gadolinium images by deconvolution given the arterial input function, AIF(t), and tissue concentration, C(t),: C(t)=CBF٠[AIF(t)⊗R(t)], where R(t) is the tissue residue function. Often AIF is found by manually inspecting tracer concentration maps which is very time consuming and operator dependent. In our study we compare 5 methods of AIF automatic selection, including a novel one. The performan...

PURPOSE To develop a postprocessing method to correct saturation of arterial input function (AIF) in T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantification of hepatic perfusion. MATERIALS AND METHODS The saturated AIF is corrected by parameterizing the first pass of the AIF as a smooth function with a single peak and minimizing a least-squares error in f...

BACKGROUND Fine milling of dry lignocellulosic biomass, without prior chemical pretreatment, can produce a high percent theoretical yield of sugars during subsequent enzymatic saccharification. However, the high sugar yields, necessary for a commercial biofuels process, are costly, with the milling energy input, necessary to achieve such yields even exceeding the energy content of the biomass. ...

Introduction: Automatic arterial input function (AIF) selection has an essential role in quantification of cerebral perfusion parameters. The purpose of this study is to develop an optimal automatic method for AIF determination in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) of glioma brain tumors by using a new preprocessing method.Material and Methods: For this study, ...

A system to heat electrons and possibly drive off-axis field-aligned current is under development on the Madison Symmetric Torus RFP. Staged experiments have reached an input power of 150kW at 3.6GHz and have produced a localized increase in SXR emission during rf injection. This measured emission is consistent with modeling in its location, energy spectrum and dependence on radial diffusion wi...

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic an...