T2 Distribution Analysis of Inflamed Bone Marrow Compartments in MR Images with Quantitative T2-mapping

Motivation: Conventional methods for imaging inflammation-related bone marrow (BM) changes are limited, necessitating advanced quantitative MRI approaches. However, capturing microscopic details poses challenges. Existing T2 distribution estimation methods' limitations led to the development of P2T2-Boot. Goal(s): We aimed to improve BM inflammation analysis using P2T2-Boot and assess its ability to differentiate healthy and inflamed BM. Approach: P2T2-Boot, a neural network for T2 distribution estimation, was developed with bootstrapping techniques, trained on simulated MRI signals, and tested on real mice data. Results: The bootstrapped model outperformed others at low Signal-to-Noise Ratios and demonstrated superior performance in distinguishing inflammatory and non-inflammatory mice. Impact: P2T2-Boot significantly enhances detecting BM inflammation, excelling in noisy conditions. Its superiority underscores its potential for advancing disease studies. The method's potential extensions make it a promising tool for advancing inflammation-related disease studies and clinical applications.