Motion-Compensated Image Reconstruction for Magnetic Resonance (MR) Imaging and for Simultaneous Positron Emission Tomography/MR Imaging

In this work, novel algorithms for 4D (3D + respiratory) and 5D (3D + respiratory + cardiac) motion-compensated (MoCo) magnetic resonance (MR) and positron emission tomography (PET) image reconstruction were developed. The focus of all methods was set on short MR acquisition times. Therefore, respiratory and cardiac patient motion were estimated on the basis of strongly undersampled radial MR data employing joint motion estimation and MR image reconstruction. In case of simultaneous PET/MR acquisitions, motion information derived from MR was incorporated into the MoCo PET reconstruction. 4D respiratory MoCo MR image reconstructions with acquisition times of 40 s achieved an image quality comparable to standard motion handling approaches, which require one order of magnitude longer MR acquisition times. Respiratory MoCo PET images using 1 min of the MR acquisition time for motion estimation revealed improved PET image quality and quantification accuracy when compared to standard reconstruction methods. Additional compensation of cardiac motion resulted in increased image sharpness of MR and PET images in the heart region and enabled time-resolved 5D imaging allowing for reconstruction of any arbitrary combination of respiratory and cardiac motion phases. The proposed methods for MoCo image reconstruction may be integrated into clinical routine, reducing MR acquisition times for improved patient comfort and increasing the diagnostic value of MR and simultaneous PET/MR examinations of the thorax and abdomen

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography

Introduction: Dynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition. Methods: A new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography. The phantom is based on high spatial and temporal information derived from real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. Results: The proposed phantom is comprised of three spatial and two temporal dimensions, including periodic physiological processes due to respiratory motion and non-periodic functional processes due to tracer kinetics. Example applications are shown in parametric [18F]FDG and [15O]H2O PET imaging, successfully generating realistic macro- and micro-parametric maps. Conclusions: The envisaged applications of this digital phantom include the development and evaluation of motion correction and 4D image reconstruction algorithms in PET and SPECT, development of protocols and methods for tracer and drug development as well as new pharmacokinetic parameter estimation algorithms, amongst others. Although the simulation platform is primarily developed for generating dynamic phantoms for emission tomography studies, it can easily be extended to accommodate dynamic MR and CT imaging simulation protocols

Development of Methodology to Estimate Fractional Flow Reserve Using Magnetic Resonance Imaging and Computational Fluid Dynamics

Fractional flow reserve is the current gold standard for evaluating severity of coronary artery disease, but it is underutilized clinically due to its invasiveness. Recent efforts have worked toward developing non-invasive alternatives, wherein medical imaging data are used to construct patient-specific computational fluid dynamics models to simulate blood flow through the coronary arteries and calculate virtual fractional flow reserve. Magnetic resonance imaging is particularly well-suited for this application due to its ability to directly quantify both angiographic geometry and flow velocity. Therefore, the purpose of this thesis was the investigation and development of magnetic resonance techniques toward defining the patient-specific boundary conditions needed in computationally estimating fractional flow reserve. In Aim 1, we performed a series of computational simulations to determine what patient-specific flow information is needed to calculate virtual fractional flow reserve. Then, we tested phase-contrast magnetic resonance in a cohort of healthy volunteers to validate its ability to quantify coronary arterial flow. In Aim 2, several novel implementations of self-gated magnetic resonance angiography were investigated for their ability to characterize coronary arterial geometry. Tests were carried out in several cohorts of adult patients with congenital heart disease and a cohort of pigs to study the use of self-navigation and both four and five–dimensional golden-angle radial sparse parallel magnetic resonance. Optimizations of both the acquisition and reconstruction frameworks were explored. Altogether, these studies advanced the use of magnetic resonance angiography in interventional cardiology.Ph.D

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'

Reconstruction Methods for Free-Breathing Dynamic Contrast-Enhanced MRI

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a valuable diagnostic tool due to the combination of anatomical and physiological information it provides. However, the sequential sampling of MRI presents an inherent tradeoff between spatial and temporal resolution. Compressed Sensing (CS) methods have been applied to undersampled MRI to reconstruct full-resolution images at sub-Nyquist sampling rates. In exchange for shorter data acquisition times, CS-MRI requires more computationally intensive iterative reconstruction methods. We present several model-based image reconstruction (MBIR) methods to improve the spatial and temporal resolution of MR images and/or the computational time for multi-coil MRI reconstruction. We propose efficient variable splitting (VS) methods for support-constrained MRI reconstruction, image reconstruction and denoising with non-circulant boundary conditions, and improved temporal regularization for breast DCE-MRI. These proposed VS algorithms decouple the system model and sparsity terms of the convex optimization problem. By leveraging matrix structures in the system model and sparsifying operator, we perform alternating minimization over a list of auxiliary variables, each of which can be performed efficiently. We demonstrate the computational benefits of our proposed VS algorithms compared to similar proposed methods. We also demonstrate convergence guarantees for two proposed methods, ADMM-tridiag and ADMM-FP-tridiag. With simulation experiments, we demonstrate lower error in spatial and temporal dimensions for these VS methods compared to other object models. We also propose a method for indirect motion compensation in 5D liver DCE-MRI. 5D MRI separates temporal changes due to contrast from anatomical changes due to respiratory motion into two distinct dimensions. This work applies a pre-computed motion model to perform motion-compensated regularization across the respiratory dimension and improve the conditioning of this highly sparse 5D reconstruction problem. We demonstrate a proof of concept using a digital phantom with contrast and respiratory changes, and we show preliminary results for motion model-informed regularization on in vivo patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138498/1/mtle_1.pd