利用分布式高效记忆物理引导深度学习,在有限的训练数据下进行大规模三维非笛卡尔冠状动脉磁共振成像重建。

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Chi Zhang, Davide Piccini, Omer Burak Demirel, Gabriele Bonanno, Christopher W Roy, Burhaneddin Yaman, Steen Moeller, Chetan Shenoy, Matthias Stuber, Mehmet Akçakaya
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引用次数: 0

摘要

目的通过克服硬件限制和训练数据可用性有限的挑战,实现大规模三维非笛卡尔冠状磁共振成像的高质量物理引导深度学习(PG-DL)重建:虽然 PG-DL 已成为一种强大的图像重建方法,但其在大规模三维非笛卡尔磁共振成像中的应用却受到硬件限制和训练数据可用性有限的阻碍。我们结合了深度学习和磁共振成像重建领域的最新进展来应对前一个挑战,并进一步提出了一种使用二维卷积神经网络的 2.5D 重建方法,该方法将三维体积视为成批的二维图像,从而用有限的训练数据来训练网络。将 PG-DL 网络的三维和 2.5D 变体与传统的高分辨率三维 kooshball 冠状动脉磁共振成像方法进行了比较:结果:在三维非笛卡尔冠状磁共振成像中,经过三维和 2.5D 处理的拟议 PG-DL 重建,在由经验丰富的心脏病专家进行图像评估时,无论在定量还是定性方面都优于所有传统方法。与三维处理相比,2.5D 变体进一步提高了血管的清晰度,在定性图像质量方面得分更高:讨论:在不影响图像大小或网络复杂性的情况下,实现了大规模三维非笛卡尔磁共振成像的PG-DL重建,而所提出的2.5D处理可在有限的训练数据下实现高质量的重建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

Object: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability.

Materials and methods: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI.

Results: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality.

Discussion: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.

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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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