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.

Tensor denoising of quantitative multi-parameter mapping.

PURPOSE: Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA). METHODS: tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions. RESULTS: Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity. CONCLUSIONS: tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.

Joint MAPLE: Accelerated joint T1 and T 2 * $$ {{\mathrm{T}}_2}

PURPOSE: Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parameter mapping techniques have been developed to help address these challenges. Here, an accelerated joint T1, T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ , frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping. METHODS: Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition. RESULTS: In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-based T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R = 16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R = 25 and R = 36 with error values less than 20%. CONCLUSION: Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability.

Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter.

OBJECTIVE: To clarify the relationship between myelin water fraction (MWF) and R1⋅R2* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWFQPM). MATERIALS AND METHODS: Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R1⋅R2* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R1·R2* and the two-pool model-MWF. The R1·R2* map was converted to MWFQPM map. MWF atlas template was generated using converted to MWF from R1·R2* per WM region. RESULTS: The mean MWF and R1·R2* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s-2, respectively. A non-linear relationship in 48 regions of the WM between MWF and R1·R2* values was observed by quadratic polynomial approximation (R2 ≥ 0.963, P < 0.0001). DISCUSSION: MWFQPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWFQPM may be generated with combined multiple WM regions.

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

PURPOSE: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors. METHODS: We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping. RESULTS: We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior. CONCLUSION: DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.

SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

PURPOSE: To develop an ultrafast and robust MR parameter mapping network using deep learning. THEORY AND METHODS: We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors and other metrics in the segmented regions of interest. RESULTS: SuperMAP achieved accurate T1rho and T2 mapping with high acceleration factors (R = 24 and R = 32). It exploited both spatial and temporal information and yielded low error (normalized mean square error of 2.7% at R = 24 and 2.8% at R = 32) and high resemblance (structural similarity of 97% at R = 24 and 96% at R = 32) to the gold standard. The network trained with retrospectively undersampled data also works well for the prospective data (with a slightly lower acceleration factor). SuperMAP is also superior to conventional methods. CONCLUSION: Our results demonstrate the feasibility of generating superfast MR parameter maps through very few undersampled parameter-weighted images. SuperMAP can simultaneously generate T1rho and T2 relaxation maps in a short scan time.

The accuracy of markerless motion capture combined with computer vision techniques for measuring running kinematics.

BACKGROUND: Markerless motion capture based on low-cost 2-D video analysis in combination with computer vision techniques has the potential to provide accurate analysis of running technique in both a research and clinical setting. However, the accuracy of markerless motion capture for assessing running kinematics compared to a gold-standard approach remains largely unexplored. OBJECTIVE: Here, we investigate the accuracy of custom-trained (DeepLabCut) and existing (OpenPose) computer vision techniques for assessing sagittal-plane hip, knee, and ankle running kinematics at speeds of 2.78 and 3.33 m s-1 as compared to gold-standard marker-based motion capture. METHODS: Differences between the markerless and marker-based approaches were assessed using statistical parameter mapping and expressed as root mean squared errors (RMSEs). RESULTS: After temporal alignment and offset removal, both DeepLabCut and OpenPose showed no significant differences with the marker-based approach at 2.78 m s-1 , but some significant differences remained at 3.33 m s-1 . At 2.78 m s-1 , RMSEs were 5.07, 7.91, and 5.60, and 5.92, 7.81, and 5.66 degrees for the hip, knee, and ankle for DeepLabCut and OpenPose, respectively. At 3.33 m s-1 , RMSEs were 7.40, 10.9, 8.01, and 4.95, 7.45, and 5.76 for the hip, knee, and ankle for DeepLabCut and OpenPose, respectively. CONCLUSION: The differences between OpenPose and the marker-based method were in line with or smaller than reported between other kinematic analysis methods and marker-based methods, while these differences were larger for DeepLabCut. Since the accuracy differed between individuals, OpenPose may be most useful to facilitate large-scale in-field data collection and investigation of group effects rather than individual-level analyses.

Multi-parameter quantitative mapping of R1, R2*, PD, and MTsat is reproducible when accelerated with Compressed SENSE.

Multi-parameter mapping (MPM) magnetic resonance imaging (MRI) provides quantitative estimates of the longitudinal and effective transverse relaxation rates R1 and R2*, proton density (PD), and magnetization transfer saturation (MTsat). Thereby, MPM enables better comparability across sites and time than conventional weighted MRI. However, for MPM, several contrasts must be acquired, resulting in prolonged measurement durations and thus preventing MPM's application in clinical routines. State-of-the-art imaging acceleration techniques such as Compressed SENSE (CS), a combination of compressed sensing and sensitivity encoding, can be used to reduce the scan time of MPM. However, the accuracy and precision of the resulting quantitative parameter maps have not been systematically evaluated. In this study, we therefore investigated the effect of CS acceleration on the fidelity and reproducibility of MPM acquisitions. In five healthy volunteers and in a phantom, we compared MPM metrics acquired without imaging acceleration, with the standard acceleration (SENSE factor 2.5), and with Compressed SENSE with acceleration factors 4 and 6 using a 32-channel head coil. We evaluated the reproducibility and repeatability of accelerated MPM using data from three scan sessions in gray and white matter volumes-of-interest (VOIs). Accelerated MPM provided precise and accurate quantitative parameter maps. For most parameters, the results of the CS-accelerated protocols correlated more strongly with the non-accelerated protocol than the standard SENSE-accelerated protocols. Furthermore, for most VOIs and contrasts, coefficients of variation were lower when calculated from data acquired with different imaging accelerations within a single scan session than from data acquired in different scan sessions with the same acceleration method. These results suggest that MPM with Compressed SENSE acceleration factors up to at least 6 yields reproducible quantitative parameter maps that are highly comparable to those acquired without imaging acceleration. Compressed SENSE can thus be used to considerably reduce the scan duration of R1, R2*, PD, and MTsat mapping, and is highly promising for clinical applications of MPM.

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

A relaxometry method that emphasizes practicality and availability.

PURPOSE: Although many methods have been proposed to quantitatively map the main MRI parameters (e.g., T1 , T2 , C × M0 ), these methods often involve special sequences not readily available on clinical scanners and/or may require long scan times. In contrast, the proposed method can readily run on most scanners, offer flexible tradeoffs between scan time and image quality, and map MRI parameters jointly to ensure spatial alignment. METHODS: The approach is based on the multi-shot spin-echo (SE) EPI sequence. The corresponding signal equation was derived and strategies for solving it were developed. As usual with multi-shot EPI, scan time can readily be traded-off against image quality by adjusting the echo train length. Validation was performed against reference relaxometry methods, in gel phantoms with varying concentrations of gadobutrol and gadoterate meglumine contrast agents. In vivo examples are further presented, from 3 neuroradiology patients. RESULTS: Bland-Altman analysis was performed: for T2 , as compared to 2D SE, bias was 0.29 ms and the 95% limits of agreement ranged from -1.15 to +1.73 ms. For T1 , compared to inversion-recovery SE (and MOLLI), bias was -20.2 ms (and -14.5 ms) and the limits of agreement ranged from -62.4 to +22.0 ms (and -53.8 to +24.9 ms). The mean relative T1 error between the proposed method and each of the 2 reference methods was similar to that of the reference methods among themselves. CONCLUSION: In the constellation of existing relaxometry methods, the proposed method is meant to stand out in terms of its practicality and availability.

Generalized Bloch model: A theory for pulsed magnetization transfer.

PURPOSE: The paper introduces a classical model to describe the dynamics of large spin-1/2 ensembles associated with nuclei bound in large molecule structures, commonly referred to as the semi-solid spin pool, and their magnetization transfer (MT) to spins of nuclei in water. THEORY AND METHODS: Like quantum-mechanical descriptions of spin dynamics and like the original Bloch equations, but unlike existing MT models, the proposed model is based on the algebra of angular momentum in the sense that it explicitly models the rotations induced by radiofrequency (RF) pulses. It generalizes the original Bloch model to non-exponential decays, which are, for example, observed for semi-solid spin pools. The combination of rotations with non-exponential decays is facilitated by describing the latter as Green's functions, comprised in an integro-differential equation. RESULTS: Our model describes the data of an inversion-recovery magnetization-transfer experiment with varying durations of the inversion pulse substantially better than established models. We made this observation for all measured data, but in particular for pulse durations smaller than 300 µs. Furthermore, we provide a linear approximation of the generalized Bloch model that reduces the simulation time by approximately a factor 15,000, enabling simulation of the spin dynamics caused by a rectangular RF-pulse in roughly 2 µs. CONCLUSION: The proposed theory unifies the original Bloch model, Henkelman's steady-state theory for MT, and the commonly assumed rotation induced by hard pulses (i.e., strong and infinitesimally short applications of RF-fields) and describes experimental data better than previous models.

Combining navigator and optical prospective motion correction for high-quality 500 µm resolution quantitative multi-parameter mapping at 7T.

PURPOSE: High-resolution quantitative multi-parameter mapping shows promise for non-invasively characterizing human brain microstructure but is limited by physiological artifacts. We implemented corrections for rigid head movement and respiration-related B0-fluctuations and evaluated them in healthy volunteers and dementia patients. METHODS: Camera-based optical prospective motion correction (PMC) and FID navigator correction were implemented in a gradient and RF-spoiled multi-echo 3D gradient echo sequence for mapping proton density (PD), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*). We studied their effectiveness separately and in concert in young volunteers and then evaluated the navigator correction (NAVcor) with PMC in a group of elderly volunteers and dementia patients. We used spatial homogeneity within white matter (WM) and gray matter (GM) and scan-rescan measures as quality metrics. RESULTS: NAVcor and PMC reduced artifacts and improved the homogeneity and reproducibility of parameter maps. In elderly participants, NAVcor improved scan-rescan reproducibility of parameter maps (coefficient of variation decreased by 14.7% and 11.9% within WM and GM respectively). Spurious inhomogeneities within WM were reduced more in the elderly than in the young cohort (by 9% vs. 2%). PMC increased regional GM/WM contrast and was especially important in the elderly cohort, which moved twice as much as the young cohort. We did not find a significant interaction between the two corrections. CONCLUSION: Navigator correction and PMC significantly improved the quality of PD, R1, and R2* maps, particularly in less compliant elderly volunteers and dementia patients.

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

Mapping grip force to motor networks.

AIM: There is ongoing debate about the role of cortical and subcortical brain areas in force modulation. In a whole-brain approach, we sought to investigate the anatomical basis of grip force whilst acknowledging interindividual differences in connectivity patterns. We tested if brain lesion mapping in patients with unilateral motor deficits can inform whole-brain structural connectivity analysis in healthy controls to uncover the networks underlying grip force. METHODS: Using magnetic resonance imaging (MRI) and whole-brain voxel-based morphometry in chronic stroke patients (n=55) and healthy controls (n=67), we identified the brain regions in both grey and white matter significantly associated with grip force strength. The resulting statistical parametric maps (SPMs) provided seed areas for whole-brain structural covariance analysis in a large-scale community dwelling cohort (n=977) that included beyond volume estimates, parameter maps sensitive to myelin, iron and tissue water content. RESULTS: The SPMs showed symmetrical bilateral clusters of correlation between upper limb motor performance, basal ganglia, posterior insula and cortico-spinal tract. The covariance analysis with the seed areas derived from the SPMs demonstrated a widespread anatomical pattern of brain volume and tissue properties, including both cortical, subcortical nodes of motor networks and sensorimotor areas projections. CONCLUSION: We interpret our covariance findings as a biological signature of brain networks implicated in grip force. The data-driven definition of seed areas obtained from chronic stroke patients showed overlapping structural covariance patterns within cortico-subcortical motor networks across different tissue property estimates. This cumulative evidence lends face validity of our findings and their biological plausibility.

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

PURPOSE: To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping. METHODS: Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T1 and T2 mapping in both simulated and actually acquired MRI data sets and was compared with supervised learning and conventional constrained reconstruction for suppressing noise and/or undersampling-induced artifacts. RESULTS: In the simulated data sets, RELAX generated good T1 /T2 maps in the presence of noise and/or undersampling artifacts, comparable to artifact/noise-free ground truth. The inclusion of a spatial total variation constraint helps improve image quality. For the in vivo T1 /T2 mapping data sets, RELAX achieved superior reconstruction quality compared with conventional iterative reconstruction, and similar reconstruction performance to supervised deep learning reconstruction. CONCLUSION: This work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self-supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models.

The LEGATOS technique: A new tissue-validated dynamic contrast-enhanced MRI method for whole-brain, high-spatial resolution parametric mapping.

PURPOSE: A DCE-MRI technique that can provide both high spatiotemporal resolution and whole-brain coverage for quantitative microvascular analysis is highly desirable but currently challenging to achieve. In this study, we sought to develop and validate a novel dual-temporal resolution (DTR) DCE-MRI-based methodology for deriving accurate, whole-brain high-spatial resolution microvascular parameters. METHODS: Dual injection DTR DCE-MRI was performed and composite high-temporal and high-spatial resolution tissue gadolinium-based-contrast agent (GBCA) concentration curves were constructed. The high-temporal but low-spatial resolution first-pass GBCA concentration curves were then reconstructed pixel-by-pixel to higher spatial resolution using a process we call LEGATOS. The accuracy of kinetic parameters (Ktrans , vp , and ve ) derived using LEGATOS was evaluated through simulations and in vivo studies in 17 patients with vestibular schwannoma (VS) and 13 patients with glioblastoma (GBM). Tissue from 15 tumors (VS) was examined with markers for microvessels (CD31) and cell density (hematoxylin and eosin [H&E]). RESULTS: LEGATOS derived parameter maps offered superior spatial resolution and improved parameter accuracy compared to the use of high-temporal resolution data alone, provided superior discrimination of plasma volume and vascular leakage effects compared to other high-spatial resolution approaches, and correlated with tissue markers of vascularity (P ≤ 0.003) and cell density (P ≤ 0.006). CONCLUSION: The LEGATOS method can be used to generate accurate, high-spatial resolution microvascular parameter estimates from DCE-MRI.

A Perspective on MR Fingerprinting.

This article reviews the basic concept of MR fingerprinting (MRF) with the goal of highlighting MRF's key contributions, putting them in the context of other quantitative MRI literature, and refining MRF's terminology. The article discusses the robustness and flexibility of MRF's signature dictionary-matching reconstruction along with more advanced MRF reconstructions. A key feature of MRF is the lack of assumptions about the signal evolution, which gives scientists the flexibility to tailor sequences for their needs. The article argues that the concept of unique fingerprints does not capture the requirements for successful parameter mapping and that an analysis of the signal's derivatives with respect to biophysical parameters, such as relaxation times, is more informative, as it allows one to evaluate the efficiency of a pulse sequence. The article points at the source of MRF's efficiency, namely, flip angle variations at the time scale of the relaxation times, and reveals that MRF's advantages are strongest at long scan times, as required for 3D imaging. Further, it outlines how MRF's flexibility can be used to design mutually tailored pulse sequences and biophysical models with the goal of improving the reproducibility of parameter mapping biological tissue. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

Endogenous assessment of myocardial injury with single-shot model-based non-rigid motion-corrected T1 rho mapping.

BACKGROUND: Cardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction. METHODS: A single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference. RESULTS: Despite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01). CONCLUSIONS: The proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders.

Simultaneous Mapping of T1 and T2 Using Cardiac Magnetic Resonance Fingerprinting in a Cohort of Healthy Subjects at 1.5T.

BACKGROUND: Cardiac MR fingerprinting (cMRF) is a novel technique for simultaneous T1 and T2 mapping. PURPOSE: To compare T1 /T2 measurements, repeatability, and map quality between cMRF and standard mapping techniques in healthy subjects. STUDY TYPE: Prospective. POPULATION: In all, 58 subjects (ages 18-60). FIELD STRENGTH/SEQUENCE: cMRF, modified Look-Locker inversion recovery (MOLLI), and T2 -prepared balanced steady-state free precession (bSSFP) at 1.5T. ASSESSMENT: T1 /T2 values were measured in 16 myocardial segments at apical, medial, and basal slice positions. Test-retest and intrareader repeatability were assessed for the medial slice. cMRF and conventional mapping sequences were compared using ordinal and two alternative forced choice (2AFC) ratings. STATISTICAL TESTS: Paired t-tests, Bland-Altman analyses, intraclass correlation coefficient (ICC), linear regression, one-way analysis of variance (ANOVA), and binomial tests. RESULTS: Average T1 measurements were: basal 1007.4±96.5 msec (cMRF), 990.0±45.3 msec (MOLLI); medial 995.0±101.7 msec (cMRF), 995.6±59.7 msec (MOLLI); apical 1006.6±111.2 msec (cMRF); and 981.6±87.6 msec (MOLLI). Average T2 measurements were: basal 40.9±7.0 msec (cMRF), 46.1±3.5 msec (bSSFP); medial 41.0±6.4 msec (cMRF), 47.4±4.1 msec (bSSFP); apical 43.5±6.7 msec (cMRF), 48.0±4.0 msec (bSSFP). A statistically significant bias (cMRF T1 larger than MOLLI T1 ) was observed in basal (17.4 msec) and apical (25.0 msec) slices. For T2 , a statistically significant bias (cMRF lower than bSSFP) was observed for basal (-5.2 msec), medial (-6.3 msec), and apical (-4.5 msec) slices. Precision was lower for cMRF-the average of the standard deviation measured within each slice was 102 msec for cMRF vs. 61 msec for MOLLI T1 , and 6.4 msec for cMRF vs. 4.0 msec for bSSFP T2 . cMRF and conventional techniques had similar test-retest repeatability as quantified by ICC (0.87 cMRF vs. 0.84 MOLLI for T1 ; 0.85 cMRF vs. 0.85 bSSFP for T2 ). In the ordinal image quality comparison, cMRF maps scored higher than conventional sequences for both T1 (all five features) and T2 (four features). DATA CONCLUSION: This work reports on myocardial T1 /T2 measurements in healthy subjects using cMRF and standard mapping sequences. cMRF had slightly lower precision, similar test-retest and intrareader repeatability, and higher scores for map quality. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:1044-1052.

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.