A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction

Liyue Shen, Wei Zhao, Dante Capaldi, John M. Pauly, Lei Xing

Abstract:

Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design
and implementation have potential flaws. Fundamentally, most deep learning models are driven entirely by data without
consideration of any prior knowledge, which dramatically increases the complexity of neural networks and limits the application
scope and model generalizability. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D
tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We
demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed
tomography imaging with ultra-sparse sampling. The study opens new avenues for data-driven biomedical imaging and promises
to provide substantially improved imaging tools for various clinical imaging and image-guided interventions.