Multiple sclerosis is characterized by inflammatory demyelination and irreversible axonal injury leading to permanent neurological disabilities. Diffusion tensor imaging demonstrates an improved capability over standard magnetic resonance differentiate axon from myelin pathologies. However, the increased cellularity vasogenic oedema associated with inflammation cannot be detected or separated axon/myelin diffusion imaging, limiting its clinical applications. A novel basis spectrum capable of...

Water/fat separation is a classical problem for in vivo proton MRI. Although many methods have been proposed to address this problem, robust water/fat remains challenge, especially the presence of large amplitude static field inhomogeneities. This challenging because nonuniqueness solution an isolated voxel. paper tackles using statistically motivated formulation that jointly estimates complete map and entire images. results difficult optimization solved effectively novel graph cut...

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in modeling of MRI images. Existing approaches focused higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space can also be mapped to matrices when the has limited spatial support slowly varying phase. Based this, we develop a novel and flexible framework for...

Compressed sensing (CS) has the potential to reduce magnetic resonance (MR) data acquisition time. In order for CS-based imaging schemes be effective, signal of interest should sparse or compressible in a known representation, and measurement scheme have good mathematical properties with respect this representation. While MR images are often compressible, second requirement is only weakly satisfied commonly used Fourier encoding schemes. This paper investigates use random CS-MRI, an effort...

Partial separability (PS) and sparsity have been previously used to enable reconstruction of dynamic images from undersampled ( <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</b> , <i xmlns:xlink="http://www.w3.org/1999/xlink">t</i> )-space data. This paper presents a new method use PS constraints jointly for enhanced performance in this context. The proposed combines the complementary advantages using unified formulation, achieving...

Abstract Water/fat separation in the presence of B 0 field inhomogeneity is a problem considerable practical importance MRI. This article describes two complementary methods for estimating water/fat images and map from Dixon‐type acquisitions. One based on variable projection (VARPRO) other linear prediction (LP). The VARPRO method very robust can be used low signal‐to‐noise ratio conditions because its ability to achieve maximum‐likelihood solution. LP computationally more efficient, shown...

There has been significant recent interest in fast imaging with sparse sampling. Conventional methods are based on Shannon-Nyquist sampling theory. As such, the number of required samples often increases exponentially dimensionality image, which limits achievable resolution high-dimensional scenarios. The partially-separable function (PSF) model previously proposed to enable data this context. Existing leverage PSF structure utilize tailored strategies, a specialized two-step reconstruction...

Purpose To propose and evaluate P‐LORAKS a new calibrationless parallel imaging reconstruction framework. Theory Methods LORAKS is flexible powerful framework that was recently proposed for constrained MRI reconstruction. based on the observation certain matrices constructed from fully sampled k‐space data should have low rank whenever image has limited support or smooth phase, made it possible to accurately reconstruct images undersampled noisy using low‐rank regularization. This paper...

Algorithms to construct/recover low-rank matrices satisfying a set of linear equality constraints have important applications in many signal processing contexts. Recently, theoretical guarantees for minimum-rank matrix recovery been proven nuclear norm minimization (NNM), which can be solved using standard convex optimization approaches. While is effective, it computationally demanding. In this work, we explore the use PowerFactorization (PF) algorithm as tool rank-constrained recovery....

Several studies comparing adult musicians and nonmusicians have shown that music training is associated with structural brain differences. It not been established, however, whether such differences result from pre-existing biological traits, lengthy musical training, or an interaction of the two factors, if comparable changes can be found in children undergoing training. As part ongoing longitudinal study, we investigated effects on developmental trajectory children's structure, over years,...

Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for fingerprinting. Specifically, describe discrete-time dynamic system model spin dynamics, and derive bound, i.e., the Cramér-Rao characterize signal-to-noise ratio (SNR) efficiency of We then formulate optimal problem, determines sequence...

Real-time cardiac MRI is a very challenging problem because of limitations on imaging speed and resolution. To address this problem, the (k,t) - space MR signal modeled as being partially separable along spatial temporal dimensions, which results in rank-deficient data matrix. Image reconstruction then formulated low-rank matrix recovery solved using emerging techniques. In paper, Power Factorization algorithm applied to efficiently recover Promising are presented demonstrate performance...

Dynamic magnetomotion of magnetic nanoparticles (MNPs) detected with magnetomotive optical coherence tomography (MM-OCT) represents a new methodology for contrast enhancement and therapeutic interventions in molecular imaging. In this study, we demonstrate vivo imaging dynamic functionalized iron oxide MNPs using MM-OCT preclinical mammary tumor model. Using targeted MNPs, images exhibit strong signals tumor, no significant were measured from tumors rats injected nontargeted or saline. The...

Purpose To propose and evaluate a novel multidimensional approach for imaging subvoxel tissue compartments called Diffusion‐Relaxation Correlation Spectroscopic Imaging. Theory Methods Multiexponential modeling of MR diffusion or relaxation data is commonly used to infer the many different microscopic that contribute signal macroscopic voxels. However, multiexponential estimation known be difficult ill‐posed. Observing this ill‐posedness theoretically reduced in higher dimensions,...

Abstract Purpose The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared‐error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, data will contain noise not a true gold standard. this work, we demonstrate that “hidden noise” present in can substantially confound standard approaches for ranking different results. Methods Using...

Abstract Noise is a major concern in many important imaging applications. To improve data signal‐to‐noise ratio (SNR), experiments often focus on collecting low‐frequency k ‐space data. This article proposes new scheme to enable extended sampling these contexts. It shown that the degradation SNR associated with can be effectively mitigated by using statistical modeling concert anatomical prior information. The method represents significant departure from most existing anatomically...

Computational acceleration on graphics processing units

Abstract Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal‐to‐noise ratio represent significant hurdles many in vivo applications. This article presents new approach to reduce noise while largely maintaining resolution weighted images, using statistical reconstruction method that takes advantage high level structural correlation observed typical datasets. Compared existing...

Purpose To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k‐space sampling for highly accelerated data acquisition. Methods We introduce joint virtual (JVC)‐generalized autocalibrating partially acquisitions (GRAPPA) to jointly reconstruct acquired with different contrast preparations, show its application in 2D, 3D, simultaneous multi‐slice (SMS) acquisitions....

Purpose To improve signal‐to‐noise ratio for diffusion‐weighted magnetic resonance images. Methods A new method is proposed denoising magnitude The formulates the problem as an maximum a posteriori} estimation based on Rician/noncentral χ likelihood models, incorporating edge prior and low‐rank model. resulting optimization solved efficiently using half‐quadratic with alternating minimization scheme. Results performance of has been validated simulated experimental data. Diffusion‐weighted...

Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those result from long-term training or pre-existing traits favoring musicality. In an attempt to begin addressing this question, we launched a longitudinal investigation of effects childhood music on cognitive, social neural development. We compared group 6- 7-year old children...