1,721,270 research outputs found
The Use of the Levenberg-Marquardt and Variable Projection Curve-Fitting Algorithm in Intravoxel Incoherent Motion Method for DW-MRI Data Analysis
No full textThe objective of this study was to evaluate the performances of different algorithms for diffusion parameters estimation in intravoxel incoherent motion method for diffusion-weighted magnetic resonance imaging (DW-MRI) data analysis. Traditionally, the method of non-linear least squares analysis by means of Levenberg–Marquardt algorithms has been used to estimate the parameters obtained from exponential decay data. In this study, we evaluated the Variable Projection curve-fitting algorithm and the performance of two non-linear regression methods when single and multiple starting points were used. Analysis was done on simulation data to which different amounts of Gaussian noise had been added. The performance of two non-linear regression methods was compared using the residual sum of squares and the number of failures in data fitting. We conclude that the VarPro algorithm is superior to the LM algorithm for curve fitting in intravoxel incoherent motion method for DW-MRI data analysis
Performances of Different Algorithms for Tracer Kinetics Parameters Estimation in Breast DCE-MRI
No full textObjective of this study was to evaluate the performances of different algorithms for tracer kinetics parameters estimation in breast Dynamic Contrast Enhanced-MRI. We considered four algorithms: two non-iterative algorithms based on impulsive and linear approximation of the Arterial Input Function respectively; and two iterative algorithms widely used for non-linear regression (Levenberg-Marquardt, LM and VARiable PROjection, VARPRO). Per each value of the kinetic parameters within a physiological range, we simulated 100 noisy curves and estimated the parameters with all algorithms. Sampling time, total duration and noise level have been chosen as in a typical breast examination. We compared the performances with respect to the Cramer-Rao Lower Bound (CRLB). Moreover, in order to gain further insight we applied the algorithms to a real breast examination. Accuracy of all the methods depends on the specific value of the parameters. The methods are in general biased: however, VARPRO showed small bias in a region of the parameter space larger than the other methods; moreover, VARPRO approached CRLB and the number of iterations were smaller than LM. In the specific conditions analyzed, VARPRO showed better performances with respect to LM and to non-iterative algorithms
A geometrical perspective on the 3TP method in DCE-MRI
No full textThe 3TP (three time points) method has been proposed for producing high-resolution pseudo-coloured maps related to the angiogenic activity in breast DCE-MRI. In the original formulation of the method, the three time-points have been chosen on an empirical basis: the algorithm for benign/malignant/uncertain classification of a voxel was to be as simple as possible and the corresponding regions of the parameters space (according to the Tofts' model) were to occupy approximately the same area. Since its inception, the method has been largely used in clinical environment, due to its simplicity and soundness. However, as only three time-points are used to evaluate the characteristics of the complex time-course of the contrast medium within capillaries, noise can result in voxel misclassification, as we show in this study.In this paper we analysed the performances of the method from a geometrical perspective, based on the concept of confidence region, and we proposed an 'optimal' choice of the three time-points, in order to reduce the misclassification to a minimum. Comparing the original 3TP method with our proposal on the basis of misclassification rate, our results show that the modified 3TP method can lead to better performance. Preliminary results on real data have been also reported. Moreover, our proposal has a sounding mathematical basis and is easily generalisable to the case of more than two parameters and to other modalities such as DCE-CT. (C) 2014 Elsevier Ltd. All rights reserved
Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models
No full textBackground/objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used in tumor
diagnosis, staging and assessment of therapy response for different types of tumors, thanks to its capability to provide
important functional information about tissue microvasculature. Tracer kinetic models used for estimating microcirculatory
parameters can be broadly categorized as conventional compartmental (CC) or distributed- parameter (DP)
models. While DP models seem to be more realistic, CC models (in particular the Tofts and the Brix models) have been
widely used in clinical investigations over the past two decades. However, to date there is no direct comparison of CC vs
DP models on real breast DCE-MRI data; moreover, a direct comparison between Tofts and Brix models, has not yet been
reported on real breast data. Therefore, the purpose of this study was two-fold: on the one hand we analyzed the
performance, on real breast DCE-MRI data, of CC vs DP models in terms of goodness-of-fit metrics; on the other hand we
compared Tofts and Brix models on the basis of real breast DCE-MRI data.
Methods: Three models were compared: two CC models (the Tofts and the Brix models) and one DP model (the ATH
model). We gathered data in two different scenarios: DCE-MRI with high temporal resolution obtained by means of a
k-space under-sampling and data sharing method known as Time-resolved angiography With Stochastic Trajectories
(TWIST) and DCE-MRI with low temporal resolution obtained by means of the Spoiled Gradient-Echo k-space scheme
known as Fast Low Angle Shot (FLASH). The performances of the three models were evaluated by means of three
goodness-of-fit metrics: the Residual Sum of Squares, the Bayesian Information Criterion and the Akaike Information
Criterion on four breast DCE-MRI examinations.
Results: Although not conclusive, the results of this study suggest that the ATH model can achieve better fit in comparison
to the Tofts and Brix models for TWIST data; and that the Brix model can achieve better fit with respect to the Tofts model
for FLASH data.
Conclusion: Given the current typical settings of clinical breast DCE-MRI examinations, there seems not to be a clear
advantage, in terms of goodness-of-fit, of ATH with respect to Tofts and Brix models; moreover, at lower temporal
resolution the Brix model can achieve better fit than the Tofts model
Breast contrast-enhanced MR imaging: semiautomatic detection of vascular map
No full textBackground
The diagnostic value of breast vascular maps using contrast-enhanced MR imaging has recently been explored. We propose a semiautomatic method to obtain breast vascular maps and to measure the number of blood vessels in the breast.
Methods
From January 2011 to December 2013, 188 patients underwent breast contrast-enhanced MRI; patients with unilateral and histopathologically confirmed breast lesions were included in this study; 123 patients had malignant lesions and 65 patients had benign tissue diagnoses. Breast semiautomatic vascular map detection was performed using Hessian matrix-based method and morphologic operators. Blood vessels detection was compared with radiologic interpretation findings to evaluate algorithm goodness. Increase in vascularity associated with ipsilateral cancer was also assessed. Chi square test was used to observe statistically significant difference.
Results
A total of 1315 blood vessels were identified using semiautomatic procedure; 1034 were correctly classified (78.7 %), 261 (19.8 %) were incorrectly classified, and 20 (1.5 %) were missing. A significant association was found between one-sided increased breast vascularity and ipsilateral malignancy (p < 0.001).
Conclusions
In conclusion, detection of vascularity increase as risk factor for developing breast cancer could be performed with semiautomatic vascular mapping of contrast-enhanced MR imaging
An overview on human error analysis and reliability assessment
No full textThere is a continuous debate about the proper role of man and machine in complex operational frameworks. Formal human analyses and risk management techniques are becoming more important part to manage the relationship between human factors and accident analyses. There are different types of Human Reliability Analysis (HRA) models. HRA methods differ in their characteristics but a common feature in all methods is the definition of the human error probability (HEP). The aim of this chapter is not to cover all of the possible HRA approaches and above not from a mathematical point of view but conceptual one. In fact, no one approach can answer all of the separate issues that can arise in human reliability. The utility of a particular approach is a function of a number of components, not just the absolute value of a method. Expert judgment is an integral part of HRA to capture information about human actions. Definitively, the aim of this chapter is twofold. It tries to respond to these questions: What is HRA? and What are the main features of the most well-known HRA methods
Influence of Parameterization on Tracer Kinetic Modeling in DCE-MRI
No full textTracer kinetic modeling in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is commonly
performed using least squares algorithms. The convergence of such algorithms and the repeatability of the estimates are
affected by the curvature of the model’s expectation surface. An adequate choice of the parameterization can reduce
curvature and thus improve parameter estimation. This study analyzes the influence of two parameterizations on the
curvature of the Tofts model. The influences of the total acquisition time and the sampling period are evaluated.
Analysis results show that using (Ktrans, ve
) can significantly reduce the curvature in a large area of the parameter space,
suggesting that curvature analysis could guide the choice of the best local parameterization in Gauss-Newton-based
algorithms. In addition, increasing the total acquisition time and decreasing the sampling period reduce the curvature.
However, only slight improvements are obtained for a total time longer than about 6 min and a sampling period shorter
than approximately 10
The importance of human error and reliability management in critical conditions and infrastructures
No full textProtection and safeguarding of critical infrastructures (such us chemical industry, oil & gas industry, nuclear industry, etc.) is an important subject of study in the contemporary society. The study of risks associated to critical infrastructure required models of good practice to investigate the complexity of processes in case of accidents. The risk management can be viewed in two ways: human error and system reliability. In other words in terms of human error it is essential to ensure the operator performance to manage a complex system or an unexpected situation. While in terms of system reliability it is essential to ensure that a system is at least as reliable as the system it is replacing. The present chapter aims to analyze the main disasters occurred in critical infrastructures related to human errors or lack of reliability of systems
sj-xlsx-2-tam-10.1177_17588359221138388 – Supplemental material for Genetic landscape of colorectal cancer patients manifesting tumor shrinkage during SARS-Cov-2 infection
No full textSupplemental material, sj-xlsx-2-tam-10.1177_17588359221138388 for Genetic landscape of colorectal cancer patients manifesting tumor shrinkage during SARS-Cov-2 infection by Alessandro Ottaiano, Mariachiara Santorsola, Luisa Circelli, Marco Cascella, Nadia Petrillo, Francesco Perri, Marika Casillo, Vincenza Granata, Monica Ianniello, Francesco Izzo, Carmine Picone, Marco Correra, Antonella Petrillo, Roberto Sirica, Gabriella Misso, Paolo Delrio, Guglielmo Nasti, Giovanni Savarese and Michele Caraglia in Therapeutic Advances in Medical Oncology</p
sj-xlsx-1-tam-10.1177_17588359221138388 – Supplemental material for Genetic landscape of colorectal cancer patients manifesting tumor shrinkage during SARS-Cov-2 infection
No full textSupplemental material, sj-xlsx-1-tam-10.1177_17588359221138388 for Genetic landscape of colorectal cancer patients manifesting tumor shrinkage during SARS-Cov-2 infection by Alessandro Ottaiano, Mariachiara Santorsola, Luisa Circelli, Marco Cascella, Nadia Petrillo, Francesco Perri, Marika Casillo, Vincenza Granata, Monica Ianniello, Francesco Izzo, Carmine Picone, Marco Correra, Antonella Petrillo, Roberto Sirica, Gabriella Misso, Paolo Delrio, Guglielmo Nasti, Giovanni Savarese and Michele Caraglia in Therapeutic Advances in Medical Oncology</p
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