Abstract
Objectives
The aim of this study is to develop a scar detection method for routine CTA imaging using deep convolutional neural networks (CNN), that relies solely on anatomical information as input and is compatible with existing clinical workflows.
Background
Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) MRI is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than CMR but is unable to reliably image scar.
Methods
A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischaemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA data set (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts.
Results
84.7\% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA derived data, with no further training, where it achieved an 88.3\% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians.
Conclusions
Automatic ischaemic scar detection can be performed from a routine cardiac CTA, without any scar specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times and guide clinical decision making.
The aim of this study is to develop a scar detection method for routine CTA imaging using deep convolutional neural networks (CNN), that relies solely on anatomical information as input and is compatible with existing clinical workflows.
Background
Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) MRI is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than CMR but is unable to reliably image scar.
Methods
A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischaemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA data set (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts.
Results
84.7\% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA derived data, with no further training, where it achieved an 88.3\% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians.
Conclusions
Automatic ischaemic scar detection can be performed from a routine cardiac CTA, without any scar specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times and guide clinical decision making.
| Original language | English |
|---|---|
| Journal | Frontiers in Cardiovascular Medicine |
| DOIs | |
| Publication status | Accepted/In press - 24 May 2021 |