Chen Zhennong, Contijoch Francisco, Colvert Gabrielle M, Manohar Ashish, Kahn Andrew M, Narayan Hari K, McVeigh Elliot
Department of Bioengineering, UC San Diego School of Engineering, La Jolla, CA, United States.
Department of Radiology, UC San Diego School of Medicine, La Jolla, CA, United States.
Front Cardiovasc Med. 2022 Jul 28;9:919751. doi: 10.3389/fcvm.2022.919751. eCollection 2022.
The presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.
Three hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing.
Volume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment.
Dynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.
左心室（LV）壁运动异常（WMA）的存在是心血管疾病患者发生不良心血管事件的独立指标。我们开发并评估了使用深度学习（DL）框架从临床四维计算机断层扫描（CT）血管造影的动态容积渲染（VR）中检测心脏壁运动异常（WMA）的能力。
回顾性评估了343项心电图门控心脏4DCT研究（年龄：61±15岁，男性占60.1%）。从6个不同视角生成左心室血池的容积渲染视频（即围绕左心室长轴每旋转60度对应的六个视图）；共产生2058个独特视频。通过评估原始4DCT数据中可见的局部缩短受损程度，对每个视频进行WMA分类的真实情况判定。通过首先使用预训练的Inception网络逐帧提取图像特征，然后使用长短期记忆网络评估特征集，对每个视频进行WMA存在情况的DL分类。数据分为60%用于5折交叉验证，40%用于测试。
容积渲染视频代表4DCT容积约800倍的数据压缩。每个视频的DL分类性能在交叉验证（准确率=93.1%，灵敏度=90.0%，特异性=95.1%，κ值：0.86）和测试（分别为90.9%、90.2%和91.4%，κ值：0.81）中都很高。每项研究的性能也很高（交叉验证：93.7%、93.5%、93.8%，κ值：0.87；测试：93.5%、91.9%、94.7%，κ值：0.87）。通过将每个视频的结果重新分类到左心室的6个区域视图中，我们表明DL对每个区域都很准确（交叉验证队列和测试队列的平均准确率分别为93.1%和90.9%）。DL分类与专家视觉评估高度一致（准确率=91.0%，κ值：0.81）。
左心室血池的动态容积渲染结合DL分类可以从心脏CT中准确检测局部WMA。
