Key result
Multimodal fusion improves inter-clinician agreement ~94% vs standard delineation for cardiac radioablation.
Why the study?
Cardiac radioablation target delineation is a challenging and tedious task requiring the integration of disparate imaging modalities, including cardiac electro-anatomical mapping.
Does a multimodal data integration process improve inter-clinician agreement and ease of use for target delineation in cardiac radioablation compared to standard slice-by-slice delineation?
Does a multimodal data integration process improve inter-clinician agreement and ease of use for target delineation in cardiac radioablation compared to standard slice-by-slice delineation?
Absolute Event Rate: 0.62% vs 0.32%
A multimodal fusion workflow integrating EAM, PET, and CT data improves inter-clinician agreement and efficiency for target delineation in cardiac radioablation.
May support multimodal workflows in cardiac radioablation planning; hypothesis-generating and requires larger validation before adoption.
BACKGROUND: Cardiac radioablation (CR) is an innovative treatment to ablate cardiac arrythmia sources by radiation therapy. CR target delineation is a challenging task requiring the exploitation of highly different imaging modalities, including cardiac electro-anatomical mapping (EAM). PURPOSE: In this work, a data integration process is proposed to alleviate the tediousness of CR target delineation by generating a fused representation of the heart, including all the information of interest resulting from the analysis and registration of electro-anatomical data, PET scan and planning computed tomography (CT) scan. The proposed process was evaluated by cardiologists during delineation trials. METHODS: The data processing pipeline was composed of the following steps. The cardiac structures of interest were segmented from cardiac CT scans using a deep learning method. The EAM data was registered to the cardiac CT scan using a point cloud based registration method. The PET scan was registered using rigid image registration. The EAM and PET information, as well as the myocardium thickness, were projected on the surface of the 3D mesh of the left ventricle. The target was identified by delineating a path on this surface that was further projected to the thickness of the myocardium to create the target volume. This process was evaluated by comparison with a standard slice-by-slice delineation with mental EAM registration. Four cardiologists delineated targets for three patients using both methods. The variability of target volumes, and the ease of use of the proposed method, were evaluated. RESULTS: All cardiologists reported being more confident and efficient using the proposed method. The inter-clinician variability in delineated target volume was systematically lower with the proposed method (average dice score of 0.62 vs. 0.32 with a classical method). Delineation times were also improved. CONCLUSIONS: A data integration process was proposed and evaluated to fuse images of interest for CR target delineation. It effectively reduces the tediousness of CR target delineation, while improving inter-clinician agreement on target volumes. This study is still to be confirmed by including more clinicians and patient data to the experiments.
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Rigal et al. (2023) studied Ventricular tachycardia (n=3). Multimodal fusion workflow vs. Standard slice-by-slice delineation with mental EAM registration was evaluated on Inter-clinician variability in delineated target volume (average dice score). A multimodal fusion workflow for cardiac radioablation target delineation improved inter-clinician agreement compared to standard slice-by-slice delineation (average dice score 0.62 vs. 0.32).
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