Autonomous Mobile Robot Amr

Abstract

This study presents a systematic review of artificial

Intelligence (Ai)–Driven Autonomous Mobile Robots

(AMRs) within scalable micro-fulfillment centers (MFCs), with a focus on their architectural configurations, operational performance, and integration within modern and demand for ultra-fast last-mile delivery have accelerated the adoption of compact, urban fulfillment models, where AMRs play a central role in enabling high-density storage, dynamic inventory handling, and real-time order processing.

This review synthesizes recent scholarly and industrial contributions to examine how AI techniques—such as

Swarm

intelligence, and predictive analytics—enhance navigation, task allocation, fleet coordination, and human–robot collaboration in constrained warehouse environments. The paper evaluates system scalability by analyzing throughput optimization, latency reduction, space utilization efficiency,

Demand

conditions. Furthermore, it investigates interoperability challenges associated with warehouse management systems (WMS), Internet of Things (IoT) infrastructures, and cloud-based

Leading Amr Deployment Frameworks Is Conducted To

identify performance trade-offs, cost implications, and implementation barriers, including energy constraints, safety compliance, and algorithmic reliability. The review also highlights emerging trends such as digital twin integration, edge AI processing, and decentralized decision-making architectures that support adaptive and self-organizing across robotics, logistics, and intelligent systems, this study provides a comprehensive analytical foundation for both academic inquiry and industrial application. The findings aim to guide future research directions toward robust, scalable, and cost-efficient AMR-enabled micro-fulfillment solutions capable of meeting the evolving demands of omnichannel retail and urban logistics environments.

Keywords: Artificial Intelligence, Autonomous Mobile Robots (AMR), Micro-Fulfillment Centers, Warehouse Automation,

Ntroduction

1.1 Background and Evolution of Micro-Fulfillment Centers The emergence of micro-fulfillment centers (MFCs) represents a structural shift in logistics architecture driven by the need for dominated supply chain operations; however, increasing demand for same-day and instant delivery has necessitated decentralized, compact fulfillment infrastructures embedded within urban environments. MFCs are designed to optimize spatial constraints while maintaining high throughput, leveraging digital infrastructure and real-time analytics for operational efficiency. The integration of cloud computing has significantly contributed to this evolution by enabling scalable data processing and real-time inventory visibility across distributed nodes, thereby enhancing decision-making precision and responsiveness (Akerele et al., 2024).

Furthermore, the evolution of MFCs is closely linked to the transformation of last-mile delivery systems, where data-informed frameworks guide infrastructure development and operational optimization. These systems rely on predictive analytics and dynamic demand forecasting to position inventory closer to end-users, reducing latency and transportation costs. In this context, MFCs function as intelligent nodes within broader logistics networks, supporting gig economy delivery models and

Accepted: 17-12-2025

International Journal of Advanced Multidisciplinary Research and Studies

The

conceptualization of such decentralized systems aligns with

Data-Driven

coordination and digital integration in logistics ecosystems

(Nwabekee Et Al., 2023). As A Result, Mfcs Have

transitioned from experimental retail innovations to critical components of scalable, technology-enabled supply chain architectures.

1.2 Role of Automation and AI in Modern Warehousing Automation and artificial intelligence (AI) have become foundational to modern warehousing, particularly within micro-fulfillment environments where operational speed and accuracy are critical. AI-driven systems enable predictive analytics, allowing warehouses to anticipate demand fluctuations, optimize inventory placement, and dynamically allocate resources. These capabilities are particularly relevant in retail-driven fulfillment models, where customer expectations for rapid delivery necessitate highly responsive

By

analyzing large volumes of transactional and behavioral data, thereby improving order accuracy and reducing processing delays (Ajiga et al., 2024).

In addition to predictive capabilities, AI enhances operational efficiency through automation of repetitive tasks and intelligent process orchestration. Autonomous Mobile Robots (AMRs), robotic picking systems, and AI-powered sorting mechanisms are increasingly deployed to streamline warehouse workflows and minimize human intervention.

Business process automation further improves coordination between warehouse management systems and customer- facing platforms, ensuring seamless integration across the supply chain. These advancements not only increase throughput but also enhance scalability by enabling

Warehouses To Handle Higher Order Volumes Without

proportional increases in labor costs (Ugbaja et al., 2024).

Redefining

warehouse operations by transforming them into intelligent, self-optimizing systems capable of supporting complex fulfillment demands.

Problem Statement And Research Gaps

Despite the rapid adoption of AI-driven Autonomous Mobile Robots (AMRs) in micro-fulfillment centers, significant challenges persist in achieving scalable, reliable, and cost-efficient implementations. Existing studies often focus on isolated aspects such as navigation algorithms, task scheduling, or warehouse automation, without providing a unified framework that integrates these components into a cohesive system architecture. This fragmentation limits the

Fulfillment

environments. Additionally, there is insufficient empirical

Evidence On How Amr Systems Perform Under Varying

demand conditions, especially in urban micro-fulfillment settings characterized by space constraints and fluctuating order volumes.

Another critical gap lies in the limited exploration of interoperability between AI-driven robotic systems and existing warehouse management infrastructures. Many implementations lack standardized integration protocols, resulting in inefficiencies and increased deployment complexity.

Energy

consumption, system resilience, and safety compliance

Large-Scale

deployments. The absence of comprehensive benchmarking frameworks also hinders comparative analysis across different AMR technologies and operational models. These gaps underscore the need for a systematic review that consolidates current knowledge and identifies pathways for scalable and efficient AMR integration in micro-fulfillment centers.

Objectives And Scope Of The Review

This review aims to systematically analyze the role of AI-

Driven Autonomous Mobile Robots In Enhancing The

operational efficiency and scalability of micro-fulfillment centers. The primary objective is to synthesize existing research on system architectures, AI algorithms, and deployment strategies that enable efficient navigation, task allocation, and fleet coordination within constrained warehouse environments. Additionally, the study seeks to evaluate performance metrics such as throughput, latency, space utilization, and energy efficiency to provide a comprehensive understanding of system effectiveness.

The scope of the review encompasses both academic and industry-based studies, focusing on recent advancements in AI technologies applied to warehouse automation. It

Ot

addresses practical challenges such as implementation costs, safety considerations, and workforce implications. By providing a holistic analysis, the study aims to bridge the

Real-World

applications, offering insights that support the development

Structure Of The Paper

This paper is organized into six main sections to provide a coherent and systematic analysis of AI-driven AMR systems in micro-fulfillment centers. Following the introduction, the second section outlines the methodology used for the systematic review, including the research design, data sources, and selection criteria. The third section examines the architecture of AI-driven AMR systems, focusing on navigation algorithms, task allocation strategies, and system integration frameworks. The fourth section presents a

Scalability,

analyzing key operational metrics such as throughput, efficiency, and cost optimization. The fifth section discusses the challenges and limitations associated with AMR

And

organizational barriers. Finally, the sixth section explores emerging trends and future research directions, highlighting opportunities for innovation and advancement in intelligent

Review Protocol And Research Questions

The review protocol adopted in this study follows a structured and reproducible methodological framework designed to synthesize interdisciplinary evidence on AI-

Driven Autonomous Mobile Robots (Amrs) In Micro-

fulfillment centers. The protocol integrates systematic

Analytical

approaches, ensuring traceability of decisions from literature International Journal of Advanced Multidisciplinary Research and Studies

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identification to synthesis. Foundational frameworks in data governance and AI-enabled control systems inform the development of the protocol, particularly in establishing auditability and reproducibility of research workflows (Mbonu et al., 2022a; Mbonu et al., 2022b). The protocol further incorporates systematic investigation techniques derived from industrial safety and operational learning models, which emphasize iterative knowledge extraction and error minimization in complex systems (Obriki & Arumosoye, 2024).

The research questions are carefully formulated to address both algorithmic and operational dimensions of AMR

Deployment. Central Questions Examine How Machine

learning algorithms enhance path planning, collision avoidance, and real-time decision-making within high- density fulfillment environments. Additional inquiries explore system interoperability with warehouse management systems, latency optimization, and scalability under dynamic demand conditions. The integration of blockchain-

Informs

questions related to trust, traceability, and coordination among distributed robotic agents (Sanni et al., 2024). Furthermore, performance evaluation metrics are aligned with infrastructure optimization models and quantitative system analysis approaches to ensure measurable outcomes such as throughput efficiency, order accuracy, and energy consumption (Ogbete & Aminu-Ibrahim, 2024; Michael & Ogunsola, 2023). This structured protocol ensures that the

Theoretical

advancements and empirical validations relevant to intelligent warehouse automation.

Nclusion And Exclusion Criteria

The inclusion criteria are designed to capture studies that provide rigorous and relevant insights into AI-driven

Micro-Fulfillment

environments. Eligible studies must demonstrate explicit application of artificial intelligence, robotics, or advanced

Those

addressing efficiency, scalability, and system resilience. Emphasis is placed on studies employing benchmarking

Optimization

frameworks, as these approaches provide quantifiable evidence of system performance (Odejobi et al., 2023; Okonkwo et al., 2024). Additionally, studies incorporating safety, sustainability, and compliance considerations are included to ensure that operational efficiency is evaluated alongside risk management and environmental impact (Obogo et al., 2024a; Obogo et al., 2024b).

Exclusion criteria eliminate studies that lack empirical validation, do not incorporate AI or automation components, or focus on domains without transferable methodologies to implementation frameworks or measurable outcomes are excluded to maintain analytical rigor. Furthermore, outdated

Technological

advancements in robotics and cloud-based systems are omitted. The criteria also exclude studies that fail to address system integration, as interoperability is a critical factor in AMR deployment. By incorporating frameworks related to emergency readiness and system resilience, the review ensures that selected studies address both operational performance and risk mitigation (Arumosoye & Obriki, 2023). The inclusion of AI-driven business process automation research further broadens the analytical scope, enabling a comprehensive understanding of how intelligent systems optimize workflows and improve efficiency across interconnected operational environments (Ugbaja et al., 2023a).

Ata Sources And Search Strategy

The data sources for this review are derived from a

Journals,

conference proceedings, and technical reports indexed across multidisciplinary and domain-specific databases. The search strategy is structured to ensure both breadth and depth, capturing foundational and emerging studies in AI- driven robotics and logistics automation. The approach is informed by data governance and regulatory traceability frameworks, which emphasize systematic classification and validation of data sources to ensure integrity and relevance (Aliliele et al., 2024a; Aliliele et al., 2024b).

Keyword

combinations and Boolean operators to refine results, incorporating terms such as “autonomous mobile robots,” “warehouse automation,” “AI logistics optimization,” and strategy integrates big data analytics techniques, including relevance scoring and trend analysis, enabling the identification of high-impact studies with significant methodological contributions (Oluoha et al., 2024).

Additionally, systematic review methodologies from supply chain analytics are applied to ensure comprehensive coverage of logistics-related innovations and operational frameworks (Omoegun et al., 2024).

Data filtering is further strengthened through the use of real- time analytics and visualization techniques, which support the identification of patterns and gaps within the literature (Ogbuefi et al., 2024). Governance frameworks are also

Applied To Ensure Consistency In Data Quality And

methodological rigor across selected studies (Ogeawuchi et al., 2023). This multi-layered search strategy ensures that the review captures a robust and representative body of literature, providing a strong foundation for subsequent

Study Selection And Quality Assessment

The study selection process is implemented through a multi- stage screening methodology designed to ensure the inclusion of high-quality and relevant studies. Initially, titles and abstracts are reviewed to identify alignment with the research focus on AI-driven AMR systems and micro- fulfillment operations. This is followed by a detailed full- text evaluation to assess methodological rigor, data validity, and relevance to the research questions. The selection criteria emphasize studies that demonstrate empirical validation, robust analytical frameworks, and measurable performance outcomes (Okonkwo et al., 2023; Ogbete et al., 2023). This approach is consistent with evaluation methodologies used in clinical and infrastructure research, where systematic filtering enhances the reliability of synthesized evidence.

Quality assessment is conducted using a structured framework that evaluates study design, analytical depth, and practical applicability. Studies are assessed based on their use of simulation models, real-world deployment data, and performance benchmarking techniques. Additional criteria include the robustness of system architectures, resilience International Journal of Advanced Multidisciplinary Research and Studies

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under operational stress, and scalability across different deployment scenarios (Odejobi et al., 2023). Asset lifecycle and infrastructure performance models are also used to evaluate long-term system sustainability and efficiency.

Implementation

(Arumosoye & Obriki, 2024). This rigorous assessment process ensures that the selected studies provide credible and actionable insights, forming a strong empirical foundation for analyzing AI-driven automation in micro- fulfillment environments.

System Design And Operational Frameworks

The system design of AI-driven Autonomous Mobile Robot (AMR) frameworks in micro-fulfillment centers is anchored on modular, scalable architectures that integrate control systems, decision layers, and execution environments. These systems typically adopt a layered design where perception, planning, and actuation components are decoupled to allow independent optimization and fault tolerance. Drawing from AI-enabled governance and control frameworks, system architectures emphasize traceability, auditability, and real- time decision feedback loops to ensure operational consistency (Mbonu et al., 2022). In addition, operational frameworks incorporate safety and compliance mechanisms analogous to industrial safety governance models, ensuring

Warehouse

environments adhere to predefined safety protocols and risk mitigation strategies (Obogo et al., 2024; Obriki & Arumosoye, 2024).

Orchestrated

workflows that align with predictive and optimization- driven frameworks commonly used in resource planning and structured around event-driven triggers, enabling robots to

Order

prioritization, and environmental constraints. Data-centric optimization models further enhance operational efficiency by identifying bottlenecks and enabling continuous system tuning through feedback mechanisms (Sanni & Wedraogo, 2024). The integration of predictive planning models ensures sustained operational uptime, particularly in high- throughput environments where system downtime directly impacts fulfillment performance (Okonkwo et al., 2024).

These frameworks collectively support resilient, adaptive, and high-performance AMR ecosystems capable of meeting the demands of modern micro-fulfillment operations.

Are

fundamentally driven by advanced AI techniques that combine probabilistic modeling, sensor fusion, and machine

Apping (Slam) Remains A Foundational Approach,

enabling robots to construct real-time environmental maps while simultaneously estimating their positions within those maps. These techniques are further enhanced through predictive modeling approaches that integrate historical movement data and environmental dynamics, allowing robots to anticipate obstacles and optimize navigation paths (Michael & Ogunsola, 2023; Ayinde, 2024). In addition,

Distributed

computation for localization tasks, enabling real-time updates and improved positional accuracy in large-scale fulfillment environments (Odejobi et al., 2023).

Advanced AI techniques also incorporate deep learning models for visual recognition and sensor interpretation, enabling AMRs to operate effectively in complex and

Dynamic Warehouse Settings. These Models Leverage

convolutional neural networks and reinforcement learning to continuously improve navigation strategies based on environmental feedback. Predictive risk modeling further enhances localization accuracy by identifying potential navigation hazards and dynamically adjusting robot trajectories). The integration of intelligent analytics frameworks ensures that navigation decisions are not only reactive but also proactive, aligning with broader system optimization goals. This convergence of AI techniques results in highly adaptive and efficient navigation systems

Micro-Fulfillment

operations. 3.3 Task Allocation, Scheduling, and Fleet Coordination Task allocation and scheduling in AMR-driven micro- fulfillment centers involve complex optimization problems that require real-time decision-making and adaptive coordination strategies. These systems utilize AI-driven scheduling algorithms to dynamically assign tasks based on factors such as robot availability, task priority, and spatial constraints. The integration of predictive models enables proactive scheduling adjustments, ensuring that high- priority orders are fulfilled efficiently while minimizing idle time and resource underutilization (Ugbaja et al., 2023b; Okonkwo et al., 2023). Additionally, resilience models borrowed from cloud workload optimization frameworks are applied to ensure system continuity and rapid recovery from disruptions (Odejobi et al., 2023).

Fleet coordination extends beyond individual task allocation to encompass system-wide optimization, where multiple robots operate collaboratively within shared environments.

Coordination strategies often employ decentralized control mechanisms, allowing robots to make localized decisions

Optimization

objectives. Blockchain-enabled workflow frameworks further enhance transparency and synchronization across distributed robotic systems, ensuring consistent task execution and minimizing conflicts (Sanni et al., 2024). The incorporation of lifecycle performance evaluation models ensures that fleet operations

Wear,

maintenance, and system scalability (Ogbete et al., 2023; Arumosoye & Obriki, 2023). These integrated approaches enable seamless coordination of large robotic fleets,

Operational

efficiency. 3.4 Integration with WMS, IoT, and Cloud Platforms Integration of AMR systems with Warehouse Management Systems (WMS), Internet of Things (IoT) devices, and cloud platforms is critical for achieving real-time visibility and operational synchronization. WMS integration enables seamless coordination between order management processes and robotic execution, ensuring that inventory data is continuously updated and aligned with physical operations.

ERP-integrated logistics frameworks further enhance this integration by providing a unified platform for managing inventory flows, order fulfillment, and resource allocation International Journal of Advanced Multidisciplinary Research and Studies

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(Omoegun et al., 2024). Cloud-based architectures support scalable data processing and enable real-time analytics, allowing organizations to monitor system performance and make data-driven decisions (Akerele et al., 2024; Ogbuefi et al., 2024).

IoT technologies play a pivotal role in enabling real-time data collection and environmental monitoring, providing critical inputs for AMR decision-making processes. Sensors embedded within warehouse infrastructure capture data on inventory levels, environmental conditions, and robot performance, which are then processed through cloud-based ensure that this integration maintains data integrity, privacy, and compliance with regulatory standards (Mbonu et al., 2022; Ogeawuchi et al., 2023). Additionally, IoT security models are essential for protecting interconnected systems from cyber threats, ensuring the reliability and safety of operations (Hassan et al., 2024) as seen in Table 1. This integrated ecosystem enables a cohesive and intelligent operational environment, where AMRs function as part of a larger, data-driven logistics network.

Table 1: Integrated Architecture of AMR Systems with WMS, IoT, and Cloud Platforms in Micro-Fulfillment Centers

Safety

4. Performance Evaluation and Scalability Analysis

Efficiency

Throughput optimization in AI-driven Autonomous Mobile Robot (AMR) systems within micro-fulfillment centers is fundamentally achieved through dynamic task allocation, predictive routing, and real-time decision intelligence.

Contemporary studies emphasize the role of AI-driven predictive analytics in anticipating demand spikes and optimizing picking sequences, thereby reducing idle robot time and improving order cycle completion rates (Ajiga et al., 2024; Ashiedu et al., 2024). The integration of ERP- enabled logistics management systems further enhances throughput by synchronizing inventory updates with robotic operations, ensuring continuous flow without bottlenecks

(Omoegun Et Al., 2024A). These Systems Leverage

reinforcement learning and heuristic optimization to minimize travel paths and coordinate multi-robot fleets, leading to measurable gains in orders processed per hour.

Empirical findings indicate that fulfillment efficiency improves significantly when AI models incorporate real- time data streams for decision-making. For instance, predictive procurement and inventory visibility frameworks ensure that stock availability aligns with demand patterns, reducing order delays and backlogs (Okonkwo et al., 2024a). Furthermore, data-centric funnel optimization models enhance order prioritization by aligning fulfillment strategies with customer demand intensity (Sanni & Wedraogo, 2024). Operational analytics frameworks also demonstrate that integrating business intelligence systems

Into Warehouse Workflows Can Improve Throughput

efficiency by over 30% in high-volume environments (Balogun et al., 2024). These findings underscore that throughput optimization is not solely dependent on robotic speed but on the orchestration of data-driven decision systems that coordinate resources, inventory, and robotic agents in real time.

4.2 Space Utilization and Warehouse Density Metrics Space utilization within micro-fulfillment centers is a critical determinant of operational efficiency, particularly in urban environments where spatial constraints demand high- density storage configurations. AI-driven AMR systems enable vertical and compact storage architectures by dynamically navigating narrow aisles and optimizing bin placement strategies. Advanced automation frameworks demonstrate that integrating intelligent storage allocation algorithms with robotic systems significantly improves warehouse density metrics, allowing facilities to store up to 40% more inventory within the same physical footprint (Ikwuanusi et al., 2024; Kisina et al., 2022). These systems rely on real-time data analytics to continuously reorganize inventory based on demand frequency, ensuring that high- turnover items are positioned for rapid retrieval.

The application of data visualization and governance models

Providing

actionable insights into storage patterns and utilization inefficiencies.

Enable

warehouse managers to monitor occupancy rates, identify underutilized zones, and implement adaptive storage strategies (Ogbuefi et al., 2024; Ogeawuchi et al., 2023).

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mobility and logistics integration highlight the importance of spatial planning in reducing congestion and improving material flow within high-density environments (Owoade et al., 2024). Automation-driven procurement and supply chain frameworks also contribute to optimized space utilization by aligning inventory levels with storage capacity constraints (Uzozie et al., 2023). Collectively, these approaches demonstrate that effective space utilization in AMR-enabled warehouses is achieved through the convergence of robotics, data analytics, and intelligent storage design.

4.3 Latency, Energy Consumption, and Cost Efficiency Latency reduction is a critical performance metric in AMR- driven micro-fulfillment systems, directly influencing order processing time and customer satisfaction. Advanced cloud- based analytics frameworks enable real-time data processing and low-latency decision-making by leveraging distributed computing architectures (Akerele et al., 2024). Optimization of server environments and data pipelines further enhances system responsiveness, ensuring that robotic commands and

Routing Decisions Are Executed With Minimal Delay

(Olamijuwon et al., 2024a). These improvements are complemented by predictive maintenance and incident management systems that reduce system downtime, thereby

Performance

(Olamijuwon et al., 2024b). Energy consumption and cost efficiency are closely linked to system optimization strategies. AI-driven models optimize robot movement and workload distribution to minimize energy usage, while cost-reduction frameworks in cloud-native applications ensure efficient resource allocation (Owoade et al., 2023). Financial analytics models also play a significant role in forecasting operational costs and identifying cost-saving opportunities within fulfillment processes (Olajide et al., 2024). Furthermore, resilience and budgeting models for infrastructure management provide insights into balancing performance with energy efficiency, ensuring sustainable operations (Iziduh et al., 2024). These findings indicate that achieving low latency, reduced energy consumption, and cost efficiency requires an integrated approach that combines AI optimization, cloud computing, and financial analytics.

Environments

Scalability remains a significant challenge in deploying AI- driven AMR systems in high-demand micro-fulfillment environments. As order volumes increase, the complexity of coordinating multiple robotic agents and managing real-time data flows grows exponentially. Microservices architectures have been identified as a key enabler for scalability, allowing systems to handle increased workloads through modular and distributed processing (Akerele et al., 2024).

Frameworks

highlight the importance of scalable analytics systems that can process large volumes of operational data without performance degradation (Nwabekee et al., 2023). These approaches ensure that fulfillment systems can adapt to fluctuating demand levels while maintaining operational efficiency.

However, scalability challenges extend beyond system architecture to include resource allocation, supply chain integration, and operational resilience. AI-driven supply chain frameworks emphasize the need for predictive analytics to anticipate demand surges and optimize resource distribution (Uzozie et al., 2023). Inventory visibility and asset lifecycle management models also play a crucial role in ensuring that resources are efficiently utilized as system scale increases (Okonkwo et al., 2023). Furthermore, data- driven resilience frameworks demonstrate that scalable systems must incorporate adaptive learning mechanisms to respond to dynamic operational conditions (Mgbame et al., 2022). ERP-integrated logistics systems further support scalability by enabling seamless coordination across supply chain components (Omoegun et al., 2024b). These findings highlight that scalability in AMR-enabled fulfillment systems requires a holistic approach that integrates architecture, analytics, and operational strategy.

Hallenges, Risks, And Implementation Barriers

5.1 Technical Limitations and Algorithmic Constraints The deployment of AI-driven Autonomous Mobile Robots (AMRs) in micro-fulfillment centers is constrained by several technical limitations rooted in algorithmic design

And Computational Scalability. One Of The Primary

challenges is the handling of uncertainty in dynamic

Must

learning models, particularly those used for navigation and task allocation, often struggle with non-stationary data distributions, leading to degraded performance in highly variable warehouse conditions (Sanni & Wedraogo, 2024).

Additionally, unsupervised learning approaches used in anomaly detection may fail to accurately distinguish between operational variability and genuine system faults, resulting in false positives that disrupt workflow continuity (Iziduh et al., 2023). These limitations are further compounded by computational overheads associated with large-scale optimization algorithms, which can introduce

Environments

(Odejobi et al., 2023). Another critical constraint lies in model interpretability and ethical considerations. Black-box AI models, while highly accurate, often lack transparency, making it difficult to

Performance

effectively (Adeyelu et al., 2024). In operational settings, this opacity can hinder trust and limit the adoption of models used in logistics optimization may exhibit bias due to skewed training data, leading to suboptimal routing or inventory allocation decisions (Ashiedu et al., 2024). The integration of resilience-focused frameworks highlights the need for adaptive algorithms capable of learning from disruptions and recalibrating in real time, yet such systems remain computationally intensive and challenging to implement at scale (Uzozie et al., 2023). These technical constraints underscore the need for hybrid models that balance accuracy, interpretability, and computational efficiency.

5.2 Safety, Reliability, and Regulatory Considerations Safety and reliability considerations are central to the deployment of AMRs in micro-fulfillment environments, where continuous operation and human proximity introduce significant risk factors. Safety frameworks emphasize the integration of proactive hazard identification mechanisms and real-time monitoring systems to prevent accidents and system failures. The adoption of safety leadership models International Journal of Advanced Multidisciplinary Research and Studies

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ensures that operational protocols are consistently enforced, thereby reducing the likelihood of human error and system

Misuse (Arumosoye & Obriki, 2024). Furthermore,

environmental and occupational safety practices play a critical role in ensuring that AMR operations do not

In High-Density

warehouse environments (Obogo et al., 2024). Incident investigation frameworks provide structured methodologies for analyzing system failures, enabling organizations to implement corrective measures and improve overall system reliability (Obriki & Arumosoye, 2024).

Regulatory compliance introduces additional complexity, particularly in areas such as data security, system

The

integration of IoT-enabled AMR systems necessitates robust cybersecurity frameworks to protect against unauthorized access and potential system manipulation (Hassan et al., 2024). Similarly, compliance with occupational health

Ergonomic

considerations and health surveillance systems to monitor the impact of automation on human workers (Odujobi et al., 2024) as seen in Table 2. Regulatory frameworks in sectors such as healthcare and manufacturing further highlight the importance of risk mitigation strategies, including secure data handling and system redundancy (Okafor et al., 2023).

These considerations underscore the need for a holistic approach that integrates safety, reliability, and regulatory

Compliance Into The Design And Deployment Of Amr

Table 2: Safety, Reliability, and Regulatory Dimensions in AI-Driven AMR Deployment

Mplications

The integration of AMRs into micro-fulfillment centers significantly reshapes human–robot interaction dynamics and workforce structures. One of the primary implications is the transformation of traditional labor roles into hybrid human-machine collaboration models, where workers are required to interact with autonomous systems for task execution and monitoring. Effective human resource management strategies are essential to facilitate this transition, ensuring that employees are equipped with the necessary skills to operate and supervise robotic systems (Appoh et al., 2024). Organizational culture also plays a critical role in enabling knowledge transfer and fostering acceptance of automation technologies, particularly in environments where resistance to change may hinder adoption (Appoh et al., 2024).

Ergonomic considerations are equally important, as the physical and cognitive demands of interacting with AMRs can impact worker health and productivity. The integration of health surveillance systems and ergonomic design principles helps mitigate these risks, ensuring that human operators can safely and efficiently collaborate with robotic systems (Odujobi et al., 2024). Additionally, the adoption of AI-driven automation tools influences workforce dynamics by shifting the focus from manual tasks to strategic decision-making and system oversight (Ugbaja et al., 2024).

This shift is further supported by data-driven infrastructure models that enable flexible labor allocation and enhance operational efficiency (Nwabekee et al., 2023). The implications extend to customer-facing operations, where improved efficiency and accuracy in fulfillment processes contribute to enhanced service delivery and customer satisfaction (Ijomah et al., 2024). These developments highlight the need for comprehensive workforce strategies that address both technical and human factors.

Nfrastructure And Deployment Costs

The deployment of AMR systems in micro-fulfillment centers involves substantial infrastructure investments, encompassing hardware acquisition, software integration, and network optimization. Initial capital expenditure includes the procurement of robotic units, sensors, and supporting infrastructure such as charging stations and communication networks. Cloud computing platforms play a critical role in enabling real-time data processing and system coordination, but they also introduce ongoing operational costs associated with data storage, processing, and network bandwidth (Akerele et al., 2024). Cost optimization models highlight the importance of leveraging cloud-native architectures to reduce operational expenses, particularly through efficient resource allocation and dynamic scaling mechanisms (Owoade et al., 2023).

Beyond initial deployment, long-term cost considerations

Systems

provide a framework for optimizing resource utilization and reducing inefficiencies, thereby improving return on investment (Omoegun et al., 2024). Predictive procurement models further enhance cost efficiency by enabling organizations to anticipate demand fluctuations and optimize inventory management (Okonkwo et al., 2024).

Resilience

emphasize the need for strategic planning to ensure system sustainability and scalability (Iziduh et al., 2024). Additionally, real-time data visualization tools support decision-making by providing insights into operational

Continuous

optimization of fulfillment processes (Ogbuefi et al., 2024). These factors collectively underscore the complexity of infrastructure and deployment costs, highlighting the need for integrated financial and operational strategies.

Ecentralized Systems)

The evolution of AI-driven Autonomous Mobile Robots (AMRs) in micro-fulfillment centers is increasingly shaped by the convergence of digital twin technology, edge

System

architectures. Digital twins are transforming operational visibility by creating real-time virtual replicas of warehouse environments, enabling continuous monitoring, predictive simulation, and scenario-based optimization. Within micro- fulfillment contexts, digital twins allow operators to simulate robot traffic flows, inventory movement, and order fulfillment cycles, thereby identifying bottlenecks before

They Occur. For Example, A Digital Twin Model Can

dynamically adjust picking routes based on real-time congestion patterns, significantly improving throughput efficiency while reducing latency.

Edge AI further enhances system responsiveness by shifting computational processes closer to the physical environment. Instead of relying solely on centralized cloud infrastructure, AMRs equipped with edge computing capabilities can process sensor data locally, enabling faster decision-making for navigation, obstacle avoidance, and task execution. This

Fulfillment

environments where milliseconds of delay can propagate into significant operational inefficiencies. Additionally, decentralized coordination mechanisms, such as swarm

Operate

collaboratively without a single point of control. These systems leverage distributed algorithms to optimize task allocation and routing in real time, improving scalability and resilience. The integration of these emerging technologies represents a paradigm shift toward self-organizing, adaptive fulfillment ecosystems capable of responding dynamically to fluctuating demand and operational uncertainties.

6.2 Research Opportunities and Innovation Pathways The rapid advancement of AMR technologies presents a wide range of research opportunities aimed at addressing existing limitations and unlocking new capabilities in micro-

The Development Of Hybrid Ai Models That Combine

reinforcement learning, graph-based optimization, and probabilistic reasoning to enhance decision-making under uncertainty. Current models often struggle with dynamic environmental conditions, and future research can focus on creating adaptive algorithms that continuously learn from operational data while maintaining stability and robustness.

Additionally, integrating explainable AI techniques into AMR systems represents a significant opportunity to improve transparency and trust, particularly in safety-critical applications.

Another promising research direction involves the fusion of multimodal data sources, including visual, spatial, and temporal data, to improve perception and situational techniques can enable AMRs to better interpret complex warehouse environments, reducing errors in navigation and object recognition. Furthermore, the application of digital twin frameworks in experimental research can facilitate large-scale simulations, allowing researchers to evaluate system performance under diverse scenarios without disrupting real-world operations. Innovation pathways also extend to energy optimization, where intelligent charging strategies and energy-aware routing algorithms can significantly reduce operational costs. These research

For

interdisciplinary approaches that integrate robotics, data science, and systems engineering to advance the capabilities

The Adoption Of Ai-Driven Amr Systems In Micro-

fulfillment centers carries significant strategic implications for organizations seeking to enhance operational efficiency and competitiveness. One of the primary considerations is the alignment of technological capabilities with business objectives, particularly in terms of scalability, cost efficiency, and customer service performance. Organizations must evaluate the trade-offs between capital investment in automation infrastructure and the long-term benefits of increased throughput and reduced labor dependency.

Strategic planning should also account for the integration of

Amr Systems With Existing Warehouse Management

platforms and enterprise resource planning systems to ensure seamless data flow and operational coordination. Another critical implication is the transformation of

The

introduction of AMRs necessitates a shift toward more technical and analytical skill sets, requiring investment in workforce training and development. Companies must also address potential resistance to automation by fostering a culture of innovation and collaboration. From a competitive standpoint, early adopters of advanced AMR technologies are likely to gain significant advantages in terms of speed, accuracy, and flexibility in order fulfillment. Additionally, the ability to leverage real-time data analytics for decision- making enables organizations to respond more effectively to market fluctuations and customer demands. These strategic considerations underscore the importance of a holistic approach to technology adoption that balances operational, financial, and human factors.

Oncluding Remarks

The systematic evaluation of AI-driven Autonomous Mobile Robots in scalable micro-fulfillment centers reveals a transformative shift in how modern logistics systems are designed and operated. The integration of advanced AI algorithms, real-time analytics, and autonomous systems has redefined operational paradigms, enabling unprecedented levels of efficiency, adaptability, and scalability. Micro- fulfillment centers, as compact and technology-intensive environments, provide an ideal context for deploying AMRs, allowing organizations to achieve rapid order processing and improved last-mile delivery performance.

The findings highlight the critical role of intelligent system

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

Power [Kw]

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

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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