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。

PEPSI：病理学增强的脑 MRI 脉冲序列不变表示

PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for  Brain MRI

Deep learning convolutional neural networks have proved to be a powerful tool
for MRI analysis. In current work, we explore the potential of the deformable
convolutional deep neural network layers for MRI data classification. We
propose new 3D deformable convolutions(d-convolutions), implement them in
VoxResNet architecture and apply for structural MRI data classification. We
show that 3D d-convolutions outperform standard ones and are effective for
unprocessed 3D MR images being robust to particular geometrical properties of
the data. Firstly proposed dVoxResNet architecture exhibits high potential for
the use in MRI data classification.

通过使用三维可变形卷积神经网络层（d-convolutions）对未经处理的三维磁共振成像进行分类，本研究表明三维可变形卷积优于标准卷积，具有对数据几何特性的鲁棒性，而首次提出的 dVoxResNet 结构在 MRI 数据分类中表现出很高的潜力。

MRI 分类的 3D 可变形卷积

3D Deformable Convolutions for MRI classification

The magnetic resonance (MR) analysis of brain tumors is widely used for
diagnosis and examination of tumor subregions. The overlapping area among the
intensity distribution of healthy, enhancing, non-enhancing, and edema regions
makes the automatic segmentation a challenging task. Here, we show that a
convolutional neural network trained on high-contrast images can transform the
intensity distribution of brain lesions in its internal subregions.
Specifically, a generative adversarial network (GAN) is extended to synthesize
high-contrast images. A comparison of these synthetic images and real images of
brain tumor tissue in MR scans showed significant segmentation improvement and
decreased the number of real channels for segmentation. The synthetic images
are used as a substitute for real channels and can bypass real modalities in
the multimodal brain tumor segmentation framework. Segmentation results on
BraTS 2019 dataset demonstrate that our proposed approach can efficiently
segment the tumor areas. In the end, we predict patient survival time based on
volumetric features of the tumor subregions as well as the age of each case
through several regression models.

通过使用卷积神经网络和生成式对抗网络，研究人员成功地应用高对比度图像来增加自动分割 MRI 脑肿瘤的准确性，预测病人生存时间。