Bootstrapped Physically-Primed Neural Networks for Robust T2 Distribution Estimation in Low-SNR Pancreatic MRI

Abstract

Estimating multi-component T2 relaxation distributions from Multi-Echo Spin Echo (MESE) MRI is a severely ill-posed inverse problem, traditionally solved using regularized non-negative least squares (NNLS). In abdominal imaging, particularly the pancreas, low SNR and residual uncorrelated noise challenge classical solvers and deterministic deep learning models. We introduce a bootstrap-based inference framework for robust distributional T2 estimation that performs stochastic resampling of the echo train and aggregates predictions across multiple subsets. This treats the acquisition as a distribution rather than a fixed input, yielding variance-reduced, physically consistent estimates and converting deterministic relaxometry networks into probabilistic ensemble predictors. Applied to the P2T2 architecture, our method uses inference-time bootstrapping to smooth noise artifacts and enhance fidelity to the underlying relaxation distribution. Noninvasive pancreatic evaluation is limited by location and biopsy risks, highlighting the need for biomarkers capable of capturing early pathophysiological changes. In type 1 diabetes (T1DM), progressive beta-cell destruction begins years before overt hyperglycemia, yet current imaging cannot assess early islet decline. We evaluate clinical utility via a test-retest reproducibility study (N=7) and a T1DM versus healthy differentiation task (N=8). Our approach achieves the lowest Wasserstein distances across repeated scans and superior sensitivity to physiology-driven shifts in the relaxation-time distribution, outperforming NNLS and deterministic deep learning baselines. These results establish inference-time bootstrapping as an effective enhancement for quantitative T2 relaxometry in low-SNR abdominal imaging.

Figures (6)

• Figure 1: Representative $T_2$ distribution estimation workflow (Control Subject).Left: Axial MRI slice (TE=77.4 ms) with the pancreatic ROI centroid indicated. Center: Signal decay analysis: The model accurately fits the physical decay curve ($R^2=0.986$), matching the predicted signal (line) to the observed echoes (dots). Right: Regional $T_2$ distribution analysis. Faint colored curves show the final bootstrapped $T_2$ distributions for individual pixels within the ROI, illustrating local spatial heterogeneity. The solid black curve denotes the spatially averaged distribution across the ROI. Dashed lines depict the Gaussian decomposition of this average into Short-$T_2$ (red) and Long-$T_2$ (blue) components.
• Figure 2: Comparison of the evaluated model architectures. The baseline MIML directly maps signals to $T_2$ distributions; P2T2 incorporates the explicit TE encoding; and the proposed Bootstrapped-P2T2 adds inference-time resampling and ensemble averaging to improve robustness and stability.
• Figure 3: Test--retest stability across anatomical regions. Boxplots show the Wasserstein distance ($W_1$) between repeated scans for the pancreatic body and tail. The proposed Bootstrapped-P2T2 model (red) exhibits consistently lower median error and reduced variance compared with the deterministic P2T2 (green) and baseline MIML (blue), demonstrating improved robustness and repeatability across both regions.
• Figure 4: Sensitivity analysis across models (Pancreas Body & Tail). (a) The combined Wasserstein distance plot compares all distribution-based models. The Bootstrapped-P2T2 model (far right in a) shows the distinct separation between T1DM (orange) and Controls (blue), reducing the overlap seen in other models. (b–c) Classical scalar metrics ($T_2$ and $M_0$) exhibit larger overlaps between groups, highlighting the improved sensitivity gained from the full-distribution modeling in (a).
• Figure 5: Representative response to glucose challenge in the pancreatic tail, using the Bootstrapped-P2T2. Left: Healthy Control subject. Right: T1DM subject. Top Row: Axial MRI (10th echo) with the pancreatic tail segmentation overlaid in red. Middle Row: Average $T_2$ distributions ($\bar{p}(T_2)$) within the ROI before (blue) and after (orange) glucose intake. Bottom Row: Cumulative Distribution Functions (CDFs) highlighting the gap between timepoints. Note the significantly larger distributional shift in the T1DM subject compared to the stable profile of the control, indicating an altered microstructural response to metabolic stress.