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Automated White Matter Fiber Tract Identification in Patients with Brain Tumors

Institution:
1Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Biomedical Engineering, Akdeniz University, Antalya, Turkey.
Publisher:
Elsevier
Publication Date:
Mar-2017
Volume Number:
13
Pages:
138-53
Citation:
Neuroimage Clin. 2017 March;13:138-53.
PubMed ID:
27981029
PMCID:
PMC5144756
Keywords:
Neurosurgery, Diffusion MRI, Tractography, Tumor, Fiber Tract, White Matter
Appears in Collections:
NAC, LMI, NCIGT, SPL
Sponsors:
U01 CA199459/CA/NCI NIH HHS/United States
R03 NS088301/NS/NINDS NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
R25 CA089017/CA/NCI NIH HHS/United States
R01 MH097979/MH/NIMH NIH HHS/United States
R21 CA198740/CA/NCI NIH HHS/United States
U01 NS083223/NS/NINDS NIH HHS/United States
Generated Citation:
O'Donnell L., Suter Y., Rigolo L., Kahali P., Zhang F., Norton I., Albi A., Olubiyi O., Meola A., Essayed W.I., Unadkat P., Aksit Ciris P., Wells III W.M., Rathi Y., Westin C-F., Golby A.J. Automated White Matter Fiber Tract Identification in Patients with Brain Tumors. Neuroimage Clin. 2017 March;13:138-53. PMID: 27981029. PMCID: PMC5144756.
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We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

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