3D Histological Reconstruction of Fiber Tracts and Direct Comparison with Diffusion Tensor MRI Tractography

ObjectiveTo evaluate the relationship between functional anisotropy (FA) and neuropsychological evaluation in concussion.MethodsDiffusion tensor MRI included FA of the Brain and neuropsychological evaluation were conducted on 10 patients with concussion who were diagnosed from April 2017 to March 2018. FA was extracted from 2 regions of interest in Corpus callosum (CC) and corticospinal tract (CT). Detailed neuropsychological testing with an emphasis on Working memory (WM) and Processing speed (PS) was also conducted. The FA value in that 2 regions were compared between the 2 groups of 5 patients (group F) who failed either in WM or PS and 5 cases (group NF) who did not admit it.ResultsMean FA values in CC and CT in the Group F were 0.70 and 0.52. Mean FA values in CC and CT in the Group NF were 0.48 and 0.55.ConclusionsOur result suggests that the FA value of CC did not explain the significant fluctuation of the neuropsychological function. However, FA value in CT were shown to explain the fluctuation of WM and PS.

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Potential of diffusion tensor MRI in the assessment of periventricular leukomalacia

Clinical Radiology ◽

10.1016/j.crad.2006.01.001 ◽

2006 ◽

Vol 61 (4) ◽

pp. 358-364 ◽

Cited By ~ 26

Author(s):

G.G. Fan ◽

B. Yu ◽

S.M. Quan ◽

B.H. Sun ◽

Q.Y. Guo

Keyword(s):

Diffusion Tensor ◽

Periventricular Leukomalacia ◽

Diffusion Tensor Mri

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IC-P-091: Diffusion Tensor MRI in Preclinical Alzheimer's Disease: Decreased Fractional Anisotropy in Parahippocampal White Matter in MCI and Older Adults with Cognitive Complaints

Alzheimer s & Dementia ◽

10.1016/j.jalz.2010.05.105 ◽

2010 ◽

Vol 6 ◽

pp. S37-S38 ◽

Cited By ~ 2

Author(s):

Yang Wang ◽

John D. West ◽

Laura A. Flashman ◽

Heather A. Wishart ◽

Laura A. Rabin ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Older Adults ◽

Alzheimer's Disease ◽

White Matter ◽

Fractional Anisotropy ◽

Diffusion Tensor ◽

Diffusion Tensor Mri ◽

Cognitive Complaints ◽

Preclinical Alzheimer’S Disease

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Independent component analysis applied to diffusion tensor MRI

Magnetic Resonance in Medicine ◽

10.1002/mrm.10046 ◽

2002 ◽

Vol 47 (2) ◽

pp. 354-363 ◽

Cited By ~ 38

Author(s):

Konstantinos Arfanakis ◽

Dietmar Cordes ◽

Victor M. Haughton ◽

John D. Carew ◽

M. Elizabeth Meyerand

Keyword(s):

Independent Component Analysis ◽

Diffusion Tensor ◽

Component Analysis ◽

Independent Component ◽

Diffusion Tensor Mri

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Relevant Biophysical Parameters Discrimination along Corticospinal Tract in Patients with Stroke Using Convolutional Neural Networks

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.51.95 ◽

2021 ◽

Vol 51 ◽

pp. 95-103

Author(s):

Abderrazek Zeraii ◽

Amine Ben Slama ◽

Lazhar Rmili ◽

Cyrine Drissi ◽

Mokhtar Mars ◽

...

Keyword(s):

Motor Neurons ◽

Corticospinal Tract ◽

Diffusion Tensor ◽

Biophysical Parameters ◽

Spinal Motor Neurons ◽

Fiber Tracts ◽

Subacute Stroke ◽

Quantitative Metrics ◽

White Matter Fiber Tracts

Stroke remains the leading source of long-term disability. As the only direct descending motor pathway, the corticospinal tract (CST) is the primary pathway to innervate spinal motor neurons and one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many distinguished traumatic situations and diseases such as stroke. Along‐tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities. In this study, we extracted DTI derived quantitative microstructural diffusion metrics along the CST tract in patients with moderate to severe subacute stroke. Respectively DTI metric of individual patient's fiber tract was then plotted. This approach may be useful for future studies that may compare in two different time (acute and chronic). The contribution of this work presents a totally computerized method of DTI image recognition based on conventional neural network (CNN) in order to supply quantitative appraisal of clinical characteristics. The obtained results have achieved an important classification (Accuracy=94.12%) when applying the CNN. The proposed methodology enables us to assess the classification of the used DTI images database within a reduced processing time. Experimental results prove the success of the proposed rating system for a suitable analysis of microstructural diffusion when compared to previous work.

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