Multi-contrast MRI acceleration has become prevalent in MR imaging, enabling
the reconstruction of high-quality MR images from under-sampled k-space data of
the target modality, using guidance from a fully-sampled auxiliary modality.
The main crux lies in efficiently and comprehensively integrating complementary
information from the auxiliary modality. Existing methods either suffer from
quadratic computational complexity or fail to capture long-range correlated
features comprehensively. In this work, we propose MMR-Mamba, a novel framework
that achieves comprehensive integration of multi-contrast features through
Mamba and spatial-frequency information fusion. Firstly, we design the
\textit{Target modality-guided Cross Mamba} (TCM) module in the spatial domain,
which maximally restores the target modality information by selectively
absorbing useful information from the auxiliary modality. Secondly, leveraging
global properties of the Fourier domain, we introduce the \textit{Selective
Frequency Fusion} (SFF) module to efficiently integrate global information in
the frequency domain and recover high-frequency signals for the reconstruction
of structure details. Additionally, we present the \textit{Adaptive
Spatial-Frequency Fusion} (ASFF) module, which enhances fused features by
supplementing less informative features from one domain with corresponding
features from the other domain. These innovative strategies ensure efficient
feature fusion across spatial and frequency domains, avoiding the introduction
of redundant information and facilitating the reconstruction of high-quality
target images. Extensive experiments on the BraTS and fastMRI knee datasets
demonstrate the superiority of the proposed MMR-Mamba over state-of-the-art MRI
reconstruction methods.

通过 MMR-Mamba 框架中 Mamba 和空间频率信息融合，从辅助模态获取的信息，实现了多对比度 MRI 加速成像和高质量重建目标图像，通过创新的策略有效融合了空间和频率领域的特征，避免冗余信息的引入。

MMR-Mamba: Mamba 与空间频率信息融合的多对比度 MRI 重建

MMR-Mamba: Multi-Contrast MRI Reconstruction with Mamba and  Spatial-Frequency Information Fusion

Out-of-distribution (OOD) detection is a critical task to ensure the
reliability and security of machine learning models deployed in real-world
applications. Conventional methods for OOD detection that rely on single-modal
information, often struggle to capture the rich variety of OOD instances. The
primary difficulty in OOD detection arises when an input image has numerous
similarities to a particular class in the in-distribution (ID) dataset, e.g.,
wolf to dog, causing the model to misclassify it. Nevertheless, it may be easy
to distinguish these classes in the semantic domain. To this end, in this
paper, a novel method called ODPC is proposed, in which specific prompts to
generate OOD peer classes of ID semantics are designed by a large language
model as an auxiliary modality to facilitate detection. Moreover, a contrastive
loss based on OOD peer classes is devised to learn compact representations of
ID classes and improve the clarity of boundaries between different classes. The
extensive experiments on five benchmark datasets show that the method we
propose can yield state-of-the-art results.

提出了一种名为 ODPC 的新方法，通过大型语言模型设计生成特定提示词来产生具有 ID 语义的 OOD 对等类，以便于检测，并采用基于 OOD 对等类的对比损失来学习紧凑的 ID 类别表示，并改善不同类别之间的界限清晰度。在五个基准数据集上进行的广泛实验表明，该方法可以得到最先进的结果。