Aniform Springback Prediction

Abstract

We propose a new penalty, the springback penalty, for constructing models to recover an unknown signal from incomplete and inaccurate measurements. Mathematically, the springback penalty is a weakly convex function. It bears various theoretical and computational advantages of both the benchmark convex ℓ1 penalty and many of its non-convex surrogates that have been well studied in the literature. We establish the exact and stable recovery theory for the recovery model using the springback penalty for both sparse and nearly sparse signals, respectively, and derive an easily implementable difference-of-convex algorithm. In particular, we show its the- oretical superiority to some existing models with a sharper recovery bound for some scenarios where the level of measurement noise is large or the amount of measurements is limited. We also demonstrate its numerical robustness regardless of the varying coherence of the sensing ma- trix. The springback penalty is particularly favorable for the scenario where the incomplete and inaccurate measurements are collected by coherence-hidden or -static sensing hardware due to its theoretical guarantee of recovery with severe measurements, computational tractability, and numerical robustness for ill-conditioned sensing matrices.

Keywords. signal recovery, compressed sensing, penalty, weakly convex, difference-of-convex al-

Ntroduction

Signal recovery aims at recovering an unknown signal from its measurements, which are often incom- plete and inaccurate due to technical, economical, or physical restrictions. Mathematically, a signal recovery problem can be expressed as estimating an unknown ¯x ∈Rn from an underdetermined linear

(1.1)

where A ∈Rm×n is a full row-rank sensing matrix such as a projection or transformation matrix (see, e.g., [3, 6, 7]), b ∈Rm \ {0} is a vector of measurements, e ∈Rm is some unknown but bounded noise perturbation in B(τ) := {e ∈Rm : ∥e∥2 ≤τ}, and the number m of measurements is considerably smaller than the size n of the signal ¯x. The set B(τ) encodes both the cases of noise-free (τ = 0) and noisy (τ > 0) measurements.

Physically, a signal of interest, or its coefficients under certain transformation, is often sparse (see, e.g., ). Hence, it is natural to seek a sparse solution to the underdetermined linear system (1.1), though it has infinitely many solutions. We say that x ∈Rn is s-sparse if ∥x∥0 ≤s, where ∥x∥0 counts the number of nonzero entries of x. To find the sparsest solution to (1.1), one may consider

(1.2)

in which ∥x∥0 serves as a penalty term of the sparsity, and it is referred to as the ℓ0 penalty for convenience. Due to the discrete and discontinuous nature of the ℓ0 penalty, the model (1.2) is NP- hard . This means the model (1.2) is computationally intractable, and this difficulty has inspired

The Springback Penalty For Robust Signal Recovery

many alternatives to the ℓ0 penalty in the literature. A fundamental proxy of the model (1.2) is the

N This Convex Model, ∥X∥1 := Pn

i=1 |xi| and it is called the ℓ1 penalty hereafter. Recall that ∥x∥1 is the convex envelope of ∥x∥0 (see, e.g., ), and it induces sparsity most efficiently among all convex penalties (see ). The BP problem (1.3) has been intensively studied in voluminous papers since the seminal works [5, 6, 13], in which various conditions have been comprehensively explored for the exact recovery via the convex model (1.3).

The BP problem (1.3) is fundamental for signal recovery, but its solution may be over-penalized because the ℓ1 penalty tends to underestimate high-amplitude components of the solution, as analyzed in . Hence, it is reasonable to consider non-convex alternatives to the ℓ1 penalty and upgrade the model (1.3) to achieve a more accurate recovery. In the literature, some non-convex penalties have been well studied, such as the smoothly clipped absolute deviation (SCAD) , the capped ℓ1 penalty , the transformed ℓ1 penalty [29, 48], and the ℓp penalty with 0 < p < 1 [9, 10, 27].

Besides, one particular penalty is the minimax concave penalty (MCP) proposed in , and it has been widely shown to be effective in reducing the bias from the ℓ1 penalty . Moreover, the so-called ℓ1−2 penalty has been studied in the literature, e.g. [14, 44, 45], to mention a few. Some of these penalties will be summarized in Section 2. In a nutshell, convex penalties are more tractable in the senses of theoretical analysis and numerical computation, while they are less effective for achieving the desired sparsity (i.e., the approximation to the ℓ0 penalty is less accurate). Non-convex penalties are generally the opposite.

Considering the pros and cons of various penalties, we are motivated to find a weakly convex penalty that can keep some favorable features from both the ℓ1 penalty and its non-convex alter- natives, and the resulting model for signal recovery is preferable in the senses of both theoretical analysis and numerical computation. More precisely, we propose the springback penalty

(1.4)

where α > 0 is a model parameter, and it should be chosen meticulously. We will show later that a larger α implies a tighter stable recovery bound. On the other hand, a too large α may lead to

Α

(x). Thus, a reasonable upper bound on α should be considered to ensure the well-definedness of the springback penalty (1.4). In the following, we will see that if the matrix A is well-conditioned (e.g., when A is drawn from a Gaussian matrix ensemble), then the requirement on α is quite loose; while if A is ill-conditioned (e.g., A is drawn from an oversampled partial DCT matrix ensemble), then generally the upper bound on α should be better discerned for the sake of designing an algorithm with theoretically provable convergence. We refer to Theorem 3.1, Theorem 4.1, Section 5.2, and Section 6.2 for more detailed discussions on the determination of α for the springback penalty (1.4) theoretically and numerically. With the springback penalty (1.4), we propose the following model

(1.5)

Mathematically, the springback penalty (1.4) is a weakly convex function, and thus the springback- penalized model (1.5) can be intuitively regarded as an “average” of the convex BP model (1.3) and the mentioned non-convex surrogates. Recall that a function f : Rn →R is α-weakly convex if

∥X∥2

2 is convex. One advantage of the model (1.5) is that various results developed in the literature on weakly convex optimization problems (e.g., [21, 31]) can be used for both theoretical analysis and algorithmic design. Indeed, the weak convexity of the springback penalty (1.4) enables us to derive sharper recovery results with fewer measurements and to design some efficient algorithms easily.

The rest of this paper is organized as follows. In the next section, we summarize some preliminaries for further analysis. In Sections 3 and 4, we establish the exact and stable recovery theory of the springback-penalized model (1.5) for sparse and nearly sparse signals, respectively.

We Also

theoretically compare the springback penalty (1.4) with some other penalties in these two sections. In Section 5, we design a difference-of-convex algorithm (DCA) for the springback-penalized model (1.5) and study its convergence.

Some numerical results are reported in Section 6 to verify our theoretical assertions, and some conclusions are drawn in Section 7.

Preliminaries

In this section, we summarize some preliminaries that will be used for further analysis.

Notations

For any x, y ∈Rn, let ⟨x, y⟩= xTy be their inner product, and let supp(x) := {1 ≤i ≤n : xi̸ = 0} be the support of x. Let I be an identity matrix whose dimension is clear in accordance with the context. Let Λ ⊆{1, 2, . . ., n} (or Λ with some super/subscripts) be an index set, and |Λ| the cardinality of Λ. For x ∈Rn and A ∈Rm×n, let xΛ ∈Rn be the vector with the same entries as x on indices Λ and zero entries on indices Λc, and let AΛ ∈Rm×|Λ| be the submatrix of A with column indices Λ. For x ∈R, sgn(x) is the sign function of x. For a convex function f, ∂(f(x)) denotes the subdifferential of f at x.

A Glance At Various Penalties

In the literature, there are a variety of convex and non-convex penalties. Below we list six of the most important ones, with x ∈Rn.

I=1

|xi|p. ⋄The transformed ℓ1 (TL1) with parameter β > 0 [29, 48]:

(Β + 1)|Xi|

β + |xi| . ⋄The minimax concave penalty (MCP) with parameter µ > 0 :

I=1

|xi|2. Note that the ℓ1 penalty is convex, the elastic net penalty is strongly convex, and the others are non-convex.

Relationship Among Various Penalties

For any nonzero vector x ∈Rn and α > 0, the springback penalty RSPB

Α

(x) is reduced to the MCP in within the ℓ∞-ball {x ∈Rn : ∥x∥∞≤µ} if α = 1/µ. The springback penalty appears to be a resemblance to the ℓ1−2 penalty, but their difference is many-sided. For instance, the gradient of ∥x∥2 is not defined at the origin.

Figure 1 displays some scalar (one-dimensional) penalties, including the ℓ1 penalty, the ℓ0.5 penalty, the transformed ℓ1 penalty with β = 1, the MCP with µ = 0.75, and the springback penalty with α = 1/µ and α = 0.15. The ℓ1−2 penalty is not plotted, as it is none other than zero in the one-dimensional case. To give a better visual comparison, we scale them to attain the point (1, 1). It is shown in Figure 1 that the springback penalty is close to the ℓ1 penalty when α = 0.15.

The springback penalty with α = 1/µ coincides with the MCP for |x| ≤µ if we do not scale them. The behavior of the springback penalty for |x| > µ attracts our interest because it turns around and heads towards the x-axis. According to Figure 1, this behavior is clearer in terms of the threshold- ing operator corresponding to the proximal mapping of the springback penalty, whose mathematical descriptions are given in Section 2.4.

-0.5

Figure 1: Scalar penalties and corresponding thresholding operators (for representing proximal map- pings with λ = 0.25): the ℓ1 penalty and the soft thresholding operator; the ℓp penalty, whose proximal mapping has no closed-form expressions (hence no thresholding operator plotted); the transformed ℓ1 penalty with β = 1, whose proximal mapping can be expressed explicitly by a thresholding oper- ator given in ; the MCP with µ = 0.75 and the firm thresholding operator; and two springback penalties with α = 1/µ and α = 0.15, and the springback thresholding operator.

As mentioned, the proposed springback penalty (1.4) balances the approximation quality of the ℓ0 penalty and the tractability in analysis and computation, and it is in between the convex and non- convex penalties. More specifically, it is in between the ℓ1 penalty and the MCP. For any x ∈Rn, we can always find a parameter µ for the MCP such that ∥x∥∞≤µ with a resulting penalty in the

Form Of ∥X∥1 −∥X∥2

2/(2µ). This penalty inherits the approximation quality of the ℓ0 penalty from the MCP and the analytical and computational advantages of the ℓ1 penalty. Inasmuch as this penalty, we consider the more general penalty (1.4) in which 1/µ is replaced by a more flexible parameter α > 0.

Proximal Mappings And Thresholding Operators

For a function R : Rn →R, as defined in , the proximal mapping of R is defined as

(2.2)

where λ > 0 is a regularization parameter. In (2.2), we slightly abuse the notation “=”.

This

mapping takes a vector x ∈Rn and maps it into a subset of Rn, which might be empty, a singleton, or a set with multiple vectors; and the image of y under this mapping is a singleton if the function R is proper closed and convex . For a given optimization model, if the proximal mapping of its

The Springback Penalty For Robust Signal Recovery

objective function has a closed-form expression, then usually it is important and necessary to consider how to take advantage of this feature for algorithmic design. When the proximal mapping of a penalty can be represented explicitly, the closed-form represen- tation is often called a thresholding operator or a shrinkage operator in the literature. For example, as analyzed in , with the soft thresholding operator

Soft(W; Λ) = Sgn(W) Max{|W| −Λ, 0},

which has been widely used in various areas such as compressed sensing and image processing, the proximal mapping (2.2) of the ℓ1 penalty can be expressed explicitly by

I = Soft(Xi; Λ),

i = 1, . . , n. The proximal mapping of a non-convex penalty, in general, does not have a closed-form expression; such cases include the ℓ1−2 penalty and the ℓp penalty with 0 < p < 1. However, there are some particular non-convex penalties whose proximal mappings can still be represented explicitly.

For

instance, the transformed ℓ1 penalty and the MCP . In particular, with the following firm

|W| ≥Μ,

which was first proposed in , it was further studied in that the proximal mapping (2.2) of the MCP can be expressed explicitly by a firm thresholding operator for the case of orthonormal designs. More specifically, the proximal mapping (2.2) of the MCP is

I = Firm(Xi; Λ, Μ),

i = 1, . . , n. Below, we show that for the springback penalty (1.4) with a well chosen α, its proximal mapping can also be expressed explicitly.

Definition 2.1 The springback thresholding operator is defined as

(2.3)

Proposition 2.1 If 1 −λα > 0, then the proximal mapping of the springback penalty (1.4) can be

I = Springback(Xi; Λ, Α),

i = 1, . . , n. Proof. When R(x) = Rℓ1(x), it follows from (2.2) that, for any z ∈Rn satisfying 0 ∈z − y + λ∂(∥z∥1), there holds zi = soft(yi; λ), i.e., z = proxλ

= (1−λα)I to be positive definite. Thus, the optimization problem

Α

(x) in (2.2), for any z ∈Rn satisfying the condition



= springback(yi; λ, α). Hence, the assertion is proved.

□

Recall that the springback penalty (1.4) is a weakly convex function. Its thresholding operator defined in (2.3) is also in between the soft and firm thresholding operators. As limµ→∞firm(w; λ, µ) = soft(w; λ), a compromising µ could be large enough such that |w| ≤µ and it reaches a certain

The Springback Penalty For Robust Signal Recovery

compromise between the soft and firm thresholding operators. In this case, we have a particular

,

|w| ≥λ. If 1/µ is replaced by a more general α > 0, then the springback thresholding operator (2.3) is recovered.

Rationale Of The Name

Springback is a concept in applied mechanics (see, e.g., ).

Figure 1 Gives More Explanations

for naming (1.4) springback. With λ = 0.25, Figure 1 displays the thresholding operators for w ∈ [−1.5, 1.5], including the soft thresholding operator, the transformed ℓ1 thresholding operator with β = 1, the firm thresholding operator with µ = 0.75, and the springback thresholding operator with α = 1/µ. The transformed ℓ1 thresholding operator enforces w with |w| ≤λ(β + 1)/β to be 0, and then its outputs approach to w as |w| increases. All the other thresholding operators enforce w with |w| ≤λ to be 0. For w ≥λ, the soft thresholding operator subtracts λ from |w| and thus causes the ℓ1 penalty to underestimate high-amplitude components; the firm thresholding operator’s outputs jump from 0 to µ until |w| exceeds µ, afterwards its output is w. For the springback thresholding operator, its outputs jump from 0 to µ until |w| exceeds µ, and afterwards its outputs still keep going along the previous jumping trajectory.

In applied mechanics, spring is related to the process of bending some materials. When the bending process is done, the residual stresses cause the material to spring back towards its original shape, so the material must be over-bent to achieve the proper bending angle. Note that the soft thresholding operator always underestimates high-amplitude components, and the components ∥x∥1 and −α

∥X∥2

in the springback penalty are decoupled. If we deem the soft thresholding operator as a process of over-bending, which stems for the component ∥x∥1, then the output of the soft thresholding operator will be sprung back toward w, which is achieved separately in consideration with the component

∥X∥2

2. Such a springback process occurs for both λ ≤|w| ≤µ and |w| ≥µ. The springback behavior is more obvious for those w with larger absolute values, and this coincides with the behavior of the springback penalty in Figure 1. That is, once |x| exceeds µ, the penalty turns around and heads towards the x-axis. This process may also be explained as a compensation of the loss of |w| with |w| ≤λ.

Springback-penalized model for sparse signal recovery In this section, we focus on the recovery of a sparse signal using the springback-penalized model (1.5). After reviewing some basic knowledge of compressed sensing, we identify some conditions for exact and robust recovery using the springback-penalized model (1.5), respectively.

Ompressed Sensing Basics

In some seminal compressed sensing papers such as [4, 13], recovery conditions have been established for the BP model (1.3). These conditions rely on the restricted isometry property (RIP) of the sensing matrix A, as proposed in .

Definition 3.1 For an index set T ⊂{1, 2, . . ., n} and an integer s with |T | ≤s, the s-restricted isometry constant (RIC) of A ∈Rm×n is the smallest δs ∈(0, 1) such that

≤(1 + Δs)∥X∥2

for all subsets T with |T | ≤s and all x ∈R|T |. The matrix A is said to satisfy the s-restricted isometry property (RIP) with δs. Denoting by xopt the minimizer of the BP problem (1.3), if A satisfies δ3s < 3(1 −δ4s) −1, then

The Springback Penalty For Robust Signal Recovery

where Cs is a constant which may only depend on δ4s. We refer to [5, 6] for more details. If the measurements are noise-free, i.e., τ = 0, then the error bound (3.1) implies exact recovery. Exact recovery is guaranteed only in the idealized situation where ¯x is s-sparse and the measurements are noise-free. If the measurements are perturbed by some noise, then the bound (3.1) is usually referred to as the robust recovery result with respect to the measurement noise. In more realistic scenarios, we can only claim that ¯x is close to an s-sparse vector, and the measurements may also be contaminated.

In such cases, we can recover ¯x with an error controlled by its distance to s-sparse vectors, and it

(3.2)

where ¯xs is the truncated vector corresponding to the s largest values of ¯x (in absolute value), and C1,s and C2,s are two constants which may only depend on δ4s. The bound (3.2) is usually referred to as the stable recovery results. Recovery conditions for other models with different penalties are usually not as extensive as the BP model (1.3). Under the framework of the RIP or some generalized versions, recovery theory for the BP model (1.3) has been generalized to the ℓp-penalized model in [9, 17]. With the unique representation property of A, stable recovery results for the MCP-penalized

−¯Xi), But Not For ∥Xopt −¯X∥2,

was obtained. We recommend the monograph for a more comprehensive and detailed exhibition on compressed sensing. Recovery guarantee using the springback-penalized model Still denoting by xopt the minimizer of the springback-penalized model (1.5), we have the following exact and robust recovery results of the model (1.5) for an s-sparse ¯x.

Theorem 3.1 (recovery of sparse signals) Let ¯x ∈Rn be an unknown s-sparse vector to be recovered. For a given sensing matrix A ∈Rm×n, let b ∈Rm be a vector of measurements from b = A¯x + e with ∥e∥2 ≤τ, and let δ3s and δ4s be the 3s- and 4s-RIC’s of A, respectively. Suppose

(3.3)

then the minimizer xopt of the problem (1.5) satisfies xopt = ¯x when τ = 0; and it satisfies

(3.5)

Proof. Let xopt = ¯x + v, and Λ0 be the support of ¯x. It is clear that vΛ0 = xopt

We Continue By Arranging The Indices In Λc

0 in order of decreasing magnitudes (in absolute value)

The Springback Penalty For Robust Signal Recovery

the indices of the 3s largest entries (in absolute value) of vΛc

Largest Entries (In Absolute Value) Of Vλc

0, and so on. The cardinal number of Λℓmay be less than 3s.

I=2

∥vΛi∥2. As the magnitude of every vt indexed by t ∈Λi+1 is less than the average of magnitudes of vt indexed

S

. Together with ∥vΛ0∥1 ≤√s∥vΛ0∥2 ≤√s∥vΛ01∥2, we have



. With the assumption δ3s < 3(1 −δ4s) −1 on A, the coefficient of ∥vΛ01∥2 in (3.6) is positive and thus

F ⟨Xopt, V⟩≤0, Then ∥Av∥2 ≥D1∥V∥2

2. If ⟨xopt, v⟩> 0, then the condition (3.3) on α guarantees

≥0,

where we use the Cauchy–Schwarz inequality. Hence we also have ∥Av∥2 ≥D1∥v∥2 2. When τ = 0, the inequality Av = A(xopt −¯x) = b −b = 0 renders 0 = ∥Av∥2 ≥D1∥v∥2

, Which

implies ∥v∥2 = 0. Thus xopt = ¯x. When τ > 0, the inequality ∥Av∥2 = ∥Axopt −A¯x∥2 ≤∥Axopt −b∥2 + ∥A¯x −b∥2 ≤2τ

□

In analysis of signal recovery models with various convex and non-convex penalties, such as the ℓ1 penalty [6, 9] and the ℓ1−2 penalty [44, 45], a linear lower bound for ∥A(xopt −¯x)∥2 is derived somehow. The proof of Theorem 3.1 mainly follows the idea of , but we derive a quadratic lower bound for the term ∥A(xopt −¯x)∥2. Thus, it is worthy noting that our results cannot be reduced to the result of the BP model (1.3) as α →0. Indeed, the quadratic bound (3.6) in our proof is reduced to a linear bound as α →0, which then leads to the same results as the BP model (1.3). However, we handle our final quadratic bound by removing its linear and constant terms and hence the obtained result cannot be reduced to the result of the BP model (1.3) as α →0.

Besides, the condition (3.3) on α is required for the springback-penalized model (1.5).

T Is

impossible to choose an α satisfying (3.3) unless we have a priori estimation on ∥xopt∥2 before solving the problem (1.5). Thus, the condition (3.3) then can be interpreted as a posterior verification in the sense that it can be verified once xopt is obtained by solving the problem (1.5).

Remark 3.1 (Posterior verification) In practice, we solve the springback-penalized model (1.5) numerically and thus obtain an approximate solution, denoted by x∗, subject to a preset accuracy ǫ > 0. That is, ∥xopt −x∗∥2 ≤ǫ. Then, the posterior verification (3.3) is guaranteed if

√S

(√1 −δ4s + √1 + δ3s)(∥x∗∥2 + ǫ).

On The Exact And Robust Recovery

In Theorem 3.1, we establish conditions for exact and robust recovery using the springback-penalized model (1.5). Table 1 lists the exact recovery conditions for five other popular models in the literature. In particular, the springback-penalized model (1.5) and the ℓ1-penalized model, i.e., the BP model (1.3), have the same RIP condition. This condition is more stringent than that of the ℓp-penalized model (0 < p < 1) but weaker than those of the transformed ℓ1- and ℓ1−2-penalized models. Beside the RIP condition, there is an additional assumption a(s) > 1 for the ℓ1−2-penalized model, where a(s) was first derived in and slightly improved in as

2

. Note that a(s) < 3 was shown in [44, 45] for both the cases. Table 1: Exact recovery conditions recovery models with various penalties.

Δ3S < 3(1 −Δ4S) −1

We then discuss robust recovery results.

√D1 →∞.

However, for an appropriate α, the bound (3.4) is informative and attractive. The robust recovery results of the ℓ1-, ℓp-, transformed ℓ1- and ℓ1−2-penalized models were shown to be linear with respect to the level of noise τ [6, 9, 44, 45, 48], in the sense of

(3.8)

where Cs is some constant. Thus, under the conditions of Theorem 3.1, the bound (3.4) for the springback-penalized model (1.5) is tighter than (3.8) in the sense of

(3.10)

Assume that the recovery conditions listed in Table 1 are satisfied for each model, respectively. Then, we can summarize their corresponding ranges of τ in Table 2 such that the robust recovery bound (3.4) of the springback-penalized model (1.5) is tighter than all the others in the sense of (3.9).

Table 2: Ranges of the level of noise such that the springback bound (3.4) is tighter than the bound (3.8) in the sense of (3.9).

Penalty

When the springback bound (3.4) is tighter than the bound (3.8)

S·A(S))2

These ranges on τ look complicated. To have a better idea, we consider a toy example with s = 20, δ3s = 1/4, δ4s = 1/3, α = 1 for the spingback penalty (1.4), and β = 1 for the trans- formed ℓ1 penalty.

Then, the springback-penalized model (1.5) would give a tighter bound in the sense of (3.9) than the ℓ1-, ℓ0.2-, ℓ0.5-, ℓ0.999-, transformed ℓ1-, and ℓ1−2-penalized models if τ > 0.1385, 0.0271, 0.2333, 0.1391, 0.0807, and 2.8652 × 10−4, respectively.

Can we further improve the robust recovery result (3.4) in Theorem 3.1? The following proposition suggests a potential improvement. Moreover, without any requirement on α, this proposition also means, even if the posterior verification (3.3) is violated sometimes, the springback-penalized model (1.5) may still give a good recovery. Note that this proposition is only of conceptual sense, because its assumption ⟨xopt, xopt −¯x⟩≤0 is not verifiable. Nevertheless, it helps us discern a possibility of achieving a better recovery bound than (3.4).

Proposition 3.1 Let ¯x ∈Rn be an unknown s-sparse vector to be recovered. For a given sensing matrix A ∈Rm×n, let b ∈Rm be a vector of measurements from b = A¯x + e with ∥e∥2 ≤τ, and let δ3s and δ4s be the 3s- and 4s-RIC’s of A, respectively. Let xopt be the minimizer of the problem (1.5) and assume ⟨xopt, xopt −¯x⟩≤0. Suppose A satisfies δ3s < 3(1 −δ4s) −1, then xopt = ¯x when τ = 0;

(3.11)

when τ ≥0, where D1 is the constant (3.5) given in Theorem 3.1 and

(3.12)

Proof. In the case of ⟨xopt, v⟩≤0, it follows straightforwardly from (3.7) that

:=D1∥V∥2

2 + D2∥v∥2. The assumption δ3s < 3(1−δ4s)−1 guarantees D2 > 0. Hence, when τ = 0, as Av = A(xopt −¯x) = 0,

We Have 0 = ∥Av∥2 ≥D1∥V∥2

2 + D2∥v∥2, which implies ∥v∥2 = 0. When τ > 0, the inequality ∥Av∥2 = ∥Axopt −A¯x∥2 ≤∥Axopt −b∥2 + ∥A¯x −b∥2 ≤2τ

+ 8D1Τ −D2

. The assertion is proved.

The Springback Penalty For Robust Signal Recovery

Remark 3.2 The robust recovery result (3.11) is always better than (3.4) in Theorem 3.1 due to the subadditivity of the square root function. Under the conditions of Proposition 3.1, the bound (3.11) for the springback-penalized model (1.5) is tighter than (3.8) in the sense of

S

. Comparing with (3.10), this improvement enlarges the value range of τ. For example, if Cs is the coefficient in the result (3.1) of the BP model (1.3) , then 1 −D2Cs/2 is approximately 0.2679.

Springback-penalized model for nearly sparse signal recov-

Ery

We then study the stable recovery of the springback-penalized model (1.5) when ¯x is nearly sparse and the measurements are noisy. Recovery guarantee using the springback-penalized model If the signal ¯x to be recovered is nearly s-sparse, then we have the following stable recovery theorem for the springback-penalized model (1.5).

Theorem 4.1 (recovery of nearly sparse signals) Let ¯x ∈Rn be an unknown vector to be recovered. For a given sensing matrix A ∈Rm×n, let b ∈Rm be a vector of measurements from b = A¯x+e with ∥e∥2 ≤τ, and let δ3s and δ4s be the 3s- and 4s-RIC’s of A, respectively. Let ¯xs ∈Rn be the truncated vector corresponding to the s largest values of ¯x (in absolute value). Suppose A satisfies δ3s < 3(1 −δ4s) −1 and α satisfies (3.3), then the minimizer xopt of the problem (1.5)

(4.1)

where D1 is the constant (3.5) given in Theorem 3.1. Proof. Let xopt = ¯x + v, and Λ0 be the support of ¯xs.



. Recall the assumption δ3s < 3(1 −δ4s) −1. The coefficient of ∥vΛ01∥2 in (4.2) is positive, and it

−4

αD1∥¯x −¯xs∥1. If ⟨xopt, v⟩> 0, then the condition (3.3) on

≥0,

which is shown in the proof of Theorem 3.1. Hence, we also have ∥Av∥2 ≥D1∥v∥2

−4

αD1∥¯x −¯xs∥1. As ∥Av∥2 = ∥Axopt −A¯x∥2 ≤∥Axopt −b∥2 + ∥A¯x −b∥2 ≤2τ, we have

□

Similar to the improvement in Proposition 3.1, the above stable recovery result can be improved as follows. Proposition 4.1 Let ¯x ∈Rn be an unknown vector to be recovered. For a given sensing matrix A ∈Rm×n, let b ∈Rm be a vector of measurements from b = A¯x + e with ∥e∥2 ≤τ, and let δ3s and δ4s be the 3s- and 4s-RIC’s of A, respectively. Let xopt be the minimizer of the problem (1.5) and assume ⟨xopt, xopt −¯x⟩≤0. Let ¯xs ∈Rn be the truncated vector corresponding to the s largest values of ¯x (in absolute value). Suppose A satisfies δ3s < 3(1 −δ4s) −1, then xopt satisfies

,

where D1 and D2 are the constants (3.5) and (3.12) given in Theorem 3.1 and Proposition 3.1, respectively.

The Springback Penalty For Robust Signal Recovery

Proof. In the case of ⟨xopt, v⟩≤0, it follows straightforwardly from the estimation (4.3) that

+ D2∥V∥2 −4

αD1∥¯x −¯xs∥1. The assumption δ3s < 3(1 −δ4s) −1 guarantees D2 > 0. Therefore, it follows from the triangle

Inequality That

∥Av∥2 = ∥Axopt −A¯x∥2 ≤∥Axopt −b∥2 + ∥A¯x −b∥2 ≤2τ.

(4.4)

which gives the improved result by solving the system of inequalities (4.4) and ∥v∥2 ≥0.

On The Stable Recovery

If ¯x is known to be s-sparse, then the estimation (4.1) in Theorem 4.1 is reduced to (3.4) in Theorem 3.1; and if the measurements are additionally noise-free, then both the estimations (3.4) and (4.1) imply exact recovery of the signal ¯x. We compare the estimation (4.1) with the estimation (3.2) for the BP model (1.3). The following comparison is based on theoretical error bounds. We are interested in the case where the estimation (4.1) is tighter than the estimation (3.2) in the sense of

(4.6)

Note that s takes values among {1, 2, . . ., n} and the right-hand side of (4.6) decreases as s increases. If the left-hand side of (4.6) is smaller than the right-hand side of (4.6) for s = 1 and the left-hand side is larger than the right-hand side for s = n, then there must exist a constant C such that the inequality (4.5) holds for s ≤C. Besides, if ¯x is known to be s-sparse, then ∥¯x −¯xs∥1 = 0 and thus (4.6) implies the existence of C without any assumption. Therefore, we have the following corollary.

Corollary 4.1 If ¯x is s-sparse, then there exists a constant C such that the inequality (4.5) holds

(4.7)

When no information of the sparsity of ¯x is known, if α satisfies

,

then there exists a constant C such that the inequality (4.5) holds for s ≤C, where C depends on α, ¯x, τ, δ3s, and δ4s. In virtue of random matrix theory, we give two examples to show that the condition δ3s < 3(1 −δ4s) −1 on A in Theorems 3.1 and 4.1 holds.

• Random Gaussian matrices: the entries of A are i.i.d. Gaussian with mean zero and variance 1/m. It was shown in [6, 7] that the condition δ3s < 3(1 −δ4s) −1 holds with overwhelming probability when s ≤C′m/ log(n/m), where C′ is a constant. Similar results were extended to sub-gaussian matrices in .

• Fourier ensemble: A is obtained by selecting m rows from the n × n discrete Fourier transform and renormalizing the columns so that they are unit-normed. If the rows are selected at random, the condition δ3s < 3(1 −δ4s) −1 holds with overwhelming probability for s ≤C′m/(log(n))4, where C′ is a constant. This was initially considered in and then improved in .

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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