A cloud GPU-based system for image reconstruction of cone beam computed tomography

Abstract

Cone-beam computed tomography (CBCT) has become more and more popular in dental and radiation therapy applications The CBCT vendors face a problem with development cost Except for the CBCT scanner itself a high-end and costly workstation with reconstruction programs also needs to be provided to the user Cloud computing could provide a solution for this problem With all the hardware and software on the cloud the newest hardware and software can be provided to the user through the Internet and the local program does not need to change For the reconstruction program dose reduction for CBCT systems remains to be a topic of concern Limited angle reconstruction with iterative image reconstruction algorithms such as maximum likelihood expectation–maximization algorithm (MLEM) has shown promises by yielding a satisfactory image quality while reducing the required projection angles and hence the radiation dose However iterative reconstruction algorithms require much more computational power To address this challenge we propose a cloud GPU-based reconstruction system to implement iterative reconstruction methods in this work Methods: We have implemented both the client- and cloud-side programs for the reconstruction system On the client side the software is configured to automatically upload the projection data to the cloud whenever a new CBCT acquisition is completed On the cloud side we have implemented a job manager to provide the non-iterative and iterative algorithms for image reconstruction with FDK and MLEM respectively Both algorithms have been implemented as CUDA programs to accelerate the reconstruction with graphical computing units (GPUs) The testing cloud-computing system was configured on a server equipped with 8 GPU cards (NVIDIA GeForce GTX 1080Ti) and we also used Google Cloud Platform (GCP) for verification Results: In the full-angle evaluation the single GPU has 1223 and 2938 times acceleration with FDK and MLEM respectively compared to a standalone CPU-based workstation Using multiple GPUs on the local server and GCP could further accelerate performance Due to the limitation of reconstruction volume the excessive usage number of GPUs would reduce the property In the limited-angle reconstruction tests MLEM with 60 projection angles and 50 iterations has a satisfied image quality under the lowest numbers of angles While using GPU that has similar computing ability the local server and GCP could have similar speeds For the combination of the different number of GPUs the speed of the MLEM can be reduced to less than one minute Conclusion: With cloud computing the cost of the high-end workstation and reconstruction software can be greatly reduced The reconstruction program implemented with CUDA has a significant acceleration This acceleration is useful for testing novel iterative image reconstruction as well as making future application of limited-angle reconstruction of clinical CBCT images a routine possibility With cloud GPU-based environments CBCT iterative reconstruction methods can be developed and tested in a faster pace that may accelerate the actual clinical application of limited angle reconstruction for CBCT dose reduction Keywords: CBCT iterative reconstruction limited angle CUDA multiple GPU reconstruction cloud computing

Date of Award	2018 Aug 16
Original language	English
Supervisor	Yu-Hua Dean Fang (Supervisor)