SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

Author:

Zhang Fan1,Noh Thomas1,Juvekar Parikshit1,Frisken Sarah F.1,Rigolo Laura1,Norton Isaiah1,Kapur Tina1,Pujol Sonia1,Wells William12,Yarmarkovich Alex3,Kindlmann Gordon4,Wassermann Demian5,San Jose Estepar Raul1,Rathi Yogesh1,Kikinis Ron16,Johnson Hans J.7,Westin Carl-Fredrik1,Pieper Steve3,Golby Alexandra J.1,O’Donnell Lauren J.1

Affiliation:

1. Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

2. Massachusetts Institute of Technology, Boston, MA

3. Isomics, Cambridge, MA

4. The University of Chicago, Chicago, IL

5. Parietal, Inria Saclay-lle de France, Neurospin CEA, Université Paris-Saclay, Palaiseau, France

6. University of Bremen and Fraunhofer MEVIS, Bremen, Germany

7. University of Iowa, Iowa City, IA

Abstract

PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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