Neighborhood resolved fiber orientation distributions (NRFOD) in automatic labeling of white matter fiber pathways

• Published in: Medical image analysis Vol. 46; pp. 130 - 145
• Main Authors: Ugurlu, Devran, Firat, Zeynep, Türe, Uğur, Unal, Gozde
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2018; Elsevier BV
• Subjects: Algorithms; Anatomy; Automation; Brain; Brain architecture; Brain stem; Brain stem fiber pathways; Bundles; Cerebellum; Clustering; Connectome; Corticospinal tract (CST); Density; Density-based clustering; Diffusion MRI; Diffusion Tensor Imaging - methods; Distance measurement; Fatigue; Fiber orientation; Fibers; Humans; Image processing systems; Image Processing, Computer-Assisted - methods; Image reconstruction; Inferior cerebellar peduncle (ICP); Lemniscus (medial); Medial lemniscus (ML); Medical imaging; Middle cerebellar peduncle (MCP); Neighborhood resolved fiber orientation distribution (NRFOD) representation; Nervous system; Neural networks; Neuroimaging; Population studies; Pyramidal tracts; Representations; Structural analysis; Substantia alba; Superior cerebellar peduncle; Superior cerebellar peduncle (SCP); Surgery; White Matter - ultrastructure; White matter fiber clustering; Medial lemniscus (ML); Corticospinal tract (CST); Inferior cerebellar peduncle (ICP); Middle cerebellar peduncle (MCP); Neighborhood resolved fiber orientation distribution (NRFOD) representation; Brain stem fiber pathways; Diffusion MRI; White matter fiber clustering; Density-based clustering; Superior cerebellar peduncle (SCP)
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2018.02.008

•A novel descriptive fiber representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers is proposed.•The proposed fiber representation allows the definition of a translation-invariant fiber-to-fiber distance measure (NRFODD), which is very useful for comparing fibers extracted from different subject spaces.•The new fiber representation and distance measure are used in an automatic fiber clustering framework that combines ROI-based and similarity-based approaches.•The proposed fiber clustering method is validated on brainstem fibers but is generalizable to other fibers in the brain. [Display omitted] Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles generally involves manual selection of regions of interest by an expert, which is subject to user bias and fatigue, hence an automation is desirable. To that end, we first present a novel anatomical representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers. The resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. A fiber-to-fiber distance measure between the proposed fiber representations is then used in a density-based clustering framework to select the clusters corresponding to the major pathways of interest. In addition, neuroanatomical priors are utilized to constrain the set of candidate fibers before density-based clustering. The proposed fiber clustering approach is exemplified on automation of the reconstruction of the major fiber pathways in the brainstem: corticospinal tract (CST); medial lemniscus (ML); middle cerebellar peduncle (MCP); inferior cerebellar peduncle (ICP); superior cerebellar peduncle (SCP). Experimental results on Human Connectome Project (HCP)’s publicly available “WU-Minn 500 Subjects + MEG2 dataset” and expert evaluations demonstrate the potential of the proposed fiber clustering method in brainstem white matter structure analysis.