A Bayesian Model to Assess T_2 Values and Their Changes Over Time in Quantitative MRI
TL;DR: This work builds posterior distributions relating the raw (spin or gradient echo) acquisitions and the relaxation time and its modifications over acquisitions and provides many estimators of the parameters distribution including the mean and the \(\alpha \)-credible intervals.
Abstract: Quantifying \(T_2\) and \(T_2^*\) relaxation times from MRI becomes a standard tool to assess modifications of biological tissues over time or differences between populations. However, due to the relationship between the relaxation time and the associated MR signals such an analysis is subject to error. In this work, we provide a Bayesian analysis of this relationship. More specifically, we build posterior distributions relating the raw (spin or gradient echo) acquisitions and the relaxation time and its modifications over acquisitions. Such an analysis has three main merits. First, it allows to build hierarchical models including prior information and regularisations over voxels. Second, it provides many estimators of the parameters distribution including the mean and the \(\alpha \)-credible intervals. Finally, as credible intervals are available, testing properly whether the relaxation time (or its modification) lies within a certain range with a given credible level is simple. We show the interest of this approach on synthetic datasets and on two real applications in multiple sclerosis.
Summary (3 min read)
- Relaxometry imaging provides a way to quantify modifications of biological tissues over time or differences between different populations.
- The problem of estimating T2 values from echo train acquisitions is discussed in many works [10, 12, 11].
- This makes the task, in the best case, complex and, in the worst, error prone with many false positive detections in the higher intensities regions.
- These posterior distributions extract the relevant information from the data and provide complete and coherent characterisations of the parameters distribution.
- In Section 2.4, the authors propose a prior to use the extended phase graph function instead of the exponential decay function used in the previous models.
2.1 Bayesian analysis of T2 relaxometry
- The Gaussian error term allows to account for measurement noise as well as for model inadequacy (due to e.g. multi exponential decay of the true signal, partial volumes or misalignment).
- Notice that the upper limit for M and positive lower limit for T2 in the prior support are needed to ensure posterior properness.
- This prior leads to invariance of the inference under reparametrisation of the exponential decay function.
- Estimators for the resulting marginal posterior p(T2|(si)) can then be computed using a Markov Chain Monte Carlo algorithm (details in Section 3).
2.2 Bayesian analysis of T2 modification
- The authors are now concerned with the following question : how to assess that the T2 value associated to a given voxel has changed between two acquisitions with a given minimal credible level.
- Xa the random variable associated to a quantity for the pre acquisitions and Xb for the post acquisitions.
- Then a voxel can be defined as negatively (resp. positively) altered at α level, if the α-credible HPD interval for C does not contain any positive (resp. negative) value (see  for a testing perspective).
2.3 Region-wise analysis
- The models proposed previously allow a voxel-wise analysis where each voxel is processed independently from others.
- Performing a grouped inference for all the voxels of a given region (e.g. lesion) can be performed by adding a supplemental layer to the model.
- Such a model allows the set of inferences over the (Cj) to be performed not independently thus dealing in a natural way with multiple comparisons by shrinking exceptional Cj toward its estimated region-wise distribution  and improving the overall inference.
- Depending on the expected regularity of the Cjs within the regions, the authors can alternatively opt for a Cauchy density or/and add an informative prior for the error variances σ2a,i and σ 2 b,i to favor goodness of the fit.
- Parallelly, a spatial Markovian regularisation can also be considered.
2.4 Using the extended phase graph
- N are obtained using sequences of multiple echoes spin echoes (e.g. CMPG sequence), the exponential decay function is only a rough approximation of the relation between T2 and the MR signals, also known as When the signals (si)i=1.
- Nevertheless, it consists of small departures from the exponential function and mainly depends on M and T2.
- Thus a reasonable choice consists in using the same priors as those derivated for the exponential function.
- Then the EPG model can be used by simply replacing ft2,m(τi) by EPG(t2,m, t1, b1, (τi)) (prior sensitivity analysis for T1 and B1 give satisfying result).
3.1 Implementation, datasets and convergence diagnosis
- For the sake of concision, for the previous models, the authors only exhibited the likelihoods, the priors and the independence assumptions.
- The resulting posteriors can be obtained using the Bayes rule.
- The authors used 10k samples (40k for the region-wise model) after discarding the 5k first ones (20k for the region-wise model).
- Convergence has been assessed using the Geweke score .
- Because the function EPG(t2, t1, b1, (τi)) is time consuming and many calls are needed, it is tabulated on a fine grid for different values of t2, t1 and b1.
- The authors run 400 estimations for different configurations of T2 and σ (realistic values are randomly given to T1, B1 and M).
- Moreover, these intervals exhibit excellent coverage properties, illustrating the interest of the derivated priors in the absence of prior information for T2.
- For different configurations of T2, σ and C, the authors analyse the specificity (denoted p−) and sensitivity (denoted p+) of the estimator for C modification (Section 2.2) for α=0.05, also known as C estimation.
- Results are summarized in Table 2 and illustrate that for the used acquisition protocol, detecting low T2 modifications in high values is limited with a 0.95 specificity level.
- The authors also observe that the region-wise model leads to strong improvements of the performances.
4 Two applications on real data
- The present method has been developed in a clinical context including a dataset of about 50 pre-post acquisitions from relapsing-remitting multiple sclerosis patients.
- The sequence parameters are the same as those used in Section 3.
- For each patient, all volumes are rigidly co-registered using a robust blockmatching method .
4.1 Assessing USPIO enhancement in MS lesion
- Ultra small superparamagnetic iron oxide is a contrast agent used to assess macrophagic activity that reduces the T2 relaxation of the surrounding tissues .
- Figure 1 displays detection with a 5% level for a patient acquired twice without USPIO injection and for a patient with potentially active lesions.
- The pre-post T2 relaxation maps illustrate the difficulty to label the lesions as enhanced or not by simply using the point estimates.
- The map for the patient without USPIO injection, for which their method does not detect any voxels, highlights this point.
- More generally, on this data the authors never detected more than 5% of the voxels.
4.2 Assessing T2 recovery in an active MS lesion
- To illustrate the interest of the credible intervals, the authors display the minimal plausible C values for the 0.05 level i.e. the lower bound of the 0.05-credible interval for [m3,m0] (Fig 2.a) and the upper one for [m6,m3] (Fig 2.b).
- These maps offer a quantitative scale to compare lesions recovery among and within patient.
- More precisely, for lesion 3, the minimal admissible C is positive for [m3,m0] (thus the interval does not contain 0) demonstrating that the lesion is recovering during this period.
- The authors also give the minimal USPIO concentration i.e. CR changes for the 5% level (Fig 2.c): they observe a good adequacy between USPIO concentration and T2 change at [m0,m3].
5 Discussion and Conclusion
- Then, the authors showed the interesting properties of their models on synthetic datasets.
- Finally, the authors exemplified the interest of the obtained credible intervals with two applications.
- As illustrated, when available, interval estimates can be more powerful than point ones to address naturally complex issues in medical imaging.
- The Bj1s are correlated in space which could be accounted for adding a supplemental layer to the model (similarly to what proposed in Section 2.3).
- The method is computationally intensive (about 2s per voxel for the EPG region-wise approach) and for more demanding applications, other strategies e.g. maximising over nuisance parameters could be investigated.
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