White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

Essayed, Walid Ibn; Zhang, Fan; Unadkat, Prashin; Cosgrove, G. Rees; Golby, Alexandra J.; O’Donnell, Lauren J.

doi:10.1016/j.nicl.2017.06.011

Cited by 181 publications

(184 citation statements)

References 192 publications

(237 reference statements)

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“…Diffusion magnetic resonance imaging (dMRI) provides the only existing technique to map the structural connections of the living human brain in a noninvasive way (Basser, Mattiello, & LeBihan, ). dMRI allows the estimation of white matter fiber tracts in the brain via a process called tractography (Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, ), which has been widely used for understanding neurological development, brain function, and brain disease, as described in several reviews (Ciccarelli, Catani, Johansen‐Berg, Clark, & Thompson, ; Essayed et al, ; Pannek, Scheck, Colditz, Boyd, & Rose, ; Piper, Yoong, Kandasamy, & Chin, ; Yamada, Sakai, Akazawa, Yuen, & Nishimura, ). White matter parcellation, that is, dividing the massive number of tractography fibers (streamline trajectories) into multiple fiber parcels (or fiber tracts), is the first and essential step to enable fiber quantification and visualization.…”

Section: Introductionmentioning

confidence: 99%

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.

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Section: Introductionmentioning

confidence: 99%

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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“…White matter tractography based on diffusion tensor imaging (DTI) has been proposed as a tool to assist localization of targets based on their white matter connectivity and has been applied to the Vim, to identify cerebellar input to the Vim (dentate-rubro-thalamic tract) with improved clinical outcomes. 9,13,17,25,26,29 Low-intensity focused ultrasound has also been proposed as a method to assist intraoperative targeting. 24 These low-intensity focused ultrasound sonications, which create no histological damage and have no MR thermometry heat signature (i.e., do not cause heating), are able to stimulate neural tissue and generate somatosensory evoked potentials when targeting the ventral posterolateral nucleus in an animal model.…”

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confidence: 99%

Volumetric analysis of magnetic resonance–guided focused ultrasound thalamotomy lesions

Harary¹,

Essayed²,

Valdés³

et al. 2018

Neurosurgical Focus

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OBJECTIVEMagnetic resonance–guided focused ultrasound (MRgFUS) thalamotomy was recently approved for use in the treatment of medication-refractory essential tremor (ET). Previous work has described lesion appearance and volume on MRI up to 6 months after treatment. Here, the authors report on the volumetric segmentation of the thalamotomy lesion and associated edema in the immediate postoperative period and 1 year following treatment, and relate these radiographic characteristics with clinical outcome.METHODSSeven patients with medication-refractory ET underwent MRgFUS thalamotomy at Brigham and Women’s Hospital and were monitored clinically for 1 year posttreatment. Treatment effect was measured using the Clinical Rating Scale for Tremor (CRST). MRI was performed immediately postoperatively, 24 hours posttreatment, and at 1 year. Lesion location and the volumes of the necrotic core (zone I) and surrounding edema (cytotoxic, zone II; vasogenic, zone III) were measured on thin-slice T2-weighted images using Slicer 3D software.RESULTSPatients had significant improvement in overall CRST scores (baseline 51.4 ± 10.8 to 24.9 ± 11.0 at 1 year, p = 0.001). The most common adverse events (AEs) in the 1-month posttreatment period were transient gait disturbance (6 patients) and paresthesia (3 patients). The center of zone I immediately posttreatment was 5.61 ± 0.9 mm anterior to the posterior commissure, 14.6 ± 0.8 mm lateral to midline, and 11.0 ± 0.5 mm lateral to the border of the third ventricle on the anterior commissure–posterior commissure plane. Zone I, II, and III volumes immediately posttreatment were 0.01 ± 0.01, 0.05 ± 0.02, and 0.33 ± 0.21 cm3, respectively. These volumes increased significantly over the first 24 hours following surgery. The edema did not spread evenly, with more notable expansion in the superoinferior and lateral directions. The spread of edema inferiorly was associated with the incidence of gait disturbance. At 1 year, the remaining lesion location and size were comparable to those of zone I immediately posttreatment. Zone volumes were not associated with clinical efficacy in a statistically significant way.CONCLUSIONSMRgFUS thalamotomy demonstrates sustained clinical efficacy at 1 year for the treatment of medication-refractory ET. This technology can create accurate, predictable, and small-volume lesions that are stable over time. Instances of AEs are transient and are associated with the pattern of perilesional edema expansion. Additional analysis of a larger MRgFUS thalamotomy cohort could provide more information to maximize clinical effect and reduce the rate of long-lasting AEs.

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“…The dMRI analysis field is developing a strong track record of community validation challenges at the Medical Image Computing and Computer Assisted Intervention (MICCAI), International Society for Magnetic Resonance in Medicine (ISMRM) and International Symposium on Biomedical Imaging (ISBI) conferences, † including challenges in tractography in phantoms, 129,131 tractography in clinical cases 132 and model reconstruction. 133,134 The clinical community is becoming increasingly aware that multi-fiber models beyond the diffusion tensor can improve anatomical accuracy of tractography, 11,[135][136][137][138][139][140][141][142] and there is increasing development of open-source dMRI software that moves beyond the diffusion tensor. 10,14,[143][144][145][146][147][148] 'Big data' is another important direction for future developments in computational dMRI.…”

Section: Future Directions In Computational Dmrimentioning

confidence: 99%

Advances in computational and statistical diffusion MRI

et al. 2017

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Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.

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White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

Cited by 181 publications

References 192 publications

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

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

Volumetric analysis of magnetic resonance–guided focused ultrasound thalamotomy lesions

Advances in computational and statistical diffusion MRI

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