Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography

Chen, Zhenrui; Tie, Yanmei; Olubiyi, Olutayo I.; Rigolo, Laura; Mehrtash, Alireza; Norton, Isaiah; Pasternak, Ofer; Rathi, Yogesh; Golby, Alexandra J.; O’Donnell, Lauren J.

doi:10.1016/j.nicl.2015.03.009

Cited by 65 publications

(74 citation statements)

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“…Images from all gradient directions were retained based on visual inspection of several patient datasets with an in-house tool indicating no gradients should be removed. Thus all 30 gradient directions were retained for analysis (Chen et al, 2015, Chen et al, 2016). We used 3D Slicer to obtain baseline images (B0, the b = 0 image in the DWI volume) and a binary brain mask derived from the DWI images.…”

Section: Methodsmentioning

confidence: 99%

“…Whole-brain tractography of both datasets was conducted using a two-tensor unscented Kalman filter method (Chen et al, 2015, Chen et al, 2016, Malcolm et al, 2010) as follows. We used default values for UKF seeding and stopping fractional anisotropy (FA) thresholds, where these defaults have previously been empirically determined across multiple datasets (Chen et al, 2015, Chen et al, 2016, Malcolm et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

“…We used default values for UKF seeding and stopping fractional anisotropy (FA) thresholds, where these defaults have previously been empirically determined across multiple datasets (Chen et al, 2015, Chen et al, 2016, Malcolm et al, 2010). In UKF tractography, the FA seeding threshold refers to the FA of an initial single-tensor fit at the seed point, which is used to initialize the multi-fiber model.…”

Section: Methodsmentioning

confidence: 99%

“…This is time consuming, difficult to standardize across patients, produces variable results across operators and software packages Bürgel et al (2009), and is complicated by the displacement of patient-specific brain anatomy due to mass effect. Furthermore, it is increasingly accepted (Caverzasi et al, 2015, Chen et al, 2015, Chen et al, 2016, Farquharson et al, 2013, Kuhnt et al, 2013, Mormina et al, 2015, Nimsky, 2014, Qazi et al, 2009) that for improved clinical anatomical accuracy, tractography must move beyond the standard diffusion tensor imaging (DTI) method, which can only represent one fiber at any location and is thus unable to model fiber crossing. Improved multi-fiber tractography methods, however, increase the difficulty of the expert selection procedure, requiring a higher number of regions of interest to restrict the selection.…”

Section: Introductionmentioning

confidence: 99%

“…We employ two-tensor unscented Kalman filter tractography (Malcolm et al, 2010), a multi-fiber tractography method that we have recently shown to be more sensitive than the clinical standard of single-tensor tractography in the presence of crossing fibers and edema (Chen et al, 2015, Chen et al, 2016). To identify tracts in a relatively robust way, despite displacement and mass effect, we use a strategy of large-scale features: major fiber tracts such as the arcuate fasciculus (AF) and corticospinal tract (CST) are quite large, traversing many image voxels, and have characteristic shapes and relationships to surrounding tracts.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automated white matter fiber tract identification in patients with brain tumors

O’Donnell

Suter

Rigolo

et al. 2017

NeuroImage: Clinical

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated white matter fiber tract identification in patients with brain tumors

O’Donnell

Suter

Rigolo

et al. 2017

NeuroImage: Clinical

Self Cite

120

133

View full text Add to dashboard Cite

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What's your name again? A review of the superior longitudinal and arcuate fasciculus evolving nomenclature

2021

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Studies of the superior longitudinal fasciculus (SLF) have multiplied in recent decades owing to methodological advances, but the absence of a convention for nomenclature remains a source of confusion. Here, we have reviewed existing nomenclatures in the context of the research studies that generated them and we have identified their agreements and disagreements. A literature search was conducted using PubMed/MEDLINE, Web‐of‐Science, Embase, and a review of seminal publications, without restrictions regarding publication date. Our search revealed that diffusion imaging, autoradiography, and fiber dissection have been the main methods contributing to tract designation. The first two have been particularly influential in systematizing the horizontal elements distant from the lateral sulcus. Twelve approaches to naming were identified, eight of them differing considerably from each other. The terms SLF and arcuate fasciculus (AF) were often used as synonyms until the second half of the 20th century. During the last 15 years, this has ceased to be the case in a growing number of publications. The term AF has been used to refer to the assembly of three different segments, or exclusively to long frontotemporal fibers. Similarly, the term SLF has been employed to denote the whole superior longitudinal associative system, or only the horizontal frontoparietal parts. As only partial correspondence can be identified among the available nomenclatures, and in the absence of an official designation of all anatomical structures that can be encountered in clinical practice, a high level of vigilance regarding the effectiveness of every oral or written act of communication is mandatory.

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Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

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There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

show abstract

Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography

Cited by 65 publications

References 50 publications

Automated white matter fiber tract identification in patients with brain tumors

Automated white matter fiber tract identification in patients with brain tumors

What's your name again? A review of the superior longitudinal and arcuate fasciculus evolving nomenclature

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

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