Validation of Non-Contrast Multiple Overlapping Thin Slab 4d-Flow Cardiac Magnetic Resonance Imaging

Rashedi, Nina; Landeras, Luis; Mor‐Avi, Victor; Genovese, Davide; Lai, Peng; Wang, Haonan; Kebed, Kalie; McClelland, Isla; Brau, Anja C. S.; Janich, Martin A.; Lang, Roberto M.; Patel, Amit R.

doi:10.1016/s0735-1097(20)32307-x

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“…Its main function is to provide power for blood flow in the body through the contraction pressure of the heart muscle. Cardiac magnetic resonance (CMR) imaging [1] is an important technique for the functional analysis of the heart. It is suitable for the accurate assessment and analysis of the local and global function of cardiac tissue structures, and it plays an important role in assisting physicians in diagnosis and treatment and improving diagnostic accuracy.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Magnetic Resonance Images Superresolution via Multichannel Residual Attention Networks

Qiu

Cheng

Wang

2021

Computational and Mathematical Methods in Medicine

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The deep neural network has achieved good results in medical image superresolution. However, due to the medical equipment limitations and the complexity of the human body structure, it is difficult to reconstruct clear cardiac magnetic resonance (CMR) superresolution images. To reconstruct clearer CMR images, we propose a CMR image superresolution (SR) algorithm based on multichannel residual attention networks (MCRN), which uses the idea of residual learning to alleviate the difficulty of training and fully explore the feature information of the image and uses the back-projection learning mechanism to learn the interdependence between high-resolution images and low-resolution images. Furthermore, the MCRN model introduces an attention mechanism to dynamically allocate each feature map with different attention resources to discover more high-frequency information and learn the dependency between each channel of the feature map. Extensive benchmark evaluation shows that compared with state-of-the-art image SR methods, our MCRN algorithm not only improves the objective index significantly but also provides richer texture information for the reconstructed CMR images, and our MCRN algorithm is better than the Bicubic algorithm in evaluating the information entropy and average gradient of the reconstructed image quality.

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Section: Introductionmentioning

confidence: 99%