Translating computational modelling tools for clinical practice in congenital heart disease

Increasingly large numbers of medical centres worldwide are equipped with the means to acquire 3D images of patients by utilising magnetic resonance (MR) or computed tomography (CT) scanners. The interpretation of patient 3D image data has significant implications on clinical decision-making and treatment planning. In their raw form, MR and CT images have become critical in routine practice. However, in congenital heart disease (CHD), lesions are often anatomically and physiologically complex. In many cases, 3D imaging alone can fail to provide conclusive information for the clinical team. In the past 20-30 years, several image-derived modelling applications have shown major advancements. Tools such as computational fluid dynamics (CFD) and virtual reality (VR) have successfully demonstrated valuable uses in the management of CHD. However, due to current software limitations, these applications have remained largely isolated to research settings, and have yet to become part of clinical practice. The overall aim of this project was to explore new routes for making conventional computational modelling software more accessible for CHD clinics. The first objective was to create an automatic and fast pipeline for performing vascular CFD simulations. By leveraging machine learning, a solution was built using synthetically generated aortic anatomies, and was seen to be able to predict 3D aortic pressure and velocity flow fields with comparable accuracy to conventional CFD. The second objective was to design a virtual reality (VR) application tailored for supporting the surgical planning and teaching of CHD. The solution was a Unity-based application which included numerous specialised tools, such as mesh-editing features and online networking for group learning. Overall, the outcomes of this ongoing project showed strong indications that the integration of VR and CFD into clinical settings is possible, and has potential for extending 3D imaging and supporting the diagnosis, management and teaching of CHD

Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N=67). Inference performed on 200 test shapes resulted in average errors of 6.01% +/-3.12 SD and 3.99% +/-0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in +/-0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with high accuracy.Comment: 22 pages, 19 figure

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy

Enhanced 3D visualization for planning biventricular repair of double outlet right ventricle: a pilot study on the advantages of virtual reality

AIMS: We aim to determine any additional benefit of virtual reality (VR) experience if compared to conventional cross-sectional imaging and standard three-dimensional (3D) modelling when deciding on surgical strategy in patients with complex double outlet right ventricle (DORV). METHODS AND RESULTS: We retrospectively selected 10 consecutive patients with DORV and complex interventricular communications, who underwent biventricular repair. An arterial switch operation (ASO) was part of the repair in three of those. Computed tomography (CT) or cardiac magnetic resonance imaging images were used to reconstruct patient-specific 3D anatomies, which were then presented using different visualization modalities: 3D pdf, 3D printed models, and VR models. Two experienced paediatric cardiac surgeons, blinded to repair performed, reviewed each case evaluating the suitability of repair following assessment of each visualization modalities. In addition, they had to identify those who had ASO as part of the procedure. Answers of the two surgeons were compared to the actual operations performed. There was no mortality during the follow-up (mean = 2.5 years). Two patients required reoperations. After review of CT/cardiac magnetic resonance images, the evaluators identified the surgical strategy in accordance with the actual surgical plan in 75% of the cases. When using 3D pdf this reached only 70%. Accordance improved to 85% after revision of 3D printed models and to 95% after VR. Use of 3D printed models and VR facilitated the identification of patients who required ASO. CONCLUSION: Virtual reality can enhance understanding of suitability for biventricular repair in patients with complex DORV if compared to cross-sectional images and other 3D modelling techniques

Investigating the Feasibility of Virtual Reality (VR) for Teaching Cardiac Morphology

Congenital heart disease (CHD) is the most common defect at birth. Effective training for clinical professionals is essential in order to provide a high standard of care for patients. Visual aids for teaching complex CHD have remained mostly unchanged in recent years, with traditional methods such as diagrams and specimens still essential for delivering educational content. Diagrams and other 2D visualisations for teaching are in most cases artistic illustrations with no direct relation to true, 3D medical data. Specimens are rare, difficult for students to access and are limited to specific institutions. Digital, patient-specific models could potentially address these problems within educational programmes. Virtual Reality (VR) can facilitate the access to digital models and enhance the educational experience. In this study, we recorded and analysed the sentiment of clinical professionals towards VR when learning about CHD. A VR application (VheaRts) containing a set of patient-specific models was developed in-house. The application was incorporated into a specialised cardiac morphology course to assess the feasibility of integrating such a tool, and to measure levels of acceptance. Attendees were clinical professionals from a diverse range of specialities. VR allowed users to interact with six different patient-derived models immersed within a 3D space. Feedback was recorded for 58 participants. The general response towards the use of VR was overwhelmingly positive, with 88% of attendees rating 4 or 5 for ‘helpfulness of VR in learning CHD’ (5-points Likert scale). Additionally, 70% of participants with no prior VR experience rated 4 or 5 for ‘intuitiveness and ease of use’. Our study indicates that VR has a high level of acceptance amongst clinical trainees when used as an effective aid for learning congenital heart disease. Additionally, we noted three specific use-cases where VR offered novel teaching experiences not possible with conventional methods

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy

Additional file 1 of Automatic segmentation of the great arteries for computational hemodynamic assessment

Additional file 1. Supplementary Materials A

Additional file 3 of Automatic segmentation of the great arteries for computational hemodynamic assessment

Additional file 3. Supplementary Materials C

Additional file 2 of Automatic segmentation of the great arteries for computational hemodynamic assessment

Additional file 2. Supplementary Materials B