Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.

IF 2.1 4区 医学 Japanese Journal of Radiology Pub Date : 2025-07-01 Epub Date: 2025-03-12 DOI:10.1007/s11604-025-01760-2
Masafumi Takafuji, Kakuya Kitagawa, Sachio Mizutani, Akane Hamaguchi, Ryosuke Kisou, Kenji Sasaki, Yuto Funaki, Kotaro Iio, Kazuhide Ichikawa, Daisuke Izumi, Shiko Okabe, Motonori Nagata, Hajime Sakuma
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Abstract

Purpose: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality for CT-LE. Therefore, this study investigated image noise and image quality with SR-DLR compared with conventional DLR (C-DLR) and hybrid iterative reconstruction (hybrid IR).

Methods and methods: We retrospectively analyzed 30 patients who underwent CT-LE using 320-row CT. The CT protocol comprised stress dynamic CT perfusion, coronary CT angiography, and CT-LE. CT-LE images were reconstructed using three different algorithms: SR-DLR, C-DLR, and hybrid IR. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scores are in terms of noise reduction, sharpness, visibility of scar and myocardial boarder, and overall image quality. Inter-observer differences in myocardial scar sizing in CT-LE by the three algorithms were also compared.

Results: SR-DLR significantly decreased image noise by 35% compared to C-DLR (median 6.2 HU, interquartile range [IQR] 5.6-7.2 HU vs 9.6 HU, IQR 8.4-10.7 HU; p < 0.001) and by 37% compared to hybrid IR (9.8 HU, IQR 8.5-12.0 HU; p < 0.001). SNR and CNR of CT-LE reconstructed using SR-DLR were significantly higher than with C-DLR (both p < 0.001) and hybrid IR (both p < 0.05). All qualitative image quality scores were higher with SR-DLR than those with C-DLR and hybrid IR (all p < 0.001). The inter-observer differences in scar sizing were reduced with SR-DLR and C-DLR compared with hybrid IR (both p = 0.02).

Conclusion: SR-DLR reduces image noise and improves image quality of myocardial CT-LE compared with C-DLR and hybrid IR techniques and improves inter-observer reproducibility of scar sizing compared to hybrid IR. The SR-DLR approach has the potential to improve the assessment of myocardial scar by CT late enhancement.

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超分辨率深度学习重建提高心肌CT后期增强质量。
目的:心肌计算机断层扫描(CT)晚期增强(LE)可以评估心肌疤痕。基于超高分辨率CT数据训练的超分辨率深度学习图像重建(SR-DLR)可以提高CT- le的图像质量。因此,本研究比较了SR-DLR与传统DLR (C-DLR)和混合迭代重建(hybrid IR)的图像噪声和图像质量。方法和方法:我们回顾性分析了30例使用320排CT行CT- le的患者。CT方案包括应力动态CT灌注、冠状动脉CT血管造影和CT- le。CT-LE图像重建采用三种不同的算法:SR-DLR、C-DLR和混合IR。图像噪声、信噪比(SNR)、对比噪声比(CNR)和定性图像质量评分是在降噪、清晰度、疤痕和心肌边界的可见性以及整体图像质量方面。还比较了三种算法在CT-LE中心肌瘢痕大小的观察者间差异。结果:与C-DLR相比,SR-DLR显著降低了35%的图像噪声(中位数6.2 HU,四分位间距[IQR] 5.6-7.2 HU vs 9.6 HU, IQR 8.4-10.7 HU;p结论:与C-DLR和混合IR技术相比,SR-DLR降低了心肌CT-LE的图像噪声,提高了图像质量,与混合IR技术相比,SR-DLR提高了疤痕大小的观察者间再现性。SR-DLR方法有可能改善CT晚期增强对心肌疤痕的评估。
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来源期刊
Japanese Journal of Radiology
Japanese Journal of Radiology Medicine-Radiology, Nuclear Medicine and Imaging
自引率
4.80%
发文量
133
期刊介绍: Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in medicine and biology. The scope of Japanese Journal of Radiology encompasses but is not restricted to diagnostic radiology, interventional radiology, radiation oncology, nuclear medicine, radiation physics, and radiation biology. Additionally, the journal covers technical and industrial innovations. The journal welcomes original articles, technical notes, review articles, pictorial essays and letters to the editor. The journal also provides announcements from the boards and the committees of the society. Membership in the Japan Radiological Society is not a prerequisite for submission. Contributions are welcomed from all parts of the world.
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