Introduction

We present an open-source software suite, SlicerDMRI (dmri.slicer.org), that enables neurosurgical planning research using diffusion magnetic resonance imaging (dMRI). dMRI measures the random molecular diffusion of water molecules and provides the only non-invasive, in-vivo technique to investigate the organization of the brain white matter (WM)₁. In neurosurgical planning research, brain mapping using dMRI can provide critical preoperative information^2,3, including preoperative maps of brain WM connections and insight into the tissue microstructure surrounding tumors. Briefly speaking, dMRI enables a computational process, called tractography⁴ to trace WM connections such as critical motor, language, visual, and somatosensory tracts. Visualization of the relationship between the lesion and these tracts can be used for preoperative planning and intraoperative neuronavigation^2,5–7. dMRI also provides quantitative microstructure measures, such as fractional anisotropy (FA) and mean diffusivity (MD), that allow assessment of changes of tissue microstructure caused by tumors and the effects of treatment^8,9.

SlicerDMRI enables the 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, an NIH-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization¹⁰. 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. SlicerDMRI has an average of over 200 downloads per month. The main functionality of SlicerDMRI includes dMRI visualization, interactive and automated tractography, tissue microstructure modeling, and quantitative analyses (see our previous paper¹¹ for a technical overview). 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 surrounding brain tumors.

In this work, we focus on a demonstration of SlicerDMRI to enable end-to-end dMRI analyses in two retrospective imaging datasets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor imaging (DTI)¹² analysis, advanced multi-fiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI.

Methods

Retrospective imaging datasets from two neurosurgical patients were selected to illustrate two different clinical scenarios: Patient-1 (69y, male) with a recurrent left frontoparietal high-grade glioma and significant surrounding edema displacing the corticospinal tracts (CST), and Patient-2 (49y, male) with a left temporoparietal high-grade glioma abutting the arcuate fasciculus (AF). Preservation of these tracts is critical for voluntary motor movements (CST)¹³, and language function (AF)¹⁴. All imaging data, including dMRI, anatomical T2w, gadolinium-enhanced T1w, ultrasound and functional MRI (fMRI) were acquired at Brigham and Women's Hospital, Boston, USA. The usage of the data was approved by the Partners Healthcare Institutional Review Board, and informed consent was obtained from the patients prior to scanning. All data analyses in the present work were performed retrospectively for research purposes only.

We first give two examples of clinical applications using conventional DTI analysis in SlicerDMRI for neurosurgical planning research. We then use the same cases to highlight advanced dMRI analyses as well as its incorporation into multi-modal analysis combined with intraoperative ultrasound.

Results and discussion

Figure-1 demonstrates the first DTI-based application for visualization and quantification of tissue microstructure in Patient-1 to give insight into tissue microstructure differences between the tumor and surrounding tissues. Tumor-related edema and mass effect have distorted the local anatomy including the FA map.

Figure-2 demonstrates the second DTI-based application for the identification of patient-specific fiber tracts in Patient-1 to inform surgical planning in order to minimize morbidity associated with disruption of critical white matter connections. The mean FA of the identified CST in the tumor-bearing hemisphere is lower than that in the healthy hemisphere, likely related to the tumor-related edema and/or infiltration.

Figure-3 demonstrates the first advanced dMRI application is based on multi-fiber Unscented Kalman Filter (UKF) tractography¹⁵ that provides an expanded range of multi-fiber models (including multi-tensor and multi-compartment models^16,17) and enables more sophisticated mathematical modeling of the white matter tissue than single-tensor DTI tractography^18–21. The UKF method with the free water model traces an apparently larger volume of fibers within the edema surrounding the tumor.

Figure-4 demonstrates the second advanced dMRI application is based on an anatomically curated white matter atlas²² and a data-driven fiber clustering pipeline^22–24 to enable automated identification of critical fiber tracts in Patient-1. Six major language white matter connections are identified from the whole brain tractography computed using the 2-tensor UKF method with the free water model.

Figure-5 demonstrates a demonstration of how SlicerDMRI enables a multi-modal analysis of dMRI combined with intraoperative ultrasound data in Patient-2. The ultrasound information demonstrates brain shift that occurs after dural opening. By integrating this brain shift information with tractography, the surgeon can make an updated estimate of how far the tracts might be^25,26. The intraoperative ultrasound image is used retrospectively here for a demonstration of software functionality.

Conclusion

SlicerDMRI provides open-source and clinician-accessible software tools for dMRI research. In this work, we demonstrate SlicerDMRI functionality as an informatics tool to enable end-to-end diffusion MRI analysis. We provide examples that demonstrate the usage of SlicerDMRI to retrospectively analyze data from brain tumor resection.

Acknowledgements

The authors gratefully acknowledge the support of NIH NCI ITCR grant U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research), P41EB015898 (National Center for Image Guided Therapy, NCIGT), P41EB015902 (Neuroimaging Analysis Center, NAC), R01MH119222, R01CA235589, R01EB027134, and HHSN261200800001E. We are also thankful for other grant support over the lifetime of 3D Slicer, including NIH R01MH074794, R01MH097979, and U54EB005149 (National Alliance for Medical Image Computing, NA-MIC).

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