While Magnetic Resonance Imaging (MRI) has significant advantages in Radiation Therapy (RT) planning and assessment, its role is limited to support of computed tomography (CT) RT due to concerns about distortion, ability to support dose calculations, and missing information to support image guidance at the treatment unit. This project pursues the hypothesis that MRI RT can be shown to be at least as accurate as CT RT, with the clear implication that MRI RT will in fact far exceed the benefits of CT.
The specific aims are: SA1 Assess and improve the geometric accuracy of routine MRI pulse sequences by combining acquisition and processing methods to minimize residual distortions, and performing phantom and patient-specific assessments to determine whether residual geometric errors are within acceptable bounds for RT SA2 Optimize patient modeling methods to create MRI-derived representations most appropriate for contouring, dose calculation, and support of image-guided positioning of patients undergoing RT SA3 Conduct virtual clinical trials to demonstrate that treatment decisions based on MRI RT are not inferior to those derived from CT RT This project will explore two methods of distortion correction (rectification and field mapping), and will characterize the geometric accuracy of MRI. Imaging techniques that enhance signals from bone, quantify fat and water distributions, and provide differential contrast from other tissues, will be analyzed via clustering techniques to label tissue/material types (e.g. free fluid, dense solid tissue, bone, various fat concentrations isolated or embedded within other tissues). These classified tissues can be assigned properties to generate electron density maps for dose calculation as well as new patient models for contouring and image guidance. A virtual clinical trial will be performed to evaluate the accuracy of MRI RT relative to CT RT. This research will support removal of redundant and potentially confounding CT scans from the planning process for certain patients. It will facilitate the use of MRI-derived physiologic, molecular, and advance morphologic information for treatment planning. It will enhance collaborative development of optimized imaging resource utilization between diagnostic radiology and radiotherapy departments in academic as well as private hospital settings. It will help optimize workflow for patients that may be treated with the evolving next generation of MRI-guided teletherapy systems. Finally it will directly support the use of emerging MRI-derived biomarkers for individualized adaptive radiotherapy, and will reduce the cost as well as complexity of longitudinal assessment of treatment outcome. In the unlikely event that CT-based radiotherapy is still demonstrated to be better, these investigations will improve the geometric integrity of MR to maximally benefit augmentation of CT-based patient models. This will also impact the value of MRI in guiding non-radiotherapy focal interventions such as surgery, focused ablation, and image-guided biopsy.

Public Health Relevance

Magnetic Resonance imaging has significant advantages over conventionally used computed tomography imaging for radiation therapy and other precise treatments such as surgery, since it better describes soft tissue boundaries, and can describe the physiologic and functional state of tissues. The limitations of MRI from the past can be overcome by new methods of analysis that we will develop and investigate. These include methods to limit geometric distortions, techniques to combine multiple types of MRI contrast to classify tissues, and studies of the impact of distortion-corrected, MRI-derived patient models on the process of radiation therapy treatment planning and implementation.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB016079-02
Application #
8641688
Study Section
Radiation Therapeutics and Biology Study Section (RTB)
Program Officer
Krosnick, Steven
Project Start
2013-04-01
Project End
2017-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
2
Fiscal Year
2014
Total Cost
$401,170
Indirect Cost
$143,183
Name
University of Michigan Ann Arbor
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Tsien, Christina; Cao, Yue; Chenevert, Thomas (2014) Clinical applications for diffusion magnetic resonance imaging in radiotherapy. Semin Radiat Oncol 24:218-26
Matakos, A; Balter, J; Cao, Y (2014) Estimation of geometrically undistorted B(0) inhomogeneity maps. Phys Med Biol 59:4945-59