Multi-contrast (MC) Magnetic Resonance Imaging (MRI) reconstruction aims to
incorporate a reference image of auxiliary modality to guide the reconstruction
process of the target modality. Known MC reconstruction methods perform well
with a fully sampled reference image, but usually exhibit inferior performance,
compared to single-contrast (SC) methods, when the reference image is missing
or of low quality. To address this issue, we propose DuDoUniNeXt, a unified
dual-domain MRI reconstruction network that can accommodate to scenarios
involving absent, low-quality, and high-quality reference images. DuDoUniNeXt
adopts a hybrid backbone that combines CNN and ViT, enabling specific
adjustment of image domain and k-space reconstruction. Specifically, an
adaptive coarse-to-fine feature fusion module (AdaC2F) is devised to
dynamically process the information from reference images of varying qualities.
Besides, a partially shared shallow feature extractor (PaSS) is proposed, which
uses shared and distinct parameters to handle consistent and discrepancy
information among contrasts. Experimental results demonstrate that the proposed
model surpasses state-of-the-art SC and MC models significantly. Ablation
studies show the effectiveness of the proposed hybrid backbone, AdaC2F, PaSS,
and the dual-domain unified learning scheme.

提出了一种新的双域核磁共振成像重建网络 DuDoUniNeXt，采用混合主干网络结构，自适应粗到精特征融合模块（AdaC2F）和部分共享浅层特征提取器（PaSS）来处理不同对比度图像的一致性和差异性信息，实验证明该模型明显优于现有的单对比度和多对比度模型。

DuDoUniNeXt: 单一和多对比度欠采样 MRI 重建的双域统一混合模型

DuDoUniNeXt: Dual-domain unified hybrid model for single and  multi-contrast undersampled MRI reconstruction

Multi-contrast magnetic resonance imaging is a significant and essential
medical imaging technique.However, multi-contrast imaging has longer
acquisition time and is easy to cause motion artifacts. In particular, the
acquisition time for a T2-weighted image is prolonged due to its longer
repetition time (TR). On the contrary, T1-weighted image has a shorter TR.
Therefore,utilizing complementary information across T1 and T2-weighted image
is a way to decrease the overall imaging time. Previous T1-assisted T2
reconstruction methods have mostly focused on image domain using whole-based
image fusion approaches. The image domain reconstruction method has the defects
of high computational complexity and limited flexibility. To address this
issue, we propose a novel multi-contrast imaging method called partition-based
k-space synthesis (PKS) which can achieve super reconstruction quality of
T2-weighted image by feature fusion. Concretely, we first decompose
fully-sampled T1 k-space data and under-sampled T2 k-space data into two
sub-data, separately. Then two new objects are constructed by combining the two
sub-T1/T2 data. After that, the two new objects as the whole data to realize
the reconstruction of T2-weighted image. Finally, the objective T2 is
synthesized by extracting the sub-T2 data of each part. Experimental results
showed that our combined technique can achieve comparable or better results
than using traditional k-space parallel imaging(SAKE) that processes each
contrast independently.

我们提出了一种名为基于分区 k 空间合成的新型多对比度成像方法，通过特征融合实现 T2 加权图像的超级重建效果，结果显示我们的技术可以与传统 k 空间并行成像方法 (SAKE) 相媲美或更好地处理每个对比度。