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
this https URL

提出了 PEPSI 方法，一种病理增强和脉冲序列不变的脑部 MRI 特征表示学习模型，它能够合成高分辨率的对比度参考图像和突出显示病变的图像，显示了其在图像合成和病变分割方面的出色能力。