发表机构
Vanderbilt University; Vanderbilt University Medical Center; Vanderbilt Memory and Alzheimer's Center（范德堡大学; 范德堡大学医学中心; 范德堡记忆与阿尔茨海默病中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对T1加权MRI的局部切片受损问题，提出SliceBridge框架，通过校正流匹配结合周围完整切片与相对位置修复受损区间，提升了切片间一致性与脑区体积估计精度。
AI 中文摘要
结构磁共振成像（MRI）图像有时会在连续的切片集上出现受损情况，采集、运动、硬件或重建效应会导致单张切片或短区间与其相邻切片不一致，而图像其余部分仍可用。这种局部受损会使后续的形态计量分析产生偏差，但丢弃或重新采集原本可用的图像成本很高。我们将此问题表述为图像修复问题：给定受损区间的位置，从周围的解剖结构和成像上下文重建这些切片。我们提出SliceBridge，这是一个基于周围完整切片及其相对切片位置的校正流匹配框架，用于修复T1加权MRI中受损的切片区间。通过区间相关的初始噪声、共享流时间和同步采样，将区间内的切片耦合起来，从而鼓励层间一致性。然后将修复后的区间插入回去，其余所有切片保持不变。我们在来自四个数据集的9877个T1加权脑部MRI体积上训练并验证了该模型，并使用干净区间 withhold 和受控受损对581个外部受试者进行了评估。与独立重建目标切片的匹配模型相比，SliceBridge在不同区间长度下将修复区间内的切片间变化误差降低了32.9%-41.3%，且在每个区间长度上都实现了更高的结构相似性（SSIM）。在受控受损案例中，SliceBridge将下游分割模型生成的脑区体积估计的中位误差从受损体积的1.95%降低至1.05%。
英文摘要
Structural magnetic resonance imaging (MRI) images are sometimes corrupted over a contiguous set of slices, where acquisition, motion, hardware, or reconstruction effects leave a single slice or short interval inconsistent with its neighbors while the rest of the image remains usable. Such localized corruption can bias downstream morphometric analysis, yet discarding or reacquiring an otherwise usable image is costly. We formulate this as an image restoration problem: given the location of the affected interval, reconstruct those slices from the surrounding anatomical and imaging context. We propose SliceBridge, a framework for restoring corrupted slice intervals in T1-weighted MRI using rectified flow matching conditioned on the surrounding intact slices and their relative slice positions. Through-plane consistency is encouraged by coupling the slices within the interval through interval-correlated initial noise, a shared flow time, and synchronized sampling. The restored interval is then inserted back, leaving all other slices unchanged. We trained and validated the model on 9,877 T1-weighted brain MRI volumes from four datasets and evaluated it on 581 external subjects using clean interval withholding and controlled corruptions. Compared with a matched model that reconstructed target slices independently, SliceBridge reduced error in slice-to-slice changes within repaired intervals by 32.9%-41.3% across interval lengths and achieved higher SSIM at every interval length. In controlled-corruption cases, SliceBridge reduced the median error in regional brain volume estimates produced by a downstream segmentation model from 1.95% in corrupted volumes to 1.05%.
