Unfolding coil localized errors from an imperfect slice profile using a structured autocalibration matrix: An application to reduce outflow effects in cine bSSFP imaging.

Purpose: Balanced steady-state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice-encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice-encoded calibration scan in addition to the 2D data, without being nearly as time-demanding as our previous method.

Single-shot diffusion trace spectroscopic imaging using radial echo planar trajectories.

Purpose: Demonstrate the feasibility and evaluate the performance of single-shot diffusion trace-weighted radial echo planar spectroscopic imaging (Trace DW-REPSI) for quantifying the trace ADC in phantom and in vivo using a 3T clinical scanner.

Theory And Methods: Trace DW-REPSI datasets were acquired in 10 phantom and 10 healthy volunteers, with a maximum b-value of 1601 s/mm and diffusion time of 10.75 ms.

Single-shot diffusion trace-weighted MR spectroscopy: Comparison with unipolar and bipolar diffusion-weighted point-resolved spectroscopy.

This study demonstrates the feasibility and performance of the point-resolved spectroscopy (PRESS)-based, single-shot diffusion trace-weighted sequence in quantifying the trace apparent diffusion coefficient (ADC) in phantom and in vivo using a 3-T MRI/MRS scanner. The single-shot diffusion trace-weighted PRESS sequence was implemented and compared with conventional diffusion-weighted (DW)-PRESS variants using bipolar and unipolar diffusion-sensitizing gradients. Nine phantom datasets were acquired using each sequence, and seven volunteers were scanned in three different brain regions to determine the range and variability of trace ADC values, and to allow a comparison of trace ADCs among the sequences.

Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging.

The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.

Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction.

Objectives: The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology.

Methods: The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance.

Alterations of gray and white matter volumes and cortical thickness in treated HIV-positive patients.

Brain structural changes in HIV identified by voxel-based morphometry (VBM) alone could arise from a variety of causes that are difficult to distinguish without further information, such as cortical thickness (CT), gyrification index (GI) or sulcal depth (SD). Hence, our goal was to assess these additional metrics in HIV using high-resolution 3D T-weighted images and investigate if surface-based morphometric (SBM) analysis would reveal significant changes in the gray matter (GM) and white matter (WM) volumes combined with alterations in cortical thickness (CT), gyrification index (GI), sulcal depth (SD). T1-w magnetization-prepared-rapid-acquisition gradient-echo (MP-RAGE) scans were acquired in 27 HIV-infected individuals on antiretroviral therapy (ART) and 15 HIV-uninfected healthy controls using a 3T MRI scanner equipped with a 16-channel head "receive" and a quadrature body "transmit" coil.

Low rank off-resonance correction for double half-echo k-space acquisitions.

The present study describes a model-based approach for correcting off-resonance in the context of double half-echo k-space acquisitions. This technique employs center-out readouts in forward and reverse directions to reduce echo-times but is sensitive to off-resonance, which manifests as pixel shifts in both directions. Demodulating the k-space signal with a constant off-resonance term per slice removes pixel shifts and results in a marked reduction in blurring.

Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer.

Objectives: This study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors.

Materials And Methods: Prospectively undersampled 5D echo-planar J-resolved spectroscopic imaging (EP-JRESI) data were acquired in nine prostate cancer (PCa) patients and three healthy males. The 5D EP-JRESI data were reconstructed using DL and compared with gradient sparsity-based Total Variation (TV) and Perona-Malik (PM) methods.

A study of 3D radial density adapted trajectories for sodium imaging.

Sodium imaging typically employs ultrashort echo time radial, density adapted and cones trajectories to capture the rapidly decaying short T2 signal. The present study considers the parameter choices involved in the use of these trajectories in terms of their impact on the resolution and signal to noise ratio. Many parameters have a strong effect on these image properties, particularly the number of spokes which impacts voxel size.

Accelerated radial echo-planar spectroscopic imaging using golden angle view-ordering and compressed-sensing reconstruction with total variation regularization.

Purpose: To implement a novel, accelerated, 2D radial echo-planar spectroscopic imaging (REPSI) sequence using undersampled radial k-space trajectories and compressed-sensing reconstruction, and to compare results with those from an undersampled Cartesian spectroscopic sequence.

Methods: The REPSI sequence was implemented using golden-angle view-ordering on a 3T MRI scanner. Radial and Cartesian echo-planar spectroscopic imaging (EPSI) data were acquired at six acceleration factors, each with time-equivalent scan durations, and reconstructed using compressed sensing with total variation regularization.

Noninvasive assessment of abdominal adipose tissues and quantification of hepatic and pancreatic fat fractions in type 2 diabetes mellitus.

The purpose of this study was to evaluate adipose tissue distributions and hepatic and pancreatic fat contents using a 6-point Dixon MRI technique in type 2 diabetes mellitus (T2DM), and to assess associations between fat distributions and biochemical markers of insulin resistance. Intra-abdominal MRI was investigated in 14 T2DM patients, 13 age- and sex-matched healthy controls (HC) and 11 young HC using a 3 T Prisma MRI scanner. All T2DM subjects completed a fasting comprehensive metabolic panel, and demographic measurements were taken according to standardized methodologies.

Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments.

This paper presents and analyzes an alternative formulation of the locally low-rank (LLR) regularization framework for magnetic resonance image (MRI) reconstruction. Generally, LLR-based MRI reconstruction techniques operate by dividing the underlying image into a collection of matrices formed from image patches. Each of these matrices is assumed to have low rank due to the inherent correlations among the data, whether along the coil, temporal, or multi-contrast dimensions.