Myocardial perfusion by first pass contrast magnetic resonance: a robust method for quantitative regional assessment of perfusion reserve index.
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Aged↗
Biomedical subjects
V Positano
Publications and source records attributed to V Positano.
Visceral fat and beta cell function in non-diabetic humans.
AIMS/HYPOTHESIS: Preferential visceral adipose tissue (VAT) accumulation has been clearly associated with insulin resistance. In contrast, the impact of visceral obesity on beta cell function is controversial. METHODS: In 62 non-diabetic women and men (age 24-69 years, BMI 21-39 kg/m2), we measured VAT and subcutaneous adipose tissue (SAT) fat mass by magnetic resonance imaging. We also measured insulin secretion and beta cell function by C-peptide deconvolution and physiological modelling of data from a frequently sampled, 75-g, 3-h OGTT, respectively. RESULTS: VAT (range 0.1-3.1 kg) was strongly related to sex, age and BMI; SAT was related to sex and BMI. Controlling for sex, age, BMI and SAT by multivariate analysis, excess VAT was associated with a clinical phenotype comprising higher plasma glucose levels, BP, heart rate and serum transaminases. The corresponding metabolic phenotype consisted of insulin resistance (partial r=-0.38) and hyperinsulinaemia (partial r=0.29). The latter, however, was appropriate for the degree of insulin resistance regardless of obesity and abdominal fat distribution. Moreover, none of the model-derived parameters describing beta cell function (glucose sensitivity, rate sensitivity and potentiation) was independently associated with excess VAT. CONCLUSIONS/INTERPRETATION: In non-diabetic Caucasian adults of either sex, preferential visceral fat deposition in itself is part of an insulin-resistant phenotype. The insulin secretory response to a physiological challenge is increased to fully compensate for the insulin resistance, but the dynamics of beta cell function (glucose sensitivity, rate sensitivity and potentiation) are largely preserved.
Adipose Tissue↗
Automated cardiac MR image segmentation: theory and measurement evaluation.
We present a new approach to magnetic resonance image segmentation with a Gradient-Vector-Flow-based snake applied to selective smoothing filtered images. The system also allows automated image segmentation in the presence of grey scale inhomogeneity, as in cardiac Magnetic Resonance imaging. Removal of such inhomogeneities is a difficult task, but we proved that using non-linear anisotropic diffusion filtering, myocardium edges are selectively preserved. The approach allowed medical data to be automatically segmented in order to track not only endocardium, which is usually a less difficult task, but also epicardium in anatomic and perfusion studies with Magnetic Resonance. The method developed proceeds in three distinct phases: (a) an anisotropic diffusion filtering tool is used to reduce grey scale inhomogeneity and to selectively preserve edges; (b) a Gradient-Vector-Flow-based snake is applied on filtered images to allow capturing a snake from a long range and to move into concave boundary regions; and (c) an automatic procedure based on a snake is used to fit both endocardium and epicardium borders in a multiphase, multislice examination. A good agreement (P<0.001) between manual and automatic data analysis, based on the mean difference+/-SD, was assessed in a pool of 907 cardiac function and perfusion images.
Algorithms↗
A model-based method for myocardium flow estimation.
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Animals↗
Combining high-performance computing and networking for advanced 3-D cardiac imaging.
This paper deals with the integration of a powerful parallel computer-based image analysis and visualization system for cardiology into a hospital information system. Further services are remote access to the hospital Web server through an internet network. The visualization system includes dynamic three-dimensional representation of two types of medical images (e.g., magnetic resonance and nuclear medicine) as well as two images in the same modality (e.g., basal versus stress images). A series of software tools for quantitative image analysis developed for supporting diagnosis of cardiac disease are also available, including automated image segmentation and quantitative time evaluation of left ventricular volumes and related indices during cardiac cycle, myocardial mass, and myocardial perfusion indices. The system has been tested both at a specialized cardiologic center and for remote consultation in diagnosis of cardiac disease by using anatomical and perfusion magnetic resonance images.
Cardiac Output↗
On-line 3D evaluation of left ventricular wall motion in magnetic resonance imaging.
In the present research a system for medical image processing has been proposed, which allows dynamic 3D visualization and successive left ventricular wall analysis. The 3D volume rendering algorithm results are presented using a friendly Graphical User Interface (GUI) giving the possibility to change interactively image processing and visualization parameters at any step, to perform simple and effective image manipulations. The left ventricular wall detection algorithm is based on the evaluation of classified volumetric data during volume rendering algorithm. In fact classification operation includes a 3D segmentation. The system has been tested in medical environment, for Magnetic Resonance (MR) images. Resulting studies have demonstrated a very high global efficiency in practical situations by using typical data volume dimensions.
Algorithms↗
Real-time multimodal medical image processing: a dynamic volume-rendering application.
In the present research, a system for medical image processing has been proposed, which allows multimodal dynamic three-dimensional (3-D) visualization interactively and in real time. The system has been conceived to support medical specialists in the diagnosis of moving organs, such as the heart during the cardiac cycle, allowing them to compare information on perfusion/contraction match as a basis for diagnosis of important cardiovascular diseases. The 3-D volume-rendering algorithm runs on a SIMD machine because of the great amount of data to be manipulated by always using the same operations. One of the features of the algorithm is the possibility to change, interactively, image processing and visualization parameters at any step, and to perform simple and effective image manipulations. Performance studies have demonstrated a very high global efficiency in practical situations by using typical data-volume dimensions. The system has been tested in the medical environment, by using magnetic resonance (MR) and single-photon emission-computed tomographic (SPECT) images.
Algorithms↗
Non-linear prediction for oesophageal voice analysis.
Herein, non-linear prediction methods are applied to oesophageal voice analysis. The research aims to investigate normal and pathological subjects, in order to improve knowledge of the oesophageal voice behaviour. Analysis is performed in the reconstructed phase space, using both non-linear prediction with local linear approximation and the S-Map method. Preliminary results seem to confirm that in normal subjects a non-linear stable deterministic behaviour takes place, while in pathological subjects the non-linear contribution reduces while the time series becomes unstable.
Adult↗