DCEMRI | Volker J Schmid

Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI

Thu, 26 May 2022 00:00:00 +0000

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Thu, 20 Jan 2022 00:00:00 +0000

Software for Magnetic Resonance Imaging

Fri, 22 Feb 2019 00:00:00 +0000

R packages for working with DCE-MRI data:

dcemriS4: R package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) for oncology applications. It provides methods for fitting voxel-wise kinetic models using Levenberg-Marquardt and using a Bayesian framework. Several kinetic models and arterial input functions are available.
oro.nifti: Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats.
cmR: cmR: cardiac magnetic resonance imaging in R
dcemri.SpatEnet: Analysing DCE-MRI using a spatially regularized Elastic Net.
dcemriboost: Voxelwise and spatially regularized boosting for DCE-MRI data.
adaptsmoFMRI: Adaptive smoothing of fMRI data.

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Sun, 01 Jan 2017 00:00:00 +0000

Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data

Fri, 01 Jan 2016 00:00:00 +0000

Neue Veröffentlichung: Spatially regularized estimation for the analysis of DCE-MRI data

Sat, 15 Mar 2014 00:00:00 +0000

Abstract

Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset.

Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data

Sat, 15 Mar 2014 00:00:00 +0000

Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging

Wed, 01 Jan 2014 00:00:00 +0000

Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets

Sun, 01 Jan 2012 00:00:00 +0000

A Bayesian hierarchical model for DCE-MRI to evaluate treatment response in a phase II study in advanced squamous cell carcinoma of the head and neck

Sat, 01 Jan 2011 00:00:00 +0000

How heterogeneous is the liver? A cluster analysis of DCE-MRI time series

Sat, 01 Jan 2011 00:00:00 +0000

Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R

Sat, 01 Jan 2011 00:00:00 +0000

Two Tissue Compartment Model in DCE-MRI: A Bayesian Approach

Fri, 01 Jan 2010 00:00:00 +0000

Semi-parametric analysis of dynamic contrast-enhanced MRI using Bayesian P-splines

Sun, 01 Jan 2006 00:00:00 +0000

Statistical analysis of pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging.

Sat, 01 Jan 2005 00:00:00 +0000