Smoothing dynamic positron emission tomography time courses using functional principal components

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error

Multimodality Quantitative Assessments of Myocardial Perfusion Using Dynamic Contrast Enhanced Magnetic Resonance and 15O-Labeled Water Positron Emission Tomography Imaging

Kinetic modeling of myocardial perfusion imaging data allows the absolute quantification of myocardial blood flow (MBF) and can improve the diagnosis and clinical assessment of coronary artery disease (CAD). Positron emission tomography (PET) imaging is considered the reference standard technique for absolute quantification, whilst oxygen-15 (15O)-water has been extensively implemented for MBF quantification. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has also been used for MBF quantification and showed comparable diagnostic performance against (¹⁵ O)-water PET studies. We investigated for the first time the diagnostic performance of two different PET MBF analysis softwares PMOD and Carimas, for obstructive CAD detection against invasive clinical standard methods in 20 patients with known or suspected CAD. Fermi and distributed parameter modeling-derived MBF quantification from DCE-MRI was also compared against (15O)-water PET, in a subgroup of six patients. The sensitivity and specificity for PMOD was significantly superior for obstructive CAD detection in both per vessel (0.83, 0.90) and per patient (0.86, 0.75) analysis, against Carimas (0.75, 0.65) and (0.81, 0.70), respectively. We showed strong, significant correlations between MR and PET MBF quantifications (r = 0.83 - 0.92). However, DP and PMOD analysis demonstrated comparable and higher hemodynamic differences between obstructive versus (no, minor, or non)-obstructive CAD, against Fermi and Carimas analysis. Our MR method assessments against the optimum PET reference standard technique for perfusion analysis showed promising results in per segment level and can support further multimodality assessments in larger patient cohorts. Further MR against PET assessments may help to determine their comparative diagnostic performance for obstructive CAD detection

Polymer Reactor Modeling, Design and Monitoring

Polymers range from synthetic plastics, such as polyacrylates, to natural biopolymers, such as proteins and DNA. The large molecular mass of polymers and our ability to manipulate their compositions and molecular structures have allowed for producing synthetic polymers with attractive properties. new polymers with remarkable characteristics are synthesized. Because of the huge production volume of commodity polymers, a little improvement in the operation of commodity-polymer processes can lead to significant economic gains. On the other hand, a little improvement in the quality of specialty polymers can lead to substantial increase in economic profits

Doctor of Philosophy

dissertationWe present a method for absolutely quantifying pharmacokinetic parameters in dynamic contrast-enhanced (DCE)-MRI. This method, known as alternating mini-mization with model (AMM), involves jointly estimating the arterial input function (AIF) and pharmacokinetic parameters from a characteristic set of measured tissue concentration curves. By blindly estimating the AIF, problems associated with AIF measurement in pharmacokinetic modeling, such as signal saturation, flow and partial volume eff ects, and small arterial lumens can be ignored. The blind estimation method described here introduces a novel functional form for the AIF, which serves to simplify the estimation process and reduce the deleterious e ffects of noise on the deconvolution process. Computer simulations were undertaken to assess the performance of the estimation process as a function of the input tissue curves. A con fidence metric for the estimation quality, based on a linear combination of the SNR and diversity of the input curves, is presented. This con fidence metric is then used to allow for localizing the region from which input curves are drawn. Local blood supply to any particular region can then be blindly estimated, along with some measure of con fidence for that estimation. Methods for evaluating the utility of the blind estimation algorithm on clinical data are presented, along with preliminary results on quantifying tissue parameters in soft-tissue sarcomas. The AMM method is applied to in vivo data from both cardiac perfusion and breast cancer scans. The cardiac scans were conducted using a dual-bolus protocol, which provides a measure of truth for the AIF. Twenty data sets were processed with this method, and pharmacokinetic parameter values derived from the blind AIF were compared with those derived from the dual-bolus measured AIF. For seventeen of the twenty datasets there were no statistically signifi cant differences in Ktrans estimates. The cardiac AMM method presented here provides a way to quantify perfusion of myocardial tissue with a single injection of contrast agent and without a special pulse sequence. The resulting parameters are similar to those given by the dual bolus method. The breast cancer scans were processed with the AMM method and the results were compared to an analysis done with the semiquantitative DCE-MRI scans. The e ffects of the temporal sampling rate of the data on the AMM method are examined. The ability of the AMM-derived parameters to distinguish benign and malignant tumors is compared to more conventional methods

Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the HP 13C MRI Consensus Group as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality