Automated white matter fiber tract identification in patients with brain tumors
| Autor: | Lauren J. O'Donnell, Laura Rigolo, Walid Ibn Essayed, Carl-Fredrik Westin, Fan Zhang, Angela Albi, Isaiah Norton, Prashin Unadkat, William M. Wells, Pelin Aksit Ciris, Alexandra J. Golby, Antonio Meola, Yogesh Rathi, Yannick Suter, Pegah Kahali, Olutayo Olubiyi |
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| Rok vydání: | 2016 |
| Předmět: |
Adult
Male medicine.medical_specialty Pathology Cognitive Neuroscience Fiber tract Neurosurgery Datasets as Topic Motor tract lcsh:Computer applications to medicine. Medical informatics lcsh:RC346-429 030218 nuclear medicine & medical imaging Diffusion MRI White matter 03 medical and health sciences Young Adult 0302 clinical medicine Atlases as Topic ddc:570 Neural Pathways medicine Image Processing Computer-Assisted Arcuate fasciculus Humans Radiology Nuclear Medicine and imaging In patient lcsh:Neurology. Diseases of the nervous system Aged Retrospective Studies Tumor Fiber (mathematics) Brain Neoplasms Regular Article Middle Aged medicine.anatomical_structure Neurosurgery Diffusion MRI Tractography Tumor Fiber tract White matter Diffusion Tensor Imaging Neurology lcsh:R858-859.7 Female Neurology (clinical) Radiology Psychology Tractography 030217 neurology & neurosurgery |
| Zdroj: | NeuroImage : Clinical NeuroImage: Clinical, Vol 13, Iss C, Pp 138-153 (2017) |
| ISSN: | 2213-1582 |
| Popis: | 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. Highlights • Spectral clustering machine learning approach for white matter tract identification • Data-driven white matter parcellation learned from healthy subjects tractography • White matter parcellation applied to 18 consecutive patients with brain tumors • Arcuate fasciculus and corticospinal tracts identified in all patients • All tracts within 3 mm of corresponding patient-specific functional activations |
| Databáze: | OpenAIRE |
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