3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning

• URL: http://arxiv.org/abs/2111.14259v1
• Date: Sun, 28 Nov 2021 22:58:00 GMT
• Title: 3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning
• Authors: Hao Li, Jianan Liu
• Abstract summary: We employ a unified 2D deep learning neural network for both 3D MRI super resolution and motion artifact reduction. We also analyzed several downsampling strategies based on the acceleration factor, and developed a controllable and quantifiable motion artifact generation method.
• Score: 8.200110925123965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shortening acquisition time and reducing the motion-artifact are two of the most essential concerns in magnetic resonance imaging. As a promising solution, deep learning-based high quality MR image restoration has been investigated to generate higher resolution and motion artifact-free MR images from lower resolution images acquired with shortened acquisition time, without costing additional acquisition time or modifying the pulse sequences. However, numerous problems still exist to prevent deep learning approaches from becoming practical in the clinic environment. Specifically, most of the prior works focus solely on the network model but ignore the impact of various downsampling strategies on the acquisition time. Besides, the long inference time and high GPU consumption are also the bottle neck to deploy most of the prior works in clinics. Furthermore, prior studies employ random movement in retrospective motion artifact generation, resulting in uncontrollable severity of motion artifact. More importantly, doctors are unsure whether the generated MR images are trustworthy, making diagnosis difficult. To overcome all these problems, we employed a unified 2D deep learning neural network for both 3D MRI super resolution and motion artifact reduction, demonstrating such a framework can achieve better performance in 3D MRI restoration task compared to other states of the art methods and remains the GPU consumption and inference time significantly low, thus easier to deploy. We also analyzed several downsampling strategies based on the acceleration factor, including multiple combinations of in-plane and through-plane downsampling, and developed a controllable and quantifiable motion artifact generation method. At last, the pixel-wise uncertainty was calculated and used to estimate the accuracy of generated image, providing additional information for reliable diagnosis.

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