GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization

TL;DR

This paper presents a GPU-accelerated iterative cone beam CT reconstruction algorithm using tight frame regularization, achieving high-quality images from undersampled data in about 5 minutes, suitable for reducing radiation dose.

Contribution

The paper introduces a novel GPU-based iterative CBCT reconstruction method employing tight frame regularization and multi-grid acceleration, improving speed and image quality over existing techniques.

Findings

Reconstructed high-quality CBCT images from undersampled data.

Achieved reconstruction in approximately 5 minutes on GPU.

Validated effectiveness in clinical head-and-neck case.

Abstract

X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. It is the goal of this paper to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight frame (TF) based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512\times512\times70 can be reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstrct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of modulation-transfer-function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.