CT reconstruction from a single X-ray image for a particular patient via progressive learning

Computed tomography(CT) has enjoyed widespread applications, especially in the assistance of clinical diagnosis and treatment.However, fast CT imaging is not available for guiding adaptive precise radiotherapy in the current radiation treatment process because the conventional CT reconstruction requires numerous projections and rich computing resources.This paper mainly studies the challenging task of 3 D CT reconstruction from a single 2 D X-ray image of a particular patient, which enables fast CT imaging during radiotherapy.It is widely known that the transformation from a 2 D projection to a 3 D volumetric CT image is a highly nonlinear mapping problem.In this paper, we propose a progressive learning framework to facilitate 2 D-to-3 D mapping.The proposed network starts training from low resolution and then adds new layers to learn increasing high-resolution details as the training progresses.In addition, by bridging the distribution gap between an X-ray image and a CT image with a novel attention-based 2 D-to-3 D feature transform module and an adaptive instance normalization layer, our network obtains enhanced performance in recovering a 3 D CT volume from a single X-ray image.We demonstrate the effectiveness of our approach on a ten-phase 4 D CT dataset including 20 different patients created from a public medical database and show its outperformance over some baseline methods in image quality and structure preservation, achieving a PSNR value of 22.76±0.708 dB and FSIM value of 0.871±0.012 with the ground truth as a reference.This method may promote the application of CT imaging in adaptive radiotherapy and provide image guidance for interventional surgery.