WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still has some limitations, such as the robustness and flexibility of existing methods have great deficiency. In this work, we propose a method to explore the k-space domain learning via robust generative modeling for flexible calibration-less PI reconstruction, coined weight-k-space generative model (WKGM). Specifically, WKGM is a generalized k-space domain model, where the k-space weighting technology and high-dimensional space augmentation design are efficiently incorporated for score-based generative model training, resulting in good and robust reconstructions. In addition, WKGM is flexible and thus can be synergistically combined with various traditional k-space PI models, which can make full use of the correlation between multi-coil data and realizecalibration-less PI. Even though our model was trained on only 500 images, experimental results with varying sampling patterns and acceleration factors demonstrate that WKGM can attain state-of-the-art reconstruction results with the well-learned k-space generative prior.Comment: 11pages, 12 figure

Model-based reconstruction of accelerated quantitative magnetic resonance imaging (MRI)

Quantitative MRI refers to the determination of quantitative parameters (T1,T2,diffusion, perfusion etc.) in magnetic resonance imaging (MRI). The ’parameter maps’ are estimated from a set of acquired MR images using a parameter model, i.e. a set of mathematical equations that describes the MR images as a function of the parameter(s). A precise and accurate highresolution estimation of the parameters is needed in order to detect small changes and/or to visualize small structures. Particularly in clinical diagnostics, the method provides important information about tissue structures and respective pathologic alterations. Unfortunately, it also requires comparatively long measurement times which preclude widespread practical applications. To overcome such limitations, approaches like Parallel Imaging (PI) and Compressed Sensing (CS) along with the model-based reconstruction concept has been proposed. These methods allow for the estimation of quantitative maps from only a fraction of the usually required data. The present work deals with the model-based reconstruction methods that are applicable for the most widely available Cartesian (rectilinear) acquisition scheme. The initial implementation was based on accelerating the T*2 mapping using Maximum Likelihood estimation and Parallel Imaging (PI). The method was tested on a Multiecho Gradient Echo (MEGE) T*2 mapping experiment in a phantom and a human brain with retrospective undersampling. Since T*2 is very sensitive to phase perturbations as a result of magnetic field inhomogeneity further work was done to address this. The importance of coherent phase information in improving the accuracy of the accelerated T*2 mapping fitting was investigated. Using alternating minimization, the method extends the MLE approach based on complex exponential model fitting which avoids loss of phase information in recovering T*2 relaxation times. The implementation of this method was tested on prospective(real time) undersampling in addition to retrospective. Compared with fully sampled reference scans, the use of phase information reduced the error of the accelerated T*2 maps by up to 20% as compared to baseline magnitude-only method. The total scan time for the four times accelerated 3D T*2 mapping was 7 minutes which is clinically acceptable. The second main part of this thesis focuses on the development of a model-based super-resolution framework for the T2 mapping. 2D multi-echo spin-echo (MESE) acquisitions suffer from low spatial resolution in the slice dimension. To overcome this limitation while keeping acceptable scan times, we combined a classical super-resolution method with an iterative model-based reconstruction to reconstruct T2 maps from highly undersampled MESE data. Based on an optimal protocol determined from simulations, we were able to reconstruct 1mm3 isotropic T2 maps of both phantom and healthy volunteer data. Comparison of T2 values obtained with the proposed method with fully sampled reference MESE results showed good agreement. In summary, this thesis has introduced new approaches to employ signal models in different applications, with the aim of either accelerating an acquisition, or improving the accuracy of an existing method. These approaches may help to take the next step away from qualitative towards a fully quantitative MR imaging modality, facilitating precision medicine and personalized treatment

Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018

This open access book features a selection of high-quality papers from the presentations at the International Conference on Spectral and High-Order Methods 2018, offering an overview of the depth and breadth of the activities within this important research area. The carefully reviewed papers provide a snapshot of the state of the art, while the extensive bibliography helps initiate new research directions

Improving Quantification in Lung PET/CT for the Evaluation of Disease Progression and Treatment Effectiveness

Positron Emission Tomography (PET) allows imaging of functional processes in vivo by measuring the distribution of an administered radiotracer. Whilst one of its main uses is directed towards lung cancer, there is an increased interest in diffuse lung diseases, for which the incidences rise every year, mainly due to environmental reasons and population ageing. However, PET acquisitions in the lung are particularly challenging due to several effects, including the inevitable cardiac and respiratory motion and the loss of spatial resolution due to low density, causing increased positron range. This thesis will focus on Idiopathic Pulmonary Fibrosis (IPF), a disease whose aetiology is poorly understood while patient survival is limited to a few years only. Contrary to lung tumours, this diffuse lung disease modifies the lung architecture more globally. The changes result in small structures with varying densities. Previous work has developed data analysis techniques addressing some of the challenges of imaging patients with IPF. However, robust reconstruction techniques are still necessary to obtain quantitative measures for such data, where it should be beneficial to exploit recent advances in PET scanner hardware such as Time of Flight (TOF) and respiratory motion monitoring. Firstly, positron range in the lung will be discussed, evaluating its effect in density-varying media, such as fibrotic lung. Secondly, the general effect of using incorrect attenuation data in lung PET reconstructions will be assessed. The study will compare TOF and non-TOF reconstructions and quantify the local and global artefacts created by data inconsistencies and respiratory motion. Then, motion compensation will be addressed by proposing a method which takes into account the changes of density and activity in the lungs during the respiration, via the estimation of the volume changes using the deformation fields. The method is evaluated on late time frame PET acquisitions using ¹⁸F-FDG where the radiotracer distribution has stabilised. It is then used as the basis for a method for motion compensation of the early time frames (starting with the administration of the radiotracer), leading to a technique that could be used for motion compensation of kinetic measures. Preliminary results are provided for kinetic parameters extracted from short dynamic data using ¹⁸F-FDG

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

An assessment of cortical function in expert and novice surgeons

Imperial Users onl