arXiv subjects

Corey A. Baron

Publications and source records attributed to Corey A. Baron.

15 recordsLinked to original sources

arXiv2025

BM4D-PC: nonlocal volumetric denoising of principal components of diffusion-weighted MR images

Purpose: Noise in diffusion-weighted MRI (dMRI) is often spatially correlated due to different acquisition and reconstruction strategies, which is not fully accounted for in current denoising strategies. Thus, we propose a novel model-based denoising method for dMRI that effectively accounts for the different noise characteristics of data. Methods: We propose a denoising strategy that incorporates full noise statistics, including the noise power spectral density (PSD), by leveraging the BM4D algorithm. Furthermore, to exploit redundancy across the diffusion MRI dataset, BM4D is applied to principal components (PC) of diffusion-weighted images (DWI) obtained through principal component analysis (PCA) decomposition of the entire DWI dataset, an approach we refer to as BM4D-PC. Importantly, our method also allows for direct estimation of both the noise map and PSD. We evaluated BM4D-PC against four existing state-of-the-art methods using in-silico and in vivo datasets, including high-resolution human and marmoset acquisitions. Results: Overall, BM4D-PC presented the best results for the metrics PSNR, SSIM and RMSE on the in-silico experiments. The in-vivo studies also showed that BM4D-PC dramatically enhanced the image quality of raw DWIs, outperforming existing denoising methods in terms of noise suppression and detail preservation, leading to improved quality of diffusion metrics. Conclusion: The proposed BM4D-PC method demonstrated state-of-the-art denoising results for dMRI using datasets from various acquisition strategies and image resolutions, potentially supporting future advances in neuroscience research.

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arXiv2023

Tensor-valued and frequency-dependent diffusion MRI and magnetization transfer saturation MRI evolution during adult mouse brain maturation

Although rodent models are a predominant study model in neuroscience research, research investigating healthy rodent brain maturation remains limited. This motivates further study of normal brain maturation in rodents to exclude confounds of developmental changes from interpretations of disease mechanisms. 11 C57Bl/6 mice (6 males) were scanned longitudinally at 3, 4, 5, and 8 months of age using frequency-dependent and tensor-valued diffusion MRI (dMRI), and Magnetization Transfer saturation (MTsat) MRI. Total kurtosis showed significant increases over time in all regions, which was driven by increases in isotropic kurtosis while anisotropic kurtosis remained stable. Increases in total and isotropic kurtosis with age were matched with increases in MTsat. Quadratic fits revealed that most metrics show a maximum/minimum around 5-6 months of age. Most dMRI metrics revealed significantly different trajectories between males and females, while the MT metrics did not. Linear fits between kurtosis and MT metrics highlighted that changes in total kurtosis found throughout normal brain development are driven by isotropic kurtosis, while differences in total kurtosis between brain regions are driven by anisotropic kurtosis. Overall, the trends observed in conventional dMRI and MT metrics are comparable to previous studies on normal brain development, while the trajectories of our more advanced dMRI metrics provide novel insight. Based on the developmental trajectories of tensor-valued dMRI and MT metrics, our results suggest myelination during brain maturation is not a main contributor to microscopic diffusion anisotropy and anisotropic kurtosis in axons. For studies that only calculate total kurtosis, we suggest caution in attributing neurobiological changes to changes in total kurtosis as we show here constant anisotropic kurtosis in the presence of increasing myelin content.

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arXiv2023

Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization

With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regularization functions based on physics, observed phenomenology, and heuristics. Moreover, several iterative methods exist that attempt to simultaneously estimate the sensitivity maps and the image. In this manuscript, we present a generalization of several existing iterative model based algorithms. We devise a calibrationless instance of this generalization that only incorporates regularization terms based on physics and the accepted compressed sensing phenomenology of sparsity in the wavelet domain. We compare the results of the new amalgamated optimization problem with existing methods on both simulated and real datasets. We show that the images reconstructed using the new method, entitled Multi-coil Compressed Sensing (MCCS), are of higher quality than existing methods in all cases studied.

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arXiv2023

Correction of high-order phase variation effects in dynamic field monitoring

Purpose: Field monitoring measures field perturbations, which can be accounted for during image reconstructions. In certain field monitoring environments, significant phase deviations can arise far from isocenter due to the finite extent of the gradient and/or main magnet. This can degrade the accuracy of field dynamics when field probes are placed near or outside the diameter spherical volume of the gradient coils and/or main magnet, leading to corrupted image quality. The objective of this work was to develop a correction algorithm that reduces errors from highly nonlinear phase variations at distant field probes in field dynamic fits. Methods: The algorithm is split into three components. Component one fits phase coefficients one spatial order at a time, while the second implements a weighted least squares solution based on probe distance. After initial fitting, component three calculates phase residuals and removes the phase for distant probes before re-fitting. Two healthy volunteers were scanned on a head-only 7T MRI using diffusion-weighted single-shot spiral and EPI sequences and field monitoring was performed. Images were reconstructed with and without phase coefficient correction and compared qualitatively. Results: The algorithm was able to correct corrupted field dynamics, resulting in image quality improvements. Significant artefact reduction was observed when correcting higher order fits, especially for diffusion weighted images. Stepwise fitting provided the most correction benefit, which was marginally improved when adding weighted least squares and phase residual corrections. Conclusion: The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved reliability of field dynamic characterization.

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arXiv2022

High-resolution single-shot spiral diffusion-weighted imaging at 7T using expanded encoding with compressed sensing

Purpose: The expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. So far, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low-SNR cases like diffusion or high-resolution MRI. Here, we investigate the extent that l1-wavelet regularization, or equivalently compressed sensing (CS), combined with expanded encoding improves trade-offs between spatial resolution, readout time and SNR for single-shot spiral diffusion-weighted imaging at 7T. The reconstructions were performed using our open-source GPU-enabled reconstruction toolbox, MatMRI, that allows inclusion of the different components of the expanded encoding model, with or without CS. Methods: In vivo accelerated single-shot spirals were acquired with five acceleration factors (2-6) and three in-plane spatial resolutions (1.5, 1.3, and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensors and computed fractional anisotropy maps. Then, simulations were used to quantitatively investigate and validate the impact of CS-based regularization on image quality when compared to a known ground truth. Results: In vivo reconstructions revealed improved image quality with retainment of small features when CS was used. Simulations showed that the joint use of the expanded encoding model and CS improves accuracy of image reconstructions (reduced mean-squared error) over the range of acceleration factors investigated. Conclusion: The expanded encoding model and CS regularization are complementary tools for single-shot spiral diffusion MRI, which enables both higher spatial resolutions and higher acceleration factors.

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arXiv2022

Tuned bipolar oscillating gradients for mapping frequency dispersion of diffusion kurtosis in the human brain

Purpose: Oscillating gradient spin echo (OGSE) sequences have demonstrated an ability to probe time-dependent microstructural features, though they often suffer from low SNR due to increased echo times. In this work we introduce frequency tuned bipolar (FTB) gradients as a variation of oscillating gradients with reduced echo time and demonstrate their utility by mapping the frequency dispersion of diffusion kurtosis in human subjects. Methods: An FTB oscillating gradient waveform is presented that provides encoding of 1.5 net oscillation periods thereby reducing the echo time of the acquisition. Simulations were performed to determine an optimal protocol based on SNR of kurtosis frequency dispersion - defined as the difference in kurtosis between pulsed and oscillating gradient acquisitions. Healthy human subjects were scanned at 7T using pulsed gradient and an optimized 23 Hz FTB protocol, which featured a maximum b-value of 2500 s/mm2. In addition, to directly compare existing methods, measurements using traditional cosine OGSE were also acquired. Results: FTB oscillating gradients demonstrated equivalent frequency dependent diffusion measurements compared to cosine modulated OGSE while enabling a significant reduction in echo time. Optimization and in vivo results suggest FTB gradients provide increased SNR of kurtosis dispersion maps compared to traditional cosine OGSE. The optimized FTB gradient protocol demonstrated consistent reductions in apparent kurtosis values and increased diffusivity in generated frequency dispersion maps. Conclusion: This work presents an alternative to traditional cosine OGSE sequences enabling more time efficient acquisitions of frequency dependent diffusion quantities as demonstrated through in vivo kurtosis frequency dispersion maps.

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arXiv2022

Model-based determination of the synchronization delay between MRI and trajectory data

Purpose: Real time monitoring of dynamic magnetic fields has recently become a commercially available option for measuring MRI k-space trajectories and magnetic fields induced by eddy currents in real time. However, for accurate image reconstructions, sub-microsecond synchronization between the MRI and trajectory data is required. In this work, we introduce a new model-based algorithm to automatically perform this synchronization using only the MRI and trajectory data. Methods: The algorithm works by enforcing consistency between the MRI data, trajectory data, and receiver sensitivity profiles by iteratively alternating between convex optimizations for (a) the image and (b) the synchronization delay. A healthy human subject was scanned at 7T using a transmit-receive coil with integrated field probes using both single shot spiral and echo-planar imaging (EPI), and reconstructions with various synchronization delays were compared to the result of the proposed algorithm. The accuracy of the algorithm was also investigated using simulations, where the acquisition delays for simulated acquisitions were determined using the proposed algorithm and compared to the known ground truth. Results: In the in vivo scans, the proposed algorithm minimized artefacts related to synchronization delay for both spiral and EPI acquisitions, and the computation time required was less than 30 seconds. The simulations demonstrated accuracy to within tens of nanoseconds. Conclusion: The proposed algorithm can automatically determine synchronization delays between MRI data and trajectories measured using a field probe system.

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arXiv2021

Characterization and Correction of Time-Varying Eddy Currents for Diffusion MRI

Purpose: Diffusion MRI (dMRI) suffers from eddy currents induced by strong diffusion gradients, which introduce artefacts that can impair subsequent diffusion metric analysis. Existing retrospective correction techniques that correct for diffusion gradient induced eddy currents do not account for eddy current decay, which is generally effective for traditional Pulsed Gradient Spin Echo (PGSE) diffusion encoding. However, these techniques do not necessarily apply to advanced forms of dMRI that require substantial gradient slewing, such as Oscillating Gradient Spin Echo (OGSE). Methods: An in-house algorithm (TVEDDY), that for the first time retrospectively models eddy current decay, was tested on PGSE and OGSE brain images acquired at 7T. Correction performance was compared to conventional correction methods by evaluating the mean-squared error (MSE) between diffusion-weighted images acquired with opposite polarity diffusion gradients. As a ground truth comparison, images were corrected using field dynamics up to third order in space measured using a field monitoring system. Results: Time-varying eddy currents were observed for OGSE, which introduced blurring that was not reduced using the traditional approach but was diminished considerably with TVEDDY and model-based reconstruction. No MSE difference was observed between the conventional approach and TVEDDY for PGSE, but for OGSE TVEDDY resulted in significantly lower MSE than the conventional approach. The field-monitoring-informed model-based reconstruction had the lowest MSE for both PGSE and OGSE. Conclusion: This work establishes that it is possible to estimate time-varying eddy currents from the diffusion data itself, which provides substantial image quality improvements for gradient-intensive dMRI acquisitions like OGSE.

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arXiv2021

Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform

The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Existing methods for generating density compensation values are either sub-optimal or only consider a finite set of points (a set of measure 0) in the optimization. This manuscript presents the first density compensation algorithm for a general trajectory that takes into account the point spread function over a set of non-zero measure. We show that the images reconstructed with Gridding using the density compensation values of this method are of superior quality when compared to density compensation weights determined in other ways. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.

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arXiv2021

Utilizing the Wavelet Transform's Structure in Compressed Sensing

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously though possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is a common sparsifying transformation used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns.

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arXiv2021

Fast Variable Density Poisson-Disc Sample Generation with Directional Variation

We present a fast method for generating random samples according to a variable density Poisson-disc distribution. A minimum threshold distance is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density Poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.

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arXiv2020

multiMap: A Gradient Spoiled Sequence for Simultaneously Measuring B1+, B0, T1/M0, T2, T2*, and Fat Fraction of a Slice

We propose multiMap, a single scan that can generate several quantitative maps simultaneously. The sequence acquires multiple images in a time-efficient manner, which can be modeled for T_2, T2*, main- and transmit-field inhomogeneity, T_1:equilibrium magnetization, and water and fat content. The sequence is constructed so that cross-dependencies between parameters are isolated. Thus, each parameter can be estimated independently. Estimates of all parameters are shown on bottle phantoms, the brain, and the knee. The results are compared to estimates from established techniques.

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arXiv2019

Unraveling the Effect of Spatial Resolution and Scan Acceleration on 3D Image-Based Navigators for Respiratory Motion Tracking in Coronary MR Angiography

Purpose: To study the accuracy of motion information extracted from beat-to-beat 3D image-based navigators (3D iNAVs) collected using a variable-density cones trajectory with different combinations of spatial resolutions and scan acceleration factors. Methods: Fully sampled, breath-held 4.4 mm 3D iNAV datasets for six respiratory phases are acquired in a volunteer. Ground truth translational and nonrigid motion information is derived from these datasets. Subsequently, the motion estimates from synthesized undersampled 3D iNAVs with isotropic spatial resolutions of 4.4 mm (acceleration factor = 10.9), 5.4 mm (acceleration factor = 7.2), 6.4 mm (acceleration factor = 4.2), and 7.8 mm (acceleration factor = 2.9) are assessed against the ground truth information. The undersampled 3D iNAV configuration with the highest accuracy motion estimates in simulation is then compared with the originally proposed 4.4 mm undersampled 3D iNAV in six volunteer studies. Results: The simulations indicate that for navigators beyond certain scan acceleration factors, the accuracy of motion estimates is compromised due to errors from residual aliasing and blurring/smoothening effects following compressed sensing reconstruction. The 6.4 mm 3D iNAV achieves an acceptable spatial resolution with a small acceleration factor, resulting in the highest accuracy motion information among all assessed undersampled 3D iNAVs. Reader scores for six volunteer studies demonstrate superior coronary vessel sharpness when applying an autofocusing nonrigid correction technique using the 6.4 mm 3D iNAVs in place of 4.4 mm 3D iNAVs. Conclusion: Undersampled 6.4 mm 3D iNAVs enable motion tracking with improved accuracy relative to previously proposed undersampled 4.4 mm 3D iNAVs.

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arXiv2019

High-Resolution, Respiratory-Resolved Coronary MRA Using a Phyllotaxis-Reordered Variable-Density 3D Cones Trajectory

Purpose: To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography using a 3D cones trajectory. Methods: To achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases. Results: In a volunteer and 5 patients, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists indicates the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01). Conclusion: The proposed method better mitigates motion artifacts in free-breathing, high-resolution coronary angiography exams compared to translational correction.

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arXiv2019

Reconstruction of Undersampled 3D Non-Cartesian Image-Based Navigators for Coronary MRA Using an Unrolled Deep Learning Model

Purpose: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model for non-rigid motion correction in coronary magnetic resonance angiography (CMRA). Methods: An unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired as part of a CMRA sequence. The unrolled model incorporates a non-uniform FFT operator to perform the data consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with $\textit{l}_{1}$-ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the $\textit{l}_{1}$-ESPIRiT and DL model-based 3D iNAVs are assessed for differences. Results: 3D iNAVs reconstructed using the DL model-based approach and conventional $\textit{l}_{1}$-ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of $\textit{l}_{1}$-ESPIRiT (20x and 3x speed increases, respectively). Conclusion: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of non-rigid motion information offered by them for correction.

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