提高基于AI的CMR生物标志物估计的扫描-重扫描精度
Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation
AI总结：
提出一种心脏生物标志物估计流程，通过图像和分割插值方法提高心尖-心底分辨率，在92名受试者的CMR数据上有效缩小了扫描-重扫描置信区间，兼顾了分割精度与纵向分析所需的标志物一致性。
摘要：
使用深度学习（DL）方法从电影心血管磁共振（CMR）数据中量化心脏生物标志物具有许多优势，例如提高准确性和加快分析速度。然而，只有少数研究关注生物标志物估计的扫描-重扫描精度，这对于可重复性和纵向分析非常重要。在此，我们提出了一种心脏生物标志物估计流程，不仅关注实现高分割精度，还关注提高所计算生物标志物的扫描-重扫描精度，即左心室和右心室射血分数，以及左心室心肌质量。我们评估了两种提高用于估计生物标志物的分割的心尖-心底分辨率的方法：一种基于图像插值，另一种基于分割插值。使用包含从92名受试者采集的扫描-重扫描电影CMR数据的数据库，我们将这两种方法的性能与插值前获得的ground truth（GT）分割和DL分割（基线）进行了比较。结果表明，与GT和基线性能相比，基于图像和基于分割的插值方法均能够缩小所有生物标志物的Bland-Altman扫描-重扫描置信区间。我们的发现强调，不仅要关注分割精度，还要关注重复扫描中生物标志物的一致性，这对于心脏功能的纵向分析至关重要。
英文摘要：
Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and faster analysis. However, only a few studies have focused on the scan-rescan precision of the biomarker estimates, which is important for reproducibility and longitudinal analysis. Here, we propose a cardiac biomarker estimation pipeline that not only focuses on achieving high segmentation accuracy but also on improving the scan-rescan precision of the computed biomarkers, namely left and right ventricular ejection fraction, and left ventricular myocardial mass. We evaluate two approaches to improve the apical-basal resolution of the segmentations used for estimating the biomarkers: one based on image interpolation and one based on segmentation interpolation. Using a database comprising scan-rescan cine CMR data acquired from 92 subjects, we compare the performance of these two methods against ground truth (GT) segmentations and DL segmentations obtained before interpolation (baseline). The results demonstrate that both the image-based and segmentation-based interpolation methods were able to narrow Bland-Altman scan-rescan confidence intervals for all biomarkers compared to the GT and baseline performances. Our findings highlight the importance of focusing not only on segmentation accuracy but also on the consistency of biomarkers across repeated scans, which is crucial for longitudinal analysis of cardiac function.
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