183 research outputs found
Joint Metabonomic and Instrumental Analysis for the Classification of Migraine Patients with 677-MTHFR Mutations
No full textMigraine is a neurological disorder that correlates with an increased risk of cerebrovascular lesions. Genetic mutations of the MTHFR gene are correlated to migraine and to the increased risk of artery pathologies. Also, migraine patients show altered hematochemical parameters, linked to an impaired platelet aggregation mechanism. Hence, the vascular assessment of migraineurs is of primary importance.
Transcranial Doppler sonography (TCD) is used to measure cerebral blood flow velocity (CBFV) and vasomotor reactivity (by an index measured during breath-holding – BHI). Aim of this study was the metabolic profiling of migraine subjects with T/T677-MTHFR and C/T677-MTHFR mutations and its correlation with CBFV and BHI.
Metabonomic multidimensional techniques were used to describe and cluster subjects. Fifty women suffering from migraine (age: 18-64; 21 with aura) underwent TCD examination, hematochemical blood analysis, Born test, and genetic tests for MTHFR mutation. Fourteen (7 with aura) had T/T677, 18 (8 with aura) had C/T677, and 18 (6 with aura) had no mutation. The total number of variables was 24.
Unsupervised and supervised techniques_showed the correlation between CBFV and BHI with mutation. Discriminant analysis allowed for classifying the subjects with 95.9% sensitivity and 89.0% specificity. Aura was not correlated to mutation or variations of instrumental data.
Our study showed that metabonomics could be effectively applied in clinical problems to show the overall correlation structure of complex systems in pathology. Specifically, our results confirmed the importance of TCD in the metabolic profiling and follow-up of migraine patients.</jats:p
CULEX - Completely User-independent Layers EXtraction: Ultrasonic carotid artery images segmentation
No full textThe analysis of the carotid wall is of paramount importance in clinical practice. In fact, the intima-media thickness is a risk index for some of the most severe acute cerebrovascular pathologies; hence, the need for an accurate segmentation of the different layers of the carotid artery. In the past ten years, a wide variety of algorithms for the carotid tunica segmentation have been proposed, but they require a certain degree of user interaction. In this paper we propose a novel approach to the completely user-independent segmentation of the carotid artery wall. Our algorithm has been designed for the extraction of the intima and media layers of the distal carotid wall, based on ultrasonic B-mode images. We evaluated the performance of the algorithm on a set of 63 images and compared the automatic segmentation to that traced by a trained operator. We obtained a mean error lower than 1.3 pixel both on the intima and media layers, which is comparable to that obtained by means of operator dependent technique
Cerebral Autoregulation Assessed by Transcranial Doppler and Near-Infrared Spectroscopy During Self-Induced Disturbances
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Automatic Computer-Based Tracings (ACT) in Longitudinal 2-D Ultrasound Images Using Different Scanners
No full textCharacterization of a Completely User - Independent Algorithm for the Segmentation of Carotid Artery Ultrasound Images
No full textThe analysis of the carotid artery wall is crucial for the diagnosis of serious cardiovascular pathologies or for the assessment of a subject's cardiovascular risk. Several algorithms have been proposed for the segmentation of ultrasound carotid artery images, but almost all require a certain degree of user interaction. We recently developed a completely user-independent algorithm for the segmentation of the common carotid artery wall; specifically, the algorithm traces the contour of the interfaces between the lumen and the intima layer and between the media and adventitia layers. In this paper we show the characterization of the algorithm in terms of segmentation error. Moreover, we compared the output of the algorithm with the segmentations manually traced by experts. We show that our algorithm 's segmentation is not statistically from that of a trained operator and that the segmentation error is lower than 1 pixel for the lumen-intima interface and 1.5 pixels for the media-adventitia interfac
A general strategy for the obtainment of biodegradable polymer shelled microbubbles as theranostic device
No full textUser-independent plaque characterization and accurate IMT measurement of carotid artery wall using ultrasound
No full textNon-invasive plaque characterization of the carotid wall is crucial for the early assessment of pathology, as well as for the monitoring of the progression of a degenerative phenomenon. Specifically, in clinical practice the carotid wall status is assessed by means of B-Mode ultrasound scans. We recently implemented an algorithm for the segmentation of the common tract of the carotid wall using ultrasound relative to healthy subjects. This paper presents a superior strategy for plaque characterization, which accurately determines both echolucent-type II and echogenic plaques in pathologic subjects. We preserve both user-independence and pixel fuzziness in our approach, thereby designing an accurate intima-media thickness (IMT). Our database consists of 20 subjects comprising of normal, stable (echogenic) and unstable (echolucent) plaques. In this database of 45 images, we demonstrate our performance with respect to the gold standard tracings to an accuracy determined as normalized error to be about 8%. The results are very promising and this algorithm is being integrated into clinical setup for automatic pathologic carotid wall analysi
Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-D ultrasound images
No full textAccurate and automatic carotid plaque characterization in contrast enhanced 2-D ultrasound images
No full textThe carotid plaques characterization is essential to decide about the possibility of surgical intervention (endarterectomy/stenting) on the patient. Soft and unstable plaques represent a major risk for the patient, as they are correlated with an augmented probability of brain infarction and emboli generation. Hence, the minimally-invasive characterization expecially of this type of carotid plaques is crucial in clinical practice. This paper presents an integrated system for the completely user-independent carotid plaque segmentation and characterization, based on ultrasound 2-D images. We show that using a ultrasound contrast agent, it is possible to segment also echolucent plaques with a percentage of misclassified pixels equal to 8%. After segmentation, the enhanced image is used to perform tissue characterization. We tested our system on 5 echolucent plaques and on 5 fibrous/stable plaques, showing that our system is capable of an accurate carotid wall segmentation and proper quantification of the percentages of blood, fat, calcium and fibrous tissue constituting the plaque. The system is very promising and it is being used in a neurology unit on patients already indicated for endarterectomy, with the purpose of correlating its output with histolog
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