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Abstract：
BACKGROUND: Cardiovascular magnetic resonance (CMR) cine imaging is still limited by long acquisition times. This study evaluated the clinical utility of an accelerated two-dimensional (2D) cine sequence with deep learning reconstruction (Sonic DL) to decrease acquisition time without compromising quantitative volumetry or image quality.
METHODS: A sub-study using 16 participants was performed using Sonic DL at two different acceleration factors (8× and 12×). Quantitative left-ventricular volumetry, function, and mass measurements were compared between the two acceleration factors against a standard cine method. Following this sub-study, 108 participants were prospectively recruited and imaged using a standard cine method and the Sonic DL method with the acceleration factor that more closely matched the reference method. Two experienced clinical readers rated images based on their diagnostic utility and performed all image contouring. Quantitative contrast difference and endocardial border sharpness were also assessed. Left- and right-ventricular volumetry, left-ventricular mass, and myocardial strain measurements were compared between cine methods using Bland-Altman plots, Pearson\'s correlation, and paired t-tests. Comparative analysis of image quality was measured using Wilcoxon-signed-rank tests and visualized using bar graphs.
RESULTS: Sonic DL at an acceleration factor of 8 more closely matched the reference cine method. There were no significant differences found across left ventricular volumetry, function, or mass measurements. In contrast, an acceleration factor of 12 resulted in a 6% (5.51/90.16) reduction of measured ejection fraction when compared to the standard cine method and a 4% (4.32/88.98) reduction of measured ejection fraction when compared to Sonic DL at an acceleration factor of 8. Thus, Sonic DL at an acceleration factor of 8 was chosen for downstream analysis. In the larger cohort, this accelerated cine sequence was successfully performed in all participants and significantly reduced the acquisition time of cine images compared to the standard 2D method (reduction of 37% (5.98/16) p < 0.0001). Diagnostic image quality ratings and quantitative image quality evaluations were statistically not different between the two methods (p > 0.05). Left- and right-ventricular volumetry and circumferential and radial strain were also similar between methods (p > 0.05) but left-ventricular mass and longitudinal strain were over-estimated using the proposed accelerated cine method (mass over-estimated by 3.36 g/m2, p < 0.0001; longitudinal strain over-estimated by 1.97%, p = 0.001).
CONCLUSIONS: This study found that an accelerated 2D cine method with DL reconstruction at an acceleration factor of 8 can reduce CMR cine acquisition time by 37% (5.98/16) without significantly affecting volumetry or image quality. Given the increase of scan time efficiency, this undersampled acquisition method using deep learning reconstruction should be considered for routine clinical CMR.
METHODS: A sub-study using 16 participants was performed using Sonic DL at two different acceleration factors (8× and 12×). Quantitative left-ventricular volumetry, function, and mass measurements were compared between the two acceleration factors against a standard cine method. Following this sub-study, 108 participants were prospectively recruited and imaged using a standard cine method and the Sonic DL method with the acceleration factor that more closely matched the reference method. Two experienced clinical readers rated images based on their diagnostic utility and performed all image contouring. Quantitative contrast difference and endocardial border sharpness were also assessed. Left- and right-ventricular volumetry, left-ventricular mass, and myocardial strain measurements were compared between cine methods using Bland-Altman plots, Pearson\'s correlation, and paired t-tests. Comparative analysis of image quality was measured using Wilcoxon-signed-rank tests and visualized using bar graphs.
RESULTS: Sonic DL at an acceleration factor of 8 more closely matched the reference cine method. There were no significant differences found across left ventricular volumetry, function, or mass measurements. In contrast, an acceleration factor of 12 resulted in a 6% (5.51/90.16) reduction of measured ejection fraction when compared to the standard cine method and a 4% (4.32/88.98) reduction of measured ejection fraction when compared to Sonic DL at an acceleration factor of 8. Thus, Sonic DL at an acceleration factor of 8 was chosen for downstream analysis. In the larger cohort, this accelerated cine sequence was successfully performed in all participants and significantly reduced the acquisition time of cine images compared to the standard 2D method (reduction of 37% (5.98/16) p < 0.0001). Diagnostic image quality ratings and quantitative image quality evaluations were statistically not different between the two methods (p > 0.05). Left- and right-ventricular volumetry and circumferential and radial strain were also similar between methods (p > 0.05) but left-ventricular mass and longitudinal strain were over-estimated using the proposed accelerated cine method (mass over-estimated by 3.36 g/m2, p < 0.0001; longitudinal strain over-estimated by 1.97%, p = 0.001).
CONCLUSIONS: This study found that an accelerated 2D cine method with DL reconstruction at an acceleration factor of 8 can reduce CMR cine acquisition time by 37% (5.98/16) without significantly affecting volumetry or image quality. Given the increase of scan time efficiency, this undersampled acquisition method using deep learning reconstruction should be considered for routine clinical CMR.
摘要：
背景：心血管磁共振（CMR）电影成像仍然受到长采集时间的限制。这项研究评估了具有深度学习重建（SonicDL）的加速二维（2D）电影序列的临床实用性，以减少采集时间而不影响定量体积或图像质量。
方法：使用SonicDL在两个不同的加速因子（8x和12x）下进行了一项使用16名参与者的子研究。定量左心室容积测定,与标准电影方法相比，将两种加速因子之间的函数和质量测量值进行了比较。在这项子研究之后，前瞻性招募了108名参与者，并使用标准电影方法和SonicDL方法进行了成像，其加速因子与参考方法更接近。两位经验丰富的临床读者根据其诊断实用程序对图像进行了评级，并执行了所有图像轮廓绘制。还评估了定量对比差异和心内膜边界清晰度。左心室和右心室容积,使用Bland-Altman图，在电影方法之间比较左心室质量和心肌应变测量值，皮尔森的相关性，和配对t检验。使用Wilcoxon符号秩检验测量图像质量的比较分析，并使用条形图进行可视化。
结果：加速因子为8的SonicDL与参考电影方法更接近。在左心室容积测量中没有发现显著差异，函数,或质量测量。相比之下,与标准电影方法相比，加速因子12导致测得的射血分数降低6％，与加速因子8的SonicDL相比，测得的射血分数降低4％。因此，选择加速因子为8的SonicDL用于下游分析。在更大的群体中，与标准2D方法相比，该加速的电影序列在所有参与者中成功执行，并且显着减少了电影图像的采集时间（减少了40％，p<0.0001)。诊断图像质量评级和定量图像质量评价两种方法在统计学上没有差异(p>0.05)。方法之间的左和右心室容积以及周向和径向应变也相似（p>0.05），但是使用提出的加速电影方法（质量高估了3.36g/m2，p<0.0001；纵向应变高估了1.97％，p=0.001)。
结论：这项研究发现，在加速因子为8时具有DL重建的加速2D电影方法可将CMR电影采集时间减少40％，而不会显着影响体积或图像质量。鉴于扫描时间效率的增加，这种使用深度学习重建的欠采样采集方法应考虑用于常规临床CMR.
方法：使用SonicDL在两个不同的加速因子（8x和12x）下进行了一项使用16名参与者的子研究。定量左心室容积测定,与标准电影方法相比，将两种加速因子之间的函数和质量测量值进行了比较。在这项子研究之后，前瞻性招募了108名参与者，并使用标准电影方法和SonicDL方法进行了成像，其加速因子与参考方法更接近。两位经验丰富的临床读者根据其诊断实用程序对图像进行了评级，并执行了所有图像轮廓绘制。还评估了定量对比差异和心内膜边界清晰度。左心室和右心室容积,使用Bland-Altman图，在电影方法之间比较左心室质量和心肌应变测量值，皮尔森的相关性，和配对t检验。使用Wilcoxon符号秩检验测量图像质量的比较分析，并使用条形图进行可视化。
结果：加速因子为8的SonicDL与参考电影方法更接近。在左心室容积测量中没有发现显著差异，函数,或质量测量。相比之下,与标准电影方法相比，加速因子12导致测得的射血分数降低6％，与加速因子8的SonicDL相比，测得的射血分数降低4％。因此，选择加速因子为8的SonicDL用于下游分析。在更大的群体中，与标准2D方法相比，该加速的电影序列在所有参与者中成功执行，并且显着减少了电影图像的采集时间（减少了40％，p<0.0001)。诊断图像质量评级和定量图像质量评价两种方法在统计学上没有差异(p>0.05)。方法之间的左和右心室容积以及周向和径向应变也相似（p>0.05），但是使用提出的加速电影方法（质量高估了3.36g/m2，p<0.0001；纵向应变高估了1.97％，p=0.001)。
结论：这项研究发现，在加速因子为8时具有DL重建的加速2D电影方法可将CMR电影采集时间减少40％，而不会显着影响体积或图像质量。鉴于扫描时间效率的增加，这种使用深度学习重建的欠采样采集方法应考虑用于常规临床CMR.
