Real-Time Lung Tumor Tracking Using a CUDA Enabled Nonrigid Registration Algorithm for MRI.

Nazanin Tahmasebi,Pierre Boulanger,Michelle Noga,Kumaradevan Punithakumar,Gino Fallone,Jihyun Yun

doi:10.1109/jtehm.2020.2989124

Abstract

Objective: This study intends to develop an accurate, real-time tumor tracking algorithm for the automated radiation therapy for cancer treatment using Graphics Processing Unit (GPU) computing. Although a previous moving mesh based tumor tracking approach has been shown to be successful in delineating the tumor regions from a sequence of magnetic resonance image, the algorithm is computationally intensive and its computation time on standard Central Processing Unit (CPU) processors is too slow to be used clinically especially for automated radiation therapy system. Method: A re-implementation of the algorithm on a low-cost parallel GPU-based computing platform is utilized to accelerate this computation at a speed that is amicable to clinical usages. Several components in the registration algorithm such as the computation of similarity metric are inherently parallel which fits well with the GPU parallel processing capabilities. Solving a partial differential equation numerically to generate the mesh deformation is one of the computationally intensive components which has been accelerated by utilizing a much faster shared memory on the GPU. Results: Implemented on an NVIDIA Tesla K40c GPU, the proposed approach yielded a computational acceleration improvement of over 5 times its implementation on a CPU. The proposed approach yielded an average Dice score of 0.87 evaluated over 600 images acquired from six patients. Conclusion: This study demonstrated that the GPU computing approach can be used to accelerate tumor tracking for automated radiation therapy for mobile lung tumors. Clinical Impact: Accurately tracking mobile tumor boundaries in real-time is important to automate radiation therapy and the proposed study offers an excellent option for fast tumor region tracking for cancer treatment.

Highlights

Tracking mobile tumors is crucial in the treatment of cancer patients using radiation therapy
The usage of Graphics Processing Unit (GPU) have been shown to be an important factor for the real-time clinical applications of the image registration algorithms [18]–[24]
Haghighi et al proposed a framework for an intensity-based symmetric registration method where the GPU implementation leads to an improved computational performance [28]

Summary

Introduction

Tracking mobile tumors is crucial in the treatment of cancer patients using radiation therapy. A hybrid radiotherapy MR-system, called Linac-MR, that allows for realtime MRI-guided radiation therapy with excellent soft tissue contrast for imaging tumors has been proposed [1]. The Linac-MR system allows for real-time adjustment of the radiation beam and can be used for the therapy, given the location of the tumor is tracked over time. One approach to track mobile tumors is to find the point correspondence over a sequence of MR images acquired over a period of time. Due to the nature of non-rigid deformation of lung tissues over breathing, a diffeomorphic based non-rigid registration algorithm has been shown to be effective in accurately tracking the tumor boundaries. The standard Central Processing Unit (CPU) based implementation of the algorithm is time-consuming and limits its clinical application

Methods

Results

Conclusion