Projection‐domain scatter correction for cone beam computed tomography using a residual convolutional neural network

• Published in: Medical physics (Lancaster) 2019-07, Vol.46 (7), p.3142-3155
• Main Authors: Nomura, Yusuke, Xu, Qiong, Shirato, Hiroki, Shimizu, Shinichi, Xing, Lei
• Format: Article
• Language: English
• Subjects: CBCT; Cone-Beam Computed Tomography; convolutional neural network; Deep Learning; IGRT; Image Processing, Computer-Assisted - methods; Monte Carlo Method; Neural Networks, Computer; quantitative imaging; scatter correction; Scattering, Radiation; Workflow
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.13583

Purpose Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatter removal. This study aims to develop an effective scatter correction method using a residual convolutional neural network (CNN). Methods A U‐net based 25‐layer CNN was constructed for CBCT scatter correction. The establishment of the model consists of three steps: model training, validation, and testing. For model training, a total of 1800 pairs of x‐ray projection and the corresponding scatter‐only distribution in nonanthropomorphic phantoms taken in full‐fan scan were generated using Monte Carlo simulation of a CBCT scanner installed with a proton therapy system. An end‐to‐end CNN training was implemented with two major loss functions for 100 epochs with a mini‐batch size of 10. Image rotations and flips were randomly applied to augment the training datasets during training. For validation, 200 projections of a digital head phantom were collected. The proposed CNN‐based method was compared to a conventional projection‐domain scatter correction method named fast adaptive scatter kernel superposition (fASKS) method using 360 projections of an anthropomorphic head phantom. Two different loss functions were applied for the same CNN to evaluate the impact of loss functions on the final results. Furthermore, the CNN model trained with full‐fan projections was fine‐tuned for scatter correction in half‐fan scan by using transfer learning with additional 360 half‐fan projection pairs of nonanthropomorphic phantoms. The tuned‐CNN model for half‐fan scan was compared with the fASKS method as well as the CNN‐based method without the fine‐tuning using additional lung phantom projections. Results The CNN‐based method provides projections with significantly reduced scatter and CBCT images with more accurate Hounsfield Units (HUs) than that of the fASKS‐based method. Root mean squared error of the CNN‐corrected projections was improved to 0.0862 compared to 0.278 for uncorrected projections or 0.117 for the fASKS‐corrected projections. The CNN‐corrected reconstruction provided better HU quantification, especially in regions near the air or bone interfaces. All four image quality measures, which include mean absolute error (MAE), mean squared error (MSE), peak signal‐to‐noise ratio (PSNR), and structural similarity (SSIM), i