Measurement of Precuneal and Hippocampal Volumes Using Magnetic Resonance Volumetry in Alzheimer's Disease

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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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Brain Informatics ◽

10.1186/s40708-020-00112-2 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 6

Author(s):

Manan Binth Taj Noor ◽

Nusrat Zerin Zenia ◽

M Shamim Kaiser ◽

Shamim Al Mamun ◽

Mufti Mahmud

Keyword(s):

Alzheimer’S Disease ◽

Parkinson’S Disease ◽

Alzheimer's Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Magnetic Resonance ◽

Neurological Disorders ◽

Magnetic Resonance Images ◽

Future Research ◽

Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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P3-363: VALIDITY OF QUALITATIVE AND SEMI-QUANTITATIVE HIPPOCAMPAL AND MEDIAL TEMPORAL LOBE ATROPHY ON MAGNETIC RESONANCE IMAGING IN COMPARISON TO VOLUMETRIC MRI ASSESSMENT IN CHINESE ALZHEIMER'S DISEASE PATIENTS

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.1725 ◽

2006 ◽

Vol 14 (7S_Part_23) ◽

pp. P1226-P1227

Author(s):

Leung-Wing Chu ◽

Simon Wong

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Temporal Lobe ◽

Medial Temporal Lobe ◽

Resonance Imaging ◽

Volumetric Mri ◽

Medial Temporal Lobe Atrophy ◽

Lobe Atrophy

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Individualized Prediction of Early Alzheimer's Disease Based on Magnetic Resonance Imaging Radiomics, Clinical, and Laboratory Examinations: A 60‐Month Follow‐Up Study

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.27689 ◽

2021 ◽

Author(s):

Lin Tang ◽

Xiaojia Wu ◽

Huan Liu ◽

Faqi Wu ◽

Rao Song ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Resonance Imaging ◽

Follow Up Study ◽

Laboratory Examinations ◽

Early Alzheimer’S Disease ◽

Individualized Prediction

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Reproducibility of Brain Volume Changes in Longitudinal Voxel-Based Morphometry Between Non-Accelerated and Accelerated Magnetic Resonance Imaging

Journal of Alzheimer s Disease ◽

10.3233/jad-210596 ◽

2021 ◽

pp. 1-10

Author(s):

Hidemasa Takao ◽

Shiori Amemiya ◽

Osamu Abe ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Gray Matter ◽

Gray Matter Volume ◽

Elderly Subjects ◽

Voxel Based Morphometry ◽

Volume Changes ◽

Matter Volume ◽

Healthy Elderly

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.

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