Accelerated Chemical Shift Encoded Cardiac MRI with Use of Resolution Enhancement Network.

IF 4.2 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Journal of Cardiovascular Magnetic Resonance Pub Date : 2024-09-05 DOI:10.1016/j.jocmr.2024.101090

Manuel A Morales, Scott Johnson, Patrick Pierce, Reza Nezafat

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引用次数: 0

Abstract

Background: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE.

Methods: FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm², 2.5 × 1.5 mm², and 3.8 × 1.9mm² resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm² to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance.

Results: FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm² (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm² (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90).

Conclusion: We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

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利用分辨率增强网络加速化学位移编码心脏磁共振成像

背景：心血管磁共振（CMR）化学位移编码（CSE）可实现心肌脂肪成像。我们试图开发一种深度学习网络（FastCSE）来加速 CSE：方法：FastCSE 建立在超分辨率生成对抗网络的基础上，并进行了扩展，以增强复值图像的清晰度。在水脂分离之前，FastCSE 会独立增强每个回波图像。FastCSE 是用 1519 名临床 3T CMR 转诊患者（56 ± 16 岁；866 名男性）的回顾性识别 cines 进行训练的。在一项针对 16 名参与者（58 ± 19 岁；7 名女性）和 5 名健康人（32 ± 17 岁；5 名女性）的前瞻性研究中，使用广义自动校准部分并行采集（GRAPPA）采集了分辨率分别为 1.5 × 1.5mm2、2.5 × 1.5 mm2 和 3.8 × 1.9 mm2 的双回波 CSE 图像。在采集分辨率为 2.5 × 1.5 mm2 和 3.8 × 1.9 mm2 的图像时，使用 FastCSE 恢复清晰度。使用定量模糊度量评估了两点 Dixon 重建获得的脂肪图像，并进行了 5 方差分析：结果：FastCSE 成功地在线重建了 CSE 图像。与分辨率为 1.5 × 1.5 mm² 的 GRAPPA 采集相比，分辨率为 2.5 × 1.5 mm² 和 3.8 × 1.9 mm² 的 FastCSE 采集减少了约 1.5 倍和 3 倍的屏气次数，而不影响脂肪的可视化，屏气次数分别从 3.0 ± 0.8 次减少到 2.0 ± 0.2 次和 1.1 ± 0.4 次。在分辨率为 2.5 × 1.5 mm2（0.31 ± 0.03 vs. 0.35 ± 0.04，P < 0.001）和 3.8 × 1.9 mm2（0.31 ± 0.03 vs. 0.42 ± 0.06，P < 0.001）的 GRAPPA 脂肪图像中，FastCSE 改善了图像清晰度并消除了振铃伪影。FastCSE图像中的模糊与1.5 × 1.5 mm²分辨率图像中的模糊相似（0.32 ±0.03 vs. 0.31 ± 0.03，P = 0.78；0.32 ± 0.03 vs. 0.31 ± 0.03，P = 0.90）：我们的研究表明，基于复值分辨率增强的深度学习加速 CSE 技术可以减少 CSE 成像中的屏气次数，而不会影响脂肪的可视化。与标准化平行成像方法相比，FastCSE 显示出相似的图像清晰度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Cardiovascular Magnetic Resonance 医学-核医学

CiteScore

10.90

自引率

12.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to: New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system. New methods to enhance or accelerate image acquisition and data analysis. Results of multicenter, or larger single-center studies that provide insight into the utility of CMR. Basic biological perceptions derived by CMR methods.