Reordering for Improved Constrained Reconstruction from Undersampled k-Space Data

Recently, there has been a significant interest in applying reconstruction techniques, like constrained reconstruction or compressed sampling methods, to undersampled k-space data in MRI. Here, we propose a novel reordering technique to improve these types of reconstruction methods. In this technique, the intensities of the signal estimate are reordered according to a preprocessing step when applying the constraints on the estimated solution within the iterative reconstruction. The ordering of the intensities is such that it makes the original artifact-free signal monotonic and thus minimizes the finite differences norm if the correct image is estimated; this ordering can be estimated based on the undersampled measured data. Theory and example applications of the method for accelerating myocardial perfusion imaging with respiratory motion and brain diffusion tensor imaging are presented

Development of whole-heart myocardial perfusion magnetic resonance imaging

Myocardial perfusion imaging is of huge importance for the detection of coronary artery disease (CAD), one of the leading causes of morbidity and mortality worldwide, as it can provide non-invasive detection at the early stages of the disease. Magnetic resonance imaging (MRI) can assess myocardial perfusion by capturing the rst-pass perfusion (FPP) of a gadolinium-based contrast agent (GBCA), which is now a well-established technique and compares well with other modalities. However, current MRI methods are restricted by their limited coverage of the left ventricle. Interest has therefore grown in 3D volumetric \whole-heart" FPP by MRI, although many challenges currently limit this. For this thesis, myocardial perfusion assessment in general, and 3D whole-heart FPP in particular, were reviewed in depth, alongside MRI techniques important for achieving 3D FPP. From this, a 3D `stack-of-stars' (SOS) FPP sequence was developed with the aim of addressing some current limitations. These included the breath-hold requirement during GBCA rst-pass, long 3D shot durations corrupted by cardiac motion, and a propensity for artefacts in FPP. Parallel imaging and compressed sensing were investigated for accelerating whole-heart FPP, with modi cations presented to potentially improve robustness to free-breathing. Novel sequences were developed that were capable of individually improving some current sequence limits, including spatial resolution and signal-to-noise ratio, although with some sacri ces. A nal 3D SOS FPP technique was developed and tested at stress during free-breathing examinations of CAD patients and healthy volunteers. This enabled the rst known detection of an inducible perfusion defect with a free-breathing, compressed sensing, 3D FPP sequence; however, further investigation into the diagnostic performance is required. Simulations were performed to analyse potential artefacts in 3D FPP, as well as to examine ways towards further optimisation of 3D SOS FPP. The nal chapter discusses some limitations of the work and proposes opportunities for further investigation.Open Acces

Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI

Purpose: To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated Magnetic Resonance Imaging (MRI) data. Methods: Scan-Specific Artifact Reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to GRAPPA and demonstrates improved robustness over other scan-specific models, such as RAKI and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded images. Results: SPARK yields 1.5x - 2x RMSE reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves performance by ~2x and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-cartesian 2D and 3D wave-encoding imaging by reducing RMSE between 20-25% and providing qualitative improvements. Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space

MRI reconstruction using Markov random field and total variation as composite prior

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field