Novel spin-lock time sampling strategies for improved reproducibility in quantitative T1ρ mapping.

This study aimed to optimize the sampling of spin-lock times (TSLs) in quantitative T1ρ mapping for improved reproducibility. Two new TSL sampling schemes were proposed: (i) reproducibility-guided random sampling (RRS) and (ii) reproducibility-guided optimal sampling (ROS). They were compared to the existing linear sampling (LS) and precision-guided sampling (PS) schemes for T1ρ reproducibility through numerical simulations, phantom experiments, and volunteer studies.

Fluid suppression in amide proton transfer-weighted (APTw) CEST imaging: New theoretical insights and clinical benefits.

Purpose: Amide proton transfer-weighted (APTw) MRI at 3T provides a unique contrast for brain tumor imaging. However, APTw imaging suffers from hyperintensities in liquid compartments such as cystic or necrotic structures and provides a distorted APTw signal intensity. Recently, it has been shown that heuristically motivated fluid suppression can remove such artifacts and significantly improve the readability of APTw imaging.

Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

Purpose: To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

Methods: Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF), , and fat-corrected B field maps.

GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging.

The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories. GRASPnet operates in the image-time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet-2D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet-3D: 3D kernels (x,y,t); and (3) GRASPnet-2D + time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t).

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters.

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation.

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

Purpose: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).

Methods: The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients.

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training.

Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training.

Methods: The current -IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization.

Integrated quantitative susceptibility and R * mapping for evaluation of liver fibrosis: An ex vivo feasibility study.

To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R * mapping using corrections for the effects of liver iron. Liver fibrosis is characterized by excessive accumulation of collagen and other extracellular matrix proteins. While collagen increases R * relaxation, measures of R * for fibrosis are confounded by liver iron, which may be present in the liver over a wide range of concentrations.

Evaluation of diffusion kurtosis imaging in stratification of nonalcoholic fatty liver disease and early diagnosis of nonalcoholic steatohepatitis in a rabbit model.

Purpose: To examine the feasibility of MR diffusion kurtosis imaging (DKI) for characterizing nonalcoholic fatty liver disease (NAFLD) and diagnosing nonalcoholic steatohepatitis (NASH).

Methods: Thirty-two rabbits on high fat diet with different severities of NAFLD were imaged at 3 T MR including diffusion weighted imaging (DWI) and DKI using b values of 0, 400, 800 s/mm with 15 diffusion directions at each b value. Apparent diffusion coefficient (ADC) was derived from the linear exponential DWI model.

Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation.

Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI.

Measurement of the liver iron concentration in transfusional iron overload by MRI R2* and by high-transition-temperature superconducting magnetic susceptometry.

Purpose: To compare measurement of the liver iron concentration in patients with transfusional iron overload by magnetic resonance imaging (MRI), using R2*, and by magnetic susceptometry, using a new high-transitiontemperature (high-Tc; operating at 77 K, cooled by liquid nitrogen) superconducting magnetic susceptometer.

Methods: In 28 patients with transfusional iron overload, 43 measurements of the liver iron concentration were made by both R2* and high-Tc magnetic susceptometry.

Results: Measurements of the liver iron concentration by R2* and high-Tc magnetic susceptometry were significantly correlated when comparing all patients (Pearson's r = 0.

Rapid automated liver quantitative susceptibility mapping.

Background: Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC.

Purpose: To develop a rapid, robust, and automated liver QSM for clinical practice.

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

Purpose: To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver.

Methods: We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time.