Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics

A steady-state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles

PURPOSE

The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality.

METHODS

An optimization framework taking into account steady-state initial longitudinal magnetization, undersampling artifacts, and mitigating overfitting by drawing from a realistic numerical thighs phantom database was developed and validated on numerical simulations and 10 healthy volunteers.

RESULTS

The sequences optimized with the proposed framework decreased the original sequence duration by 30% (8 s per repetition instead of 11.2 s) while showing improved accuracy (SSIM going up from 96% to 99% forF F $$ FF $$ , from 93% to 96% forT 1 H 2 O $$ T{1}_{H2O} $$ on numerical simulations) and precision, especially when compared with sequences optimized through other means.

CONCLUSIONS

The proposed framework paves the way for fast 3D quantification ofF F $$ FF $$ andT 1 H 2 O $$ T{1}_{H2O} $$ in the skeletal muscle.

A flexible MRF approach to improve kinetic rate estimation with bSSFP-based hyperpolarized [1-13C]pyruvate MRI

PURPOSE

In this work, we adopt the MR fingerprinting (MRF) framework and leverage its flexibility in quantitative pulse sequence design to propose improved balanced steady-state free precession (bSSFP)-based hyperpolarized Carbon-13 (13C) acquisitions for robust metabolic conversion rate quantification.

METHODS

Spectrally selective bSSFP-based acquisitions with variable RF excitation were implemented for [1-13C]pyruvate and used in conjunction with prior implementation of [1-13C]lactate selective bSSFP imaging. MRF framework parameter estimation was performed using dictionary-based template matching. Influences of bSSFP-based acquisitions and sigmoid RF excitation scheme were assessed with simulation experiments and Monte Carlo evaluation. Methods were then compared using experimental data from rat kidney acquired on a clinical 3 T scanner.

RESULTS

Simulations indicated that combining bSSFP-based acquisitions and variable RF excitation (MRF-Sigmoid) exhibited bias <0.1% across the majority (86%) of combinations of pyruvate-to-lactate conversion rate (kPL) and noise level investigated when estimating kPL with the MRF framework. bSSFP-based experiments, with and without sigmoid excitation scheme, showed lower variance in fits at all levels of kPL and noise investigated compared to the method used in prior work by this group (hybrid gradient echo). Positive, linear correlations were found for in vivo voxel-wise estimates of kPL in healthy rat kidneys when comparing all experiment methods. MRF-Sigmoid experiment design increased pyruvate cumulative SNR by 3.5-fold over hybrid gradient echo while maintaining similar lactate cumulative SNR.

CONCLUSION

The use of the MRF framework for kPL estimation demonstrates the feasibility of dictionary-based template matching and can be used to accurately estimate physiologically relevant kPL and improve cumulative SNR.

Efficient 3D FISP-MRF at 0.55 T using long spiral readouts and concomitant field effect mitigation

PURPOSE

To demonstrate the feasibility of SNR-efficient 3D fast imaging with steady state precession MR fingerprinting (FISP-MRF) using long spiral readouts with mitigation of concomitant field effects at 0.55 T.

METHODS

Fourteen FISP-MRF sequences with different spiral readout lengths (2.9 ms to 22.0 ms) were implemented with the open-source Pulseq framework. Datasets were reconstructed using a low-rank and subspace model-based reconstruction combined with MaxGIRF spatial encoding model. Concomitant field-induced blurring and MRF precision were evaluated using reconstructed images and quantitative maps acquired from an ACR phantom, an ISMRM/NIST system phantom, and 2 healthy volunteers.

RESULTS

A simulation study shows that the SNR of time-series images would increase by ∼2× in white matter as a spiral readout increased from 2.9 ms to 22.0 ms. Empirically, MRF T1 and T2 standard deviations of in-vivo white matter were reduced by ∼50 %, and concomitant field mitigation was necessary. Residual spatial blurring was non-negligible for readouts ≥16.5 ms, suggesting an operating regime (2.9-16.5 ms) for 3D FISP-MRF at 0.55 T.

CONCLUSION

We demonstrate SNR-efficient 3D FISP-MRF using long spiral readouts in conjunction with concomitant field-induced blurring mitigation. A wide operating regime with improved precision is feasible only after concomitant field mitigation.

Time-resolved MR fingerprinting for T2* signal extraction: MR fingerprinting meets echo planar time-resolved imaging

PURPOSE

This study leverages the echo planar time-resolved imaging (EPTI) concept in MR fingerprinting (MRF) framework for a new time-resolved MRF (TRMRF) approach, and explores its capability for fast simultaneous quantification of multiple MR parameters including T1, T2, T2*, proton density, off resonance, and B1 +.

METHODS

The proposed TRMRF method uses the concept of EPTI to track the signal change along the EPI echo train for T2* weighting with a k-t Poisson-based sampling order designed for acquisition. A two-dimensional decomposition algorithm was designed for the image reconstruction, enabling fast and precise subspace modeling. The accuracy of proposed method was evaluated by a T1/T2 phantom. The feasibility was demonstrated through 5 healthy volunteer brain studies.

RESULTS

In the phantom studies, T1, T2, and T2* maps of TRMRF correlated strongly with gold-standard methods. The concordance correlation coefficients are 0.9999, 0.9984 and 0.9978, and R2s are 0.9998, 0.9971, and 0.9983. In the in vivo studies, quantitative maps were acquired with 5 healthy volunteers. TRMRF was demonstrated to have comparable results with spiral MRF and gradient-echo EPTI. TRMRF scans using 16, 10, and 6 s per slice were also evaluated to demonstrate the capability of shorter scan times.

CONCLUSION

A new approach is proposed to exploit the advantage of EPTI in the MRF framework. We demonstrate in phantom and in vivo experiments that T1, T2, T2*, proton density, off resonance, and B1 + can be simultaneously quantified within 6 s/slice by TRMRF.

Quantitative molecular imaging using deep magnetic resonance fingerprinting

Deep learning-based saturation transfer magnetic resonance fingerprinting (MRF) is an emerging approach for noninvasive in vivo imaging of proteins, metabolites and pH. It involves a series of steps, including sample/participant preparation, image acquisition schedule design, biophysical model formulation and artificial intelligence and computational model training, followed by image acquisition, deep reconstruction and analysis. Saturation transfer-based molecular MRI has been slow to reach clinical maturity and adoption for clinical practice due to its technical complexity, semi-quantitative contrast-weighted nature and long scan times needed for the extraction of quantitative molecular biomarkers. Deep MRF provides solutions to these challenges by providing a quantitative and rapid framework for extracting biologically and clinically meaningful molecular information. Here we define a complete protocol for quantitative molecular MRI using deep MRF. We describe in vitro sample preparation and animal and human scan considerations, and provide intuition behind the acquisition protocol design and optimization of chemical exchange saturation transfer (CEST) and semi-solid magnetization transfer (MT) quantitative imaging. We then extensively describe the building blocks for several artificial intelligence models and demonstrate their performance for different applications, including cancer monitoring, brain myelin imaging and pH quantification. Finally, we provide guidelines to further modify and expand the pipeline for imaging a variety of other pathologies (such as neurodegeneration, stroke and cardiac disease), accompanied by the related open-source code and sample data. The procedure takes between 48 min (for two proton pools or in vitro imaging) and 57 h (for complex multi-proton pool in vivo imaging) to complete and is suitable for graduate student-level users.

SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting

Background

Magnetic resonance fingerprinting (MRF) is a rapid imaging technique for simultaneous mapping of multiple tissue properties such as T1 and T2 relaxation times. However, conventional pattern matching reconstruction and iterative low rank reconstruction methods may not take full advantage of the spatiotemporal content of MRF data and can require significant computational resources with long reconstruction times. Deep learning reconstruction using a three-dimensional (3D) convolutional neural network (CNN)-based method may enable high-quality, rapid MRF reconstruction. Evaluation of such proposed deep learning reconstruction methods for MRF is needed to clarify whether deep learning techniques adapted from other MR image reconstruction problems will yield benefits when employed in MRF applications. The objective of this study is to design and evaluate a novel deep learning framework (SuperMRF) that directly transforms undersampled parameter-weighted 3D Cartesian MRF data into quantitative T1 and T2 maps, bypassing traditional pattern-matching in MRF.

Methods

In contrast to conventional MRF where only the temporal evolution of each voxel is used for quantification, SuperMRF exploits both two-dimensional spatial and one-dimensional temporal information with a 3D CNN for reconstruction. Controlled simulation experiments were performed using reference parameter maps from in vivo knee scans of healthy volunteers. To evaluate the robustness to noise, we trained our network using clean data and tested it on simulated noisy data. Conventional inner product-based pattern matching and state-of-the-art iterative low rank reconstruction techniques were used for comparison. The performance of all methods was evaluated with respect to structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized mean squared error (NMSE). Prospective real-world MRF scans were performed in four volunteer subjects using the trained network from simulations and cartilage and muscle T1 and T2 values were compared between conventional pattern matching, low rank reconstruction, and SuperMRF.

Results

SuperMRF estimated accurate T1 and T2 mapping in a highly accelerated scan (15× undersampling in k-space with a 20-fold reduction in the number of acquired MRF frames) with low error (NMSE of 5%) and high resemblance (SSIM of 94%) to reference quantitative maps. SuperMRF was observed to be superior to the conventional and low rank MRF reconstruction methods in terms of NMSE, SSIM, and robustness to noise. In prospective real-world data, SuperMRF provided comparable T1 and T2 maps as compared to low rank MRF. The only significantly different cartilage and muscle values in prospective data across the three reconstruction methods were those from conventional MRF T2.

Conclusions

Our results demonstrate that the proposed SuperMRF can achieve rapid, robust reconstruction with reduced frames in addition to k-space undersampling, outperforming the conventional and state-of-the-art reconstruction methods in simulation and providing comparable results to low rank reconstruction in prospective real-world subjects.

B 1 + $$ {\mathrm{B}}_1^{+} $$ mapping near metallic implants using turbo spin echo pulse sequences

PURPOSE

To propose a B1 + mapping technique for imaging of body parts containing metal hardware, based on magnitude images acquired with turbo spin echo (TSE) pulse sequences.

THEORY AND METHODS

To encode the underlying B1 +, multiple (two to four) TSE image sets with various excitation and refocusing flip angles were acquired. To this end, the acquired signal intensities were matched to a database of simulated signals which was generated by solving the Bloch equations taking into account the exact sequence parameters. The retrieved B1 + values were validated against gradient-recalled and spin echo dual angle methods, as well as a vendor-provided TurboFLASH-based mapping sequence, in gel phantoms and human subjects without and with metal implants.

RESULTS

In the absence of metal, phantom experiments demonstrated excellent agreement between the proposed technique using three or four flip angle sets and reference dual angle methods. In human subjects without metal implants, the proposed technique with three or four flip angle sets showed excellent correlation with the spin echo dual angle method. In the presence of metal, both phantoms and human subjects revealed a narrow range of B1 + estimation with the reference techniques, whereas the proposed technique successfully resolved B1 + near the metal. In select cases, the technique was implemented in conjunction with multispectral metal artifact reduction sequences and successfully applied for B1 + shimming.

CONCLUSION

The proposed technique enables resolution of B1 + values in regions near metal hardware, overcoming susceptibility-related and narrow-range limitations of standard mapping techniques.

Incorporating Spatial and Spectral Saturation Modules Into MR Fingerprinting

In this work, we introduce spatial and chemical saturation options for artefact reduction in magnetic resonance fingerprinting (MRF) and assess their impact on T1 and T2 mapping accuracy. An existing radial MRF pulse sequence was modified to enable spatial and chemical saturation. Phantom experiments were performed to demonstrate flow artefact reduction and evaluate the accuracy of the T1 and T2 maps. As an in vivo demonstration, MRF of the prostate was performed on an asymptomatic volunteer using saturation modules to reduce flow-related artefacts. T1, T2 and B1 + maps obtained with and without saturation modules were compared. Application of spatial saturation in prostate MRF reduced streaking artefacts from the femoral vessels. When saturation is enabled T1 accuracy is preserved, and T2 accuracy remains acceptable up to approximately 100 ms. Chemical and spatial saturation can be incorporated into MRF sequences with limited impact on T1 accuracy. Further sequence optimisation may be needed to accurately estimate long T2 components. Spatial saturation modules have potential in prostate MRF applications as a means to reduce flow-related artefacts.

Three-dimensional high-isotropic-resolution MR fingerprinting optimized for 0.55 T

PURPOSE

To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.

METHODS

We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.55T scanners, with additional features incorporated to improve the image quality of quantitative brain and musculoskeletal imaging, including (i) improved k-space sampling efficiency, (ii) Cramér-Rao lower bound optimized flip-angle pattern for specified T1 and T2 at 0.55 T, (iii) gradient trajectory correction, (iv) attention-based denoising, and (v) motion estimation and correction.

RESULTS

The proposed MRF acquisition and reconstruction framework can provide high-quality 1.2-mm isotropic whole-brain quantitative maps and 1-mm isotropic knee quantitative maps, each acquired in 4.5 min. The proposed method was validated in both phantom and in vivo brain and knee studies.

CONCLUSION

By proposing novel methods and integrating advanced techniques, we achieved high-isotropic-resolution MRF on a 0.55T scanner, demonstrating enhanced efficiency, motion resilience, and quantitative accuracy.

Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI

OBJECTIVE

We extend the traditional framework for estimating subspace bases in quantitative MRI that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps.

METHODS

To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression.

RESULTS

Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with minimal cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications.

CONCLUSION

The proposed method permits subspace reconstruction with a more compact basis, thereby offering significant computational savings.

SIGNIFICANCE

Efficient subspace reconstruction facilitates the validation and translation of advanced quantitative MRI techniques, e.g., magnetization transfer and diffusion.

Approximating R1 and R2: A Quantitative Approach to Clinical Weighted MRI

Weighted MRI images are widely used in clinical as well as open-source neuroimaging databases. Weighted images such as T1-weighted, T2-weighted, and proton density-weighted (T1w, T2w, and PDw, respectively) are used for evaluating the brain's macrostructure; however, their values cannot be used for microstructural analysis, as they lack physical meaning. Quantitative MRI (qMRI) relaxation rate parameters (e.g., R1 and R2) do contain microstructural physical meaning. Nevertheless, qMRI is rarely done in large-scale clinical databases. Currently, the weighted images ratio T1w/T2w is used as a quantifier to approximate the brain's microstructure. In this paper, we test three additional quantifiers that approximate quantitative maps, which can help bring quantitative MRI to the clinic for easy use. Following the signal equations and using simple mathematical operations, we combine the T1w, T2w, and PDw images to estimate the R1 and R2 maps. We find that two of these quantifiers (T1w/PDw and T1w/ln(T2w)) can approximate R1, and that (ln(T2w/PDw)) can approximate R2, in 3 datasets that were tested. We find that this approach also can be applied to T2w scans taken from widely available DTI (Diffusion Tensor Imaging) datasets. We tested these quantifiers on both in vitro phantom and in vivo human datasets. We found that the quantifiers accurately represent the quantitative parameters across datasets. Finally, we tested the quantifiers within a clinical context, and found that they are robust across datasets. Our work provides a simple pipeline to enhance the usability and quantitative accuracy of MRI weighted images.

UltimateSynth: MRI Physics for Pan-Contrast AI

Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analysis, as computational tools are often tailored to specific MRI types and lack generalizability across the full spectrum of scans used in healthcare. Here, we introduce a versatile framework for the development and validation of pan-contrast AI models that can exhaustively cater to the full spectrum of scans achievable with MRI, enabling model deployment across scanner models, scan types, and age groups. Core to our framework is UltimateSynth, a technology that combines tissue physiology and MR physics in synthesizing realistic images across a comprehensive range of contrasts to bolster the AI development life cycle through efficient data labeling, generalizable model training, and thorough performance benchmarking. UltimateSynth is a platform for pan-contrast generalization of contrast-specific tools. We showcase the effectiveness of UltimateSynth by training an off-the-shelf U-Net to generalize anatomical segmentation across over 150,000 unique MRI contrasts, achieving robust tissue volumetric quantification with exceptionally low variability below 2%.

B1-MRF: Large dynamic range MRF-based absolute B 1 + mapping in the human body at 7T

PURPOSE

This study aims to map the transmit magnetic field (B 1 + $$ {B}_1^{+} $$ ) in the human body at 7T using MR fingerprinting (MRF), with a focus on achieving high accuracy and precision across a large dynamic range, particularly at low flip angles (FAs).

METHODS

A FLASH-based MRF sequence (B1-MRF) with highB 1 + $$ {B}_1^{+} $$ sensitivity was developed. Phantom and in vivo abdominal imaging were performed at 7T, and the results were compared with established reference methods, including a slow but precise preparation-based method (PEX), saturated TurboFLASH (satTFL), actual flip angle imaging (AFI) and Bloch-Siegert shift (BSS).

RESULTS

The MRF signal curve was highly sensitive toB 1 + $$ {B}_1^{+} $$ , while T1 sensitivity was comparatively low. The phantom experiment showed good agreement ofB 1 + $$ {B}_1^{+} $$ to PEX for a T1 range of 204-1691 ms evaluated at FAs from 0° to 70°. Compared to the references, a dynamic range increase larger than a factor of two was determined experimentally. In vivo liver scans showed a strong correlation between B1-MRF, satTFL, and RPE-AFI in a low body mass index (BMI) subject (18.1 kg/m2). However, in larger BMI subjects (≥25.5 kg/m2), inconsistencies were observed in lowB 1 + $$ {B}_1^{+} $$ regions for satTFL and RPE-AFI, while B1-MRF still provided consistent results in these regions.

CONCLUSION

B1-MRF provides accurate and preciseB 1 + $$ {B}_1^{+} $$ maps over a wide range of FAs, surpassing the capabilities of existing methods in the FA range < 60°. Its enhanced sensitivity at low FAs is advantageous for various applications requiring preciseB 1 + $$ {B}_1^{+} $$ estimates, potentially advancing the frontiers of ultra-high field (UHF) body imaging at 7T and beyond.

3D Creatine Kinase Imaging (CKI) for In Vivo Whole-Brain Mapping of Creatine Kinase Reaction Rates with 31P-Magnetization Transfer MR Fingerprinting

The creatine kinase (CK) is a key enzyme involved in brain bioenergetics, playing a key role in brain function and the pathogenesis of neurological and psychiatric diseases, but imaging its activity noninvasively in the human brain in vivo remains a significant challenge. This study aims to advance the magnetization transfer (MT)- 31P magnetic resonance fingerprinting (MRF) for 3D Creatine Kinase Imaging (CKI). The method was implemented and validated on a clinical 7 Tesla MRI scanner. It enables whole-brain mapping of CK reaction rates for the first time, showing robust reproducibility for 25-minute scan sessions. CKI acquisition also provided simultaneous mapping of adenosine triphosphate and phosphocreatine concentration ratios, phosphocreatine longitudinal relaxation time, andB 0 maps. Furthermore, a functional CKI (fCKI) study demonstrated the first CK activation map in response to visual stimulation, revealing a mean 15% increase in CK rates in the visual cortex. The novel imaging modalities, CKI and fCKI, have the potential to offer new insights into brain bioenergetics both at rest and during activity.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index

PURPOSE

For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design.

METHODS

By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects.

RESULTS

The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences.

CONCLUSION

We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI

Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.

High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting

PURPOSE

This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time.

METHODS

We developed 3D visualization of short transverse relaxation time component (ViSTa)-MRF, which combined ViSTa technique with MR fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multicompartment fitting that could introduce bias and/or noise from additional assumptions or priors.

RESULTS

The in vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in vivo results of 1 mm- and 0.66 mm-isotropic resolution datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30× slower with lower SNR. Furthermore, we applied the proposed method to enable 5-min whole-brain 1 mm-iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post-mortem brain samples.

CONCLUSIONS

In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at 1 and 0.66 mm isotropic resolution in 5 and 15 min, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.

Deep image prior cine MR fingerprinting with B1 + spin history correction

PURPOSE

To develop a deep image prior (DIP) reconstruction for B1 + -corrected 2D cine MR fingerprinting (MRF).

METHODS

The proposed method combines low-rank (LR) modeling with a DIP to generate cardiac phase-resolved parameter maps without motion correction, employing self-supervised training to enforce consistency with undersampled spiral k-space data. Two implementations were tested: one approach (DIP) for cine T1 , T2 , and M0 mapping, and a second approach (DIP with effective B1 + estimation [DIP-B1]) that also generated an effective B1 + map to correct for errors due to RF transmit inhomogeneities, through-plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low-rank motion-corrected (LRMC), DIP, and DIP-B1. Results were compared to diastolic ECG-triggered MRF, MOLLI, and T2 -prep bSSFP. Additionally, bright-blood and dark-blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements.

RESULTS

DIP and DIP-B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high-resolution details. Within-segment variability in the myocardium (reported as the coefficient of variation for T1 /T2 ) was lowest for DIP-B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP-B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was -1.1% compared to reference cine scans.

CONCLUSION

A DIP reconstruction for 2D cine MRF enabled cardiac phase-resolved mapping of T1 , T2 , M0 , and the effective B1 + with improved noise suppression and precision compared to LR and LRMC.

Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model

PURPOSE

To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters.

THEORY AND METHODS

We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency.

RESULTS

We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports.

CONCLUSION

The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain

PURPOSE

Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system.

METHODS

Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains.

RESULTS

The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol.

CONCLUSION

Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI

In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective radiofrequency (RF) pulses to perform spatial encoding along the slice dimension in a 3D imaging experiment. In the first part of the paper, we use the Cramér-Rao lower bound to examine the potential estimation theoretic benefits of using phase constraints. The results suggest that phase constraints can be used to improve experimental efficiency and enable acceleration, but only if the RF encoding matrix is complex-valued and appropriately designed. In the second part of the paper, we use simulations of RF-encoded data to test the benefits of phase constraints combined with optimized RF-encodings, and find that the theoretical benefits are indeed borne out empirically. These results provide new insights into the potential benefits of phase constraints for RF-encoded data, and provide a solid theoretical foundation for future practical explorations.

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.

Water diffusion and T2 quantification in transient-state MRI: the effect of RF pulse sequence

In quantitative measurement of the T 2 value of tissues, the diffusion of water molecules has been recognized as a confounder. This is most notably so for transient-state quantitative mapping techniques, which allow simultaneous estimation of T 1 and T 2 . In prior work, apparently conflicting conclusions are presented on the level of diffusion-induced bias on the T2 estimate. So far there is a lack of studies on the effect of the RF pulse angle sequence on the level of diffusion-induced bias. In this work, we show that the specific transient-state RF pulse sequence has a large effect on this level of bias. In particular, the bias level is strongly influenced by the mean value of the RF pulse angles. Also, for realistic values of the spoiling gradient area, we infer that the diffusion-induced bias is negligible for non-liquid human tissues; yet, for phantoms, the effect can be substantial (15% of the true T 2 value) for some RF pulse sequences. This should be taken into account in validation procedures.

High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting

Purpose

This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time.

Methods

We developed 3D ViSTa-MRF, which combined Visualization of Short Transverse relaxation time component (ViSTa) technique with MR Fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multi-compartment fitting that could introduce bias and/or noise from additional assumptions or priors.

Results

The in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in-vivo results of 1mm- and 0.66mm-iso datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30x slower with lower SNR. Furthermore, we applied the proposed method to enable 5-minute whole-brain 1mm-iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post-mortem brain samples.

Conclusions

In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at 1mm and 0.66mm isotropic resolution in 5 and 15 minutes, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.

Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI

We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.

Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule.

Only-train-once MR fingerprinting for B0 and B1 inhomogeneity correction in quantitative magnetization-transfer contrast

PURPOSE

To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B0 and B1 variations.

METHODS

An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Zref ) through Bloch equations at multiple saturation power levels.

RESULTS

The B0 and B1 errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B0 and B1 inhomogeneities.

CONCLUSION

The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.

MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification

Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.

3D magnetic resonance fingerprinting on a low-field 50 mT point-of-care system prototype: evaluation of muscle and lipid relaxation time mapping and comparison with standard techniques

OBJECTIVE

To implement magnetic resonance fingerprinting (MRF) on a permanent magnet 50 mT low-field system deployable as a future point-of-care (POC) unit and explore the quality of the parameter maps.

MATERIALS AND METHODS

3D MRF was implemented on a custom-built Halbach array using a slab-selective spoiled steady-state free precession sequence with 3D Cartesian readout. Undersampled scans were acquired with different MRF flip angle patterns and reconstructed using matrix completion and matched to the simulated dictionary, taking excitation profile and coil ringing into account. MRF relaxation times were compared to that of inversion recovery (IR) and multi-echo spin echo (MESE) experiments in phantom and in vivo. Furthermore, B₀ inhomogeneities were encoded in the MRF sequence using an alternating TE pattern, and the estimated map was used to correct for image distortions in the MRF images using a model-based reconstruction.

RESULTS

Phantom relaxation times measured with an optimized MRF sequence for low field were in better agreement with reference techniques than for a standard MRF sequence. In vivo muscle relaxation times measured with MRF were longer than those obtained with an IR sequence (T₁: 182 ± 21.5 vs 168 ± 9.89 ms) and with an MESE sequence (T₂: 69.8 ± 19.7 vs 46.1 ± 9.65 ms). In vivo lipid MRF relaxation times were also longer compared with IR (T₁: 165 ± 15.1 ms vs 127 ± 8.28 ms) and with MESE (T₂: 160 ± 15.0 ms vs 124 ± 4.27 ms). Integrated ΔB₀ estimation and correction resulted in parameter maps with reduced distortions.

DISCUSSION

It is possible to measure volumetric relaxation times with MRF at 2.5 × 2.5 × 3.0 mm³ resolution in a 13 min scan time on a 50 mT permanent magnet system. The measured MRF relaxation times are longer compared to those measured with reference techniques, especially for T₂. This discrepancy can potentially be addressed by hardware, reconstruction and sequence design, but long-term reproducibility needs to be further improved.

Adaptive model-based Magnetic Resonance

PURPOSE

Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time.

METHODS

We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T₂ s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T₂ , which was used to guide the selection of sequence parameters in real time.

RESULTS

Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T₂ for n-acetyl-aspartate by a factor of 2.5.

CONCLUSION

Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.

Mitigating undersampling errors in MR fingerprinting by sequence optimization

PURPOSE

To develop a method for MR Fingerprinting (MRF) sequence optimization that takes both the applied undersampling pattern and a realistic reference map into account.

METHODS

A predictive model for the undersampling error leveraging on perturbation theory was exploited to optimize the MRF flip angle sequence for improved robustness against undersampling artifacts. In this framework parameter maps from a previously acquired MRF scan were used as reference. Sequences were optimized for different sequence lengths, smoothness constraints and undersampling factors. Numerical simulations and in vivo measurements in eight healthy subjects were performed to assess the effect of the performed optimization. The optimized MRF sequences were compared to a conventionally shaped flip angle pattern and an optimized pattern based on the Cramér-Rao lower bound (CRB).

RESULTS

Numerical simulations and in vivo results demonstrate that the undersampling errors can be suppressed by flip angle optimization. Analysis of the in vivo results show that a sequence optimized for improved robustness against undersampling with a flip angle train of length 400 yielded significantly lower median absolute errors in T 1 : 5 . 6 % ± 2 . 9 % and T 2 : 7 . 9 % ± 2 . 3 % compared to the conventional ( T 1 : 8 . 0 % ± 1 . 9 % , T 2 : 14 . 5 % ± 2 . 6 % ) and CRB-based ( T 1 : 21 . 6 % ± 4 . 1 % , T 2 : 31 . 4 % ± 4 . 4 % ) sequences.

CONCLUSION

The proposed method is able to optimize the MRF flip angle pattern such that significant mitigation of the artifacts from strong k-space undersampling in MRF is achieved.

A fast MR fingerprinting simulator for direct error estimation and sequence optimization

Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity.

Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping

Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.

Three-dimensional multi-parameter brain mapping using MR fingerprinting

The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T1, T2 and T1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T1, T2, T1ρ, were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T1/T2/T1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T1, T2 and T1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.

On Ambiguity in Linear Inverse Problems: Entrywise Bounds on Nearly Data-Consistent Solutions and Entrywise Condition Numbers

Ill-posed linear inverse problems appear frequently in various signal processing applications. It can be very useful to have theoretical characterizations that quantify the level of ill-posedness for a given inverse problem and the degree of ambiguity that may exist about its solution. Traditional measures of ill-posedness, such as the condition number of a matrix, provide characterizations that are global in nature. While such characterizations can be powerful, they can also fail to provide full insight into situations where certain entries of the solution vector are more or less ambiguous than others. In this work, we derive novel theoretical lower- and upper-bounds that apply to individual entries of the solution vector, and are valid for all potential solution vectors that are nearly data-consistent. These bounds are agnostic to the noise statistics and the specific method used to solve the inverse problem, and are also shown to be tight. In addition, our results also lead us to introduce an entrywise version of the traditional condition number, which provides a substantially more nuanced characterization of scenarios where certain elements of the solution vector are less sensitive to perturbations than others. Our results are illustrated in an application to magnetic resonance imaging reconstruction, and we include discussions of practical computation methods for large-scale inverse problems, connections between our new theory and the traditional Cramér-Rao bound under statistical modeling assumptions, and potential extensions to cases involving constraints beyond just data-consistency.

Efficient performance analysis and optimization of transient-state sequences for multiparametric magnetic resonance imaging

In transient-state multiparametric MRI sequences such as Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT), MR fingerprinting, or hybrid-state imaging, the flip angle pattern of the RF excitation varies over the sequence. This gives considerable freedom to choose an optimal pattern of flip angles. For pragmatic reasons, most optimization methodologies choose for a single-voxel approach (i.e., without taking the spatial encoding scheme into account). Particularly in MR-STAT, the context of spatial encoding is important. In the current study, we present a methodology, called BLock Analysis of a K-space-domain Jacobian (BLAKJac), which is sufficiently fast to optimize a sequence in the context of a predetermined phase-encoding pattern. Based on MR-STAT acquisitions and reconstructions, we show that sequences optimized using BLAKJac are more reliable in terms of actually achieved precision than conventional single-voxel-optimized sequences. In addition, BLAKJac provides analytical tools that give insights into the performance of the sequence in a very limited computation time. Our experiments are based on MR-STAT, but the theory is equally valid for other transient-state multiparametric methods.

The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches

PURPOSE

Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T₂ , T₁ , or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter.

METHODS

We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T₂ s, and a functional MRS experiment designed to estimate fractional change ( δ $$ \delta $$ ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T₂ in multi-echo and δ $$ \delta $$ in functional MRS.

RESULTS

When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible.

CONCLUSION

Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.

CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction

PURPOSE

To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction.

METHODS

A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side.

RESULTS

DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side.

CONCLUSION

Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.

MR Fingerprinting for Liver Tissue Characterization: A Histopathologic Correlation Study

Background Liver MR fingerprinting (MRF) enables simultaneous quantification of T1, T2, T2*, and proton density fat fraction (PDFF) maps in single breath-hold acquisitions. Histopathologic correlation studies are desired for its clinical use. Purpose To compare liver MRF-derived metrics with separate reference quantitative MRI in participants with diffuse liver disease, evaluate scan-rescan repeatability of liver MRF, and validate MRF-derived measurements for histologic grading of liver biopsies. Materials and Methods This prospective study included participants with diffuse liver disease undergoing MRI from July 2021 to January 2022. Participants underwent two-dimensional single-section liver MRF and separate reference quantitative MRI. Linear regression, Bland-Altman plots, and coefficients of variation were used to assess the bias and repeatability of liver MRF measurements. For participants undergoing liver biopsy, the association between mapping and histologic grading was evaluated by using the Spearman correlation coefficient. Results Fifty-six participants (mean age, 59 years ± 15 [SD]; 32 women) were included to compare mapping techniques and 23 participants were evaluated with liver biopsy (mean age, 52.7 years ± 12.7; 14 women). The linearity of MRF with reference measurements in participants with diffuse liver disease (R² value) for T1, T2, T2*, and PDFF maps was 0.86, 0.88, 0.54, and 0.99, respectively. The overall coefficients of variation for repeatability in the liver were 3.2%, 5.5%, 7.1%, and 4.6% for T1, T2, T2*, and PDFF maps, respectively. MRF-derived metrics showed high diagnostic performance in differentiating moderate or severe changes from mild or no changes (area under the receiver operating characteristic curve for fibrosis, inflammation, steatosis, and siderosis: 0.62 [95% CI: 0.52, 0.62], 0.92 [95% CI: 0.88, 0.92], 0.97 [95% CI: 0.96, 0.97], and 0.74 [95% CI: 0.57, 0.74], respectively). Conclusion Liver MR fingerprinting provided repeatable T1, T2, T2*, and proton density fat fraction maps in high agreement with reference quantitative mapping and may correlate with pathologic grades in participants with diffuse liver disease. © RSNA, 2022 Online supplemental material is available for this article.

Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints

PURPOSE

To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues.

METHODS

The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted. The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference.

RESULTS

The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 5.8% with the conventional, two-step approach to 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.9% and 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN).

CONCLUSION

The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.

Optimisation of data acquisition towards continuous cardiac Magnetic Resonance Fingerprinting applications

PURPOSE

Assess and optimise acquisition parameters for continuous cardiac Magnetic Resonance Fingerprinting (MRF).

METHODS

Different acquisition schemes (flip angle amplitude, lobe size, T2-preparation pulses) for cardiac MRF were assessed in simulations and phantom and demonstrated in one healthy volunteer. Three different experimental designs were evaluated using central composite and fractional factorial designs. Relative errors for T1 and T2 were calculated for a wide range of realistic T1 and T2 value combinations. The effect of different designs on the accuracy of T1 and T2 was assessed using response surface modelling and Cohen's f calculations.

RESULTS

Larger flip angle amplitudes lead to an improvement of T2 accuracy and precision for simulations and phantom experiments. Similar effects could also be shown qualitatively in in-vivo scans. Accuracy and precision of T1 were robust to different design parameters with improved values for faster flip angle variation. Cohen's f showed that T2-preparation pulses influence the accuracy of T2. The number of pulses used is the most important parameter. Without T2-preparation pulses, RMSE were 3.0 ± 8.09 % for T1 and 16.24 ± 14.47 % for T2. Using those pulses reduced the RMSE to 2.3 ± 8.4 % for T1 and 14.11 ± 13.46 % for T2. Nonetheless, even if the improvement is significant, RMSE are still too high for reliable quantification.

CONCLUSION

In contrast to previous study using triggered MRF sequences using < 30° flip angles, large flip angle amplitudes led to better results for continuous cardiac MRF sequences. T2-preparation pulse can improve the accuracy of T2 estimation but lead to longer scan times.

3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T

Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1 , T2, T1ρ relaxation times, and B1 + were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

PURPOSE

Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time.

METHODS

We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps.

RESULTS

T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts.

CONCLUSION

We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T₂, T₂*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T₂, and T₁-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.

Artificial intelligence in cardiac magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T₁ and T₂ mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.

An efficient approach to optimal experimental design for magnetic resonance fingerprinting with B-splines

PURPOSE

To introduce a computationally efficient approach to optimizing the data acquisition parameters of MR Fingerprinting experiments with the Cramér-Rao bound.

METHODS

This paper presents a new approach to the optimal experimental design (OED) problem for MR Fingerprinting, which leverages an early observation that the optimized data acquisition parameters of MR Fingerprinting experiments are highly structured. Specifically, the proposed approach captures the desired structure by representing the sequences of data acquisition parameters with a special class of piecewise polynomials known as B-splines. This incorporates low-dimensional spline subspace constraints into the OED problem, which significantly reduces the search space of the problem, thereby improving the computational efficiency. With the rich B-spline representations, the proposed approach also allows for incorporating prior knowledge on the structure of different acquisition parameters, which facilitates the experimental design.

RESULTS

The effectiveness of the proposed approach was evaluated using numerical simulations, phantom experiments, and in vivo experiments. The proposed approach achieves a two-order-of-magnitude improvement of the computational efficiency over the state-of-the-art approaches, while providing a comparable signal-to-noise ratio efficiency benefit. It enables an optimal experimental design problem for MR Fingerprinting with a typical acquisition length to be solved in approximately 1 min.

CONCLUSIONS

The proposed approach significantly improves the computational efficiency of the optimal experimental design for MR Fingerprinting, which enhances its practical utility for a variety of quantitative MRI applications.

Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders

Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.

Active Sampling for Accelerated MRI with Low-Rank Tensors

Magnetic resonance imaging (MRI) is a powerful imaging modality that revolutionizes medicine and biology. The imaging speed of high -dimensional MRI is often limited, which constrains its practical utility. Recently, low-rank tensor models have been exploited to enable fast MR imaging with sparse sampling. Most existing methods use some pre-defined sampling design, and active sensing has not been explored for low-rank tensor imaging. In this paper, we introduce an active low-rank tensor model for fast MR imaging. We propose an active sampling method based on a Query-by-Committee model, making use of the benefits of low-rank tensor structure. Numerical experiments on a 3-D MRI data set with Cartesian sampling designs demonstrate the effectiveness of the proposed method.

3D MR fingerprinting using Seiffert spirals

PURPOSE

Seiffert spirals were recently explored as an efficient way to traverse 3D k-space compared to traditional 3D techniques. Several studies have shown the ability of 3D MR fingerprinting (MRF) techniques to acquire T1 and T2 relaxation maps in a short period of time. However, these sequences do not sample across a large region of 3D k-space every TR, especially in the way that Seiffert trajectories can.

METHODS

A 3D MRF sequence was designed using 8 Seiffert spirals rotated in 3D k-space, with flip angle modulation for T1 and T2 sensitivity. The sequence was compared to an MRF sequence using a 2D spiral rotated in 3D k-space using the tiny golden angle acquisition with similar resolution/readout duration. Both sequences were evaluated using simulations, phantom validation, and in vivo imaging.

RESULTS

In all experiments, the Seiffert spiral MRF sequence performed similar to if not better than the multi-axis 2D spiral MRF sequence. Strong intraclass correlation coefficients (> 0.9) were found between conventional and MRF sequences in phantoms, whereas the in vivo results showed slightly less aliasing artifact with the Seiffert trajectory.

CONCLUSION

In this study, Seiffert spirals were used within the MRF framework to acquire high-resolution T1 and T2 relaxation time maps in less than 2.5 min. The reduced aliasing artifacts seen with the Seiffert sequence suggests that sampling over 3D k-space evenly each TR can improve quantification or shorten scan times.