Document Type


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IEEE Access

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Cardiac image display, web-based architecture, beating heart, raymarching, bidirectional visual synchronization, information streaming


Clinical treatment delivery often involves multiple medical professionals, where collaborative teamwork is crucial to ensure the quality of diagnosis and therapeutic decision making. With the development of Internet of Medical Things (IoMT) and its applications in mobile health (mHealth), healthcare services can be delivered to remote users. However, a challenging situation arises when the data are volumetric and dynamic, it will be difficult to achieve real-time performance in information streaming and data dynamic rendering and synchronization over Internet, as is the case with cardiac procedures, where both the anatomy and dynamic function characteristics of the organ must be considered. To address this issue, we have developed new algorithms and a web-based collaborative software architecture to display dynamic volumetric data in a web browser to enable dynamic data sharing with remote healthcare professionals. We illustrate our system using the diagnosis and treatment planning of an atrial septal defect (ASD) repair as an example to evaluate the capabilities of our platform. In this example, a series of three-dimensional (3D) ultrasound images are registered and fused with the high-quality magnetic resonance (MR) cardiac data to provide complementary information; virtual septal occluder patches are modeled and integrated into the beating heart views to facilitate the planning of the procedure to occlude the ASD, and feedback is streamed amongst all participating professionals. Our algorithm runs on a Node.js server with WebSocket protocol to synchronize dynamic heart rendering, so all the connected users can observe the same four-dimensional (4D) display of the dual-modality heart data during the process of examining cardiac anatomy, performing functional analysis, and sharing treatment strategies across distributed geographic locations. The presented computational models and software architecture create a new vehicle for collaborative exploration and manipulation of dynamic volumetric medical datasets.

Funding Source

This work was supported by the Illinois State University, including the Research Grant, the Seed Grant, the Travel Funding of the School of Information Technology, the University Research Grant, the Publication Incentive Program of the College of Arts and Sciences, and the University Publication Support Program.


This article first appeared in IEEE Access, volume 11, 2023. DOI: 10.1109/ACCESS.2022.3232275.

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