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Second Order Correctness of Perturbation Bootstrap M-Estimator of Multiple Linear Regression Parameter∗

Debraj Dasa And S. N. Lahirib

Abstract. Consider the multiple linear regression model yi = x′

Iβ + Ǫi, Where Ǫi’S Are Independent

and identically distributed random variables, xi’s are known design vectors and β is the p × 1 vector of parameters. An effective way of approximating the distribution of the M-estimator ¯βn, after proper centering and scaling, is the Perturbation Bootstrap Method. In this current work, second order results of this non-naive bootstrap method have been investigated. Second order correctness is important for reducing the approximation error uniformly to o(n−1/2) to get better inferences. We show that the classical studentized version of the bootstrapped estimator fails to be second order correct. We introduce an innovative modification in the studentized version of the bootstrapped statistic and show that the modified bootstrapped pivot is second order correct (S.O.C.) for approximating the distribution of the studentized M-estimator. Additionally, we show that the Perturbation Bootstrap continues to be S.O.C. when the errors ǫi’s are independent, but may not be identically distributed. These findings establish perturbation Bootstrap approximation as a significant improvement over asymptotic normality in the regression M-estimation.

Keywords: M-Estimation, S.O.C., Perturbation Bootstrap, Edgeworth Expansion, Studentization, Residual Bootstrap, Generalized Bootstrap, Wild Bootstrap. 1.

(1.1)

where y1, . . , yn are responses, ǫ1, . , ǫn are independent and identically distributed (IID) random variables with common distribution F (say), x1, . , xn are known non random design vectors and β is the p-dimensional vector of parameters.

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Das D. and Lahiri S. N. Suppose ¯βn is the M-estimator of β corresponding to the objective function Λ(·) i.e.

I=1 Λ(Yi −X′

it). Now if ψ(·) is the derivative of Λ(·), then ¯βn is the M- estimator corresponding to the score function ψ(·) and is defined as the solution of the

Xiψ(Yi −X′

iβ) = 0. It is known [cf. Huber(1981)] that under some conditions on the objective function, design vectors and error distribution F; ( ¯βn −β) with proper scaling has an asymptotically normal distribution with mean 0 and dispersion matrix σ2Ip where σ2 = Eψ2(ǫ1)/E2ψ′(ǫ1).

After introduction of bootstrap by Efron in 1979 as a resampling technique, it has been widely used as a distributional approximation method. Resampling from the naive empirical distribution of the centered residuals in a regression setup, called residual bootstrap, was introduced by Freedman (1981). Freedman (1981) and Bickel and Freedman (1981b) had shown that given data, the conditional distribution of √n(β∗

N −¯Βn) Converges To The Same

normal distribution as the distribution of √n( ¯βn −β) when ¯βn is the usual least square estimator of β, that is, when Λ(x) = x2. It implies that the residual bootstrap approximation to the exact distribution of the least square estimator is first order correct as in the case of normal approximation. The advantage of the residual bootstrap approximation over normal approximation for the distribution of linear contrasts of least square estimator for general p was first shown by Navidi (1989) by investigating the underlying Edgeworth Expansion (EE); although heuristics behind the same was given by Liu (1988) in restricted case p = 1.

Consequently, EE for the general M-estimator of β was obtained by Lahiri (1989b) when p = 1; whereas the same for the multivariate least square estimator was found by Qumsiyeh (1990a). EE of standardized and studentized versions of the general M-estimator in multiple linear regression setup was first obtained by Lahiri (1992). Lahiri (1992) also established the second order results for residual bootstrap in regression M-estimation.

A natural generalization of sampling from the naive empirical distribution is to sample from a weighted empirical distribution to obtain the bootstrap sample residuals. Broadly, the resulting bootstrap procedure is called the weighted or generalized bootstrap. It was introduced by Mason and Newton (1992) for bootstrapping mean of a collection of IID ran- dom variables. Mason and Newton (1992) considered exchangeable weights and established its consistency. Lahiri (1992) established second order correctness of generalized bootstrap in approximating the distribution of the M-estimator for the model (1.1) when the weights are chosen in a particular fashion depending on the design vectors. Wellner and Zhan (1996) proved the consistency of infinite dimensional generalized bootstrapped M-estimators. Con-

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sequently, Chatterjee and Bose (2005) established distributional consistency of generalized bootstrap in estimating equations and showed that generalized bootstrap can be used in order to estimate the asymptotic variance of the original estimator. Chatterjee and Bose (2005) also mentioned the bias correction essential for achieving second order correctness.

An important special case of generalized bootstrap is the bayesian bootstrap of Rubin (1981). Rao and Zhao (1992) showed that the distribution function of M-estimator for the model (1.1) can be approximated consistently by bayesian bootstrap. See the monograph of Barbe and Bertail (2012) for an extensive study of generalized bootstrap.

A close relative to the generalized bootstrap procedure is the wild bootstrap. It was in- troduced by Wu (1986) in multiple linear regression model (1.1) with errors ǫi’s being het- eroscedastic. Beran (1986) justified wild bootstrap method by pointing out that the distri- bution of the least square estimator can be approximated consistently by the wild bootstrap approximation. Second order results of wild bootstrap in heteroscedastic regression model was first established by Liu (1988) when p = 1. Liu (1988) also showed that usual residual bootstrap is not capable of approximating the distribution of the least square estimator upto second order in heteroscedastic setup and described a modification in resampling procedure which can establish second order correctness. For general p, the heuristics behind achieving second order correctness by wild bootstrap in homoscedastic least square regression were discussed in Mammen (1993). Recently, Kline and Santos (2011) developed a score based bootstrap method depending on wild bootstrap in M-estimation for the homoscedastic model (1.1) and established consistency of the procedure for Wald and Lagrange Multiplier type tests for a class of M-estimators under misspecification and clustering of data.

A novel bootstrap technique, called the perturbation bootstrap was introduced by Jin, Ying, and Wei (2001) as a resampling procedure where the objective function having a U- process structure was perturbed by non-negative random quantities. Jin, Ying, and Wei (2001) showed that in standardized setup, the conditional distribution of the perturbation resampling estimator given the data and the distribution of the original estimator have the same limiting distribution which means this resampling method is first order correct without studentization. In a recent work, Minnier, Tian, and Cai (2011) also applied this perturbation resampling method in penalized regression setup such as Adaptive Lasso, SCAD, lq penalty and showed that the standardized perturbed penalized estimator is first order correct. But, second order properties of this new bootstrap method have remained largely unexplored in the context of multiple linear regression. In this current work, the perturbation bootstrap approximation is shown to be S.O.C. for the distribution of studentized M-estimator for the regression model (1.1). An extension to the case of independent and non-IID errors is also established, showing the robustness of perturbation bootstrap towards the presence

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Das D. and Lahiri S. N. of heteroscedasticity. Therefore, besides the existing bootstrap methods, the perturbation bootstrap method can also be used in regression M-estimation for making inferences regard- ing the regression parameters and higher order accuracy can be achieved than the normal approximation.

A classical way of studentization in bootstrap setup, in case of regression M-estimator and for IID errors, is to consider the studentization factor to be σ∗

N Being The Perturbation

bootstrapped estimator of β, defined in Section 2. Although the residual bootstrapped es- timator is S.O.C. after straight-forward studentization, the same pivot fails to be S.O.C. in the case of perturbation bootstrap. Two important special cases are considered as examples in this respect. The reason behind this failure is that although the bootstrap residuals are sufficient in capturing the variability of the bootstrapped estimator in residual bootstrap, it is not enough in the case of perturbation resampling. Modifications have been proposed as remedies and are shown to be S.O.C. The modifications are based on the novel idea that the variability of the random perturbing quantities G∗

I (1 ≤I ≤N) Along With The Bootstrap

residuals are required to capture the variability of the perturbation bootstrapped estimator; whereas individually they are not sufficient. For technical details, see Section 4.2 and Section 5.1.

With a view to establish second order correctness, we start with the standardized setup and then proceed to studentization. First, we find a two-term EE of the conditional density of a suitable stochastic approximation of the concerned bootstrapped pivot and then we show that it is the required two-term EE corresponding to the bootstrapped pivot. The result then follows by comparing the EE of the bootstrapped pivot with that of underlying original pivot. The techniques that are to be used in finding EE have been demonstrated and discussed in Bhattacharya and Ghosh (1978), Bhattacharya and Rao (1986), Navidi (1989) and Lahiri (1992).

A significant volume of work is available in bootstrapping M-estimators. We will con- clude this section by briefly reviewing the literature. Bootstrapping M-estimators in linear model has been studied by Navidi(1989), Lahiri (1992, 1996), Rao and Zhao (1992), Qum- siyeh (1994), Karabulut and Lahiri (1997), Jin, Ying and Wei (2001), Hu (2001), El Bantli (2004) among others. And in the applications other than linear model, bootstrapping in M-estimation and its subclasses has been investigated by Arcones and Gin´e (1992), Lahiri (1994), Wellner and Zhan (1996), Allen and Datta (1999), Hu and Kalbfleisch (2000), Hlavka (2003), Wang and Zhou (2004), Chatterjee and Bose (2005), Ma and Kosorok (2005), Lahiri and Zhu (2006), Cheng and Huang (2010), Feng et. al. (2011), Lee (2012), Cheng (2015), among others.

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The rest of the paper is organized as follows. Perturbation bootstrap is described briefly in Section 2. Section 3 states the assumptions and motivations behind considering those assumptions. Main results for IID case, along with the modification in bootstrap studenti- zation, are stated in Section 4. An extension to the case of independent and non-IID errors is proposed in Section 5. An outline of the proofs are given in Section 6. Section 7 states concluding remarks. The details of the proofs are available in a supplementary material Das and Lahiri (2017).

2. Description Of Perturbation Bootstrap

In the perturbation bootstrap, the objective function Λ(·) has been perturbed several times by a non-negative random quantity to get a bootstrapped estimate of β. It has nothing to do with residuals in resampling stage, unlike the residual and weighted bootstrap. More precisely, the perturbation bootstrap estimator β∗

I

or in terms of the score function ψ(·), as the solution of the vector equation

Where G∗

i , i ∈{1, . . , n} are non-negative and non-degenerate completely known random variables, considered as perturbation quantities. Note that, if µG∗is the mean of G∗

I ¯Βn,

i ∈{1, . . , n}, are the residuals corresponding to the M-estimator ¯βn. This observation will be helpful in finding a suitable stochastic approximation in bootstrap regime. For details, see Section 6.

The central idea of the perturbation bootstrap is to draw a relatively large collection of

1 , . . . , G∗B

n ) : b = 1, . . , B} from the distribution of G∗

1 And Then To

find the conditional empirical distribution of √n(β∗

I = 0

for each b ∈{1, . . , B}; to approximate the distribution of √n( ¯βn −β) asymptotically. As a result the bootstrapped distribution may be used as an approximation to the original distribution, just like the normal approximation, in constructing confidence intervals and testing of hypotheses regarding β.

6

Das D. and Lahiri S. N. Now, in the perturbation bootstrap M-estimation, G∗

I ’S Can Be Thought Of As Weight Cor-

responding to the ith data point (xi, yi). To make it easier to understand, consider the least

Indicating That The Perturbing Quantities G∗

i ’s can be thought of as weights. Remark 2.1. Consider the least square estimator ˆβn. Then keeping the asymptotic prop- erties fixed, the perturbation bootstrap version ˆβ∗

I = X′

i ˆβn + ˆǫiti, i ∈{1, . . , n} and {t1, . , tn} is a set of IID random variables independent of {ǫ1, . , ǫn} with Et1 = 0, Var(t1) = 1. Additionally, one needs E(t3

1) = 1

for establishing second order correctness of wild bootstrap approximation [cf. Liu (1988), Mammen (1993)]. Now Looking at (2.3) and (2.4) and in view of assumption (A.5)(ii), it can be said that the perturbation bootstrap coincides with the wild bootstrap in least square setup. Therefore one can view perturbation bootstrap as a generalization of the wild bootstrap in regression M-estimation.

Remark 2.2. There is a basic difference between perturbation bootstrap and weighted bootstrap with respect to the construction of the bootstrapped estimator. Whereas in the perturbation bootstrap, the bootstrapped estimator is defined through the non-negative and non-degenerate random perturbations of the objective function; in weighted bootstrap, the bootstrapped estimator is defined through bootstrap samples drawn from a weighted empir- ical distribution. See for example the construction of the weighted bootstrapped estimator corresponding to Theorem 2.3 of Lahiri (1992) and compare it with our construction as

7

stated in Section 2. However, as pointed out by a referee, one can think of the perturba- tion bootstrap, defined in Section 2, as the weighted bootstrap version of some statistical functional if the design vectors are random. Suppose, {(x1, y1) . . , (xn, yn)} are IID with underlying probability measure Q. Then one can write



for some statistical functional T(·). Define empirical measures Qn = n−1 Pn

1(Xi, Yi)Wi Where

1(·) is the indicator function and {W1, . . , Wn} are

I , I ∈{1, . . . , N}. The

weighted bootstrap of general statistical functionals of only the IID random variables is con- sidered in the monograph of Barbe and Bertail (2012). Second order correctness of weighted bootstrap of standardized mean of IID random variables was established by Haeusler et. al.

(1992) under two choices of weights. One choice is the non-negative IID weights and the other one is the self-normalized sum of non-negative IID random variables. Their results were extended by Barbe and Bertail (2012) for general statistical functionals in IID case when the weights are self-normalized sum of non-negative IID random variables [cf. Corol- lary 4.1 of Barbe and Bertail (2012)]. For general M-estimation, Chatterjee (1999) showed that weighted bootstrap estimator is generally biased and established its second order cor- rectness after properly correcting for the bias. To the best of our knowledge, there is no second order result available in the literature under studentized setup for general statistical functional. In this article, we have assumed the design vectors to be non-random, implying that our setup fits neither in the general statistical functional setup of Barbe and Bertail (2012) nor in the general M-estimation setup of Chatterjee (1999); although Theorem 5.1 continue to hold when the design is random. Throughout the article we consider weights to be non-negative IID. Our main motivation is to explore second order results in studentized setup which, unlike the standardized (i.e., the known variance) case, is applicable in practice.

Further, we prove our results in the situation when errors are heteroscedastic. We establish all our second order correctness results without requiring any bias correction.

3. Assumptions

Suppose, xi = (xi1, xi2, . . , xip)′. Define, Dn ≡D = (

I2, Xi2Xi3, . . . , Xi2Xip, . . . , X2

ip)′ . Note that for any constants ai, . . , an ∈R, Pn

I=1 Aixix′

i = 0. Hence, {z1, . . , zn} are linearly independent

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Das D. and Lahiri S. N.

If And Only If {Xix′

i : 1 ≤i ≤n} are linearly independent. Therefore, rn = the rank of

I=1 Ziz′

i is nondecreasing in n. So, if r = max{rn : n ≥1} then without loss of generality (w.l.g.), we can assume that rn = r for all n ≥q. Consider canonical decomposition of



where L is a q × q non-singular matrix. Partition L as L′ = [L′

Iψ′(Ǫ1))′. ˘Zi = (Z′

i, n−1)′. Let, ΦV denotes the normal distribution with mean 0 and dispersion matrix V and φV is the density of ΦV. Write ΦV = Φ and φV = φ when V is the identity matrix. h′, h′′ denote respectively first and second derivatives of real valued function h that is twice differentiable.

Also ||.|| denotes euclidean norm.For any set B ∈Rp and ǫ > 0, δB denotes the boundary of B, |B| denotes the cardinality of B and Bǫ = {x : x ∈Rp and d(x, B) < ǫ} where d(x, B) = inf{||x −y|| : y ∈B}. For a function f : Rl →R and a non-negative integral

J F Denotes Αj Times Partial

derivative of f with respect to the jth component of its argument, 1 ≤j ≤l. Also assume that (e1, . . , ep)′ is the standard basis of Rp. Let, P∗and E∗respectively denote conditional bootstrap probability and conditional expectation of G∗

1 Given Data. The Class Of Sets B

denotes the collection of borel subsets of Rp satisfying

Next We State The Assumptions:

(A.1) ψ(·) is twice differentiable and ψ′′(·) satisfies a Lipschitz condition of order α for some 0 < 2α ≤1.

(A.2)

(i) An →A1 as n →∞for some positive definite matrix A1.

I=1 Viv′

i) →A2 as n →∞for some non-singular matrix A2, where expectation is with respect to F.

I=1 ˜Vi˜V′

i) →A3 as n →∞for some non-singular matrix A3 where ˜vi is defined as same way as vi with zi being replaced by ˘zi.

(A.3)

(i) Eψ(ǫ1) = 0 and σ2 = Eψ2(ǫ1)/E(ψ′(ǫ1)) ∈(0, ∞). (ii) E|ψ(ǫ1)|4 + E|ψ′(ǫ1)|4 + E|ψ′′(ǫ1)|2 < ∞.

(A.4) G∗

i and ǫi are independent for all 1 ≤i ≤n.

1 −Μg∗

 < 1.

I,¯Z′

i)′ where ¯xi = xiψ(¯ǫi), ¯zi = ziψ′(¯ǫi); {¯ǫ1, . . , ¯ǫn} being the set of residuals.

I Is Non-

singular for sufficiently large n. Hence, without loss of generality the canonical decomposition

B′ = Ik

where k = p + q and B is a k × k non-singular matrix. Define k × 1 vector ˘vi by

1 ≤I ≤N

To find valid EE in the perturbation bootstrap regime, the following condition [cf. Navidi

(1989)] Is Also Required:

(A.7) There exists a δ > 0 such that −Kn(δ)/logγn →∞where Bn(δ) = {1 ≤i ≤n :

It)2 > Δγ2

n for all t ∈Rk with ||t||2 = 1}, Kn(δ) = |Bn(δ)|, the cardinality of

The Set Bn(Δ), And Γn = (Pn

i=1 ||˘vi||4)1/2. But note that the condition (A.7) has already been satisfied in our set up due to Lemma 6.2 and the proposition in Lahiri (1992).

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Das D. and Lahiri S. N. Now we briefly explain the assumptions. Assumption (A.1) is smoothness condition on the score function ψ(·). This condition is essential for obtaining a Taylor’s expansion of ψ(·) around regression errors. Assumption (A.2) presents the regularity conditions on the design vectors necessary to find EE. For the validity of asymptotic normality of the regression M- estimator, only (A.2)(i) is enough [cf. Huber (1981)]; whereas additional condition (A.2)(ii) is required for the validity of the EE. (A.2)(iii) states atmost how fast the L2 norm of the design vectors can increase to get a valid EE. This condition is somewhat stronger than the condition (C.6) assumed in Lahiri (1992); although there was a reduction in accuracy of bootstrap approximation due to this relaxation. This type of conditions are quite common in the literature of edgeworth expansions in regression setup; see for example Navidi (1989), Qumsiyeh (1990a). We now state an example where assumption (A.2) (iii) is fulfilled.

Example 3.1. Suppose, {X(1), . . , X(p)} is a set of independent random vectors where X(j) = (X1j, . , Xnj)′ is a vector of n IID copies of the non-degenerate random variable X1j, j ∈{1, . , p}. Define, p × p matrix M = ((mjk))j,k=1,...,p where mjk = E(X2

1J) = 0 And E|X1J|8 < ∞For All

j ∈{1, . . , p} and det(M)̸ = 0. Then for the design matrix X, assumption (A.2) (iii) holds with probability 1 (w.p. 1).

Proof :

For the design matrix X, xi = (Xi1, Xi2, . . , Xip)′ and zi = (X2

, Xi2Xi3, . . . , Xi2Xip, . . . , X2

ip)′ for i ∈{1, . . , n}. First note that if all the entries of X are IID then the condition det(M)̸ = 0 is redundant. By Kolmogorov strong law of large numbers, An = n−1D2 →diag

I →N W.P.1, For Some Positive

definite matrix N. This implies that ||L|| = O(n−1/2) w.p.1 and hence

(3.3)

Therefore, our claim follows from (3.2) and (3.3).

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Assumption (A.3) is the moment condition on the error variables through the score func- tion ψ(·). (A.3)(i) is generally assumed to establish asymptotic normality. Assumption (A.4)

Is Inherent In The Present Setup, Since G∗

i ’s are introduced by us to define the bootstrapped estimator whereas ǫi’s are already present in the process of data generation. The conditions present in Assumption (A.5) are moment and smoothness conditions on the perturbing quan-

Tities G∗

i ’s, required for the valid two term EE in bootstrap setup. The Cramer’s condition is very common in the literature of edgeworth expansions. Cramer’s condition is satisfied when

1)2) Has A Non-Degenerate Compo-

nent which is absolutely continuous with respect to Lebesgue measure [cf. Hall (1992)]. An

1 Is Beta(Γ, Δ) Where 3Γ = Δ = 3/2. Also One Can

investigate Generalized Beta family of distributions for more choices of the distribution of

G∗

1. Assumption (A.6) is the Cramer’s condition on the errors. Although this assumption is not needed for obtaining EE of the bootstrapped estimators, it is needed for obtaining EE for the original M-estimator.

Note that the condition (A.7) is somewhat abstract. Hence as pointed out by a referee, some clarification would be helpful. To this end, it is worth mentioning that to find formal EE for the standardized bootstrapped pivot (see section 4.1), the most difficult step is to



. Now it is easy to see that for any |α| ≤p + q + 4, |DαE∗eit′T∗

N|

is bounded above by a sum of n|α|-terms, each of which is bounded above by

N And C(Α) Is A Constant Which

depends only on α.



Hence, in view of Cramer’s condition (A.5) (iii) and Lemma 6.2, if there exists a sequence of sets {Jn}n≥1 such that Jn ⊂{1, . . , n} and for all i ∈Jn, γ−1

12

Das D. and Lahiri S. N.

Again |I∗C

n ∩Jn| ≥|Jn| −|α| and γn ≥kn−1. Therefore, to achieve (3.4), it is enough to have

N2(P+Q)+4 · Θ|Jn|−(P+Q+4) = O(N−1/2)

Hence due to Lemma 6.2, it is enough to have |Jn| ≥an−C ·log γn for some positive constant C and a sequence of constants {an} increasing to ∞. This observation together with (3.5) justifies condition (A.7).

We will denote the assumptions (A.1)-(A.5) by (A.1)′-(A.5)′ when (A.2) and (A.5) are respectively defined with (ii)′ and (iii)′ instead of (ii) and (iii).

4.1. Rate Of Perturbation Bootstrap Approximation

Here we will state the approximation results both in standardized and studentized setup. It is well known that √n ¯βn has asymptotic variance σ2A−1

N ( ¯Βn −Β). Now To Define The Standardized

version of the corresponding bootstrapped statistic β∗

N, We Need Its Conditional Asymptotic

variance, given the data. Using Taylor’s expansion, it is quite easy to get the conditional

¯A−1

2n are well defined for sufficiently large sample size n due to the assumption (A.2)(i) and (A.3)(ii). Hence, the standardized bootstrapped M-estimator F∗

¯A1N, ¯A1/2

2n being defined in terms of the spectral decomposition of ¯A2n; although it can be defined in many different ways [cf. Lahiri (1994)]. Under some regularity conditions, both the distribution of Fn and the conditional distribution of F∗

N Can Be Shown

to be approximated asymptotically by a Normal distribution with mean 0 and variance Ip. Hence, it is straightforward that perturbation bootstrap approximation to the distribution

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of the M-estimator is first order correct. The second order result in standardized case is formally stated in Theorem 4.1. Proposition 4.1.

Suppose, the assumptions (A.1)-(A4), (A.5)(i) hold. Then there exist constant C1 > 0 and a sequence of Borel sets Q1n ⊆Rn, such that P((ǫ1, . . , ǫn) ∈Q1n) →1 as n →∞, and given (ǫ1, . , ǫn) ∈Q1n, n ≥C1 such that there exists a sequence of statistics

≥1 −Δnn−1/2

where δn ≡δn(ǫ1, . . , ǫn) tends to 0. Theorem 4.1.

Let {Β∗

n}n≥1 be a sequence of statistics satisfying Proposition 4.1 depending on (ǫ1, . . , ǫn). Assume, the assumptions (A.1)-(A.5) hold. (a) Then there exist constant C2 > 0 and a sequence of Borel sets Q2n ⊆Rn and

1 And On Ψ(·), Ψ′(·)

& ψ′′(·) through the residuals {¯ǫ1, . . , ¯ǫn} such that given (ǫ1, . , ǫn) ∈Q2n, with

N(X) = (1 + N−1/2A∗

n(x, ψ, G∗))φ(x) and δn ≡δn(ǫ1, . . , ǫn) tends to 0. (b) Suppose in addition assumption (A.6)(i) holds. Then we have,

= Op(N−1/2)

Now, the quantity σ2 is mostly unavailable in practical circumstances. Hence, the non- pivotal quantity like Fn is very rare in use in providing valid inferences. It is more reasonable to explore the asymptotic properties of a pivotal quantity, like the studentized version of the M-estimator ¯βn. Depending on the observed residuals ¯ǫi = yi −xi′ ¯βn, i ∈{1, . . , n}, the

N = N−1 Pn

i=1 ψ2(¯ǫi). Hence, the studentized M-estimator in regression setup may be

N A1/2

n ( ¯βn −β). Define the studentized version of the corresponding

Are As

defined earlier.

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Das D. and Lahiri S. N. Theorem 4.2. Suppose, the assumptions (A.1)-(A.5) hold.

(a) Then there exist constant C3 > 0 and a sequence of Borel sets Q3n ⊆Rn and

1 And On Ψ(·), Ψ′(·)

& ψ′′(·) through the residuals {¯ǫ1, . . , ¯ǫn}, such that given (ǫ1, . , ǫn) ∈Q3n, with

N(X) = (1 + N−1/2˜A∗

n(x, ψ, G∗))φ(x) and δn ≡δn(ǫ1, . . , ǫn) tends to 0. Suppose in addition assumption (A.6)(i)′ holds. Then (b) for the collection of Borel sets B defined by (3.1),

= Op(N−1/2)

(c) if 2Eψ2(ǫ1)Eψ(ǫ1)ψ′(ǫ1)̸ = Eψ′(ǫ1)Eψ3(ǫ1), then there exists ǫ > 0 such that,

= 1

Remark 4.1. Proposition 4.1 states that there exists a sequence of perturbation boot-

Strapped Estimator Β∗

n within a neighborhood of length C.n−1/2(logn)1/2 around the original M-estimator ¯βn outside a set of bootstrap probability op(n−1/2). This existence result is es- sential in finding valid EEs in bootstrap regime. This can be compared with Theorem 2.3 (a) of Lahiri (1992), where similar kind of result was shown in case of residual and generalized bootstrap.

Remark 4.2. Note that, where as the error term in approximating the distribution of M- estimator by perturbation bootstrap is of order Op(n−1/2) in the prevalent studentize setup, it reduces the order of the error of approximation to op(n−1/2) in simple standardized setup.

This means that the difference between coefficients corresponding to the term n−1/2 in the EEs of original and bootstrapped estimator can be made arbitrarily small in standardized setup, but not in usual studentized setup.

Remark 4.3. To understand part (c) of Theorem 4.2, consider the usual least square estimator. In least square setup, the condition in the Theorem 4.2 (c) reduces to Eǫ3̸ = 0. This simply means that if the studentization in perturbation bootstrapped version is performed analogously as in case of original least square estimator, then the bootstrap distribution can

15

not correct the original distribution upto second order. If this is investigated more deeply, then it can be observed that the usual studentized perturbation bootstrap approximation can not correct for the skewness of the error distribution F.

4.1.1. Examples

Theorem 4.2 concludes that the standard way of performing studentization of the boot- strapped estimator is first order correct. In order to show that the usual studentized setup is not second order correct, we consider following two important special cases with ψ(x) = x.

Example 4.1

Consider the observations {y1, . . , yn} are coming from the distribution F with a location shift µ. This in terms of regression model becomes

Yi = Μ + Ǫi

Hence, in this setup p = 1, β = µ and xi = 1 for all i ∈{1, . . , n}. It can be shown that in this setup, ˜ξn(·) and ˜ξ∗

11 As Well As ˜B∗

31 are not converging respectively to ˜b11 and ˜b31 in probability and hence the perturbation bootstrap method is not second order correct in the above setup when the bootstrapped estimator is studentized in the usual manner.

16

Das D. and Lahiri S. N. where β0 and β1 are parameters of interest and ǫi’s are IID errors. This model, in terms of our multivariate linear regression structure, can be written as yi = ˜x′

Iβ + Ǫi Where Β = (Β0, Β1)′

and ˜xi = (1, xi)′. Hence, the EEs of the original and bootstrapped estimators upto the order o(n−1/2), after usual studentization, respectively becomes

E1, . . . , Ep

′ is the standard basis of Rp, j = 1 or 2, γ1 is the coefficient of skewness

I . ¯A2N Is As

defined in general setup with ˜xi in place of xi for i ∈{1, . . , n}. The form of the coefficients

31

are given in the supplementary material Das and Lahiri (2017) for all (j1, j2) ∈{(a, b) : a, b ∈{0, 1, 2, 3} and a + b = 3}.

Note That, The Coefficients ˜B(J)

11 , 1 ≤j ≤p, all can not vanish together unless γ1 = 0 and

Can Not Converge To ˜B(J)

11 unless γ1 = 0. Similarly, it can be shown that same condition is required to have the closeness of the coefficients ˜b(j,3−j)

. Hence, The

two EEs can not get closer unless γ1 = 0, similar to the Example 4.1. This is exactly what is stated in the part (c) of Theorem 4.2 in most general form. 4.2.

As It Has Been Seen That H∗

n, the usual studentized version of the perturbation bootstrapped estimator is not attending the desired optimal rate op(n−1/2), so in the perspective of statis- tical inference, perturbation bootstrap is not advantageous over asymptotic normal approx- imation. For the sake of obtaining second order correctness, define the modified studentized

Authors:

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

94143, Usa.

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

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

14Jlvmi Consulting Llc, Dousman, Wi, Usa

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

Abstract

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

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

Introduction

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

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

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

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

●

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

●

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

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

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

Hyperpolarized 13C-Pyruvate Preparation

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

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

General Considerations

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

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

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

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

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

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

Personnel

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

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

Equipment And Facility

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

Material Handling

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

Pharmacy Kit Filling And Assembling

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

Quality Control And Dose Release

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

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

The Final Dose Release And Injection

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

Some Key Challenges

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

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

Current Practices

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

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

In House

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

Summary

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

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

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

Mri System Setup And Calibrations

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

Imaging System

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

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

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

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

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

Rf Coils

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

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

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

Provide B1 Transmit Across The Fov (B1

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

B1

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

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

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

Homogeneous B1

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

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

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

(1)

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

Tx = Transmit

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

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

Phantoms

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

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

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

+) And Receive (B1

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

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

Prescan Calibration

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

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

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

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

+ Inhomogeneity As Well

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

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

Power [Kw]

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

8

13C-bicarbonate doped with dimethyl silicone, various

Maximum Values

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

Summary

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

+ Profiles. The

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

For Calibration Of B1

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

Acquisition And Reconstruction

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

+ Inhomogeneity,

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

Acquisition And Reconstruction Methods

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

Mrs/I Methods Specifically

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

Chemical Shift

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

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

Their Application To Different

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

The Majority Of

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

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

Prostate Studies

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

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

Heart Studies

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

Brain Studies

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

Abdomen And Breast Studies

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

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

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

1H Imaging

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

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

Reported Study Parameters

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

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

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

(B)

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

Summary

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

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

Data Analysis And Quantification

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

Metrics

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

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

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

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

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

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

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

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

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

Visualization

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

Metrics

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

Parameter Encoding

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

Anatomical Context

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

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