PRISM：基于微结构拟合的固定点恢复分析-合成可微方法
PRISM: Differentiable Analysis-by-Synthesis for Fixel Recovery in Diffusion MRI
AI总结：
PRISM通过端到端的空间块拟合显式多室模型，结合CSF、灰质和白质纤维方向，利用排斥和稀疏先验实现纤维恢复，优于现有方法。
摘要：
扩散磁共振成像微结构拟合是非凸的，通常逐体素进行，限制了狭窄交叉处纤维峰值恢复。本文引入PRISM，一种可微的分析-合成框架，通过空间块端到端拟合显式多室前向模型。模型结合脑脊液（CSF）、灰质，最多K个白质纤维室（棍棒和飞艇），以及受限室，通过排斥和稀疏先验实现显式纤维方向和软模型选择。PRISM支持快速MSE目标和Rician负对数似然（NLL），联合学习sigma而无需先验信息。包含轻量级噪声校准模块（平滑偏置场和每测量尺度/偏移）以提高鲁棒性，并在干净数据测试中正则化到身份。在合成交叉纤维数据（SNR=30；五种方法，16种交叉角度）上，PRISM在最佳匹配角度误差3.5度（95%召回率），优于基线方法（MSMT-CSD，6.8度，83%召回率）；在NLL模式下，误差降至2.3度（99%召回率），可分辨到20度交叉。在DiSCo1幻影（NLL模式）中，PRISM在所有四个跟踪角度上优于CSD基线，最佳相关系数为0.934（25度）vs. MSMT-CSD的0.920。全脑HCP拟合（约741k体素，MSE模式）在单个GPU上约12分钟内完成，随机种子下结果几乎一致。
英文摘要：
Diffusion MRI microstructure fitting is nonconvex and often performed voxelwise, which limits fiber peak recovery in narrow crossings. This work introduces PRISM, a differentiable analysis-by-synthesis framework that fits an explicit multi-compartment forward model end-to-end over spatial patches. The model combines cerebrospinal fluid (CSF), gray matter, up to K white-matter fiber compartments (stick-and-zeppelin), and a restricted compartment, with explicit fiber directions and soft model selection via repulsion and sparsity priors. PRISM supports a fast MSE objective and a Rician negative log-likelihood (NLL) that jointly learns sigma without oracle information. A lightweight nuisance calibration module (smooth bias field and per-measurement scale/offset) is included for robustness and regularized to identity in clean-data tests. On synthetic crossing-fiber data (SNR=30; five methods, 16 crossing angles), PRISM achieves 3.5 degrees best-match angular error with 95% recall, which is 1.9x lower than the best baseline (MSMT-CSD, 6.8 degrees, 83% recall); in NLL mode with learned sigma, error drops to 2.3 degrees with 99% recall, resolving crossings down to 20 degrees. On the DiSCo1 phantom (NLL mode), PRISM improves connectivity correlation over CSD baselines at all four tracking angles (best r=.934 at 25 degrees vs. .920 for MSMT-CSD). Whole-brain HCP fitting (~741k voxels, MSE mode) completes in ~12 min on a single GPU with near-identical results across random seeds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
