scholarly journals Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis

2017 ◽  
Vol 5 ◽  
Author(s):  
Benedetta Biffi ◽  
Jan L. Bruse ◽  
Maria A. Zuluaga ◽  
Hopewell N. Ntsinjana ◽  
Andrew M. Taylor ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Shape Analysis ◽  
Magnetic Resonance Images ◽  
Cardiac Motion ◽  
Statistical Shape Analysis ◽  
Motion Patterns ◽  
Statistical Shape

scholarly journals Bulk Flow and Near Wall Hemodynamics of the Rabbit Aortic Arch and Descending Thoracic Aorta: A 4D PC-MRI Derived Computational Fluid Dynamics Study

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
D. S. Molony ◽  
J. Park ◽  
L. Zhou ◽  
C. C. Fleischer ◽  
H. Y. Sun ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Magnetic Resonance ◽  
Aortic Arch ◽  
Shape Analysis ◽  
Helical Flow ◽  
Flow Index ◽  
Statistical Shape Analysis ◽  
Statistical Shape

Animal models offer a flexible experimental environment for studying atherosclerosis. The mouse is the most commonly used animal, however, the underlying hemodynamics in larger animals such as the rabbit are far closer to that of humans. The aortic arch is a vessel with complex helical flow and highly heterogeneous shear stress patterns which may influence where atherosclerotic lesions form. A better understanding of intraspecies flow variation and the impact of geometry on flow may improve our understanding of where disease forms. In this work, we use magnetic resonance angiography (MRA) and 4D phase contrast magnetic resonance imaging (PC-MRI) to image and measure blood velocity in the rabbit aortic arch. Measured flow rates from the PC-MRI were used as boundary conditions in computational fluid dynamics (CFD) models of the arches. Helical flow, cross flow index (CFI), and time-averaged wall shear stress (TAWSS) were determined from the simulated flow field. Both traditional geometric metrics and shape modes derived from statistical shape analysis were analyzed with respect to flow helicity. High CFI and low TAWSS were found to colocalize in the ascending aorta and to a lesser extent on the inner curvature of the aortic arch. The Reynolds number was linearly associated with an increase in helical flow intensity (R = 0.85, p < 0.05). Both traditional and statistical shape analyses correlated with increased helical flow symmetry. However, a stronger correlation was obtained from the statistical shape analysis demonstrating its potential for discerning the role of shape in hemodynamic studies.

Functional information from magnetic resonance imaging

1995 ◽  
Vol 53 ◽  
pp. 84-85
Author(s):  
Alan P. Koretsky ◽  
Afonso Costa e Silva ◽  
Yi-Jen Lin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Cancer Biology ◽  
Imaging Modality ◽  
Magnetic Resonance Images ◽  
Great Success ◽  
Functional Information ◽  
Resonance Imaging ◽  
High Resolution Mri

Magnetic resonance imaging (MRI) has become established as an important imaging modality for the clinical management of disease. This is primarily due to the great tissue contrast inherent in magnetic resonance images of normal and diseased organs. Due to the wide availability of high field magnets and the ability to generate large and rapidly switched magnetic field gradients there is growing interest in applying high resolution MRI to obtain microscopic information. This symposium on MRI microscopy highlights new developments that are leading to increased resolution. The application of high resolution MRI to significant problems in developmental biology and cancer biology will illustrate the potential of these techniques.In combination with a growing interest in obtaining high resolution MRI there is also a growing interest in obtaining functional information from MRI. The great success of MRI in clinical applications is due to the inherent contrast obtained from different tissues leading to anatomical information.

Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

2018 ◽  
Vol 15 (12) ◽  
pp. 1151-1160 ◽  
Author(s):  
Zihan Jiang ◽  
Huilin Yang ◽  
Xiaoying Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Shape Analysis ◽  
Disease Assessment ◽  
Statistical Shape Analysis ◽  
Superior Part ◽  
Statistical Shape

Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects. Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC). Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC. Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.

Automatic statistical shape analysis of human cerebral asymmetry in 3D MRI

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S99
Author(s):  
A Pepe ◽  
L Zhao ◽  
J Tohka ◽  
J Koikkalainen ◽  
J Hietala ◽  
...  
Keyword(s):  
Shape Analysis ◽  
Statistical Shape Analysis ◽  
3D Mri ◽  
Cerebral Asymmetry ◽  
Statistical Shape

Statistical shape analysis of putamen in early-onset Parkinson’s disease

2021 ◽  
Vol 209 ◽  
pp. 106936
Author(s):  
Deniz Sigirli ◽  
Senem Turan Ozdemir ◽  
Sevda Erer ◽  
Ibrahim Sahin ◽  
Ilker Ercan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Shape Analysis ◽  
Early Onset ◽  
Statistical Shape Analysis ◽  
Statistical Shape ◽  
Early Onset Parkinson’S Disease

Hippocampal and amygdalar morphological abnormalities in Alzheimer’s disease based on three Chinese MRI datasets

2021 ◽  
Vol 18 ◽  
Author(s):  
Yuanyuan Wei ◽  
Nianwei Huang ◽  
Yong Liu ◽  
Xi Zhang ◽  
Silun Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Shape Analysis ◽  
Early Stage ◽  
Disease Status ◽  
Brain Structures ◽  
Statistical Shape Analysis ◽  
Social Significance ◽  
Statistical Shape ◽  
Shape Characteristics

Background: Early detection of Alzheimer’s disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. Objective: In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. Methods: We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. Results: We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. Conclusion: Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.

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