Deep residual network-based projection interpolation and post-processing techniques for thoracic patient CBCT reconstruction

Abstract

Background: Projection interpolation can be used to reduce streak artifacts caused by sparse sampling in cone-beam computed tomography (CBCT) image reconstruction. Conventional analytical interpolation methods create additional blur and artifacts at locations away from the centers of reconstructed CBCT image slices. We hypothesize that the deep learning (DL) interpolation technique may mitigate these limitations.

Purpose: The purpose of this study is to develop a DL-based technique that interpolates sparsely sampled real patient CBCT projections before reconstruction and post-process reconstructed images for improved image quality and reduced patient imaging dose.

Methods: Real patient CBCT projections are acquired from projection angles that are not exactly evenly spread. The proposed method first linearly interpolates under-sampled projections according to densely sampled angles. Each set of projections is a projection stack of three axes: x, y, and z that represent width, height, and number of projections. The proposed technique re-slices the stack of interpolated projections along y-axis, and each acquired x-z plane slice is processed by a deep residual U-Net (DRU) model to augment the slice's image quality. The resulting slices are reassembled into a stack of densely-sampled projections to be reconstructed into a CBCT volume with the FDK algorithm. A second DRU model further post-processes the reconstructed CBCT volume to improve the image quality. The proposed technique is compared with conventional linear interpolation on sparsely-sampled real patient CBCT projection data (76, 98, and 136 extracted from 680 projections in half-fan geometry). A quantitative analysis is conducted with metrics of peak-signal-to-noise-ratio (PSNR), structural-similarity-index-measure (SSIM), and root-mean-square-error (RMSE).

Results: The PSNR, SSIM, and RMSE results of CBCT reconstructed using DRU optimized projections (98-680 projections in half-fan geometry; 49-340 projections in full-fan geometry) are substantially improved compared to those reconstructed using sparsely sampled projections and those reconstructed using linearly interpolated projections. The application of the DRU post-processing technique further improves the image quality. The reconstructed CBCT with combined workflow using 98 sparsely sampled projections for half-fan geometry and 49 projections for full-fan geometry achieved reasonable image and reduced patient imaging dose by 86%. The reconstruction time is about 15 s in addition to a regular FDK reconstruction.

Conclusion: The combined workflow is the first DL CBCT projection interpolation technique that is demonstrated to work on real patient projection data. Preliminary results demonstrate that the proposed DL interpolation and post-processing techniques performed well in reducing the artifacts of reconstructed CBCT images using under-sampled patient projections with substantially reduced patient imaging dose.

Keywords: CBCT; deep learning; post‐processing; projection interpolation.