MR diffusion tensor imaging (DTI) and neuropsychological testing for neuronal connectivity in Alzheimer's disease (AD) patients

2004 ◽  
Author(s):  
Jianhui Zhong ◽  
Hongyan Ni ◽  
Tong Zhu ◽  
Sven Ekholm ◽  
Voyko Kavcic
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Neuropsychological Testing ◽  
Neuronal Connectivity ◽  
Mr Diffusion ◽  
Mr Diffusion Tensor Imaging ◽  
Diffusion Tensor Imaging Dti

scholarly journals MR Parameter Map Suite: ITK Classes for Calculating Magnetic Resonance T2 and T1 Parameter Maps

2008 ◽  
Author(s):  
Don Bigler ◽  
Mark Meadowcroft ◽  
Xiaoyan Sun ◽  
Jeffrey Vesek ◽  
Alex Dresner ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Non Invasive ◽  
Mr Diffusion ◽  
Mr Diffusion Tensor Imaging ◽  
Diffusion Tensor Imaging Dti

This document describes a suite of new multi-threaded classes for calculating magnetic resonance (MR) T2 and T1 parameter maps implemented using the Insight Toolkit ITK (www.itk.org). Similar to MR diffusion tensor imaging (DTI), MR T2 and T1 parameter maps provide a non-invasive means for quantitatively measuring disease or pathology in-vivo. Included in the suite are classes for reading proprietary Bruker 2dseq and Philips PAR/REC images and example programs and data for validating the new classes.

scholarly journals Diffusion tensor imaging for Alzheimer's disease: A review of concepts and potential clinical applicability

2009 ◽  
Vol 3 (4) ◽  
pp. 268-274 ◽  
Author(s):  
Luciano de Gois Vasconcelos ◽  
Sonia Maria Dozzi Brucki ◽  
Andrea Parolin Jackowiski ◽  
Orlando Francisco Amodeo Bueno
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Cognitive Assessments ◽  
Clinical Role ◽  
Pubmed Database ◽  
Apparent Diffusion ◽  
And Diffusion ◽  
Diffusion Tensor Imaging Dti

Abstract In view of the urgent need to identify an early and specific biomarker for Alzheimer's disease (AD), a PubMed database search was performed using the terms "Alzheimer disease" and "Diffusion Magnetic Resonance Imaging" to enable review of Diffusion tensor imaging (DTI) concepts and its potential clinical role in AD evaluation. Detailed analysis of selected abstracts showed that the main DTI measures, fractional anisotropy and apparent diffusion coefficient, indicators of fiber tract integrity, provide a direct assessment of WM fibers and may be used as a new biomarker for AD. These findings were found to correlate with cognitive assessments, rates of AD progression and were also able to differentiate among groups including mild cognitive impairment, AD, and other dementias. Despite several consistent DTI findings in AD patients, there is still a lack of knowledge and studies on the DTI field. DTI is not yet ready for clinical use, and requires extensive further research in order to achieve this goal.

scholarly journals Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maurizio Bergamino ◽  
Ryan R. Walsh ◽  
Ashley M. Stokes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Free Water ◽  
Potential Impact ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Pathology ◽  
Diffusion Tensor Imaging Dti

AbstractMagnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer’s disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer’s disease.

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.

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