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.

翻译：从多回波自旋回波（MESE）MRI数据中估计多组分T2弛豫分布是一个严重的不适定逆问题，传统上采用正则化非负最小二乘法（NNLS）求解。在腹部成像（尤其是胰腺）中，低信噪比和残留的非相关噪声对经典求解器及确定性深度学习模型构成挑战。我们提出一种基于自举的推断框架，用于实现稳健的分布式T2估计。该框架通过对回波序列进行随机重采样，并在多个子集上聚合预测结果，将采集过程视为分布而非固定输入，从而产生方差缩减、物理一致的估计值，并将确定性弛豫测量网络转化为概率集成预测器。应用于P2T2架构时，本方法利用推断阶段的自举过程平滑噪声伪影，并增强对底层弛豫分布的保真度。胰腺的无创评估受限于其解剖位置和活检风险，这凸显了对能够捕捉早期病理生理变化生物标志物的迫切需求。在1型糖尿病（T1DM）中，进行性β细胞破坏在明显高血糖发生前数年即已开始，但现有成像技术无法评估早期胰岛衰退。我们通过重测复现性研究（N=7）和T1DM与健康人群的区分任务（N=8）评估临床效用。本方法在重复扫描中取得了最低的Wasserstein距离，并对弛豫时间分布中生理驱动的变化表现出卓越的敏感性，其性能优于NNLS及确定性深度学习基线。这些结果表明，推断阶段的自举过程能有效增强低信噪比腹部成像中定量T2弛豫测量的性能。