Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease

PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN. We also acquired actual real-time undersampled radial data in patients with congenital heart disease (CHD), and compare CNN reconstruction to Compressed Sensing (CS). METHODS: A 3D (2D plus time) CNN architecture was developed, and trained using 2276 gold-standard paired 3D data sets, with 14x radial undersampling. Four sampling schemes were tested, using 169 previously unseen 3D 'synthetic' test data sets. Actual real-time tiny Golden Angle (tGA) radial SSFP data was acquired in 10 new patients (122 3D data sets), and reconstructed using the 3D CNN as well as a CS algorithm; GRASP. RESULTS: Sampling pattern was shown to be important for image quality, and accurate visualisation of cardiac structures. For actual real-time data, overall reconstruction time with CNN (including creation of aliased images) was shown to be more than 5x faster than GRASP. Additionally, CNN image quality and accuracy of biventricular volumes was observed to be superior to GRASP for the same raw data. CONCLUSION: This paper has demonstrated the potential for the use of a 3D CNN for deep de-aliasing of real-time radial data, within the clinical setting. Clinical measures of ventricular volumes using real-time data with CNN reconstruction are not statistically significantly different from the gold-standard, cardiac gated, BH techniques

Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease

PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN. We also acquired actual real-time undersampled radial data in patients with congenital heart disease (CHD), and compare CNN reconstruction to Compressed Sensing (CS). METHODS: A 3D (2D plus time) CNN architecture was developed, and trained using 2276 gold-standard paired 3D data sets, with 14x radial undersampling. Four sampling schemes were tested, using 169 previously unseen 3D 'synthetic' test data sets. Actual real-time tiny Golden Angle (tGA) radial SSFP data was acquired in 10 new patients (122 3D data sets), and reconstructed using the 3D CNN as well as a CS algorithm; GRASP. RESULTS: Sampling pattern was shown to be important for image quality, and accurate visualisation of cardiac structures. For actual real-time data, overall reconstruction time with CNN (including creation of aliased images) was shown to be more than 5x faster than GRASP. Additionally, CNN image quality and accuracy of biventricular volumes was observed to be superior to GRASP for the same raw data. CONCLUSION: This paper has demonstrated the potential for the use of a 3D CNN for deep de-aliasing of real-time radial data, within the clinical setting. Clinical measures of ventricular volumes using real-time data with CNN reconstruction are not statistically significantly different from the gold-standard, cardiac gated, BH techniques

Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging

International audienceThe Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using compressed sensing. It has then been extended to address 3D imaging using either stacks of 2D sampling patterns or a local 3D strategy that optimizes a single sampling trajectory at a time. 2D SPARKLING actually performs variable density sampling (VDS) along a prescribed target density while maximizing sampling efficiency and meeting the gradient-based hardware constraints. However, 3D SPARKLING has remained limited in terms of acceleration factors along the third dimension if one wants to preserve a peaky point spread function (PSF) and thus good image quality.In this paper, in order to achieve higher acceleration factors in 3D imaging while preserving image quality, we propose a new efficient algorithm that performs optimization on full 3D SPARKLING. The proposed implementation based on fast multipole methods (FMM) allows us to design sampling patterns with up to 10^7 k-space samples, thus opening the door to 3D VDS. We compare multi-CPU and GPU implementations and demonstrate that the latter is optimal for 3D imaging in the high-resolution acquisition regime (600µm isotropic). Finally, we show that this novel optimization for full 3D SPARKLING outperforms stacking strategies or 3D twisted projection imaging through retrospective and prospective studies on NIST phantom and in vivo brain scans at 3 Tesla. Overall the proposed method allows for 2.5-3.75x shorter scan times compared to GRAPPA-4 parallel imaging acquisition at 3 Tesla without compromising image quality

A Quantitative MRI Protocol for Assessing Matrix and Mineral Densities and Degree of Mineralization of Human Cortical Bone

Two categories of bone disease, osteoporosis and osteomalacia, affect bone in different ways: bone mineral and matrix are lost in roughly equal proportions in osteoporosis, while only mineral is depleted in osteomalacia. The difference between these disorders is in bone mineralization: the mass of mineral per volume of bone matrix, excluding pore spaces. Standard clinical examinations measure x-ray attenuation to infer mineral density. However, bone mineral density alone cannot fully describe bone health. Advances in solid-state 31P and 1H magnetic resonance imaging (MRI) have enabled quantification of the densities of extremely short-lived bone mineral 31P and matrix-bound water 1H signals as surrogates for bone mineral and matrix densities. The ratio of these two measurements provides the degree of mineralization of bone (DMB). In this dissertation, the relaxation properties of bone mineral 31P and water 1H were analyzed, the surrogacy of bound water concentration for bone matrix density was established, and measurements of bone mineral 31P and matrix-associated water 1H densities in human bone specimens were designed and implemented on clinical scanners. Although bone mineral 31P longitudinal relaxation time (T1) increased and effective transverse relaxation time (T2*) decreased with increasing field strength, the predicted signal-to-noise ratio (SNR) increased slightly. Also, the short-T2* fraction of bone water calculated by 1H bi-component fitting was correlated with porosity and matrix density at 1.5 T, but these associations weakened as field strength increased. In contrast, short-transverse relaxation time (T2) fraction was highly correlated with gold-standard measurements, suggesting the superiority of T2-based methods for separation of bound and pore water fractions. Additionally, single adiabatic inversion-recovery zero echo time (SIR-ZTE) 1H density was correlated negatively with porosity and positively with matrix and mineral densities, suggesting that this MRI method provides a surrogate measure of bone matrix density. Finally, both bone mineral 31P and matrix-associated 1H densities in human cortical bone specimens were correlated negatively with porosity and age, and positively with peripheral quantitative computed tomography (pQCT) density. As expected, DMB was uncorrelated with porosity, age, or pQCT density. This work established the feasibility of image-based quantification of bone mineral and bound water densities using clinical hardware

Magnetic Resonance Imaging of Short-T2 Tissues with Applications for Quantifying Cortical Bone Water and Myelin

The human body contains a variety of tissue species with short T2 ranging from a few microseconds to hundreds of microseconds. Detection and quantification of these short-T2 species is of considerable clinical and scientific interest. Cortical bone water and myelin are two of the most important tissue constituents. Quantification of cortical bone water concentration allows for indirect estimation of bone pore volume and noninvasive assessment of bone quality. Myelin is essential for the proper functioning of the central nervous system (CNS). Direct assessment of myelin would reveal CNS abnormalities and enhance our understanding of neurological diseases. However, conventional MRI with echo times of several milliseconds or longer is unable to detect these short-lived MR signals. Recent advances in MRI technology and hardware have enabled development of a number of short-T2 imaging techniques, key among which are ultra-short echo time (UTE) imaging, zero echo time (ZTE) imaging, and sweep imaging with Fourier transform (SWIFT). While these pulse sequences are able to detect short-T2 species, they still suffer from signal interference between different T2 tissue constituents, image artifacts and excessive scan time. These are primary technical hurdles for application to whole-body clinical scanners. In this thesis research, new MRI techniques for improving short-T2 tissue imaging have been developed to address these challenges with a focus on direct detection and quantification of cortical bone water and myelin on a clinical MRI scanner. The first focus of this research was to optimize long-T2 suppression in UTE imaging. Saturation and adiabatic RF pulses were designed to achieve maximum long-T2 suppression while maximizing the signal from short-T2 species. The imaging protocols were optimized by Bloch equation simulations and were validated using phantom and in vivo experiments. The results show excellent short-T2 contrast with these optimized pulse sequences. The problem of blurring artifacts resulting from the inhomogeneous excitation profile of the rectangular pulses in ZTE imaging was addressed. The proposed approach involves quadratic phase-modulated RF excitation and iterative solution of an inverse problem formulated from the signal model of ZTE imaging and is shown to effectively remove the image artifacts. Subsequently image acquisition efficiency was improved in order to attain clinically-feasible scan times. To accelerate the acquisition speed in UTE and ZTE imaging, compressed sensing was applied with a hybrid 3D UTE sequence. Further, the pulse sequence and reconstruction procedure were modified to enable anisotropic field-of-view shape conforming to the geometry of the elongated imaged object. These enhanced acquisition techniques were applied to the detection and quantification of cortical bone water. A new biomarker, the suppression ratio (a ratio image derived from two UTE images, one without and the other with long-T2 suppression), was conceived as a surrogate measure of cortical bone porosity. Experimental data suggest the suppression ratio may be a more direct measure of porosity than previously measured total bone water concentration. Lastly, the feasibility of directly detecting and quantifying spatially-resolved myelin concentration with a clinical imager was explored, both theoretically and experimentally. Bloch equation simulations were conducted to investigate the intrinsic image resolution and the fraction of detectable myelin signal under current scanner hardware constraints. The feasibility of quantitative ZTE imaging of myelin extract and lamb spinal cord at 3T was demonstrated. The technological advances achieved in this dissertation research may facilitate translation of short-T2 MRI methods from the laboratory to the clinic

New Techniques and Optimizations of Short Echo-time 1H MRI with Applications in Murine Lung

Although x-ray computed tomography (CT) is a gold standard for pulmonary imaging, it has high ionizing radiation, which puts patients at greater risk of cancer, particularly in a longitudinal study with cumulative doses. Magnetic resonance imaging (MRI) doesn\u27t involve exposure to ionizing radiation and is especially useful for visualizing soft tissues and organs such as ligaments, cartilage, brain, and heart. Many efforts have been made to apply MRI to study lung function and structure of both humans and animals. However, lung is a unique organ and is very different from other solid organs like the heart and brain due to its complex air-tissue interleaved structure. The magnetic susceptibility differences at the air-tissue interfaces result in very short T2* (~ 1 ms) of lung parenchyma, which is even shorter in small-animal MRI (often at higher field) than in human MRI. Both low proton density and short T2* of lung parenchyma are challenges for pulmonary imaging via MRI because they lead to low signal-to-noise ratio (SNR) in images with traditional Cartesian methods that necessitate longer echo times (≥ 1 ms). This dissertation reports the work of optimizing pulmonary MRI techniques by minimizing the negative effects of low proton density and short T2* of murine lung parenchyma, and the application of these techniques to imaging murine lung. Specifically, echo time (TE) in the Cartesian sequence is minimized, by simultaneous slice select rephasing, phase encoding and read dephasing gradients, in addition to partial Fourier imaging, to reduce signal loss due to T2* relaxation. Radial imaging techniques, often called ultra-short echo-time MRI or UTE MRI, with much shorter time between excitation and data acquisition, were also developed and optimized for pulmonary imaging. Offline reconstruction for UTE data was developed on a Linux system to regrid the non-Cartesian (radial in this dissertation) k-space data for fast Fourier transform. Slabselected UTE was created to fit the field-of-view (FOV) to the imaged lung without fold-in aliasing, which increases TE slightly compared to non-slab-selected UTE. To further reduce TE as well as fit the FOV to the lung without aliasing, UTE with ellipsoidal k-space coverage was developed, which increases resolution and decreases acquisition time. Taking into account T2* effects, point spread function (PSF) analysis was performed to determine the optimal acquisition time for maximal single-voxel SNR. Retrospective self-gating UTE was developed to avoid the use of a ventilator (which may cause lung injury) and to avoid possible prospective gating errors caused by abrupt body motion. Cartesian gradient-recalled-echo imaging (GRE) was first applied to monitor acute cellular rejection in lung transplantation. By repeated imaging in the same animals, both parenchymal signal and lung compliance were measured and were able to detect rejection in the allograft lung. GRE was also used to monitor chronic cellular rejection in a transgenic mouse model after lung transplantation. In addition to parenchymal signal and lung compliance, the percentage of highdensity lung parenchyma was defined and measured to detect chronic rejection. This represents one of the first times quantitative pulmonary MRI has been performed. For 3D radial UTE MRI, 2D golden means (1) were used to determine the direction of radial spokes in k-space, resulting in pseudo-random angular sampling of spherical k-space coverage. Ellipsoidal k-space coverage was generated by expanding spherical coverage to create an ellipsoid in k-space. UTE MRI with ellipsoidal k-space coverage was performed to image healthy mice and phantoms, showing reduced FOV and enhanced in-plane resolution compared to regular UTE. With this modified UTE, T2* of lung parenchyma was measured by an interleaved multi-TE strategy, and T1 of lung parenchyma was measured by a limited flip angle method (2). Retrospective self-gating UTE with ellipsoidal k-space coverage was utilized to monitor the progression of pulmonary fibrosis in a transforming growth factor (TGF)-α transgenic mouse model and compared with histology and pulmonary mechanics. Lung fibrosis progression was not only visualized by MRI images, but also quantified and tracked by the MRIderived lung function parameters like mean lung parenchyma signal, high-density lung volume percentage, and tidal volume. MRI-derived lung function parameters were strongly correlated with the findings of pulmonary mechanics and histology in measuring fibrotic burden. This dissertation demonstrates new techniques and optimizations in GRE and UTE MRI that are employed to minimize TE and image murine lungs to assess lung function and structure and monitor the time course of lung diseases. Importantly, the ability to longitudinally image individual animals by these MRI techniques minimizes the number of animals required in preclinical studies and increases the statistical power of future experiments as each animal can serve at its own control