Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI

Abstract

Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities. Most of the state-ofthe-art approaches have achieved improvement through the development of network architectures for fixed undersampling patterns, without fully exploiting the complementary information between modalities. Although existing under-sampling pattern learning algorithms can be simply modified to allow the fully-sampled T1-weighted MR image to assist the pattern learning, no significant improvement on the reconstruction task can be achieved. To this end, we propose an iterative framework to optimize the undersampling pattern for MRI acquisition of another modality that can complement the fully-sampled T1-weighted MR image at different under-sampling factors, while jointly optimizing the T1-assisted MRI reconstruction model. Specifically, our proposed method exploits the difference of latent information between the two modalities for determining the sampling patterns that can maximize the assistance power of T1-weighted MR image in improving the MRI reconstruction. We have demonstrated superior performance of our learned under-sampling patterns on a public dataset, compared to commonly used under-sampling patterns and state-of-the-art methods that can jointly optimize both the reconstruction network and the under-sampling pattern, up to 8-fold under-sampling factor.

Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

“…A series of traditional algorithms [15,31] attempt to explore prior context information from T1-weighted MR images for super-resolving T2weighted or spectroscopy MR images. [9,14,39] further devise CNN models to exploit such inter-modality context information. However, directly fusing features of multiple modalities via convolutions with small kernels can not sufficiently leverage the intermodality dependencies.…”

DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network

“…As such, paired MRI data of common and T1ce modalities are very scarce but indispensable in clinical practice, especially in tumor diagnosis. In such context, there has been great interest in research topics related to missing modality [22,28,29,25].…”