Lipid suppression in CSI with spatial priors and highly undersampled peripheral k-space

Mapping [superscript 1]H brain metabolites using chemical shift imaging is hampered by the presence of subcutaneous lipid signals, which contaminate the metabolites by ringing due to limited spatial resolution. Even though chemical shift imaging at spatial resolution high enough to mitigate the lipid artifacts is infeasible due to signal-to-noise constraints on the metabolites, the lipid signals have orders of magnitude of higher concentration, which enables the collection of high-resolution lipid maps with adequate signal-to-noise. The previously proposed dual-density approach exploits this high signal-to-noise property of the lipid layer to suppress truncation artifacts using high-resolution lipid maps. Another recent approach for lipid suppression makes use of the fact that metabolite and lipid spectra are approximately orthogonal, and seeks sparse metabolite spectra when projected onto lipid-basis functions. This work combines and extends the dual-density approach and the lipid-basis penalty, while estimating the high-resolution lipid image from 2-average k-space data to incur minimal increase on the scan time. Further, we exploit the spectral-spatial sparsity of the lipid ring and propose to estimate it from substantially undersampled (acceleration R = 10 in the peripheral k-space) 2-average in vivo data using compressed sensing and still obtain improved lipid suppression relative to using dual-density or lipid-basis penalty alone.National Institutes of Health (U.S.) (Grant NIH R01 EB007942)National Science Foundation (U.S.) (Grant 0643836)Siemens-MIT AllianceMIT-Center for Integration of Medicine and Innovative Technology (Medical Engineering Fellowship

Simultaneously Sparse Solutions to Linear Inverse Problems with Multiple System Matrices and a Single Observation Vector

A linear inverse problem is proposed that requires the determination of multiple unknown signal vectors. Each unknown vector passes through a different system matrix and the results are added to yield a single observation vector. Given the matrices and lone observation, the objective is to find a simultaneously sparse set of unknown vectors that solves the system. We will refer to this as the multiple-system single-output (MSSO) simultaneous sparsity problem. This manuscript contrasts the MSSO problem with other simultaneous sparsity problems and conducts a thorough initial exploration of algorithms with which to solve it. Seven algorithms are formulated that approximately solve this NP-Hard problem. Three greedy techniques are developed (matching pursuit, orthogonal matching pursuit, and least squares matching pursuit) along with four methods based on a convex relaxation (iteratively reweighted least squares, two forms of iterative shrinkage, and formulation as a second-order cone program). The algorithms are evaluated across three experiments: the first and second involve sparsity profile recovery in noiseless and noisy scenarios, respectively, while the third deals with magnetic resonance imaging radio-frequency excitation pulse design.Comment: 36 pages; manuscript unchanged from July 21, 2008, except for updated references; content appears in September 2008 PhD thesi

MRI estimates of brain iron concentration in normal aging: Comparison of field-dependent (FDRI) and phase (SWI) methods

Different brain structures accumulate iron at different rates throughout the adult life span. Typically, striatal and brain stem structures are higher in iron concentrations in older than younger adults, whereas cortical white matter and thalamus have lower concentrations in the elderly than young adults. Brain iron can be measured in vivo with MRI by estimating the relaxivity increase across magnetic field strengths, which yields the Field-Dependent Relaxation Rate Increase (FDRI) metric. The influence of local iron deposition on susceptibility, manifests as MR phase effects, forms the basis for another approach for iron measurement, Susceptibility-Weighted Imaging (SWI), for which imaging at only one field strength is sufficient. Here, we compared the ability of these two methods to detect and quantify brain iron in 11 young (5 men, 6 women; 21 to 29 years) and 12 elderly (6 men, 6 women; 64 to 86 years) healthy adults. FDRI was acquired at 1.5 T and 3.0 T, and SWI was acquired at 1.5 T. The results showed that both methods detected high globus pallidus iron concentration regardless of age and significantly greater iron in putamen with advancing age. The SWI measures were more sensitive when the phase signal intensities themselves were used to define regions of interest, whereas FDRI measures were robust to the method of region of interest selection. Further, FDRI measures were more highly correlated than SWI iron estimates with published postmortem values and were more sensitive than SWI to iron concentration differences across basal ganglia structures. Whereas FDRI requires more imaging time than SWI, two field strengths, and across-study image registration for iron concentration calculation, FDRI appears more specific to age-dependent accumulation of non-heme brain iron than SWI, which is affected by heme iron and non-iron source effects on phase.National Institutes of Health (U.S.) (Grant AG017919)National Institutes of Health (U.S.) (Grant AA005965)National Institutes of Health (U.S.) (Grant AA017168

Simulating magnetic nanoparticle behavior in low-field MRI under transverse rotating fields and imposed fluid flow

In the presence of alternating-sinusoidal or rotating magnetic fields, magnetic nanoparticles will act to realign their magnetic moment with the applied magnetic field. The realignment is characterized by the nanoparticle's time constant, ττ. As the magnetic field frequency is increased, the nanoparticle's magnetic moment lags the applied magnetic field at a constant angle for a given frequency, Ω, in rad s[superscript −1]. Associated with this misalignment is a power dissipation that increases the bulk magnetic fluid's temperature which has been utilized as a method of magnetic nanoparticle hyperthermia, particularly suited for cancer in low-perfusion tissue (e.g., breast) where temperature increases of between 4 and 7 °C above the ambient in vivo temperature cause tumor hyperthermia. This work examines the rise in the magnetic fluid's temperature in the MRI environment which is characterized by a large DC field, B[subscript 0]. Theoretical analysis and simulation is used to predict the effect of both alternating-sinusoidal and rotating magnetic fields transverse to B[subscript 0]. Results are presented for the expected temperature increase in small tumors (~1 cm radius) over an appropriate range of magnetic fluid concentrations (0.002–0.01 solid volume fraction) and nanoparticle radii (1–10 nm). The results indicate that significant heating can take place, even in low-field MRI systems where magnetic fluid saturation is not significant, with careful the goal of this work is to examine, by means of analysis and simulation, the concept of interactive fluid magnetization using the dynamic behavior of superparamagnetic iron oxide nanoparticle suspensions in the MRI environment. In addition to the usual magnetic fields associated with MRI, a rotating magnetic field is applied transverse to the main B[subscript 0] field of the MRI. Additional or modified magnetic fields have been previously proposed for hyperthermia and targeted drug delivery within MRI. Analytical predictions and numerical simulations of the transverse rotating magnetic field in the presence of B[subscript 0] are investigated to demonstrate the effect of Ω, the rotating field frequency, and the magnetic field amplitude on the fluid suspension magnetization. The transverse magnetization due to the rotating transverse field shows strong dependence on the characteristic time constant of the fluid suspension, τ. The analysis shows that as the rotating field frequency increases so that Ωτ approaches unity, the transverse fluid magnetization vector is significantly non-aligned with the applied rotating field and the magnetization's magnitude is a strong function of the field frequency. In this frequency range, the fluid's transverse magnetization is controlled by the applied field which is determined by the operator. The phenomenon, which is due to the physical rotation of the magnetic nanoparticles in the suspension, is demonstrated analytically when the nanoparticles are present in high concentrations (1–3% solid volume fractions) more typical of hyperthermia rather than in clinical imaging applications, and in low MRI field strengths (such as open MRI systems), where the magnetic nanoparticles are not magnetically saturated. The effect of imposed Poiseuille flow in a planar channel geometry and changing nanoparticle concentration is examined. The work represents the first known attempt to analyze the dynamic behavior of magnetic nanoparticles in the MRI environment including the effects of the magnetic nanoparticle spin-velocity. It is shown that the magnitude of the transverse magnetization is a strong function of the rotating transverse field frequency. Interactive fluid magnetization effects are predicted due to non-uniform fluid magnetization in planar Poiseuille flow with high nanoparticle concentrations.R. J. Shillman Career Development AwardThomas and Gerd Perkins Professorship AwardMIT Dean's FellowshipNational Institutes of Health (U.S.) (Award R01 EB007942

Local SAR in parallel transmission pulse design

The management of local and global power deposition in human subjects (specific absorption rate, SAR) is a fundamental constraint to the application of parallel transmission (pTx) systems. Even though the pTx and single channel have to meet the same SAR requirements, the complex behavior of the spatial distribution of local SAR for transmission arrays poses problems that are not encountered in conventional single-channel systems and places additional requirements on pTx radio frequency pulse design. We propose a pTx pulse design method which builds on recent work to capture the spatial distribution of local SAR in numerical tissue models in a compressed parameterization in order to incorporate local SAR constraints within computation times that accommodate pTx pulse design during an in vivo magnetic resonance imaging scan. Additionally, the algorithm yields a protocol-specific ultimate peak in local SAR, which is shown to bound the achievable peak local SAR for a given excitation profile fidelity. The performance of the approach was demonstrated using a numerical human head model and a 7 Tesla eight-channel transmit array. The method reduced peak local 10 g SAR by 14–66% for slice-selective pTx excitations and 2D selective pTx excitations compared to a pTx pulse design constrained only by global SAR. The primary tradeoff incurred for reducing peak local SAR was an increase in global SAR, up to 34% for the evaluated examples, which is favorable in cases where local SAR constraints dominate the pulse applications.Siemens CorporationNational Institutes of Health (U.S.) (Grant NIH R01EB006847)National Institutes of Health (U.S.) (Grant NIH R01EB007942)National Center for Research Resources (U.S.) (Grant P41RR14075)Siemens-MIT Allianc

MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping

Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ[subscript 1] and ℓ[subscript 2] norm regularized QSM algorithms. These regularized approaches solve for the underlying magnetic susceptibility distribution, a sensitive measure of the tissue iron concentration, that gives rise to the observed signal phase. Regularized QSM methodology also involves a pre-processing step that removes, by dipole fitting, unwanted background phase effects due to bulk susceptibility variations between air and tissue and requires data acquisition only at a single field strength. For validation, performances of the two QSM methods were measured against published estimates of regional brain iron from postmortem and in vivo data. The in vivo comparison was based on data previously acquired using Field-Dependent Relaxation Rate Increase (FDRI), an estimate of MRI relaxivity enhancement due to increased main magnetic field strength, requiring data acquired at two different field strengths. The QSM analysis was based on susceptibility-weighted images acquired at 1.5 T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5 T and 3.0 T. Both datasets were collected in the same healthy young and elderly adults. The in vivo estimates of regional iron concentration comported well with published postmortem measurements; both QSM approaches yielded the same rank ordering of iron concentration by brain structure, with the lowest in white matter and the highest in globus pallidus. Further validation was provided by comparison of the in vivo measurements, ℓ[subscript 1]-regularized QSM versus FDRI and ℓ[subscript 2]-regularized QSM versus FDRI, which again yielded perfect rank ordering of iron by brain structure. The final means of validation was to assess how well each in vivo method detected known age-related differences in regional iron concentrations measured in the same young and elderly healthy adults. Both QSM methods and FDRI were consistent in identifying higher iron concentrations in striatal and brain stem ROIs (i.e., caudate nucleus, putamen, globus pallidus, red nucleus, and substantia nigra) in the older than in the young group. The two QSM methods appeared more sensitive in detecting age differences in brain stem structures as they revealed differences of much higher statistical significance between the young and elderly groups than did FDRI. However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.National Institutes of Health (U.S.) (Grant NIH R01 EB007942)National Institutes of Health (U.S.) (Grant AG019717)National Institutes of Health (U.S.) (Grant AA005965)National Institutes of Health (U.S.) (Grant AA017168)National Institutes of Health (U.S.) (Grant EB008381)National Science Foundation (U.S.) (Grant 0643836)Siemens CorporationSiemens-MIT AllianceMIT-Center for Integration of Medicine and Innovative Technology (Medical Engineering Fellowship

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

Joint Frequency and Image Space Learning for Fourier Imaging

We demonstrate that neural network layers that explicitly combine frequency and image feature representations are a versatile building block for analysis of imaging data acquired in the frequency space. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The joint learning schemes proposed and analyzed in this paper enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures. This is in contrast to most current deep learning approaches for image reconstruction that apply learned data manipulations solely in the frequency space or solely in the image space. We demonstrate the advantages of joint convolutional learning on three diverse tasks: image reconstruction from undersampled acquisitions, motion correction, and image denoising in brain and knee MRI. We further demonstrate advantages of the joint learning approaches across training schemes using a wide variety of loss functions. Unlike purely image based and purely frequency based architectures, the joint models produce consistently high quality output images across all tasks and datasets. Joint image and frequency space feature representations promise to significantly improve modeling and reconstruction of images acquired in the frequency space. Our code is available at https://github.com/nalinimsingh/interlacer.Comment: 16 pages, 13 figures, image reconstruction, motion correction, denoising, magnetic resonance imaging, deep learnin

Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging

Purpose: To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. Methods: Data from our multi-contrast acquisition was embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R=6 (2D) and R=4x4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than three minutes. Results: Across all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary k-space sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplarily slices and quantitative error metrics. Conclusion: By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R=16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams

Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or “QSIP.” The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B[subscript 0] inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase, and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in vivo FDRI: statistically significant Spearman correlations ranging from Rho=0.905 to Rho=1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions.National Institutes of Health (U.S.) (Grant P41EB015902)National Institutes of Health (U.S.) (Grant P41RR013218)National Institutes of Health (U.S.) (Grant P41EB015898)National Institutes of Health (U.S.) (Grant P41RR019703)National Institutes of Health (U.S.) (Grant T32EB0011680-06)National Institutes of Health (U.S.) (Grant K05AA017168)National Institutes of Health (U.S.) (Grant R01AA012388