Augmentation of CBCT Reconstructed from Under-sampled Projections using Deep Learning.

Researchers

Journal

Modalities

Models

Abstract

Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this study, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled CBCT. For training, CBCT images were reconstructed using TV based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR). SR-CNN substantially enhanced image details in the TV based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 halffan projections to image quality comparable to the reference fullysampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared to the conventional FDK and TV based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in undersampled 3D/4D-CBCT, which can be very valuable for image guided radiotherapy.