Kravchenko Dmitrij, Isaak Alexander, Mesropyan Narine, Peeters Johannes M, Kuetting Daniel, Pieper Claus C, Katemann Christoph, Attenberger Ulrike, Emrich Tilman, Varga-Szemes Akos, Luetkens Julian A
Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
Quantitative Imaging Laboratory Bonn, Bonn, Germany.
Eur Radiol. 2025 May;35(5):2877-2887. doi: 10.1007/s00330-024-11145-0. Epub 2024 Oct 23.
To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.
Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cine: 1.89 × 1.96 mm, reconstructed at 1.04 × 1.04 mm) and at a low-resolution (2.98 × 3.00 mm, reconstructed at 1.04 × 1.04 mm). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cine). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student's paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.
Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cine was shorter than cine (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (-19.5 ± 4.3 vs -19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).
Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.
Question Cine CMR acquisitions are time-intensive and vulnerable to artifacts. Findings Low-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35-42% without a significant difference in volumetric results or subjective image quality. Clinical relevance DL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.
比较标准分辨率平衡稳态自由进动（bSSFP）电影图像与低分辨率采集但采用深度学习（DL）超分辨率算法重建的电影图像。
前瞻性采集健康志愿者和患者的心血管磁共振（CMR）电影数据集（短轴和四腔心视图），分为正常分辨率（电影：1.89×1.96毫米，重建后为1.04×1.04毫米）和低分辨率（2.98×3.00毫米，重建后为1.04×1.04毫米）。低分辨率图像采用压缩感知DL去噪和分辨率提升进行重建（电影）。评估左心室射血分数（LVEF）、舒张末期容积指数（LVEDVi）和应变。计算表观信噪比（aSNR）和对比噪声比（aCNR）。采用5分李克特量表评估主观图像质量。采用学生配对t检验、威尔科克森配对符号秩检验和组内相关系数（ICC）进行统计分析。
分析了30名参与者（37±16岁；20名健康志愿者和10名患者）。短轴视图电影全层采集持续时间比电影短（57.5±8.7秒对98.7±12.秒；p<0.0001）。在以下方面未观察到差异：LVEF（59±7对59±7%；ICC：0.95[95%置信区间：0.94，0.99]；p=0.17）、LVEDVi（85.0±13.5对84.4±13.7毫升/米；ICC：0.99[0.98，0.99]；p=0.12）、纵向应变（-19.5±4.3对-19.8±3.9%；ICC：0.94[0.88，0.97]；p=0.52）、短轴aSNR（81±49对69±38；p=0.32）、aCNR（53±31对45±27；p=0.33）或主观图像质量（5.0[四分位距4.9，5.0]对5.0[四分位距4.7，5.0]；p=0.99）。
对较低空间分辨率采集的电影图像进行深度学习重建可使采集时间减少42%，屏气时间缩短，且不影响容积测量结果或图像质量。
问题电影CMR采集耗时且易出现伪影。发现使用DL超分辨率进行低分辨率提升重建可使采集时间减少35 - 42%，容积测量结果或主观图像质量无显著差异。临床意义对较低空间分辨率采集的bSSFP电影图像进行DL超分辨率重建可减少采集时间，同时保持诊断准确性，通过缩短屏气时间提高电影成像的临床可行性。
