Evaluation of cinematic volume rendering open-source and commercial solutions for the exploration of congenital heart data

Irum Baseer, Israel Valverde, Abdel H. Moustafa, Josep Blat, Oscar Camara

Room: 104

2023-10-25T03:45:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
Exploring Innovative Medical Visualization: We've ventured into the realm of medical imaging, investigating advanced techniques to improve decision-making in congenital heart disease cases. Traditional methods, which depend on basic lighting models, often lead to distortions in complex cardiac data. Enter cinematic rendering (CR), an impressive three-dimensional photo-realistic visualization method. Our study thoroughly examined the effects of CR on heart anatomy visualization, utilizing the open-source MeVisLab framework, and compared its performance to a commercial clinical tool.
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Cinematic rendering, open-source, commercial tool, congenital heart data


Detailed anatomical information is essential to optimize medical decisions for surgical and pre-operative planning in patients with congenital heart disease. The visualization techniques commonly used in clinical routine for the exploration of complex cardiac data are based on multi-planar reformations, maximum intensity projection, and volume rendering, which rely on basic lighting models prone to image distortion. On the other hand, cinematic rendering (CR), a three-dimensional visualization technique based on physically-based rendering methods, can create volumetric images with high fidelity. However, there are a lot of parameters involved in CR that affect the visualization results, thus being dependent on the user's experience and requiring detailed evaluation protocols to compare available solutions. In this study, we have analyzed the impact of the most relevant parameters in a CR pipeline developed in the open-source version of the MeVisLab framework for the visualization of the heart anatomy of three congenital patients and two adults from CT images. The resulting visualizations were compared to a commercial tool used in the clinics with a questionnaire filled in by clinical users, providing similar definitions of structures, depth perception, texture appearance, realism, and diagnostic ability.