End2end-ALARA: Approaching the ALARA Law in CT Imaging with End-to-end Learning

Computed tomography (CT) examination poses radiation injury to patient. A consensus performing CT imaging is to make the radiation dose as low as reasonably achievable, i.e. the ALARA law. In this paper, we propose an end-to-end learning framework, named End2end-ALARA, that jointly optimizes dose modulation and image reconstruction to meet the goal of ALARA in CT imaging. End2end-ALARA works by building a dose modulation module and an image reconstruction module, connecting these modules with a differentiable simulation function, and optimizing the them with a constrained hinge loss function. The objective is to minimize radiation dose subject to a prescribed image quality (IQ) index. The results show that End2end-ALARA is able to preset personalized dose levels to gain a stable IQ level across patients, which may facilitate image-based diagnosis and downstream model training. Moreover, compared to fixed-dose and conventional dose modulation strategies, End2end-ALARA consumes lower dose to reach the same IQ level. Our study sheds light on a way of realizing the ALARA law in CT imaging.