ESTRO 2022 - Abstract Book

S287

Abstract book

ESTRO 2022

Conclusion This study shows that automatic applicator-based image registration for MR-IGABT could be achieved by combining classical image registration algorithms with modern deep learning methods. This is an important step towards future applications for monitoring organ motion during treatment and reducing dosimetric uncertainties.

PD-0324 A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction

L. Shen 1 , W. Zhao 2 , J. Pauly 3 , L. Xing 2

1 Stanford University, Electrical Engineering, Stanford , USA; 2 Stanford University, Radiation Oncology, Stanford, USA; 3 Stanford University, Electrical Engineering, Stanford, USA Purpose or Objective 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 physical priors, which dramatically increases the complexity of neural networks and limits the application scope and generalizability of the resultant models. Here we establish a geometry-informed deep learning framework for ultra-sparse tomographic image reconstruction. We introduce a novel mechanism for the integration of geometric priors of the imaging system. We demonstrate that the seamless inclusion of known geometric priors is essential to enhance the performance of volumetric computed tomography imaging with ultra-sparse sampling. Materials and Methods We propose a geometry-informed deep learning framework for 3D tomographic image reconstruction and illustrate the way that the known imaging geometry is integrated into the dual-domain deep learning. Specifically, the proposed framework consists of three modules: a) 2D projection generation network is developed to learn to generate novel-view projections from the given sparse views; b) geometric back-projection operator transforms the 2D projections to 3D images, referred to as geometry preserving images (GPI), which geometrically relates the pixelated 2D input data to the corresponding ray lines in 3D space; and c) 3D image refinement network learns to refine the GPI to generate the final 3D images. To evaluate feasibility of the proposed approach, we conduct experiments on a dataset containing 1018 lung CT images, where 80% and 20% data are used for training and testing. The projection images are digitally produced from CT images using geometry consistent with a clinical on-board cone-beam CT system for radiation therapy.