PEPSI：用于脑部MRI的病理增强脉冲序列不变表征
PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
AI总结：
针对现有脑部MRI分析方法依赖特定脉冲序列、难以适配临床多样数据的问题，提出病理增强的脉冲序列不变表征模型PEPSI，通过合成图像训练实现跨模态协同，在图像合成和病理分割任务上表现优异。
摘要：
数据驱动的机器学习方法在MRI扫描分析领域已取得显著进展。然而，现有大多数MRI分析方法都是针对特定MR脉冲序列（MR对比度）设计的，通常要求采集数据接近各向同性。这限制了它们在多样真实临床数据中的适用性——临床扫描常因序列参数、分辨率和扫描方向的不同而存在外观差异，在存在病理的情况下尤为明显。本文提出PEPSI，这是首个面向脑部MRI的病理增强、脉冲序列不变的特征表征学习模型。PEPSI完全基于合成图像训练，采用一种新颖的病理编码策略，支持在病理多样、存在模态缺失的多个数据集间进行协同训练。尽管不同MR脉冲序列下的病理外观存在差异，或是采集图像存在质量问题（如分辨率、方向、伪影等），PEPSI仍能生成捕捉解剖结构的参考对比度（MP-RAGE）高分辨率图像，以及专门突出病理区域的图像。实验表明，与当前最优的对比度无关合成模型相比，PEPSI在图像合成方面表现出色，它能准确重建解剖结构，同时区分病理组织与正常组织。我们进一步在五个涵盖白质高信号和卒中病灶的公开数据集上验证了PEPSI特征用于下游病理分割的效率与有效性。代码可在https://github.com/peirong26/PEPSI获取。
英文摘要：
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR pulse sequences (MR contrasts) and usually require nearly isotropic acquisitions. This limits their applicability to diverse real-world clinical data, where scans commonly exhibit variations in appearances due to being obtained with varying sequence parameters, resolutions, and orientations -- especially in the presence of pathology. In this paper, we propose PEPSI, the first pathology-enhanced, and pulse-sequence-invariant feature representation learning model for brain MRI. PEPSI is trained entirely on synthetic images with a novel pathology encoding strategy, and enables co-training across datasets with diverse pathologies and missing modalities. Despite variations in pathology appearances across different MR pulse sequences or the quality of acquired images (e.g., resolution, orientation, artifacts, etc), PEPSI produces a high-resolution image of reference contrast (MP-RAGE) that captures anatomy, along with an image specifically highlighting the pathology. Our experiments demonstrate PEPSI's remarkable capability for image synthesis compared with the state-of-the-art, contrast-agnostic synthesis models, as it accurately reconstructs anatomical structures while differentiating between pathology and normal tissue. We further illustrate the efficiency and effectiveness of PEPSI features for downstream pathology segmentations on five public datasets covering white matter hyperintensities and stroke lesions. Code is available at https://github.com/peirong26/PEPSI.
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。
