Speaker
Tom Hughes,The University of Texas at Austin
Abstract
Computational Medicine is a rapidly growing field. I would divide the history of Computational Medicine into two eras, one prior to the widespread use of medical imaging modalities and one since. In the former era, Computational Medicine was in its infancy and the relevance of results, often computed on very simplified geometric configuration, was limited and had very little impact on clinical practice. Since the clinical installation of imaging technology, the situation has changed significantly and continues to progress. The early days of Computational Mechanics saw a similar evolution, but as computers became more powerful and ubiquitous in engineering design offices, the footprint of Computational Mechanics became pervasive. Problems that could not be solved early on, such as full vehicle crash analysis in the 1980s, became routine by the late 1990s, and now we can say that cars are fully designed on computers.
The fidelity of imaging modalities continues to advance and seems to behave similarly to the way Moore’s Law did for the number of transistors on a microchip. It has been said that an analog of Moore’s Law is now at work in medical imaging, and it is clearly a driver for the development and application of Computational Medicine. The future of Computational Medicine may look like the present of Computational Mechanics, which also continues to move forward.
The problems facing Computational Medicine are the complexity of organs and physiological processes, the current resolution of imaging modalities with reference to particular diseases, the relevant time scales to affect patient specific clinical decision making, and how to deliver the results of Computational Medicine analyses to the clinician. The major technical pacing item is the accurate and efficient creation of computational models, which is also still the major bottleneck in the engineering design-through-analysis process. Clearly, Isogeometric Analysis has a role to play in addressing the image-based modeling problem of Computational Medicine. In engineering, the predominant modeling problem is building models from Computer Aided Design (CAD) files. A question that is at present unanswered is should a CAD file first be created of the organs under consideration, or should the computational model be directly built from the segmentation of the organs, e.g., the coronary arteries, cancerous tumors, etc.? Another question is where does AI and Machine Learning fit into the process of developing computational models from imaging data? These technologies are already of use in some clinically available technologies, e.g., HeartFlow, Inc.
I do not have answers to all the questions, but I do have examples of how immersed (i.e., unfitted) Isogeometric Analysis is being used in Computational Medicine, both from the model building and equation solving perspectives. In my presentation, I will focus on recent Isogeometric Analysis work on the detection and risk-assessment of vulnerable plaques in the walls of coronary arteries and a way to model essential sub-voxel (i.e., “invisible”) features.
In my opinion, research opportunities going forward abound for immersed Isogeometric Analysis applications in Computational Medicine.