Fast geometric distortion correction using a deep neural network: Implementation for the 1 Tesla MRI‐Linac system. Issue 9 (2nd August 2020)

• Record Type: Journal Article
• Authors: Li, Mao; Shan, Shanshan; Chandra, Shekhar S.; Liu, Feng; Crozier, Stuart
• Is Part Of: Medical physics. Volume 47:Issue 9(2020)
• Page Start: 4303
• Page End: 4315
• Publication Date: 2020-08-02
• Subjects: geometric distortion correction -- gradient nonlinearity -- MRI‐Linac -- neural network -- phantom; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14382
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Combining high‐resolution magnetic resonance imaging (MRI) with a linear accelerator (Linac) as a single MRI‐Linac system provides the capability to monitor intra‐fractional motion and anatomical changes during radiotherapy, which facilitates more accurate delivery of radiation dose to the tumor and less exposure to healthy tissue. The gradient nonlinearity (GNL)‐induced distortions in MRI, however, hinder the implementation of MRI‐Linac system in image‐guided radiotherapy where highly accurate geometry and anatomy of the target tumor is indispensable. Methods: To correct the geometric distortions in MR images, in particular, for the 1 Tesla (T) MRI‐Linac system, a deep fully connected neural network was proposed to automatically learn the intricate relationship between the undistorted (theoretical) and distorted (real) space. A dataset, consisting of spatial samples acquired by phantom measurement that covers both inside and outside the working diameter of spherical volume (DSV), was utilized for training the neural network, which offers the ability to describe subtle deviations of the GNL field within the entire region of interest (ROI). Results: The performance of the proposed method was evaluated on MR images of a three‐dimensional (3D) phantom and the pelvic region of an adult volunteer scanned in the 1T MRI‐Linac system. The experimental results showed that the severe geometric distortions within the entire ROI had been successfully corrected with an error less than the pixel size. Also, the presented network is highly efficient, which achieved significant improvement in terms of computational efficiency compared to existing methods. Conclusions: The feasibility of the presented deep neural network for characterizing the GNL field deviations in the 1T MRI‐Linac system was demonstrated in this study, which shows promise in facilitating the MRI‐Linac system to be routinely implemented in real‐time MRI‐guided radiotherapy.