Imaging System

The main prerequisite for a given MRI scanner to be capable of supporting studies with HP 13C is its “broadband” capability to transmit and receive radiofrequency (RF) signal at the frequency of 13C, which is around 4 times lower than 1H. This does not come as a default on clinical MR devices. The transmit power of the broadband amplifier should also be sufficient to support the intended flip angle and RF pulse shape with the employed transmission RF coil(s) for 13C. Most studies to date use relatively low flip angles (< 90 degrees) for HP 13C in order to preserve polarization for time-resolved imaging. The capability to receive 13C signal on multiple channels is also desirable to increase SNR, as discussed further in the “RF coils” section.

The choice of magnetic field strength is primarily dependent on the metabolites’ frequency separation due to chemical shift dispersion and 1H imaging. High field strengths do not enhance hyperpolarized 13C signal as they do for 1H because the signal strength in a HP experiment relies on manipulating the population of quantum energy states outside of the MRI scanner.

However, the injected HP 13C-pyruvate and its metabolic products have greater frequency separation at higher fields, and it may thus be easier to separate and quantify these resonances at higher fields. This comes at the cost of a reduction in the achievable T2* and often reduced T1. As the initial polarization is independent of the imaging field strength it has been proposed that the increased T2* at 1.5T can potentially be exploited to increase SNR by adapting the acquisition bandwidth or reduce off-resonance imaging effects in cases when the decay of the transverse magnetization is dominated by T2* (73). In practice, 3T has been used in all published human 13C-pyruvate studies surveyed (Supporting Table S1), and comprises the majority of scanners currently in use for human studies (Table 3). A field strength of 3T is well-suited for 1H MRI anatomical reference and correlative imaging.

Stronger and more rapidly slewing magnetic field gradients support more rapid spatial encoding, particularly for metabolite-specific single-shot imaging using echo-planar imaging (EPI) or spiral imaging (See “Acquisition and Reconstruction”). Although the spatial resolution acquired for HP 13C imaging is typically much coarser than for 1H MRI, the factor of ~4 in gyromagnetic ratio leads to the same reduction factor in performance of the gradient system, so 13C experiments are potentially more limited by gradient hardware performance. To date, all human studies have used the commercially-available integrated gradient systems provided in clinical MRI scanners.

Optimization of scanner design has understandably focused on minimization of artifacts in 1H MRI, where devices such as room lights, the gradient amplifiers, and the motors driving the patient bed are checked to ensure that they do not produce RF interference at the 1H frequency, but artifacts may arise at other frequencies. Eddy current compensation is also not always appropriately adjusted for nuclei at other frequencies (74). In order to optimize for 13C, many sites have performed checks on phantoms for RF interference, gradient artifacts, and eddy currents (74), including the use of post-hoc gradient impulse response function characterisation and correction, and some vendors have fixed these issues as well.

Rf Coils

For HP 13C imaging studies in humans, RF coils for both 1H and 13C nuclei are needed, with 1H MRI providing an anatomical reference for registration and optional additional multiparametric MRI readouts. At the Larmor frequency of 13C nuclei, the relative contributions from coil noise compared to sample noise increase compared to 1H (73,75), although sample noise still is likely the dominant contributor for human-sized coils at 32.1MHz - the resonance frequency of 13C nuclei at 3T.

The key requirement for human 13C-pyruvate RF coils are that the coil geometry and sensitive volume must cover the volume of interest in the subject. Table 2 and Figure 4 shows coil configurations that have been used and optimized for applications in different anatomic regions.

Volume resonators are most commonly used for transmit, as they surround the subject to

Provide B1 Transmit Across The Fov (B1

+). While 1H relies on a large birdcage (“body”) coil built into the scanner, 13C transmit coils must be placed inside the bore. This takes up valuable space within the magnet, and also has led to the use of designs with relatively inhomogeneous

B1

+. Many human studies have used Helmholz pair resonators for transmit, including the “clamshell coil”, which has a notably inhomogeneous B1

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

angles that control the use of the hyperpolarized magnetization and creates errors in common HP metrics (9,76). The exception are head coils, where birdcage designs with highly

Homogeneous B1

+ can be placed around the head while easily fitting inside the bore. As with 1H MRI, higher SNR can typically be achieved by smaller receive coil elements, such as surface coils or phased arrays, and the majority of 13C receive coils used have layouts similar to 1H phased arrays.

RF coil quality control is important to ensure proper functioning of the coils to provide consistent imaging quality, especially with limited natural abundance 13C signal in vivo. It typically involves 1) a physical integrity check of the coil cables and connectors and 2) phantom SNR tests to check the coil’s performance and to monitor it over time (see Phantoms below). An useful reference for RF coil quality control is outlined in the MRI accreditation program of the American College of Radiology (77) and can be adapted for 13C coils.

Notably, configurations for brain and prostate studies used dual-tuned 1H/13C coil designs, which greatly simplify workflow and registration of 1H and 13C images, as no switching of coils is needed.

(1)

Table 2: RF coil configurations reported for human HP [1-13C]pyruvate studies.

Tx = Transmit

coil, RX = receive coil. The commonly used “clamshell” TX coil is a Helmholz pair design. For 1H RF configurations, all used the Body coil for TX unless otherwise noted, and “repositioned” indicates the 13C coil was removed for 1H imaging. One representative reference is listed for each configuration. The RF coil configurations reported in the reviewed papers are shown in Supporting Table S1.

Figure 4: Examples of RF coil configurations used for human HP [1-13C]pyruvate brain studies. (A,B) 13C Clamshell TX (Helmholz pair) and 2× 4-channel paddle RX arrays. (C) 13C Birdcage volume TX and 32-channel RX array (RX array slides into TX coil). (D) 13C Birdcage volume TX and 24-channel RX array, combined with a 1H 8-channel RX array. Image reproduced with permission from Ref (16).

Phantoms

Since hyperpolarized magnetization is non-renewable, phantoms containing 13C nuclei are important to: 1) test the multi-nuclear capabilities of the imaging system, including all parts of the signal excitation and receive chain; 2) perform calibration measurements before a scan with hyperpolarized nuclei; and 3) perform necessary pre-scan adjustments (see “Prescan Calibration” section). The phantoms currently in use are listed in Table 3. Their composition must provide sufficient 13C signal, with additional considerations of conductivity, stability, chemical shift(s) present, potential for dynamic imaging, and cost. The phantom geometries are typically either compact, in order to be used alongside the subject during a HP scan, or large enough to mimic the inner volume of a RF coil for system testing.

One popular compact design contains enriched 13C-urea at high concentration, typically 8 M, which provides a single resonance, placed inside a small container ~1 mL. The most common recipe mixes 13C-urea in a 90% water/10% glycerol solution, with glycerol used to increase the urea solubility and doping with a Gd-based contrast agent to shorten T1 which increases the potential SNR per unit time. For example, when Dotarem is added at a 3:1000 volume ratio the 13C-urea T1 is around 500 ms and T2 is around 100 ms. However, when testing pulse sequences influenced by T1 and T2, doping should be used carefully. This phantom is suitable for frequency calibration, transmit gain calibration, sequence testing, and as a fiducial marker when placed next to a patient. However, enriched 13C-urea has a relatively high cost compared to natural abundance compounds.

For larger volumes (>100 ml), the phantoms most often used contain undiluted ethylene glycol, glycerol, or dimethyl silicone. These compounds have sufficiently high carbon concentrations to provide sufficient 13C signal even with the 1.1% natural abundance of 13C. These larger phantoms matching the inner volume of an RF coil are useful for coil testing, including transmit

+) And Receive (B1

-) coil profile mapping, as well as to mimic acquisitions using in vivo FOV requirements. In this case, size and conductivity should match the expected subject size in order to mimic coil loading and get a realistic estimation of B1+. Large-volume natural abundance urea phantoms have also been used by some sites, but suffer from higher conductivity compared to biological tissues. Typically, it is easier to increase the conductivity and hence coil loading of the non-conductive phantom by adding NaCl to match physiological loading (16,78).

Dynamic phantoms that aim to mimic metabolite kinetics have also been developed (79–81), and have the potential to more closely mimic the HP experiment, but so far these are not widely used.

Prescan Calibration

Prior to performing an MRI acquisition, the so-called prescan procedure is used to set the shim parameters to maximize B0 homogeneity over the field of view (FOV) or a specific region of interest (ROI), the scanner center frequency (CF), the RF transmit gain, and the receiver gain.

While this calibration procedure is usually automated for 1H, the lack of sufficient natural abundance 13C signal prevents use of automated methods. (Although natural abundance 13C lipid signal has been detected, there are so far no reports on using this signal for prescan.) Table 3 shows current practices across sites.

Maximizing B0 homogeneity is independent of the nucleus and is therefore performed prior to 13C imaging using the 1H water signal and existing shimming tools, such as by a standard automated process (“Auto Shimming”) or using high order shimming routines. Similarly, the 13C CF can be calculated from the 1H CF using a predetermined scaling factor that depends on the target chemical shift (82). Another common approach used is to have a small, high-concentration 13C phantom, e.g. 8M 13C-urea, integrated in the RF coil or placed next to the scan subject (1). The reference frequency can also be based on real-time measurements after the HP injection but prior to imaging (83). Both the CF and B0 shimming are critical when using spectrally-selective RF pulses, as inmetabolite-specific imaging methods, where the desired excitation bandwidths are typically very narrow and frequency offsets can lead to a failure mode that is only apparent after injection.

The calibration of the RF transmit power is typically performed on a small, high-concentration 13C phantom placed near the region of interest during the scan or on a large 13C phantom of similar size and coil loading as the subject, prior to the subject scan. Reference power is often done by sweeping the power in a pulse-acquire sequence (53,62), or the Bloch-Siegert method (52,84). When using a small phantom, the location of the phantom, B1

+ Inhomogeneity As Well

as any shielding effects, e.g., when the phantom is integrated into a coil (1), may degrade the accuracy. Other methods include real-time Bloch-Siegert method measurements after the HP injection (83), and using the stronger natural abundance 23Na signal that is close enough to the 13C resonance frequency to be detected by 13C coils (82).

The receiver gain is predetermined, either systematically based on independent phantom measurements and assuming the dose and polarization of the HP compound is known prior to injection, or based on past HP imaging studies.

Power [Kw]

Phantom(s) - during study Phantom(s) - before study 13C Frequency

8

13C-bicarbonate doped with dimethyl silicone, various

Maximum Values

Table 3: Summary of the imaging systems, phantoms, and prescan procedures used at sites currently performing HP 13C-pyruvate human studies. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. *Previously performed studies with a Siemens 3T Tim Trio. The imaging systems, phantoms, and prescan procedures reported in the reviewed papers are shown in Supporting Table S1.

Summary

Commercially available 3T MRI systems are by far the most commonly used for human HP 13C-pyruvate studies, although a systematic investigation of the impact of B0 has only recently been investigated (73). The multi-nuclear RF transmit and receive chain has proven sufficient for current acquisition strategies, although many sites have observed artifacts due to RF interference, gradient interference, and residual eddy currents when operating at the 13C frequency. A variety of 13C RF coils, tailored for numerous anatomical targets, have been successfully demonstrated, with the main limitation that most transmit coils take up a lot of additional space inside the bore and provide relatively inhomogeneous B1

+ Profiles. The

phantoms used have converged into generally 2 categories - small phantoms containing 13C-enriched compounds that can be used during the study and human-sized phantoms containing compounds with high carbon concentrations but without 13C enrichment that are used to test and calibrate the coils. There are no standardized compositions or geometry, and dynamic phantoms that recapitulate in vivo kinetics would be desirable but are still an emerging area. Prescan calibration procedures were not well defined in most publications, so we surveyed individual sites to determine current practices. Calibration procedures for the B0 field (13C CF and shimming) for most sites take advantage of 1H signal and methods, while methods

For Calibration Of B1

+ is more variable across sites, likely a reflection of remaining challenges in how to perform this calibration. Standardization of both phantoms and calibration procedures would synergistically improve the robustness and reproducibility of HP 13C studies.

Acquisition And Reconstruction

Data acquisition strategies in human HP [1-13C]pyruvate MRI studies must account for multiple chemical shifts, efficiently utilize the non-renewable HP magnetization, and acquire data quickly relative to metabolism and relaxation decay processes. These studies require spectral encoding to separate metabolites, necessitating pulse sequences that efficiently encode up to 5D data (3 spatial + 1 spectral + 1 temporal dimension). RF pulses must efficiently sample without immediately saturating the non-renewable HP magnetization, and sequences must acquire data quickly and be robust to both experimental and physiologic variation (e.g. B1

+ Inhomogeneity,

variation in perfusion) to ensure reproducibility and minimize scan-to-scan variability. This section covers current successful practices for data acquisition in human [1-13C]pyruvate studies, and accompanying 1H imaging, from different anatomic regions, including scan parameters and image reconstruction.

Acquisition And Reconstruction Methods

The acquisition methods used in human [1-13C]pyruvate studies can be classified into 3 categories: 1) MR spectroscopy or MR spectroscopic imaging (“MRS/I”), 2) chemical shift encoding methods, and 3) metabolite-specific imaging (Fig. 5).

Mrs/I Methods Specifically

resolve a spectrum that can be analyzed to extract expected as well as unexpected resonances, making this approach very robust. It was used in many initial studies (1).

Chemical Shift

encoding methods, most commonly the Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) method, use imaging sequences acquired with multiple TEs and rely on a model-based separation of expected chemical shifts (85).

Metabolite-specific imaging methods use specialized RF pulses that are spatially and spectrally selective to excite individual metabolites which are then typically imaged with fast k-space trajectories such as echo planar imaging (EPI) or spirals (86).

Their Application To Different

organ systems is described below. The image reconstruction methods used in human [1-13C]pyruvate studies have typically been conventional methods (e.g. FFT, non-uniform FFT, or equivalent). The incorporation of accelerated imaging and advanced reconstruction methods including parallel imaging (4,57,87) and compressed sensing (7) has also been applied in human studies for improved spatial resolution, temporal resolution and coverage, but have the potential for additional artifacts as well as SNR losses due to ill-conditioning of the reconstruction (e.g. g-factor).

The Majority Of

published studies do not use accelerated imaging indicating the resolution and coverage achievable without acceleration is currently adequate for successful data collection. Performing coil combination, even with fully sampled data has also been shown to have specific challenges for HP human images: using naive sum-of-squares methods suffer from high noise amplification in the relatively low SNR regime of HP [1-13C]pyruvate (compared to 1H), motivating several HP 13C-specific methods that include data-driven coil sensitivity estimation which have shown obvious improvements over sum-of-squares (11).

More recently denoising techniques have been applied as post-processing of human HP data(41,42,44). The techniques applied are based on spatial-temporal singular value decomposition for unsupervised estimation of signal and noise components. They have shown improvements in apparent SNR in the brain and liver, while care must be taken to choose parameters such as the rank threshold to avoid oversmoothing and overfitting to the estimated signal components.

Prostate Studies

Prostate cancer was the first human application of HP [1-13C]pyruvate (1), and data was acquired with MRS/I methods: 1D dynamic MRS, single-slice 2D dynamic echo-planar spectroscopic imaging (EPSI), and single time point 3D EPSI. Advances in imaging strategies led to the development and application of new acquisition schemes, including undersampled 3D EPSI with compressed-sensing (7), model-based chemical shift encoding methods that use a priori information (47,59), and metabolite-specific EPI (10), all of which can provide volumetric whole-organ coverage and dynamic acquisitions.

The pyruvate bolus arrival in the prostate can vary by ± 10 s between patients, necessitating dynamic imaging to reliably and consistently capture the pyruvate bolus (18). For this reason, all currently ongoing studies acquire dynamic data. While MRS/I, chemical shift encoding, and metabolite-specific imaging can all achieve dynamic imaging, chemical shift encoding and metabolite-specific imaging provide greater dynamic and volumetric coverage (85). For scan prescriptions, the FOV is designed to provide full prostate coverage and typically to match the orientation of the anatomic imaging used for registration. Flip angles used in current studies are constant through time, as quantification with a variable-through-time flip scheme is highly sensitive to bolus timing (8) and errors in the RF transmit (B1 +) field (76).

Heart Studies

Data acquisition methods for 13C imaging in the heart must be designed to meet the demands of significant cardiac motion and blood flow. To cope with the periodic cardiac motion, most human heart studies to date used gating to the diastolic window, the longest cardiac cycle interval, which has reduced motion (2,22,28,30,35,36,38,45,52). The duration of the diastolic window limits the available data sampling time, making cardiac acquisitions the most time-constrained of the HP 13C MRI applications. The most common acquisition approach is metabolite-specific imaging with spiral k-space trajectories (2). Their single-shot imaging capability makes these methods particularly robust to motion effects. Furthermore, spiral k-space trajectories provide rapid k-space coverage and relatively benign flow and motion artifacts. The majority of studies have used 2D multi-slice acquisitions, but 3D encoding has also been used successfully (35).

Brain Studies

For HP 13C MRI of the human brain, the majority of studies have also used 2D (slice selective) acquisitions (10–12,14,16,28,33,40,41,44,51,53,60), with a trend toward volumetric coverage using 2D multi-slice metabolite-specific imaging. 3D metabolite-specific imaging of the whole brain, with phase encoding of the slice direction (34,57), has been shown to provide similar SNR efficiency (88) compared with multislice imaging. A number of studies have employed MRS/I (5,6,29,31–33,50,55) resulting in a spectrum from each voxel, which has the advantage of not requiring a priori information about which peaks to encode. This was important in early brain studies when it was not known which peaks would be detectable. Chemical shift encoding, using a set of images with different echo times and an iterative reconstruction of the individual resonances (i.e. the IDEAL approach (85)), has also been used (12,49,54), with the drawback that coverage in the slice direction was limited due to the time required to acquire multiple echo time images.

Abdomen And Breast Studies

The fundamental approaches to data acquisition and reconstruction in the abdomen and breast are largely similar to the aforementioned applications, but demand attention to particular challenges associated with these anatomic regions, especially relating to respiratory motion.

Although it has been shown that a basic 2D MRSI approach based on phase encoding and FID readout can be successfully applied for HP 13C imaging in breast (15) and kidney (13), major advantages in terms of spatiotemporal resolution and coverage have been realized using tailored approaches based on metabolite-specific imaging (43,62) and chemical shift encoding (43), which have facilitated multi-slice or 3D dynamic acquisitions over large FOVs in the abdomen (4,37,46).

The significant respiratory motion encountered in these regions can directly blur 13C images, and has further favored these rapid acquisition strategies. Motion also degrades B0 homogeneity, which can shift frequency-selective excitation profiles and introduce artifacts into rapid imaging readouts. This makes accurate determination of the acquisition center frequency and shimming essential in these regions which often cover large FOVs. (See “Prescan Calibration” section for more information). In some studies, breath-holding was used to minimize motion effects and enforce frame-to-frame data consistency (42). A pragmatic and reasonably effective approach for dealing with respiratory motion during 13C data acquisition is an initial breath-hold (as long as can be tolerated), followed by free-breathing (46,62).

1H Imaging

Collection of 1H imaging data is essential both for prescribing the 13C acquisition and for interpretation of the resulting 13C data. Multi-planar 1H scouts are acquired prior to 13C acquisition to enable graphical prescription of the 13C imaging region. All human HP 13C-pyruvate imaging studies acquire conventional MRI scans (e.g. T1- and T2-weighted volumes) for anatomic reference, aiming to cover at least the full 13C FOV. Acquiring these anatomic scans as close as possible to the time of 13C imaging (immediately before or after) minimizes potential misregistration between the data sets. Depending on the application, other advanced 1H sequences are also acquired (e.g. diffusion-weighted imaging for cancer imaging).

When contrast-enhanced data is acquired, it is done after 13C imaging, as paramagnetic contrast agents will accelerate 13C relaxation.

Reported Study Parameters

Figures 5 and 6, and Supporting Table S2 shows the reported acquisition study parameters for human HP [1-13C]pyruvate studies published as of September 2022. Figure 5 shows a mixture of MRS/I, metabolite-specific imaging, and chemical shift encoding methods have been successfully used, where spectroscopy-based methods have become less prevalent in recent studies. Figure 6 shows the acquisition timing, including the important start time and interval/temporal resolution, is quite variable across studies.

Figure 5: Acquisition methods used in published HP [1-13C]pyruvate human studies published up to September 2022, classified into: MR spectroscopy and spectroscopy imaging (MRS/I); chemical shift encoding methods, such as IDEAL, that use multiple TEs and model-based reconstructions; and metabolite-specific imaging methods that use spectrally-selective excitation to image a single resonance at a time.

Figure 6: Temporal acquisition characteristics reported in HP [1-13C]pyruvate human studies published up to September 2022. (a) Reported referencing of acquisition start times.

(B)

Acquisition start times reported when using dynamic imaging and when timing was reported relative to the end of the injection. (c) Temporal resolutions. “Not Applicable” indicates dynamic imaging was not used.

Summary

Three general categories of acquisition strategies have been used successfully for human HP 13C-pyruvate studies: MRS/I, model-based chemical shift encoding (e.g. IDEAL) methods, and metabolite-specific imaging methods. These have enabled successful studies in the prostate, heart, brain, abdomen, and breast. Recent studies increasingly have used the imaging-based strategies of metabolite-specific imaging and chemical shift encoding which are the fastest methods, although a heads-to–head comparison between techniques has not been performed.

Metabolite-specific imaging is quite popular because of its speed and compatibility with single-shot imaging, but is sensitive to B0 field variations and thus requires careful calibrations. Nearly all studies surveyed acquired data dynamically, allowing measurement of the bolus and metabolite kinetics. The exact timings and associated flip angles vary quite widely across reported studies, with no consensus yet as to how to choose these parameters. Image reconstruction is typically done directly using Fourier Transform methods, and accelerated imaging strategies are uncommon.

Data Analysis And Quantification

This section covers the analysis of data from human HP [1-13C]pyruvate studies, including modeling and metrics, visualization, as well as considerations for how to store data and metadata. Depending on study design, the analysis may need to give quantitative or semi-quantitative output reflecting a biological process or may just reflect a contrast between different regions of interest for quantitative evaluation.

Metrics

Figure 7: HP [1-13C]pyruvate raw data (A) have typically been quantified using four categories of metrics depending on the acquisition. Data acquired as a single time point are often quantified using normalized metabolite images or metabolite ratios (B). Dynamic data can be quantified using normalized metabolite images or metabolite ratios (B), or with metabolite timings such as time-to-peak (TTP) or pharmacokinetic (PK) models (C). The latter two require the data to be time-resolved. [1-13C]alanine and 13C-bicarbonate are analyzed similarly to [1-13C]lactate but omitted here for display.

Metabolite images are commonly used as summary metrics for HP MRI data, often including some form of normalization as well as summed over time as an area under the time curve (AUC) (17). These are analogous to the visual evaluation that is most used for routine clinical work (89,90). In these metabolite images, we expect that the [1-13C]pyruvate AUC signal is predominantly weighted towards perfusion and uptake, while [1-13C]lactate, [1-13C]alanine and 13C-bicarbonate AUCs represent metabolic conversion. The strength of this approach lies in its simplicity and relatively few underlying assumptions. Limitations to the use of single-metabolite images or AUCs include sensitivity to inhomogeneous coil profiles (57,87,91), the acquisition strategy and acquisition parameters, pyruvate polarization and concentration level, and signal relaxation rates (92). Further, the reader must be careful to interpret all the images in conjunction to better understand the underlying biology; for example, increased [1-13C]lactate in the presence of decreased [1-13C]pyruvate delivery can have a very different meaning compared to increased [1-13C]lactate with increased [1-13C]pyruvate delivery.

In an attempt to address variations in coil sensitivity, polarization level, and pyruvate delivery, AUC images are often computed by normalizing to a specified parameter, such as the maximum pyruvate or average lactate signals, or presented as a ratio such as lactate/pyruvate or divided by “total Carbon” - the sum total of HP 13C signal observed across all metabolites. The AUC ratios between metabolites and pyruvate are proportional to the corresponding forward kinetic rates (81,93), but are not directly comparable to rate constants when magnetization loss rates (e.g. relaxation and losses due to signal excitation) differ between studies. Similarly, the ratios between the produced metabolites (e.g. bicarbonate/lactate) can reflect the balance between downstream metabolic pathways (12,55). Care must be taken to consider how AUC images are calculated and normalized before comparing values between studies.

To further quantify the interpretation, pharmacokinetic (PK) modeling approaches were developed to compute the apparent kinetics of pyruvate-to-metabolite exchange (92,94–99). These yield semi-quantitative to quantitative apparent rate constants, given in s-1. Some models require a vascular input function, while others avoid this requirement (95). PK models can explicitly account for acquisition-specific details such as excitation angle and repetition time, and thus may reduce the effects of these details on quantification. An input-less model, provided in the Hyperpolarized-MRI-Toolbox (https://github.com/LarsonLab/hyperpolarized-mri-toolbox) (100) and thus frequently employed for human data, has been shown to fit well and robustly to prostate and brain data (8,20). PK models are quantitative in nature, arguably provide more relevant biological information (8,20), and appear to be reproducible across sites (51). However, rate constants derived from PK models are still apparent rates, and likely do not reflect a single biological characteristic.

Some additional considerations include whether complex or magnitude data is used, as the noise behaviors will impact the analysis differently. Additionally, cut-off thresholds or other criteria may be used to identify and avoid voxels with insufficient SNR before analysis to improve robustness (20,41).

Regardless of the analysis approach, the underlying biology is not always clearly represented by the data; instead, the metrics may be influenced by perfusion, barrier permeability, intercellular shuttles, enzyme activities, co-substrate concentrations, or combinations thereof, depending on the organ and disease of interest (19,43,94,101–103). This may be addressed by incorporating complementary information. As an example, HP 13C pyruvate data is influenced by perfusion, and thus addition of perfusion MRI could be important for interpretation (98,104,105).

All the methods outlined above have been explored in clinical studies, described in Supporting Table 3 and summarized in Figure 8. As of September 2022, approximately 52% of studies involving human subjects report rate constants derived from a PK model with a few different models reported. A nearly equal fraction (51%) of the studies report AUC ratio values.

Approximately 66% of these studies report metabolite-specific images or AUC values. About 40% report SNR values; this metric is particularly frequent in manuscripts that describe technical developments for clinical HP MRI. Approximately 16% of these studies summarize model-free metrics, and 10% report measurements from a single timepoint. Most studies report a combination of quantities.

Figure 8: Reported metrics used for analysis in HP [1-13C]pyruvate human studies published up to September 2022.

Visualization

A wide variety of approaches have been used for visualizing data from human HP 13C-MRI studies. The challenges and practical considerations are: 1) choosing the appropriate metrics to display, 2) how to encode the parameters (e.g. the colormap), and 3) choosing how to provide anatomical context and other multi-parametric data. The choice of visualization also depends on the goal which could be for diagnostic interpretation, but also quality control, reproducibility among readers and publication.

Metrics

The choice of HP 13C metrics is described in detail above. At this stage in HP 13C development where there is no standardized metric, often a combination of metabolite images and ratios or PK model parameters are shown.

Parameter Encoding

The mapping function chosen should provide an adequate, often quantitative, impression of the parameter mapped. There is a consensus in the visualization field that perceptually uniform maps are best suited to visualize continuous parameters, like the greyscale typically used by radiologists as well as other monochrome (black to blue) and color ranges (fire-type, rainbow-type) (106,107). Multi-color heatmaps have been the most frequently employed method for HP 13C data, while greyscale has infrequently been used but it ensures there is no coloring-based bias as well as facilitating later reuse (Fig. 9a). Among the color schemes employed in the clinical HP 13C literature, fire-type scheme seems to be the most common [similar to “Plasma” or “Inferno” in matplotlib.org]. Next most commonly employed is the rainbow-type scheme [similar to “Rainbow” in matplotlib.org].

Anatomical Context

HP MRI faces the challenge that it does not necessarily depict the anatomical features, similar to PET, and thus requires an anatomical reference. Most often, a grayscale anatomical image is overlaid with a HP colormap (Fig. 9c,d). This approach is very intuitive, but can skew perception as the grey-scale anatomical reference may affect the brightness of the HP data (e.g. signal in the skull). This bias does not occur when showing adjacent maps (Fig. 9a, b). Here, anatomical outlines may help to provide reference (Fig. 9b).

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