Propriospinal myoclonus: Utility of magnetic resonance diffusion tensor imaging and fiber tracking

2007 ◽  
Vol 22 (10) ◽  
pp. 1506-1509 ◽  
Author(s):  
Emmanuel Roze ◽  
Emmanuelle Apartis ◽  
Marie Vidailhet ◽  
Valérie Cochen ◽  
Yara Beaugendre ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Propriospinal Myoclonus

Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging

2007 ◽  
Vol 107 (3) ◽  
pp. 488-494 ◽  
Author(s):  
Jeffrey I. Berman ◽  
Mitchel S. Berger ◽  
Sungwon Chung ◽  
Srikantan S. Nagarajan ◽  
Roland G. Henry
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Bipolar Electrode ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Motor Pathway ◽  
Magnetic Source ◽  
Subcortical Stimulation

Object Resecting brain tumors involves the risk of damaging the descending motor pathway. Diffusion tensor (DT)–imaged fiber tracking is a noninvasive magnetic resonance (MR) technique that can delineate the subcortical course of the motor pathway. The goal of this study was to use intraoperative subcortical stimulation mapping of the motor tract and magnetic source imaging to validate the utility of DT-imaged fiber tracking as a tool for presurgical planning. Methods Diffusion tensor-imaged fiber tracks of the motor tract were generated preoperatively in nine patients with gliomas. A mask of the resultant fiber tracks was overlaid on high-resolution T1- and T2-weighted anatomical MR images and used for stereotactic surgical navigation. Magnetic source imaging was performed in seven of the patients to identify functional somatosensory cortices. During resection, subcortical stimulation mapping of the motor pathway was performed within the white matter using a bipolar electrode. Results A total of 16 subcortical motor stimulations were stereotactically identified in nine patients. The mean distance between the stimulation sites and the DT-imaged fiber tracks was 8.7 ±3.1 mm (±standard deviation). The measured distance between subcortical stimulation sites and DT-imaged fiber tracks combines tracking technique errors and all errors encountered with stereotactic navigation. Conclusions Fiber tracks delineated using DT imaging can be used to identify the motor tract in deep white matter and define a safety margin around the tract.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

scholarly journals Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

2021 ◽  
Vol 22 (10) ◽  
pp. 5216
Author(s):  
Koji Kamagata ◽  
Christina Andica ◽  
Ayumi Kato ◽  
Yuya Saito ◽  
Wataru Uchida ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Neurodegenerative Diseases ◽  
Diffusion Magnetic Resonance Imaging ◽  
New Technologies ◽  
Diffusion Tensor ◽  
Free Water ◽  
Diffusion Kurtosis Imaging ◽  
Resonance Imaging

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Changes in the corticospinal tract after wearing prosthesis in bilateral transtibial amputation

2017 ◽  
Vol 41 (5) ◽  
pp. 507-511
Author(s):  
Sang Yoon Lee ◽  
Si Hyun Kang ◽  
Don-Kyu Kim ◽  
Kyung Mook Seo ◽  
Hee Joon Ro ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Primary Motor Cortex ◽  
Corticospinal Tract ◽  
Diffusion Tensor ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Transtibial Amputation

Background:After amputation, the brain is known to be reorganized especially in the primary motor cortex. We report a case to show changes in the corticospinal tract in a patient with serial bilateral transtibial amputations using diffusion tensor imaging.Case Description and Methods:A 78-year-old man had a transtibial amputation on his left side in 2008 and he underwent a right transtibial amputation in 2011. An initial brain magnetic resonance imaging with a diffusion tensor imaging was performed before starting rehabilitation on his right transtibial prosthesis, and a follow-up magnetic resonance imaging with diffusion tensor imaging was performed 2 years after this.Findings and Outcomes:In the initial diffusion tensor imaging, the number of fiber lines in his right corticospinal tract was larger than that in his left corticospinal tract. At follow-up diffusion tensor imaging, there was no definite difference in the number of fiber lines between both corticospinal tracts.Conclusion:We found that side-to-side corticospinal tract differences were equalized after using bilateral prostheses.Clinical relevanceThis case report suggests that diffusion tensor imaging tractography could be a useful method to understand corticomotor reorganization after using prosthesis in transtibial amputation.

Enhanced Fiber Tractography Using Edema Correction: Application and Evaluation in High-Grade Gliomas

Neurosurgery ◽  
2021 ◽  
Author(s):  
Fraser Henderson Jr ◽  
Drew Parker ◽  
Anupa A Vijayakumari ◽  
Mark Elliott ◽  
Timothy Lucas ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Free Water ◽  
High Grade Glioma ◽  
Fiber Tracking ◽  
High Grade ◽  
Peritumoral Edema ◽  
Fiber Tractography ◽  
Motor Pathways

Abstract BACKGROUND A limitation of diffusion tensor imaging (DTI)-based tractography is peritumoral edema that confounds traditional diffusion-based magnetic resonance metrics. OBJECTIVE To augment fiber-tracking through peritumoral regions by performing novel edema correction on clinically feasible DTI acquisitions and assess the accuracy of the fiber-tracks using intraoperative stimulation mapping (ISM), task-based functional magnetic resonance imaging (fMRI) activation maps, and postoperative follow-up as reference standards. METHODS Edema correction, using our bi-compartment free water modeling algorithm (FERNET), was performed on clinically acquired DTI data from a cohort of 10 patients presenting with suspected high-grade glioma and peritumoral edema in proximity to and/or infiltrating language or motor pathways. Deterministic fiber-tracking was then performed on the corrected and uncorrected DTI to identify tracts pertaining to the eloquent region involved (language or motor). Tracking results were compared visually and quantitatively using mean fiber count, voxel count, and mean fiber length. The tracts through the edematous region were verified based on overlay with the corresponding motor or language task-based fMRI activation maps and intraoperative ISM points, as well as at time points after surgery when peritumoral edema had subsided. RESULTS Volume and number of fibers increased with application of edema correction; concordantly, mean fractional anisotropy decreased. Overlay with functional activation maps and ISM-verified eloquence of the increased fibers. Comparison with postsurgical follow-up scans with lower edema further confirmed the accuracy of the tracts. CONCLUSION This method of edema correction can be applied to standard clinical DTI to improve visualization of motor and language tracts in patients with glioma-associated peritumoral edema.

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