Relationship between Carotid Computed Tomography Dual-Energy and Brain Leukoaraiosis

Saba, Luca; Sanfilippo, Roberto; Balestrieri, Antonella; Zaccagna, Fulvio; Argiolas, Giovanni Maria; Suri, Jasjit S.; Montisci, Roberto

doi:10.1016/j.jstrokecerebrovasdis.2017.04.016

Cited by 8 publications

(4 citation statements)

References 38 publications

(46 reference statements)

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“…1). Atherosclerosis has been also linked to neuronal diseases such as dementia [14], leukoaraiosis [15,16], and Alzheimer's [17], renal diseases [18,19], arthritis [20], and coronary artery diseases [21].…”

Section: Introductionmentioning

confidence: 99%

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Biswas¹,

Saba

Omerzu

et al. 2021

J Digit Imaging

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Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

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Section: Introductionmentioning

confidence: 99%

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Biswas¹,

Saba

Omerzu

et al. 2021

J Digit Imaging

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“…CTA indicates plaque composition with fatty, fibrotic, and calcified plaques displaying different values of HUs. 46 , 47 Fatty plaques are associated with a higher risk of stroke than calcified plaques. 9 , 48 , 49 The present work adds up to previous studies and altogether, these data may help to better characterize the relationship between histology and routine preoperative modalities, which could be critical to improve surgical screening of carotid lesions.…”

Section: Discussionmentioning

confidence: 99%

In vitro analysis of carotid lesions using a preliminary microwave sensor to detect vulnerable plaques: Correlation with histology, Duplex ultrasound examination, and computed tomography scanner: The Imaging and Microwave Phenotyping Assessment of Carotid stenosis Threat (IMPACT) study

Shahbaz,

Charpentier,

Ponnaiah

et al. 2024

JVS-Vascular Science

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“…It indirectly explained the relationship between white matter lesions and carotid plaque. In addition, some studies have pointed out that the volume of LA is related to the attenuation of the CT value of carotid plaque [32]. However, there are many controversies in current studies.…”

Section: Relationship Between La and Carotid Atherosclerosismentioning

confidence: 97%

Overview-Research Progress of Leukoaraiosis

Chen¹,

Bian²

2022

JBBS

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In 1987, Hachinski et al. [1] proposed Leukoaraiosis (LA) as an imaging academic term. Since then, LA and other cerebrovascular diseases have become the research focus of scholars at home and abroad. However, the molecular, cellular and pathogenesis of cerebrovascular disease are still unclear. Cerebral small vessel disease is caused by the lesions of perforating arteries, capillaries and veins of the brain. Modern imaging technology makes it possible to classify these diseases that can not be directly distinguished in clinical practice. The imaging features of cerebral infarction include cerebral microvascular atrophy. At present, a large number of studies have been carried out around LA, such as the pathogenesis, risk factors, imaging manifestations and classification, pathophysiological changes, hemodynamics, gene polymorphism and so on. In addition, although LA belongs to the category of cerebral small vessel disease, more scholars believe that there are countless links between large artery atherosclerosis and LA, and to some extent, have the same pathogenesis. This paper reviews the following aspects.

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Relationship between Carotid Computed Tomography Dual-Energy and Brain Leukoaraiosis

Abstract: The results of our study suggest that the associations between HU attenuation of the carotid artery plaques (with the exclusion of calcified plaques) and the volume of LA are emphasized at low keV energy levels.

Cited by 8 publications

References 38 publications

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

In vitro analysis of carotid lesions using a preliminary microwave sensor to detect vulnerable plaques: Correlation with histology, Duplex ultrasound examination, and computed tomography scanner: The Imaging and Microwave Phenotyping Assessment of Carotid stenosis Threat (IMPACT) study

Overview-Research Progress of Leukoaraiosis

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