Diffusion imaging with balanced steady state free precession

Balanced steady state free precession (bSSFP) offers high signal efficiency and relative motion insensitivity. In this study, diffusion weighted bSSFP (DW-bSSFP) was introduced by modifying standard bSSFP sequence with two pairs of balanced bipolar diffusion gradients. The diffusion effect was analyzed and described in closed forms. It was found to be coupled to the transverse and longitudinal relaxation, flip angle and spin phase advance per TR. Such coupling was demonstrated in phantom experiment at 7T. Preliminary DW-bSSFP imaging experiment was performed in rat brain in vivo for diffusion tensor imaging, yielding parametric maps qualitatively similar to those obtained with an 8-shot DW-EPI protocol. The proposed DW-bSSFP approach can provide a new means of diffusion imaging with high resolution, relative motion insensitivity and short diffusion time. Such approach may lead to improved and new diffusion characterization of neural tissues, abdominal organs, myocardium and musculoskeletal tissues. © 2012 IEEE.published_or_final_versio

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

Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions

Longitudinal analysis of new multiple sclerosis lesions with magnetization transfer and diffusion tensor imaging

Objective The potential of magnetization transfer imaging (MTI) and diffusion tensor imaging (DTI) for the detection and evolution of new multiple sclerosis (MS) lesions was analyzed. Methods Nineteen patients with MS obtained conventional MRI, MTI, and DTI examinations bimonthly for 12 months and again after 24 months at 1.5 T MRI. MTI was acquired with balanced steady-state free precession (bSSFP) in 10 min (1.3 mm$^{3}$ isotropic resolution) yielding both magnetization transfer ratio (MTR) and quantitative magnetization transfer (qMT) parameters (pool size ratio (F), exchange rate (kf), and relaxation times (T1/T2)). DTI provided fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Results At the time of their appearance on MRI, the 21 newly detected MS lesions showed significantly reduced MTR/F/kf and prolonged T1/T2 parameters, as well as significantly reduced FA and increased AD/MD/RD. Significant differences were already observed for MTR 4 months and for qMT parameters 2 months prior to lesions’ detection on MRI. DTI did not show any significant pre-lesional differences. Slightly reversed trends were observed for most lesions up to 8 months after their detection for qMT and less pronounced for MTR and three diffusion parameters, while appearing unchanged on MRI. Conclusions MTI provides more information than DTI in MS lesions and detects tissue changes 2 to 4 months prior to their appearance on MRI. After lesions’ detection, qMT parameter changes promise to be more sensitive than MTR for the lesions’ evolutional assessment. Overall, bSSFP-based MTI adumbrates to be more sensitive than MRI and DTI for the early detection and follow-up assessment of MS lesions. Clinical relevance statement When additionally acquired in routine MRI, fast bSSFP-based MTI can complement the MRI/DTI longitudinal lesion assessment by detecting MS lesions 2–4 months earlier than with MRI, which could implicate earlier clinical decisions and better follow-up/treatment assessment in MS patients. Key Points • Magnetization transfer imaging provides more information than DTI in multiple sclerosis lesions and can detect tissue changes 2 to 4 months prior to their appearance on MRI. • After lesions’ detection, quantitative magnetization transfer changes are more pronounced than magnetization transfer ratio changes and therefore promise to be more sensitive for the lesions’ evolutional assessment. • Balanced steady-state free precession–based magnetization transfer imaging is more sensitive than MRI and DTI for the early detection and follow-up assessment of multiple sclerosis lesions

High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced steady-state free precession frequency profile

We propose to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, that is, mean, axial, and radial diffusivity (MD, AD, and RD), fractional anisotropy (FA), as well as the spherical coordinates (azimuth Φ and inclination ϴ) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased (e.g. in corpus callosum). The inclination ϴ was well predicted for anisotropic WM structures, while the azimuth Φ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultrahigh field strengths in clinically relevant scan times

Hybrid-State Free Precession in Nuclear Magnetic Resonance

The dynamics of large spin-1/2 ensembles in the presence of a varying magnetic field are commonly described by the Bloch equation. Most magnetic field variations result in unintuitive spin dynamics, which are sensitive to small deviations in the driving field. Although simplistic field variations can produce robust dynamics, the captured information content is impoverished. Here, we identify adiabaticity conditions that span a rich experiment design space with tractable dynamics. These adiabaticity conditions trap the spin dynamics in a one-dimensional subspace. Namely, the dynamics is captured by the absolute value of the magnetization, which is in a transient state, while its direction adiabatically follows the steady state. We define the hybrid state as the co-existence of these two states and identify the polar angle as the effective driving force of the spin dynamics. As an example, we optimize this drive for robust and efficient quantification of spin relaxation times and utilize it for magnetic resonance imaging of the human brain

Emerging imaging techniques in spondyloarthritis dual-energy computed tomography and new MRI sequences

Imaging of the sacroiliac joint plays a critical role in the classification of patients with axial spondyloarthritis. New imaging techniques are emerging, changing the way clinicians look at the sacroiliac joint. This article introduces the novel techniques in imaging of spondyloarthritis, including dual-energy computed tomography and new MRI sequences, with a focus on the imaging of bone marrow edema and erosions of the sacroiliac joint

Influence of Longitudinal Position on the Evolution of Steady-State Signal in Cardiac Cine Balanced Steady-State Free Precession Imaging

Background: Emerging quantitative cardiac magnetic resonance imaging (CMRI) techniques use cine balanced steady-state free precession (bSSFP) to measure myocardial signal intensity and probe underlying physiological parameters. This correlation assumes that steady-state is maintained uniformly throughout the heart in space and time. Purpose: To determine the effects of longitudinal cardiac motion and initial slice position on signal deviation in cine bSSFP imaging by comparing two-dimensional (2D) and three-dimensional (3D) acquisitions. Material and Methods: Nine healthy volunteers completed cardiac MRI on a 1.5-T scanner. Short axis images were taken at six slice locations using both 2D and 3D cine bSSFP. 3D acquisitions spanned two slices above and below selected slice locations. Changes in myocardial signal intensity were measured across the cardiac cycle and compared to longitudinal shortening. Results: For 2D cine bSSFP, 46% ± 9% of all frames and 84% ± 13% of end-diastolic frames remained within 10% of initial signal intensity. For 3D cine bSSFP the proportions increased to 87% ± 8% and 97% ± 5%. There was no correlation between longitudinal shortening and peak changes in myocardial signal. The initial slice position significantly impacted peak changes in signal intensity for 2D sequences (P \u3c 0.001). Conclusion: The initial longitudinal slice location significantly impacts the magnitude of deviation from steady-state in 2D cine bSSFP that is only restored at the center of a 3D excitation volume. During diastole, a transient steady-state is established similar to that achieved with 3D cine bSSFP regardless of slice location