Magnetic resonance fingerprinting in multiple sclerosis

BACKGROUND

In this cross sectional study, we used MRF to investigate tissue properties of normal-appearing white matter, gray matter, and lesions in relapsing remitting MS (n = 21), secondary progressive MS (n = 16) and healthy controls (n = 9). A FISP-based MRF sequence was used for acquisition, imaging time 5 min 15 s. MRF T1 and T2 relaxation times were measured from lesional tissue, normal-appearing frontal white matter, corpus callous, thalamus, and caudate. Differences between healthy controls and MS were examined using ANCOVA adjusted for age and sex. Spearman rank correlations were assessed between T1 and T2 relaxation times and clinical measures.

OBJECTIVES

To examine brain T1 and T2 values using magnetic resonance fingerprinting (MRF) in healthy controls and MS.

METHODS

The subjects included 21 relapsing-remitting (RR) MS, 16 secondary progressive (SP) MS, and 9 age- and sex-matched HC without manifest neurological disease participating in a longitudinal MRI study. A 3T/ FISP-based MRF sequence was acquired. Regions of interest were drawn for lesions and normal appearing white matter. ANCOVA adjusted for age and sex were used to compare the groups with significance set at 0.05.

RESULTS

A step-wise increase in T1 and T2 relaxation times was found between healthy controls, relapsing remitting MS, and secondary progressive MS. Significant differences were found in T1 and T2 between MS and healthy controls in the frontal normal-appearing white matter, corpus callosum, and thalamus (p < 0.04 for all). Significant differences in T1 and T2 between RR and SPMS were found in the frontal normal-appearing white matter and T2 lesions (p < 0.02 for all). T1 relaxation from the frontal normal-appearing white matter correlated with the Expanded Disability Status Scale [ρ = 0.62, p < 0.001], timed 25 foot walk (ρ = 0.45, p = 0.01), 9 hole peg test (ρ = 0.62, p < 0.001), and paced auditory serial addition test (ρ = -0.4, p = 0.01).

CONCLUSION

These results suggest that MRF may be a clinically feasible quantitative approach for characterizing tissue damage in MS.

Breast cancer imaging with glucosamine CEST (chemical exchange saturation transfer) MRI: first human experience

OBJECTIVES

This study aims to evaluate the feasibility of imaging breast cancer with glucosamine (GlcN) chemical exchange saturation transfer (CEST) MRI technique to distinguish between tumor and surrounding tissue, compared to the conventional MRI method.

METHODS

Twelve patients with newly diagnosed breast tumors (median age, 53 years) were recruited in this prospective IRB-approved study, between August 2019 and March 2020. Informed consent was obtained from all patients. All MRI measurements were performed on a 3-T clinical MRI scanner. For CEST imaging, a fat-suppressed 3D RF-spoiled gradient echo sequence with saturation pulse train was applied. CEST signals were quantified in the tumor and in the surrounding tissue based on magnetization transfer ratio asymmetry (MTRasym) and a multi-Gaussian fitting.

RESULTS

GlcN CEST MRI revealed higher signal intensities in the tumor tissue compared to the surrounding breast tissue (MTRasym effect of 8.12 ± 4.09%, N = 12, p = 2.2 E-03) with the incremental increase due to GlcN uptake of 3.41 ± 0.79% (N = 12, p = 2.2 E-03), which is in line with tumor location as demonstrated by T₁W and T₂W MRI. GlcN CEST spectra comprise distinct peaks corresponding to proton exchange between free water and hydroxyl and amide/amine groups, and relayed nuclear Overhauser enhancement (NOE) from aliphatic groups, all yielded larger CEST integrals in the tumor tissue after GlcN uptake by an averaged factor of 2.2 ± 1.2 (p = 3.38 E-03), 1.4 ± 0.4 (p =9.88 E-03), and 1.6 ± 0.6 (p = 2.09 E-02), respectively.

CONCLUSION

The results of this initial feasibility study indicate the potential of GlcN CEST MRI to diagnose breast cancer in a clinical setup.

KEY POINTS

• GlcN CEST MRI method is demonstrated for its the ability to differentiate between breast tumor lesions and the surrounding tissue, based on the differential accumulation of the GlcN in the tumors. • GlcN CEST imaging may be used to identify metabolic active malignant breast tumors without using a Gd contrast agent. • The GlcN CEST MRI method may be considered for use in a clinical setup for breast cancer detection and should be tested as a complementary method to conventional clinical MRI methods.

GLINT: GlucoCEST in neoplastic tumors at 3 T-clinical results of GlucoCEST in gliomas

Objective

Clinical relevance of dynamic glucose enhanced (DGE) chemical exchange saturation transfer (CEST) imaging has mostly been demonstrated at ultra-high field (UHF) due to low effect size. Results of a cohort study at clinical field strength are shown herein.

Materials and methods

Motion and field inhomogeneity corrected T1ρ‐based DGE (DGE⍴) images were acquired before, during and after a d-glucose injection with 6.3 s temporal resolution to detect accumulation in the brain. Six glioma patients with clear blood–brain barrier (BBB) leakage, two glioma patients with suspected BBB leakage, and three glioma patients without BBB leakage were scanned at 3 T.

Results

In high-grade gliomas with BBB leakage, d-glucose uptake could be detected in the gadolinium (Gd) enhancing region as well as in the tumor necrosis with a maximum increase of ∆DGE⍴ around 0.25%, whereas unaffected white matter did not show any significant DGE⍴ increase. Glioma patients without Gd enhancement showed no detectable DGE⍴ effect within the tumor.

Conclusion

First application of DGE⍴ in a patient cohort shows an association between BBB leakage and DGE signal irrespective of the tumor grade. This indicates that glucoCEST corresponds more to the disruptions of BBB with Gd uptake than to the molecular tumor profile or tumor grading.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00982-5.

What do we know about dynamic glucose-enhanced (DGE) MRI and how close is it to the clinics? Horizon 2020 GLINT consortium report

Cancer is one of the most devastating diseases that the world is currently facing, accounting for 10 million deaths in 2020 (WHO). In the last two decades, advanced medical imaging has played an ever more important role in the early detection of the disease, as it increases the chances of survival and the potential for full recovery. To date, dynamic glucose-enhanced (DGE) MRI using glucose-based chemical exchange saturation transfer (glucoCEST) has demonstrated the sensitivity to detect both d-glucose and glucose analogs, such as 3-oxy-methyl-d-glucose (3OMG) uptake in tumors. As one of the recent international efforts aiming at pushing the boundaries of translation of the DGE MRI technique into clinical practice, a multidisciplinary team of eight partners came together to form the “glucoCEST Imaging of Neoplastic Tumors (GLINT)” consortium, funded by the Horizon 2020 European Commission. This paper summarizes the progress made to date both by these groups and others in increasing our knowledge of the underlying mechanisms related to this technique as well as translating it into clinical practice.

MRzero - Automated discovery of MRI sequences using supervised learning

PURPOSE

A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies.

METHODS

The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T 1 and T 2 , and Δ B 0 . As a proof of concept, we use both conventional MR images and T 1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time.

RESULTS

In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo.

CONCLUSIONS

Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.

Structure or Exchange? On the Feasibility of Chemical Exchange Detection with Balanced Steady-State Free Precession in Tissue - An In Vitro Study

Balanced steady-state free precession imaging has recently been suggested for chemical exchange detection (bSSFPX). The objective of this work is to investigate the contributions of microstructural, chemical shift and chemical exchange effects to the asymmetry of the bSSFP profile at field strengths of 3 T and 9.4 T. To this end, in vitro bSSFPX experiments are performed for a range of repetition times and flip angles in glucose water solutions with different MnCl₂ concentrations and tissue homogenates obtained from the brainstem of pig brains. The experimental results are compared to multi-pool Bloch-McConnell simulations. Additionally, the influence of white matter tract geometry is analyzed ex vivo in pig brain hemispheres measured at two different angles with respect to B₀ . The detectable bSSFP profile asymmetry in glucose solutions with tissue-like relaxation times and white matter homogenates was consistent with Bloch-McConnell simulations but relatively small. In intact white matter tracts, the asymmetry was dominated by structural effects with a strong dependency on tract orientation relative to B₀ . In tracts perpendicular to B₀ , the asymmetry was ≈ 3-4 times higher than in the homogenates, thus barely affected by chemical exchange effects. In conclusion, chemical exchange-related bSSFP profile asymmetries are detectable in tissue homogenates, however, the observed asymmetry level is generally low and prone to confounding structural effects rendering in vivo chemical exchange detection with bSSFP challenging in the brain.

Adaptive denoising for chemical exchange saturation transfer MR imaging

High image signal-to-noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data-driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z-spectra with artificially introduced noise. Three different data-driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z-spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data-driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements.

Partial volume mapping using magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T₁ resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRF-mapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.

Possible artifacts in dynamic CEST MRI due to motion and field alterations

PURPOSE

Dynamic CEST studies such as dynamic glucose enhanced imaging, have gained a lot of attention recently. The expected CEST effects after injection are rather small in tissue especially at clinical field strengths (0.5-2%). Small movements during the dynamic CEST measurement together with a subtraction-based evaluation can lead to pseudo CEST effects of the same order of magnitude. These artifacts are studied herein.

METHODS

A brain tumor patient 3D-CEST baseline scan without glucose injection performed at 3 T is used to generate a virtual dynamic measurement introducing different kinds of simulated motion and B₀ shifts.

RESULTS

Minor motion (0.6 mm translations) and B₀ artifacts (7 Hz shift) can lead to pseudo effects in the order of 1% in dynamic CEST imaging. Especially around tissue interfaces such as CSF borders or tumor affected areas, the pseudo effect patterns are non-intuitive and can be mistaken as dynamic agent uptake.

CONCLUSION

Correction or mitigation for small motions is crucial for dynamic CEST imaging, especially in subjects with lesions. Concomitant B₀ alterations can as well induce pseudo CEST effects at 3 T.

3D gradient echo snapshot CEST MRI with low power saturation for human studies at 3T

Purpose

For clinical implementation, a chemical exchange saturation transfer (CEST) imaging sequence must be fast, with high signal‐to‐noise ratio (SNR), 3D coverage, and produce robust contrast. However, spectrally selective CEST contrast requires dense sampling of the Z‐spectrum, which increases scan duration. This article proposes a compromise: using a 3D snapshot gradient echo (GRE) readout with optimized CEST presaturation, sampling, and postprocessing, highly resolved Z‐spectroscopy at 3T is made possible with 3D coverage at almost no extra time cost.

Methods

A 3D snapshot CEST sequence was optimized for low‐power CEST MRI at 3T. Pulsed saturation was optimized for saturation power and saturation duration. Spectral sampling and postprocessing (B₀ correction, denoising) was optimized for spectrally selective Lorentzian CEST effect extraction. Reproducibility was demonstrated in 3 healthy volunteers and feasibility was shown in 1 tumor patient.

Results

Low‐power saturation was achieved by a train of 80 pulses of duration t_p = 20 ms (total saturation time t_sat = 3.2 seconds at 50% duty cycle) with B₁ = 0.6 μT at 54 irradiation frequency offsets. With the 3D snapshot CEST sequence, a 180 × 220 × 54 mm field of view was acquired in 7 seconds per offset. Spectrally selective CEST effects at +3.5 and –3.5 ppm were quantified using multi‐Lorentzian fitting. Reproducibility was high with an intersubject coefficient of variation below 10% in CEST contrasts. Amide and nuclear overhauser effect CEST effects showed similar correlations in tumor and necrosis as show in previous ultra‐high field work.

Conclusion

A sophisticated CEST tool ready for clinical application was developed and tested for feasibility.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting

PURPOSE

To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori.

THEORY

Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution.

METHODS

An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels.

RESULTS

Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue.

CONCLUSIONS

The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding

PURPOSE

In radial imaging, projections may become "miscentered" due to gradient errors such as delays and eddy currents. These errors may result in image artifacts and can disrupt the reliability of direct current (DC) navigation. The proposed parallel imaging-based technique retrospectively estimates trajectory error from miscentered radial data without extra acquisitions, hardware, or sequence modification.

THEORY AND METHODS

After phase correction, self-calibrated GRAPPA operator gridding (GROG) weights are iteratively applied to shift-miscentered projections toward the center of k-space. A search algorithm identifies the shift that aligns the peak k-space signals by maximizing the sum-of-squares DC signal estimate of each projection. The algorithm returns a trajectory estimate and a corrected radial k-space signal.

RESULTS

Data from a spherical phantom, the head, and the heart demonstrate that image reconstruction with the estimated trajectory restores image quality and reduces artifacts such as streaks and signal voids. The DC signal level is increased and variability is reduced.

CONCLUSION

Retrospective phase correction and iterative application of GROG can be used to successfully estimate the trajectory error in two-dimensional radial acquisitions for improved image reconstruction without requiring extra data acquisition or sequence modification.

Parallel MR imaging

Parallel imaging is a robust method for accelerating the acquisition of magnetic resonance imaging (MRI) data, and has made possible many new applications of MR imaging. Parallel imaging works by acquiring a reduced amount of k-space data with an array of receiver coils. These undersampled data can be acquired more quickly, but the undersampling leads to aliased images. One of several parallel imaging algorithms can then be used to reconstruct artifact-free images from either the aliased images (SENSE-type reconstruction) or from the undersampled data (GRAPPA-type reconstruction). The advantages of parallel imaging in a clinical setting include faster image acquisition, which can be used, for instance, to shorten breath-hold times resulting in fewer motion-corrupted examinations. In this article the basic concepts behind parallel imaging are introduced. The relationship between undersampling and aliasing is discussed and two commonly used parallel imaging methods, SENSE and GRAPPA, are explained in detail. Examples of artifacts arising from parallel imaging are shown and ways to detect and mitigate these artifacts are described. Finally, several current applications of parallel imaging are presented and recent advancements and promising research in parallel imaging are briefly reviewed.