Keywords: Quantitative Imaging, CEST & MT, MRF, Amine, Glutamate

Motivation: To obtain quantitative glutamate CEST and MT maps in the brain with higher resolution than spectroscopic imaging.

Goal(s): To develop a CEST-MRF pulse sequence and deep learning reconstruction approach for rapid quantitative glutamate imaging.

Approach: CEST-MRF pulse sequence with an acquisition schedule optimized by deep learning was developed to measure glutamate exchange rate and volume fractions. Quantitative maps were obtained using a neural network trained on physics-derived signals.

Results: The proposed approach yields water T1 and T2 relaxation maps, glutamate exchange and volume fraction maps and the semi-solid exchange and volume fraction maps in a scan time of less than 2 minutes.

Impact: The proposed quantitative glutamate-sensitive CEST-MRF technique can lead to improved diagnosis and treatment response evaluation in patients with brain tumors given that glutamate dysregulation is a key aspect of tumor growth.

Keywords: Quantitative Imaging, Heart

Motivation: Co-registered quantification of relevant tissue characteristics including T1, T2 and ADC has the potential to aid the diagnosis of various cardiomyopathies.

Goal(s): Our goal was to produce co-registered cardiac T1, T2 and ADC maps using free-breathing MultiParametric single shot Echo Planer Imaging (MP-EPI).

Approach: Pixel-wise value quantifications of T1, T2, and ADC were calculated and compared in a static phantom and 5 healthy subjects following respiratory motion correction.

Results: The MP-EPI technique demonstrated consistency with reference measurements (R>0.97) for T1 and T2 values, with no significant differences observed in human subjects (p>0.2), and normal ADC values.

Impact: Free breathing, co-registered multiparametric maps from EPI-based images may provide complementary information for the diagnosis and characterization of comprehensive myocardiopathies. This method has the potential to expand to include other tissue characterization in the heart, such as T2*, and CEST.

Keywords: Quantitative Imaging, Precision & Accuracy, Parameter Estimation, Magnetization transfer, MR fingerprinting

Motivation: Neural-network (NN)-based estimators trained with the mean-squared error criterion have a non-negligible bias which impedes inter-method comparability and the clinical adoption of quantitative MRI methods.

Goal(s): To develop fast, accurate, precise, and reproducible quantitative MRI estimators that are reliable in the face of pathology.

Approach: We explicitly penalize the bias of the NN's estimates during training and study the resulting NN's bias and variance properties for a magnetization transfer model.

Results: The proposed method reduces the NN's variable bias throughout parameter space, achieves a variance close to the theoretical minimum, and shows excellent concordance with parameter maps estimated using non-linear least-squares in vivo.

Impact: NNs trained with the proposed strategy are approximately minimum variance unbiased estimators and are therefore well-suited for the development, validation, and translation of new quantitative biomarkers, particularly for multi-compartment biophysical models such as magnetization transfer or diffusion in white matter.

Keywords: Quantitative Imaging, Brain, CRLB optimization, Machine Learning, Fast-Fitting, QuantoRAGE

Motivation: T₁ and T₂ relaxometry provide valuable information for early pathology detection but require long scan and fitting times for high-resolution whole-brain mapping.

Goal(s): To reduce acquisition and map generation times for simultaneous T₁ and T₂ mapping.

Approach: The proposed accelerated sequence uses two scans instead of the original four, with parameters optimized using the Cramér-Rao lower bound (CRLB). A neural network fast fitting model is employed to drastically reduce parameter quantification times.

Results: The optimized technique reduced total acquisition time from 18:20 to 9:10 minutes and fitting time from several hours to 2 min for the entire brain.

Impact: Using QuantoRAGE, simultaneous T₁ and T₂ relaxometry of the whole brain with high isotropic resolution can be acquired in 9 minutes with quantitative maps generated within few seconds per slice. Both aspects bring quantitative MRI closer to clinical applications.

Keywords: Quantitative Imaging, Precision & Accuracy, Validation; Cross-vendor

Motivation: To address the unmet need for a cross-platform, multiparametric technique to facilitate data harmonization across different sites.

Goal(s): To implement and evaluate a fully transparent 3D multiparametric mapping for multisite neuroimaging.

Approach: A multiparametric mapping technique, 3D-QALAS, was implemented in Pulseq. The acquired T1 and T2 maps were compared within-scanner, cross scanners, software versions, sites, and vendors.

Results: The Pulseq implementation exhibited significantly higher reproducibility than vendor-native implementations, particularly for T2 values, in both phantom and in vivo studies. This approach enabled ADNI-compliant field-of-view sizes with 1mm isotropic resolution within 5 minutes, while maintaining a cross-platform coefficient of variation below 4%.

Impact: An open-source implementation across different vendors and scanners, along with a consistent reconstruction and fitting pipeline, improved measurement reproducibility. This approach facilitates data harmonization, version control and error-propagation assessment, making it also suitable for extracting quantitative information for downstream analysis.

Keywords: Pulse Sequence Design, MR Fingerprinting

Motivation: Due to significantly different relaxation times and image contrasts between neonate and adult brain, MR sequences optimized for adults are sub-optimal and may produce measurement bias in pediatric scans.

Goal(s): Optimizing MR fingerprinting pulse sequence for neuroimaging across age ranges.

Approach: We predicted and minimized measurement errors using the systematic error index model with a digital neonate brain phantom to optimize MRF sequence parameters. Optimized sequences were compared against an adult-optimized sequence via simulation and in vivo scans.

Results: Optimized sequences showed improved image quality and accuracy for infant scans and maintained accuracy for adult scans in both simulation and in vivo experiments.

Impact: We present the first application of the systematic error index model for MRF sequence optimization for brain scans across age ranges to achieve high measurement accuracy with reduced scan time.

Keywords: Pulse Sequence Design, Multi-Contrast

Motivation: Existing 3D multi-parametric imaging methods suffers from long scan times, and decent MRA contrasts are yet to be provided.  

Goal(s): To develop a 3-minute 3D high resolution multi-parametric imaging method that offeres whole brain imaging with TOF quality MRA results.

Approach: A simplified version of the MULTIPLEX method, namely MULTIPLEX-Lite, is proposed. Compared to the original MULTIPLEX method, addtional MRA results are offered, while removing the T1 and PD mappings to further improve scan time. 

Results: The proposed MULTIPLEX-Lite method offered whole brain high resolution (<1mm3 voxel volume) MRA, PDW, T1W, aT1W, SWI, R2* maps and QSM in one 3-minute scan.

Impact: The proposed MULTIPLEX-Lite method is among the most feasible and practical solutions for routine clinically friendly 3D high-resolution multi-parametric imaging practices. 

Keywords: Quantitative Imaging, Relaxometry, T1, MT, magnetization transfer

Motivation: T₁-estimates vary substantially throughout the literature.

Goal(s): To provide evidence that magnetization transfer (MT) explains most of this variability.

Approach: We simulated 16 literature T₁-mapping approaches with an MT model and fitted a T₁-value to each simulated dataset. We then modified a global set of MT parameters to best explain the T₁-variability.

Results: We found that MT explains 71% of the literature's T₁-variability. The largest reduction and minimal Bayesian and Akaike information criteria were achieved when incorporating two recent advances in MT modeling: describing the semi-solid pool's spin dynamics with the generalized Bloch model and removing commonly-used constraints on the semi-solid pool's T₁.

Impact: Our results suggest that T₁ should be considered a semi-quantitative metric in biological tissue, meaning comparisons between different T₁-mapping methods and validations in doped-water phantoms are of limited value.

Keywords: Quantitative Imaging, Liver, R2*; fat droplet size; Monte Carlo simulations; phantom

Motivation: Liver fat (hepatic steatosis) can confound R₂^*-based iron quantification in chemical shift encoded MRI, while the size of fat droplets may also affect liver R₂^*. However, it is infeasible to experimentally investigate the influence of fat droplet size in vivo on liver R₂^* due to tissue complexity.

Goal(s): To investigate the influence of fat droplet size on liver R₂^* at both 1.5T and 3.0T.

Approach: Monte Carlo simulation and phantom studies.

Results: Liver R₂^* demonstrates a positive linear relationship with proton density fat fraction and remains relatively unaffected by fat droplet size.

Impact: These findings may benefit phantom design and understanding of the underlying mechanisms of R₂^* characteristics in the presence of hepatic steatosis.

Keywords: Quantitative Imaging, Multi-Contrast, Multiparametric Mapping, Myelin Water Imaging, Relaxometry, Radial Stack-of-Stars Trajectory

Motivation: Multiparametric MRI of the brain can be used to improve the assessment of neurological diseases. However, the long scan time hinders its clinical applications.

Goal(s): This study aims to develop a technique for fast whole brain multiparametric mapping.

Approach: A dual-flip-angle stack-of-stars (SOS) blipped multi-gradient-echo sequence was developed to accelerate the acquisition. A novel joint-sparsity-constrained multicomponent T₂^*-T₁ spectrum estimation algorithm was proposed to improve the quantification of myelin water fraction (MWF).

Results: The in vivo experiments have demonstrated good agreement between results of accelerated SOS and the reference, as well as good repeatability between two repeated accelerated SOS scans.

Impact: Our technique can provide robust whole brain multiparametric mapping of MWF, T₁, proton density (PD), R₂^*, magnetic susceptibility (QSM), and B₁ transmit field (B₁⁺) with a two-minute scan, which has a great potential for neurological applications, such as multiple sclerosis.

Keywords: Quantitative Imaging, Image Reconstruction, Accelerated parameter mapping, Self-supervised; Zero-shot self-supervised learning

Motivation: Ground-truth-free (GT-free) deep learning (DL) approaches are expected to lower the cost of training DL models in accelerated quantitative MRI, but their performance has not been well compared to supervised approaches, and their application to quantitative MRI is still limited.

Goal(s): Evaluation of the effectiveness of GT-free approaches in quantitative MRI.

Approach: Three quantitative MRI methods (variable flip angle, multi-slice multi-echo, double echo steady state) were used to compare model-based DL architectures with three learning schemes: supervised learning, self-supervised learning, and zero-shot self-supervised learning in multiple acceleration factors.

Results: GT-free deep Learning approaches had high performance comparable to SL in many cases.

Impact: In this study, we compared GT-free approaches with SL and showed that they had high performance comparable to SL in many cases. These results indicate that GT-free approaches are applicable to a variety of sequences in accelerated quantitative MRI.

Keywords: Preclinical Image Analysis, Preclinical, Abdomen, T1 Mapping

Motivation: Well established techniques for fast 3D T1 mapping with cartesian/radial trajectories are prone to respiratory artifacts.Previously established non-cartesian sequences have mitigated the influence of motion artifacts, though still suffer from long measurement times.

Goal(s): Implementation of  a novel 3D dual-echo rosette k-space trajectory for preclinical UTE MRI(PETALUTE) for abdominal imaging of both anatomical and quantitative T1 measurements and retrospective 4-fold acceleration.

Approach: PETALUTE(resolution 0.24x0.24x0.24mm³,accelerated scan-time 2:15min) acquisition for T1 mapping via variable flip angle method and evaluation of T1 values and acceleration effects.

Results: High-resolution non-gated abdominal imaging with the ability to clearly distinguish anatomy and T1 values,that did not deprecate when accelerated.

Impact: Well established methods for T1 mapping using cartesian/radial trajectories suffer from motion artifacts due to long acquisition duration.PETALUTE,a novel 3D dual-echo rosette k-space trajectory for preclinical UTE-MRI,is able to generate high-resolution non-gated abdominal anatomical images and T1 mapping in ~2min.

Keywords: Quantitative Imaging, Quantitative Imaging

Motivation: Quantitative MRI (qMRI) offers sensitive and specific measures to study age-related microstructural changes in the brain. However, models assessing age trajectories in qMRI brain properties are often incomparable among centers.

Goal(s): Develop normative models reflecting aging trajectories and assess the impact of bi-centric, non-fully matched protocols in brain aging studies.

Approach: Investigating age trajectories in cortical regions using polynomial regression models, focusing on quantitative R1, R2*, and susceptibility mapping (QSM).

Results: We validated data harmonization by observing the impact on normative trajectories using bicentric data, where we noted significantly different maturation and aging inflections for R1 and R2* trajectories across cortical regions.

Impact: This bi-centric, multi-parameter qMRI study investigates age-dependent variations across cortical regions, offering a valuable reference for subsequent qMRI aging research and emphasizing age effects on the cortical surface.

Keywords: MR Fingerprinting, Cancer

Motivation: Cardiac MRF (cMRF) allows for comprehensive myocardial tissue characterization in a single scan and has been investigated at 1.5T/3T. However, cMRF has not been demonstrated at low-field.

Goal(s): Investigate the feasibility of a bSSFP-cMRF sequence for simultaneous T1 and T2 mapping at 0.55T.

Approach: The proposed approach considers bSSFP radial readouts with varying IR and T2-preparation pulses over 16 heartbeats.  bSSFP-cMRF was evaluated in phantoms and healthy subjects in comparison to reference maps.

Results: bSSFP-cMRF at 0.55T shows excellent agreement with reference values in phantom and good image quality in healthy subjects with T1 and T2 values agreeing with the literature.

Impact: The simultaneous quantification of T1 and T2 at 0.55T in a single cardiac-MRF scan of 16s could provide an alternative to higher field scanners, allowing for a more accessible way to assess cardiovascular disease.

Keywords: MR Fingerprinting, Kidney, MRF; low-rank;

Motivation: Subjective qualitative T2-weighted, T1-weighted (with and without contrast) and fat suppressed images are currently used to characterize renal masses. Characterization could be improved and standardized by using objective, generalizable, quantiative criteria. 

Goal(s): In this work, 2D single-breathhold, high-resolution T1/T2/T2*/PDFF mapping rosette MRF is deployed for kidney tissue characterization. 

Approach: A novel MRF reconstruction is introduced to enable reduced MRF data collection time, incorporating separate low-rank models along the TE and TR domains, Hermitian symmetry via virtual coils, and a dictionary-patch based regularization.

Results: In vivo results in healthy subjects demonstrate 2D 1x1x5 mm³ T1/T2/T2*/PDFF MRF kidney mapping in a single breath-hold.

Impact: Simultaneous mapping of T1/T2/T2*/PDFF in the kidney in a single high-resolution breath-hold scan via the proposed MRF approach is feasible. This technique could bolster traditional pre-/post- contrast renal mass protocols with objective characterization methods.

Keywords: MR Fingerprinting, Brain, 23Na, 1H

Motivation: Develop a method for high-resolution multinuclear fingerprinting (MNF).

Goal(s): Generate high-resolution multi-parametric maps of proton and sodium nuclei.

Approach: The method consists of two steps:
1- Simultaneous acquisition of ¹H/²³Na MR fingerprinting (MRF) data resulting in high-resolution ¹H maps and low-resolution ²³Na maps
2- Application of a super-resolution algorithm to match the in-plane resolution of ²³Na maps to the in-plane high-resolution of the ¹H maps.

Results: Multinuclear fingerprinting (MNF) can generate high-resolution ¹H density, T₁, and T₂ maps and ²³Na density, T₁, T_2long, and T_2short maps in brain from simultaneous ¹H/²³Na MRF data acquired at 7 T in 21 min.

Impact: This method provides a novel approach towards the investigation of sodium maps as biomarkers for neurological diseases.

Keywords: Signal Modeling, Susceptibility

Motivation: Quantitative susceptibility mapping (QSM) provides valuable clinical information and is widely used, especially at ultra-high field (7T). Due to long echo times, QSM acquisitions are extra sensitive to the changes in B₀ ($$$\delta\textbf{B}_0$$$), such as those secondary to motion.

Goal(s): To provide purely data-driven motion and $$$\delta\textbf{B}_0$$$ correction for QSM. 

Approach: We use the self-navigated DISORDER k-space re-ordering, originally proposed for motion correction, to additionally estimate $$$\delta\textbf{B}_0$$$. Within-scan motion and $$$\delta\textbf{B}_0$$$ are then retrospectively corrected during image reconstruction.

Results: We show improved reconstruction in all 5 scanned volunteers when additionally correcting $$$\delta\textbf{B}_0$$$. This directly improves QSM.

Impact: The proposed method can result in improved image quality when scanning in presence of motion and , e.g. due to heavy breathing. Combining this approach with an optimized QSM protocol will provide motion- and -robust QSM.

Keywords: Quantitative Imaging, Liver

Motivation: The utility of uncertainty to ensure a reliable learning-based parametric mapping in quantitative MRI is underexplored.

Goal(s): This study aimed to develop a reliable method for quantitative T1rho mapping of liver using uncertainty-based deep learning.

Approach: We proposed a parametric map refinement approach that trained the model probabilistically to estimate uncertainty in predicted T1rho values. The uncertainty map was used to enhance mapping performance and identify unreliable values in the region of interest. 

Results:  Testing on 51 patients with liver fibrosis showed a mapping error of less than 3% and simultaneous uncertainty estimation. 

Impact: Our work demonstrates potential of saving scan time while preserving T1rho quantification accuracy. It is also shown that incorporating uncertainty estimation in the T1rho mapping network can improve the reliability of predicted values.

Keywords: Myocardium, Cardiomyopathy, Quantitative Imaging

Motivation: Cardiac parametric mapping with electrocardiogram-triggered single-shot acquisition has compromised spatial resolution due to limited quiescent period for data acquisition.

Goal(s): To develop a high-resolution simultaneous myocardial T1, T2 and T1ρ mapping technique.

Approach: Enhance the spatial resolution while reducing the FOV to make the acquisition window fit in the mid-diastolic quiescent period in the cardiac cycle. Subsequently, employ the ROVir technique to eliminate fold-over artifacts arising from FOV reduction.

Results: The proposed technique achieved high-resolution multi-parametric mapping without a loss of quantitative precision.

Impact: A novel framework was proposed to shorten the acquisition window and improve the spatial resolution of electrocardiogram-triggered cardiac parametric mapping beyond common k-space undersamping. High-resolution myocardial parametric mapping can provide more precise and reliable diagnostic information.

Keywords: Quantitative Imaging, Quantitative Imaging, 4D flow MRI, hemodynamics

Motivation: Reproducibility is fundamentally an issue for quantitative MRI, and any human intervention required for processing can be a significant source of variability. 

Goal(s): This study aims to improve reproducibility in quantitative 4D flow MRI by removing all human input from processing, using AI-driven tools.

Approach: Hemodynamic parameters quantified by a fully automated neural-network-based processing tool for 4D flow MRI were compared to quantifications performed by two sets of human observers.

Results: Moderate but appreciable limits of agreement were observed between quantifications performed by different human observers.  Quantified values from fully-automated processing were comparable to those from humans, but all inter-observer variability was eliminated.

Impact: This study offers a stable baseline for improving measurement reliability in quantitative 4D flow MRI by removing all manual human inputs required.