Erdem Sukran, Erdem Orhan, Hussain M Tarique, Greil F Gerald, Zou Qing
Division of Pediatric Cardiology, Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX 75235, USA.
Department of Advanced Data Analytics, University of North Texas, Denton, TX 76205, USA.
Rev Cardiovasc Med. 2025 Jul 22;26(7):37399. doi: 10.31083/RCM37399. eCollection 2025 Jul.
Cardiovascular magnetic resonance (CMR) is a time-consuming, yet critical imaging method. In contrast, while rapid techniques accelerate image acquisition, these methods can also compromise image quality. Meanwhile, the effectiveness of Adaptive CS-Net, a vendor-supported deep-learning magnetic resonance (MR) reconstruction algorithm, for non-contrast three-dimensional (3D) whole-heart imaging using relaxation-enhanced angiography without contrast and triggering (REACT) remains uncertain.
Thirty participants were prospectively recruited for this study. Each underwent non-contrast imaging that included a modified REACT sequence and a standard 3D balanced steady-state free precession (bSSFP) sequence. The REACT data were acquired through six-fold undersampling and reconstructed offline using both conventional compressed sensing (CS) and an Adaptive CS-Net algorithm. Subjective and objective image quality assessments, as well as cross-sectional area measurements of selected vessels, were conducted to compare the REACT images reconstructed using Adaptive CS-Net against those reconstructed using conventional CS, as well as the standard bSSFP sequence. For a statistical comparison of image quality across these three image sets, the nonparametric Friedman test was performed, followed by Dunn's post-hoc test.
The Adaptive CS-Net and CS-reconstructed REACT images exhibited superior image quality for pulmonary veins, neck, and upper thoracic vessels compared to the standard 3D bSSFP sequence. Adaptive CS-Net and CS reconstructed REACT images displayed significantly higher contrast-to-noise ratio (CNR) compared to those reconstructed using the 3D bSSFP sequence (all -values < 0.05) for the left upper (5.40, 5.53, 0.97), left lower (6.33, 5.84, 2.27), right upper (5.49, 6.74, 1.18), and right lower pulmonary veins (6.71, 6.41, 1.26). Additionally, REACT methods showed a statistically significant improvement in CNR for both the ascending aorta and superior vena cava compared to the 3D bSSFP sequence.
The Adaptive CS-Net reconstruction for the REACT images consistently delivered superior or comparable image quality compared to the CS technique. Notably, the Adaptive CS-Net reconstruction provides significantly enhanced image quality for pulmonary veins, neck, and upper thoracic vessels compared to 3D bSSFP.
心血管磁共振成像（CMR）是一种耗时但至关重要的成像方法。相比之下，快速技术虽能加速图像采集，但这些方法也可能损害图像质量。同时，供应商支持的深度学习磁共振（MR）重建算法自适应CS-Net，在使用无对比剂和触发的弛豫增强血管造影术（REACT）进行非对比剂三维（3D）全心成像中的有效性仍不确定。
本研究前瞻性招募了30名参与者。每位参与者均接受了非对比剂成像，包括改良的REACT序列和标准的三维稳态自由进动（bSSFP）序列。REACT数据通过六倍欠采样采集，并使用传统压缩感知（CS）和自适应CS-Net算法进行离线重建。进行主观和客观图像质量评估，以及对选定血管的横截面积测量，以比较使用自适应CS-Net重建的REACT图像与使用传统CS重建的图像以及标准bSSFP序列。为了对这三个图像集的图像质量进行统计比较，进行了非参数Friedman检验，随后进行Dunn事后检验。
与标准的3D bSSFP序列相比，自适应CS-Net和CS重建的REACT图像在肺静脉、颈部和上胸部血管方面表现出更高的图像质量。与使用3D bSSFP序列重建的图像相比，自适应CS-Net和CS重建的REACT图像在左上（5.40, 5.53, 0.97）、左下（6.33, 5.84, 2.27）、右上（5.49, 6.74, 1.18）和右下肺静脉（6.71, 6.41, 1.26）的对比噪声比（CNR）显著更高（所有P值<0.05）。此外，与3D bSSFP序列相比，REACT方法在升主动脉和上腔静脉的CNR方面也有统计学上的显著改善。
与CS技术相比，REACT图像的自适应CS-Net重建始终能提供更高或相当的图像质量。值得注意的是，与3D bSSFP相比，自适应CS-Net重建为肺静脉、颈部和上胸部血管提供了显著更高的图像质量。
