Fat fraction mapping using bSSFP Signal Profile Asymmetries for Robust multi-Compartment Quantification (SPARCQ)

Purpose: To develop a novel quantitative method for detection of different tissue compartments based on bSSFP signal profile asymmetries (SPARCQ) and to provide a validation and proof-of-concept for voxel-wise water-fat separation and fat fraction mapping. Methods: The SPARCQ framework uses phase-cycled bSSFP acquisitions to obtain bSSFP signal profiles. For each voxel, the profile is decomposed into a weighted sum of simulated profiles with specific off-resonance and relaxation time ratios. From the obtained set of weights, voxel-wise estimations of the fractions of the different components and their equilibrium magnetization are extracted. For the entire image volume, component-specific quantitative maps as well as banding-artifact-free images are generated. A SPARCQ proof-of-concept was provided for water-fat separation and fat fraction mapping. Noise robustness was assessed using simulations. A dedicated water-fat phantom was used to validate fat fractions estimated with SPARCQ against gold-standard 1H MRS. Quantitative maps were obtained in knees of six healthy volunteers, and SPARCQ repeatability was evaluated in scan rescan experiments. Results: Simulations showed that fat fraction estimations are accurate and robust for signal-to-noise ratios above 20. Phantom experiments showed good agreement between SPARCQ and gold-standard (GS) fat fractions (fF(SPARCQ) = 1.02*fF(GS) + 0.00235). In volunteers, quantitative maps and banding-artifact-free water-fat-separated images obtained with SPARCQ demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ fat fraction was 0.0512. Conclusion: The SPARCQ framework was proposed as a novel quantitative mapping technique for detecting different tissue compartments, and its potential was demonstrated for quantitative water-fat separation.Comment: 20 pages, 7 figures, submitted to Magnetic Resonance in Medicin

Fat-free noncontrast whole-heart CMR with fast and power-optimized off-resonant water excitation pulses

Background: Cardiovascular MRI (CMR) faces challenges due to the interference of bright fat signals in visualizing anatomical structures. Effective fat suppression is crucial when using whole-heart CMR. Conventional methods often fall short due to rapid fat signal recovery and water-selective off-resonant pulses come with tradeoffs between scan time and RF energy deposit. A lipid-insensitive binomial off-resonant (LIBOR) RF pulse is introduced, addressing concerns about RF energy and scan time for CMR at 3T. Methods: A short LIBOR pulse was developed and implemented in a free-breathing respiratory self-navigated whole-heart sequence at 3T. A BORR pulse with matched duration, as well as previously used LIBRE pulses, were implemented and optimized for fat suppression in numerical simulations and validated in healthy subjects (n=3). Whole-heart CMR was performed in healthy subjects (n=5) with all four pulses. The SNR of ventricular blood, skeletal muscle, myocardium, and subcutaneous fat, and the coronary vessel sharpness and length were compared. Results: Experiments validated numerical findings and near homogeneous fat suppression was achieved with all pulses. Comparing the short pulses (1ms), LIBOR reduced the RF power two-fold compared with LIBRE, and three-fold compared with BORR, and LIBOR significantly decreased overall fat SNR. The reduction in RF duration shortened the whole-heart acquisition from 8.5min to 7min. No significant differences in coronary arteries detection and sharpness were found when comparing all four pulses. Conclusion: LIBOR enabled whole-heart CMR under 7 minutes at 3T, with large volume fat signal suppression, while reducing RF power compared with LIBRE and BORR. LIBOR is an excellent candidate to address SAR problems encountered in CMR where fat suppression remains challenging and short RF pulses are required.Comment: 25 pages, 7 figures, 2 table

A robust broadband fat suppressing phaser T2 preparation module for cardiac magnetic resonance imaging at 3T

Purpose: Designing a new T2 preparation (T2-Prep) module in order to simultaneously provide robust fat suppression and efficient T2 preparation without requiring an additional fat suppression module for T2-weighted imaging at 3T. Methods: The tip-down RF pulse of an adiabatic T2 preparation (T2-Prep) module was replaced by a custom-designed RF excitation pulse that induces a phase difference between water and fat, resulting in a simultaneous T2 preparation of water signals and the suppression of fat signals at the end of the module (now called a phaser adiabatic T2-Prep). Using numerical simulations, in vitro and in vivo ECG-triggered navigator gated acquisitions of the human heart, the blood, myocardium and fat signal-to-noise ratio and right coronary artery (RCA) vessel sharpness using this approach were compared against previously published conventional adiabatic T2-Prep approaches Results: Numerical simulations predicted an increased fat suppression bandwidth and decreased sensitivity against transmit magnetic field inhomogeneities using the proposed approach, while preserving the water T2 preparation capabilities. This was confirmed by the tissue signals acquired on the phantom and the in vivo MRA, which show similar blood and myocardium SNR and CNR and significantly reduced fat SNR compared to the other methods tested. As a result, the RCA conspicuity was significantly increased and the motion artifacts were visually decreased. Conclusion: A novel fat-suppressing T2-preparation method was developed and implemented that demonstrated robust fat suppression and increased vessel sharpness compared with conventional techniques, while preserving its T2 preparation capabilities.Comment: 23 pages, 5 figures, submitted to Magnetic Resonance in Medicin

Advances in machine learning applications for cardiovascular 4D flow MRI

Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow