Optimizing Brachial Plexus Diffusion Tensor Imaging

Objectives: This study aimed (1) to quantify the differential impact of deep learning (DL) reconstruction on brachial plexus (BP) diffusion tensor imaging (DTI) image quality and quantitative metrics compared with conventional single-shot echo-planar imaging (EPI), (2) to clarify the clinical utility and limitations of integrating simultaneous multislice (SMS) acceleration with DL DTI in the challenging BP anatomy, and (3) to determine the optimal, time-efficient DTI protocol for robust BP tractography. Materials and Methods: Twenty-seven healthy volunteers were prospectively enrolled (stage 1: n = 14; stage 2: n = 13). All DTI acquisitions used a single-shot EPI sequence. In the first stage, DTI with DL reconstruction was compared with a conventional non-DL DTI. In the second stage, the integration of SMS acceleration into DL DTI protocols was assessed across 5 protocol sets. Fiber tract number, tract length, tract-based DTI metrics, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image consistency, and interreader agreement were assessed in the BP postganglionic nerve roots and spinal cord. Results: In the first stage, DL DTI demonstrated significantly higher tractography success rates, fiber numbers, and fiber lengths in the BP ( P < 0.05). DL substantially improved interreader agreement and image consistency, with the Dice similarity coefficient (DSC) increasing from 0.537 to 0.740 ( P = 0.006). DL reconstruction resulted in changes in anatomy-specific fractional anisotropy, with a significant decrease in BP fractional anisotropy but an increase in spinal cord fractional anisotropy. In the second stage, SMS-integrated protocols produced significantly lower tractography success and degraded image quality compared with non-SMS DL protocols ( P < 0.05). SMS integration resulted in lower SNR and CNR ( P < 0.05), and reduced image consistency (DSC decreased from 0.727 to 0.486, P = 0.015). The DL12-2 protocol (12 diffusion directions and two b800 acquisitions) was identified as the optimal time-efficient solution. Conclusions: DL reconstruction significantly improves the feasibility and reliability of BP DTI and affects quantitative metrics in an anatomy-specific manner. In contrast, the integration of SMS acceleration with single-shot EPI remains technically challenging in the high-susceptibility BP region. Our findings underscore that anatomy-specific optimization is essential for successful clinical integration of DL-enhanced DTI.