Self‐Supervised Noise Reduction in Low‐Dose Cone Beam Computed Tomography (CBCT) Using the Randomly Dropped Projection Strategy

Abstract

Deep learning‐based denoising methods have proved efficient for medical imaging. Obtaining a three‐dimensional representation of a scanned object is essential, such as in the computed tomography (CT) system. A sufficient radiation dose needs to be irradiated to a scanned object to obtain a high‐quality image. However, the radiation dose is insufficient in many cases due to hardware limitations or health care issues. A deep learning‐based denoising method can be a solution to obtaining good images, even when the radiation dose is insufficient. However, most existing deep learning‐based denoising methods require numerous paired low‐dose CT (LDCT) images and normal‐dose CT (NDCT) images. It is almost impossible to obtain numerous well‐paired LDCT and NDCT images. Self‐supervised denoising methods were proposed to train a denoising neural network on only noisy images. These methods can be applied to the projection domain in LDCT. However, applying denoising in the projection image domain is a challenging task, because the projection images for LDCT have extremely weak signals. To solve this problem, we propose a noise reduction method based on the dropped projection strategy. The proposed method works by first reconstructing the 3D image with the degraded versions of the projection images generated by Bernoulli sampling. Subsequently, the denoising neural network is trained to restore the signal dropped out by Bernoulli sampling in the projection image domain. As such, the method we propose solves the over‐smoothing problem in previous methods and is able to be trained with a small amount of data. We verified the performance of our proposed method on the SPARE challenge dataset and the in‐house lithium polymer dataset. The experiments on two datasets show that the proposed method outperforms the conventional denoising methods by at least 4.47 dB of PSNR value.