scholarly journals Statistical Shape Analysis of Ascending Thoracic Aortic Aneurysm: Correlation between Shape and Biomechanical Descriptors

2020 ◽  
Vol 10 (2) ◽  
pp. 28 ◽  
Author(s):  
Federica Cosentino ◽  
Giuseppe M Raffa ◽  
Giovanni Gentile ◽  
Valentina Agnese ◽  
Diego Bellavia ◽  
...  
Keyword(s):  
Aortic Aneurysm ◽  
Shape Analysis ◽  
Thoracic Aortic Aneurysm ◽  
Aortic Surgery ◽  
Principal Component ◽  
Aortic Dilatation ◽  
Statistical Shape Analysis ◽  
Statistical Shape ◽  
Personalized Approach ◽  
And Function

An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allowed to reduce the complex ATAA anatomy to a few shape modes, which were correlated to shear stress and aortic strain, as determined by computational analysis. Findings demonstrated that shape modes are associated to specific morphological features of aneurysmal aorta as the vessel tortuosity and local bulging of the ATAA. A predictive model, built with principal shape modes of the ATAA wall, achieved better performance in stratifying surgically operated ATAAs versus monitored ATAAs, with respect to a baseline model using the maximum aortic diameter. Using current imaging resources, this study demonstrated the potential of SSA to investigate the association between shape and function in ATAAs, with the goal of developing a personalized approach for the treatment of the severity of aneurysmal aorta.

scholarly journals On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis

2021 ◽  
Vol 8 (5) ◽  
pp. 66
Author(s):  
Salvatore Cutugno ◽  
Tommaso Ingrassia ◽  
Vincenzo Nigrelli ◽  
Salvatore Pasta
Keyword(s):  
Aortic Valve ◽  
Shape Analysis ◽  
Ventricular Remodeling ◽  
Left Ventricular Remodeling ◽  
Demographic Data ◽  
Principal Component ◽  
Left Ventricular ◽  
Statistical Shape Analysis ◽  
Statistical Shape ◽  
And Function

The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of n=86 patients underwent computed tomography (CT) for the evaluation of valvulopathy were segmented to obtain the LV surface and then were automatically aligned to a reference template by rigid registrations and transformations. Shape modes of the anatomical LV variation induced by the degree of AS were assessed by principal component analysis (PCA). The first shape mode represented nearly 50% of the total variance of LV shape in our patient population and was mainly associated to a spherical LV geometry. At Pearson’s analysis, the first shape mode was positively correlated to both the end-diastolic volume (p<0.01, R=0.814) and end-systolic volume (p<0.01, and R=0.922), suggesting LV impairment in patients with severe AS. A predictive model built with PCA-related shape modes achieved better performance in stratifying the occurrence of adverse events with respect to a baseline model using clinical demographic data as risk predictors. This study demonstrated the potential of SSA approaches to detect the association of complex 3D shape features with functional LV parameters.

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

Abstract 15129: Outcomes Related to Age of Elective Surgery and Age of Dissection Within Genetically Triggered Aneurysm Conditions: The Gentac Experience

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Kathryn W Holmes ◽  
Scott A Lemaire ◽  
Richard B Devereux ◽  
William J Ravekes ◽  
Shaine A Morris ◽  
...  
Keyword(s):  
Aortic Aneurysm ◽  
Thoracic Aortic Aneurysm ◽  
Elective Surgery ◽  
Aortic Surgery ◽  
Reference Population ◽  
Ehlers Danlos Syndrome ◽  
Aortic Replacement ◽  
Fourth Decade ◽  
Ascending Aortic Replacement ◽  
Aortic Dissections

Introduction: The GenTAC Registry ( G enetically Triggered T horacic A ortic Aneurysms and Cardiovascular C onditions) followed patients with aortopathies over 8 years among 8 centers with the goal of evaluating cardiovascular outcomes. Methods: Enrollment initiated in 2007, and data were collected until 2015. We included diagnoses with >100 participants: Bicuspid aortic valve with aneurysm (BAV, n=879), Marfan syndrome (MFS, n=861), Familial thoracic aortic aneurysm or dissection (FTAAD, n=378), Other thoracic aortic aneurysm at < 50 years of age (Other<50, n=524), Turner syndrome (TS, n=298), Vascular Ehlers Danlos syndrome (VEDS, n=149), and Loeys-Dietz syndrome (LDS, n=121). We identified patients who underwent elective ascending aortic replacement, total unique dissections, and time to first dissection. With MFS as a reference population and adjusted for sex, endpoints were analyzed by a Firth penalized Cox-PH regression model to account for diagnosis groups with low event numbers. Results: LDS participants at a mean age of (24.5 ± 15.0y) were youngest at elective aortic surgery followed by MFS (32.3 ±12.3y), TS (37.6 ±13.6y), VEDS (35.0 ±SD 7.4y), Other<50 (40.3 ±SD 10.3y), FTAAD (42.9 ±14.2y), and BAV(49.4 ± 13.8 y). Dissections were reported in all diagnosis groups with a total of 472 unique dissections in 3210 patients (14%). Mean age at first dissection was in the third decade for LDS, TS, MFS, VEDS and in the fourth decade for BAV, FTAD, and Other<50. Adjusted hazard ratio for time to first dissection was higher in LDS, 1.77 (95%CI 1.14- 2.77), compared to MFS and other diagnosis groups (Figure 1). Conclusions: Reported aortic dissections were prominent in the GenTAC cohort. Despite elective surgery at a younger age, LDS patients had a higher hazard risk of dissection compared to other diagnosis groups.

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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