School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
Comput Biol Med. 2022 Nov;150:106191. doi: 10.1016/j.compbiomed.2022.106191. Epub 2022 Oct 15.
The aim of this study is to develop an automated method of regional scar detection on clinically standard computed tomography angiography (CTA) using encoder-decoder networks with latent space classification.
Localising scar in cardiac patients can assist in diagnosis and guide interventions. Magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE) is the clinical gold standard for scar imaging; however, it is commonly contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is widely used as a first-line imaging modality of cardiac applications.
A dataset of 79 patients with both clinically indicated MRI LGE and subsequent CTA scans was used to train and validate networks to classify septal and lateral scar presence within short axis left ventricle slices. Two designs of encoder-decoder networks were compared, with one encoding anatomical shape in the latent space. Ground truth was established by segmenting scar in MRI LGE and registering this to the CTA images. Short axis slices were taken from the CTA, which served as the input to the networks. An independent external set of 22 cases (27% the size of the cross-validation set) was used to test the best network.
A network classifying lateral scar only achieved an area under ROC curve of 0.75, with a sensitivity of 0.79 and specificity of 0.62 on the independent test set. The results of septal scar classification were poor (AUC < 0.6) for all networks. This was likely due to a high class imbalance. The highest AUC network encoded anatomical shape information in the network latent space, indicating it was important for the successful classification of lateral scar.
Automatic lateral wall scar detection can be performed from a routine cardiac CTA with reasonable accuracy, without any scar specific imaging. This requires only a single acquisition in the cardiac cycle. In a clinical setting, this could be useful for pre-procedure planning, especially where MRI is contraindicated. Further work with more septal scar present is warranted to improve the usefulness of this approach.
本研究旨在开发一种使用具有潜在空间分类的编解码器网络对临床标准 CT 血管造影(CTA)进行区域疤痕检测的自动化方法。
在心脏病患者中定位疤痕可以辅助诊断并指导干预。心脏磁共振成像(MRI)结合钆延迟增强(LGE)是疤痕成像的临床金标准;然而,它通常是禁忌症。CTA 是一种替代成像方式,禁忌症较少,广泛用于心脏应用的一线成像方式。
使用具有临床指征的 MRI LGE 以及随后的 CTA 扫描的 79 名患者数据集来训练和验证网络,以对短轴左心室切片中的间隔和外侧疤痕存在进行分类。比较了两种编解码器网络设计,其中一种在潜在空间中对解剖形状进行编码。通过对 MRI LGE 中的疤痕进行分割并将其注册到 CTA 图像上来建立地面真实。从 CTA 中获取短轴切片,作为网络的输入。使用独立的外部 22 例数据集(交叉验证集的 27%)来测试最佳网络。
仅对侧壁疤痕进行分类的网络在独立测试集上的 AUC 为 0.75,灵敏度为 0.79,特异性为 0.62。对于所有网络,间隔疤痕分类的结果都很差(AUC<0.6)。这可能是由于高类别不平衡造成的。编码网络潜在空间中解剖形状信息的 AUC 最高网络表明,这对于成功分类外侧疤痕很重要。
从常规心脏 CTA 可以以合理的准确性自动检测外侧壁疤痕,而无需任何特定于疤痕的成像。这仅需要在心脏周期中进行一次采集。在临床环境中,这对于术前规划特别有用,特别是在 MRI 禁忌的情况下。需要更多存在间隔疤痕的进一步工作来提高这种方法的实用性。