scholarly journals Magnetic resonance diffusion tensor imaging and fiber-tracking diffusion tensor tractography in the management of spinal astrocytomas

2016 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alessandro Landi
Keyword(s):  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Diffusion Tensor Tractography

scholarly journals DIFFUSION TENSOR TRACTOGRAPHY

Neurosurgery ◽  
2008 ◽  
Vol 63 (3) ◽  
pp. 452-459 ◽  
Author(s):  
Wai Hoe Ng ◽  
Dennis Lai-Hong Cheong ◽  
Kathleen Joy Khu ◽  
Govidasamy Venkatesh ◽  
Yee Kong Ng ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Fractional Anisotropy ◽  
Corticospinal Tract ◽  
Tumor Volume ◽  
Diffusion Tensor ◽  
Diffusion Tensor Tractography ◽  
Significant Difference ◽  
Fiber Number ◽  
And Diffusion

ABSTRACT OBJECTIVE Benign extracerebral lesions such as meningiomas may cause hemiparesis by compression and deviation without infiltrating the white matter. We used magnetic resonance diffusion tensor imaging and diffusion tensor tractography to investigate the effects of benign extracerebral lesions on the corticospinal tract (CST). METHODS Thirteen patients with extracerebral lesions (11 benign meningiomas and 2 benign cysts) underwent magnetic resonance diffusion tensor imaging and diffusion tensor tractography of the CST using fiber assignment by continuous tractography. The CST was reconstructed and assessed by comparing the ipsilateral and unaffected contralateral fibers. The tumor volume, relative fractional anisotropy, fiber deviation, relative fiber number, and relative fiber per voxel were compared between patients without and with temporary presurgical hemiparesis. RESULTS Seven patients without hemiparesis and five patients with temporary hemiparesis were analyzed; one patient had permanent weakness and was excluded from analysis. There was no significant difference in the tumor volume, relative fractional anisotropy, presence of cerebral edema, or CST deviation between groups. In patients with temporary hemiparesis, the median relative fiber number (mean, 0.35 ± 0.32) and relative fiber per voxel (mean, 0.49 ± 0.14) were significantly reduced compared with patients without hemiparesis (0.92 ± 0.55, P = 0.04; and 0.96 ± 0.28, P < 0.01, respectively). CONCLUSION In patients with benign extracerebral lesions, reduction in fiber number and fiber per voxel, but not fiber deviation, correlated with temporary hemiparesis. Clinical recovery was possible even if the CST fibers detected by diffusion tensor tractography were reduced by benign extracerebral lesions.

Magnetic Resonance Imaging Diffusion Tensor Tractography: Evaluation of Anatomic Accuracy of Different Fiber Tracking Software Packages

2014 ◽  
Vol 81 (1) ◽  
pp. 144-150 ◽  
Author(s):  
Guenther C. Feigl ◽  
Wolfgang Hiergeist ◽  
Claudia Fellner ◽  
Karl-Michael M. Schebesch ◽  
Christian Doenitz ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Diffusion Tensor Tractography ◽  
Resonance Imaging ◽  
Software Packages

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.

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