  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.
