An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography

Frontiers in Oncology

卷:9

Pratik Mukherjee¹  Michael Wahl³  Olivier Morin⁴  Steve Braunstein⁴  Christopher Chapman⁴  Roland Henry⁵  Julia Owen⁶  Kesshi Jordan⁷  Bagrat Amirbekian⁷ 

[1] Center for Imaging of Neurodegenerative Disease, San Francisco VA Medical Center, San Francisco, CA, United States;

[2] Department of Neurology, University of California, San Francisco, San Francisco, CA, United States;

[3] Department of Radiation Oncology, Samaritan Pastega Regional Cancer Center, Corvallis, OR, United States;

[4] Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States;

[5] Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States;

[6] Department of Radiology, University of Washington, Seattle, WA, United States;

[7] Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States;

关键词: tractography;    glioma;    code:Python;    neuro oncology;    radiation therapy (radiotherapy);    diffusion MRI (dMRI);   

DOI  :  10.3389/fonc.2019.00810

来源: DOAJ

【 摘 要 】

There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.

【 授权许可】

Unknown