Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.
Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.
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使用基于深度学习的重建算法提高延迟钆增强图像质量及其对心肌瘢痕定量的影响
DOI：
10.1007/s00330-020-07461-w
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发表时间：
2021-06
影响因子：
5.9
通讯作者：
Hirsch A
中科院分区：
文献类型：
--
作者：
van der Velde N;Hassing HC;Bakker BJ;Wielopolski PA;Lebel RM;Janich MA;Kardys I;Budde RPJ;Hirsch A
本研究旨在评估基于深度学习（DL）的重建算法对延迟钆增强（LGE）图像质量的影响，并评价其对瘢痕定量的作用。
60例疑似或已知心肌病患者（年龄46±17岁，50%为男性）接受了心脏磁共振成像（CMR）检查。短轴LGE图像分别使用常规重建方法以及具有0% - 100%可调降噪（NR）水平的深度学习网络（DLRecon）进行重建。使用5分制量表（差到优）对标准LGE图像和降噪75%的DLRecon图像的质量进行评分。在30例有LGE的患者中，使用高于远隔心肌不同标准差（SD）的阈值技术以及在不同降噪水平图像中使用半高全宽（FWHM）技术对瘢痕大小进行定量。
DLRecon图像质量高于标准LGE图像（主观质量评分3.3±0.5比3.6±0.7，p < 0.001）。使用SD方法时，瘢痕大小随降噪水平升高而增加。在100%降噪水平下，与标准LGE图像相比，使用2SD、4SD和6SD定量方法时，瘢痕大小分别增加36%、87%和138%（所有p值均< 0.001）。然而，使用FWHM方法时，未发现瘢痕大小有差异（p = 0.06）。
使用基于深度学习的重建算法可显著提高LGE图像质量。然而，该算法对瘢痕定量有重要影响，这取决于所使用的定量技术。由于FWHM方法不受降噪影响，因此更受青睐。临床医生在使用基于深度学习的重建算法时，应了解其对瘢痕定量的影响。
• 使用基于深度学习的重建算法（该算法旨在通过降噪技术重建高信噪比图像），基于（主观）视觉评估的图像质量以及延迟钆增强图像的清晰度显著提高。
• 当使用高于远隔心肌不同标准差的阈值技术对瘢痕大小进行定量时，应特别注意，因为这些先进的图像增强算法影响较大。
• 当使用基于降噪的深度学习算法时，建议使用半高全宽方法对瘢痕大小进行定量，因为该方法对噪声水平最不敏感，并且与视觉延迟钆增强评估的一致性最佳。
在线版本包含补充材料，可在10.1007/s00330 - 2020 - 07461 - w获取。
The aim of this study was to assess the effect of a deep learning (DL)–based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06). LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning–based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment. The online version contains supplementary material available at 10.1007/s00330-020-07461-w.
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DOI：
10.1016/j.jcmg.2018.07.015
发表时间：
2019-08
期刊：
JACC. Cardiovascular imaging
影响因子：
--
作者：
Halliday BP;Baksi AJ;Gulati A;Ali A;Newsome S;Izgi C;Arzanauskaite M;Lota A;Tayal U;Vassiliou VS;Gregson J;Alpendurada F;Frenneaux MP;Cook SA;Cleland JGF;Pennell DJ;Prasad SK
通讯作者：
Prasad SK
影响因子：
24
作者：
Amado, LC;Gerber, BL;Lima, JAC
通讯作者：
Lima, JAC
影响因子：
2.1
作者：
Liu, Dongting;Ma, Xiaohai;Fan, Zhanming
通讯作者：
Fan, Zhanming
影响因子：
2.5
作者：
Kim, Ki Hwan;Park, Sung-Hong
通讯作者：
Park, Sung-Hong
实时心血管MR具有时空伪影抑制，使用先天性心脏病中的概念深度学习。
影响因子：
3.3
作者：
Hauptmann A;Arridge S;Lucka F;Muthurangu V;Steeden JA
通讯作者：
Steeden JA