“…For this reason, the 17-segment model has previously been proposed. 8 Previously, angulation from the long axis of the body to the cardiac long axis could be performed by means of several manual rotation steps. After angulation, the 17 segments were manually delineated according to the 17-segment model definitions.…”
Section: Discussionmentioning
confidence: 99%
“… 9 The cardiac planes can be generated from body planes by means of angulation. This angulation can be performed manually, 8 but may also be performed semiautomated. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…As a potential solution for this problem, the use of the American Heart Association (AHA) 17-segment model has previously been proposed as structured approach for the identification and subsequent targeting and treatment of the proarrhythmic region(s). 8 However, radiotherapy planning systems do not yet have implemented tools available to angulate and segment the heart according to this AHA 17-segment model statement. 9 In order to standardize treatments and to promote accurate communication and identification of the target for cardiac radioablation, a robust and reproducible method for the angulation and generation of the segments is of clear importance.…”
Section: Introductionmentioning
confidence: 99%
“…As a potential solution for this problem, the use of the American Heart Association (AHA) 17-segment model has previously been proposed as structured approach for the identification and subsequent targeting and treatment of the proarrhythmic regions. 8 …”
“…For this reason, the 17-segment model has previously been proposed. 8 Previously, angulation from the long axis of the body to the cardiac long axis could be performed by means of several manual rotation steps. After angulation, the 17 segments were manually delineated according to the 17-segment model definitions.…”
Section: Discussionmentioning
confidence: 99%
“… 9 The cardiac planes can be generated from body planes by means of angulation. This angulation can be performed manually, 8 but may also be performed semiautomated. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…As a potential solution for this problem, the use of the American Heart Association (AHA) 17-segment model has previously been proposed as structured approach for the identification and subsequent targeting and treatment of the proarrhythmic region(s). 8 However, radiotherapy planning systems do not yet have implemented tools available to angulate and segment the heart according to this AHA 17-segment model statement. 9 In order to standardize treatments and to promote accurate communication and identification of the target for cardiac radioablation, a robust and reproducible method for the angulation and generation of the segments is of clear importance.…”
Section: Introductionmentioning
confidence: 99%
“…As a potential solution for this problem, the use of the American Heart Association (AHA) 17-segment model has previously been proposed as structured approach for the identification and subsequent targeting and treatment of the proarrhythmic regions. 8 …”
“… 5 The readily available 17-segment model, often employed in cardiac imaging, can be utilized to streamline the targeting process and provide a common vocabulary between radiation experts and cardiac electrophysiologists. 15 …”
Section: Treatment Planning and Radiation Deliverymentioning
BackgroundCardiac 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).PurposeIn 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.MethodsThe 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.ResultsAll 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.ConclusionsA 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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