Attention Hybrid Variational Net for Accelerated MRI Reconstruction

• URL: http://arxiv.org/abs/2306.12365v1
• Date: Wed, 21 Jun 2023 16:19:07 GMT
• Title: Attention Hybrid Variational Net for Accelerated MRI Reconstruction
• Authors: Guoyao Shen, Boran Hao, Mengyu Li, Chad W. Farris, Ioannis Ch. Paschalidis, Stephan W. Anderson, Xin Zhang
• Abstract summary: The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. We propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domain.
• Score: 7.046523233290946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. Multiple deep learning-based structures have been proposed for MRI reconstruction using CS, both in the k-space and image domains as well as using unrolled optimization methods. However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image). Herein, we propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domain. We evaluate our method on a well-known open-source MRI dataset and a clinical MRI dataset of patients diagnosed with strokes from our institution to demonstrate the performance of our network. In addition to quantitative evaluation, we undertook a blinded comparison of image quality across networks performed by a subspecialty trained radiologist. Overall, we demonstrate that our network achieves a superior performance among others under multiple reconstruction tasks.

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