Rapid and accurate T2 mapping from multi-spin-echo data using Bloch-simulation-based reconstruction

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single-Shot Ultrafast MR T2 Mapping

BACKGROUND

Meningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times.

PURPOSE

To investigate the potential of T2 maps derived from multiple overlapping-echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki-67 index, and to compare the diagnostic efficiency of two different region-of-interest (ROI) placements (whole-tumor and contrast-enhanced, respectively).

STUDY TYPE

Prospective.

PHANTOM/SUBJECTS

A phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma.

FIELD STRENGTH/SEQUENCE

3 T scanner. MOLED, T2-weighted spin-echo sequence, T2-dark-fluid sequence, and postcontrast T1-weighted gradient echo sequence.

ASSESSMENT

Two ROIs were delineated: the whole-tumor area (ROI1) and contrast-enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki-67 index were reviewed by a neuropathologist according to the 2021 classification criteria.

STATISTICAL TESTS

Linear regression, Bland-Altman analysis, Pearson's correlation analysis, independent t test, Mann-Whitney U test, Kruskal-Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P-value significance level of 0.05.

RESULTS

The MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = -1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki-67 index (r = 0.237-0.374).

DATA CONCLUSION

MOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki-67 index.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

DeepEMC-T2 mapping: Deep learning-enabled T2 mapping based on echo modulation curve modeling

PURPOSE

Echo modulation curve (EMC) modeling enables accurate quantification of T2 relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps from fewer echoes.

METHODS

DeepEMC-T2 mapping was developed using a modified U-Net to estimate both T2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T2/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T2 mapping was evaluated in seven experiments.

RESULTS

Compared to the reference, DeepEMC-T2 mapping achieved T2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T2 mapping all enabled more accurate T2 estimation.

CONCLUSIONS

DeepEMC-T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without dictionary matching. Accurate T2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.

Validation of conductivity tensor imaging against diffusion tensor magnetic resonance electrical impedance tomography

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) and electrodeless conductivity tensor imaging (CTI) are two emerging modalities that can quantify low-frequency tissue anisotropic conductivity properties by assuming similar properties underlie ionic mobility and water diffusion. While both methods have potential applications to estimating neuro-modulation fields or formulating forward models used for electrical source imaging, a direct comparison of the two modalities has not yet been performed in-vitro or in-vivo. Therefore, the aim of this study was to test the equivalence of these two modalities. We scanned a tissue phantom and the head of human subject using DT-MREIT and CTI protocols and reconstructed conductivity tensor and effective low frequency conductivities. We found both gray and white matter conductivities recovered by each technique were equivalent within 0.05 S/m. Both DT-MREIT and CTI require multiple processing steps, and we further assess the effects of each factor on reconstructions and evaluate the extent to which different measurement mechanisms potentially cause discrepancies between the two methods. Finally, we discuss the implications for spectral models of measuring conductivity using these techniques. The study further establishes the credibility of CTI as an electrodeless non-invasive method of measuring low frequency conductivity properties.

Improved quantitative parameter estimation for prostate T2 relaxometry using convolutional neural networks

OBJECTIVE

Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate.

MATERIALS AND METHODS

Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness.

RESULTS

In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels.

DISCUSSION

This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma

PURPOSE

To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI.

METHODS

A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset.

RESULTS

For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions.

CONCLUSION

The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

Efficient standardization of clinical T2-weighted images: Phase-conjugacy e-CAMP with projected gradient descent

PURPOSE

To standardize T  2 $$ {}_2 $$ -weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T  2 $$ {}_2 $$ maps with the goal of removing scanner- and/or protocol-specific heterogeneity.

METHODS

The T  2 $$ {}_2 $$ map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD).

RESULTS

The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T  2 $$ {}_2 $$ mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T  2 $$ {}_2 $$ maps compared to the strict real-value enforcement. It is worth noting that the algorithm works well with challenging T  2 $$ {}_2 $$ w-TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error.

CONCLUSION

The novel methodological development creates an efficient algorithm that allows for T  2 $$ {}_2 $$ map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T  2 $$ {}_2 $$ maps from TSE data with typical clinical parameters has not been previously reported.

Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review

Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.

Fast and accessible T2 mapping using off-resonance corrected DESPOT2 with application to 3D prostate

PURPOSE

Most T1 and T2 mapping take long acquisitions or needs specialized sequences not widely accessible on clinical scanners. An available solution is DESPOT1/T2 (Driven equilibrium single pulse observation of T1/T2). DESPOT1/T2 uses Spoiled gradient-echo (SPGR) and balanced Steady-State Free Precession (bSSFP) sequences, offering an accessible and reliable way for 3D accelerated T1/T2 mapping. However, bSSFP is prone to off-resonance artifacts, limiting the application of DESPOT2 in regions with high susceptibility contrasts, like the prostate. Our proposal, DESPO+, employs the full bSSFP and SPGR models with a dictionary-based method to reconstruct 3D T1/T2 maps in the prostate region without off-resonance banding.

METHODS

DESPO+ modifies the bSSFP acquisition of the original variable flip angle DESPOT2. DESPO+ uses variable repetition and echo times, employing a dictionary-based method of the full bSSFP and SPGR models to reconstruct T1, T2, and Proton Density (PD) simultaneously. The proposed DESPO+ method underwent testing through simulations, T1/T2 phantoms, and on fourteen healthy subjects.

RESULTS

The results reveal a significant reduction in T2 map banding artifacts compared to the original DESPOT2 method. DESPO+ approach reduced T2 errors by up to seven times compared to DESPOT2 in simulations and phantom experiments. We also synthesized in-vivo T1-weighted/T2-weighted images from the acquired maps using a spin-echo model to verify the map's quality when lacking a reference. For in-vivo imaging, the synthesized images closely resemble those from the clinical MRI protocol, reducing scan time by around 50% compared to traditional spin-echo T1-weighted/T2-weighted acquisitions.

CONCLUSION

DESPO+ provides an off-resonance insensitive and clinically available solution, enabling high-resolution 3D T1/T2 mapping and synthesized T1-weighted/T2-weighted images for the entire prostate, all achieved within a short scan time of 3.6 min, similar to DESPOT1/T2.

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.

Mitigating slice cross-talk in multi-slice multi-echo spin echo T2 mapping

PURPOSE

To investigate whether a T2 inter-slice variation could occur when a multi-slice multi-echo spin echo (MESE) sequence is used for image acquisition and to propose an enhanced method for reconstructing T2 maps that can effectively address and correct these variations.

METHODS

Bloch simulations were performed accounting for the direct saturation effect to evaluate magnetization changes in multi-slice 2D MESE sequence. Experimental phantom scans were performed to validate these simulations. An improved version of the dictionary-based reconstruction approach was proposed, enabling the creation of a multi-slice dictionary of echo modulation curves (EMC). The corresponding method has been assayed considering inter-slice T2 variation with phantoms and in lower leg.

RESULTS

Experimental and numerical study illustrate that direct saturation leads to a bias of EMCs. This bias during the T2 maps reconstructions using original single-slice EMC-dictionary method led to inter-slice T2 variation of 2.03% in average coefficient of variation (CV) in agarose phantoms, and up to 2.8% in vivo (for TR = 2 s, slice gap = 0%). A reduction of CV was observed when increasing the gap up to 100% (0.36% in phantoms, and up to 1.5% in vivo) or increasing TR up to 4 s (0.76% in phantoms, and up to 1.9% in vivo). Matching the multi-slice experimental data with multi-slice dictionaries provided a reduced CV of 0.54% in phantoms and up to 2.3% in vivo.

CONCLUSION

T2 values quantified from multi-slice MESE images using single-slice dictionaries are biased. A dedicated multi-slice EMC method providing the appropriate dictionaries can reduce the inter-slice T2 variation.

Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study

PURPOSE

To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping.

METHODS

This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers.

RESULTS

Quantitative maps obtained with 3D-QALAS showed high linearity (R2  = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis.

CONCLUSION

The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

Improvements in Between-Vendor MRI Harmonization of Renal T2 Mapping using Stimulated Echo Compensation

BACKGROUND

T2 mapping is valuable to evaluate pathophysiology in kidney disease. However, variations in T2 relaxation time measurements across MR scanners and vendors may occur requiring additional correction.

PURPOSE

To harmonize renal T2 measurements between MR vendor platforms, and use an extended-phase-graph-based fitting method ("StimFit") to correct stimulated echoes and reduce between-vendor variations.

STUDY TYPE

Prospective.

SUBJECTS

8 healthy "travelling" volunteers (37.5% female, 32 ± 6 years) imaged on four MRI systems across three vendors at four sites, 10 healthy volunteers (50% female, 32 ± 8 years) scanned multiple times on a given MR scanner for repeatability evaluation. ISMRM/NIST system phantom scanned for evaluation of T2 accuracy.

FIELD STRENGTH/SEQUENCE

3T, multiecho spin-echo sequence.

ASSESSMENT

T2 images fit using conventional monoexponential fitting and "StimFit." Mean absolute percentage error (MAPE) of phantom measurements with reference T2 values. Average cortex and medulla T2 values compared between MR vendors, with masks obtained from T2 -weighted images and T1 maps. Full-width-at-half-maximum (FWHM) T2 distributions to evaluate local homogeneity of measurements.

STATISTICAL TESTS

Coefficient of variation (CV), linear mixed-effects model, analysis of variance, student's t-tests, Bland-Altman plots, P-value <0.05 considered statistically significant.

RESULTS

In the ISMRM/NIST phantom, "StimFit" reduced the MAPE from 4.9%, 9.1%, 24.4%, and 18.1% for the four sites (three vendors) to 3.3%, 3.0%, 6.6%, and 4.1%, respectively. In vivo, there was a significant difference in kidney T2 measurements between vendors using a monoexponential fit, but not with "StimFit" (P = 0.86 and 0.92, cortex and medulla, respectively). The intervendor CVs of T2 measures were reduced from 8.0% to 2.6% (cortex) and 7.1% to 2.8% (medulla) with StimFit, resulting in no significant differences for the CVs of intravendor repeat acquisitions (P = 0.13 and 0.05). "StimFit" significantly reduced the FWHM of T2 distributions in the cortex and whole kidney.

DATA CONCLUSION

Stimulated-echo correction reduces renal T2 variation across MR vendor platforms.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical T2-Weighted Images

RATIONALE AND OBJECTIVES

MR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T2-weighted (T2w) images, reflect not only the biologically relevant T2 of tissue but also hardware and acquisition parameters that vary from site to site. Quantitative T2 mapping avoids these confounds because it quantitatively isolates the biological parameter of interest, thus representing a universal standardization across sites. However, efforts to incorporate quantitative mapping sequences into routine clinical practice have seen slow adoption. Here we show, for the first time, that the routine T2w complex raw dataset can be successfully regarded as a quantitative mapping sequence that can be reconstructed with classical optimization methods and physics-based constraints.

MATERIALS AND METHODS

While previous constrained reconstruction methods are unable to reconstruct a T2 map based on this data, the expanding-constrained alternating minimization for parameter mapping (e-CAMP), which employs stepwise initialization, a linearized version of the exponential model and a phase conjugacy constraint, is demonstrated to provide useful quantitative maps directly from a vendor T2w single image data.

RESULTS

This paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired T2w images taken on both phantom and brain.

CONCLUSION

Because T2w scans are included in nearly every protocol, this approach could open the door to creating large, standardized datasets without requiring widespread changes in clinical protocols.

Time-efficient, high-resolution 3T whole-brain relaxometry using 3D-QALAS with wave-CAIPI readouts

PURPOSE

Volumetric, high-resolution, quantitative mapping of brain-tissue relaxation properties is hindered by long acquisition times and SNR challenges. This study combines time-efficient wave-controlled aliasing in parallel imaging (wave-CAIPI) readouts with the 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS), enabling full-brain quantitative T1 , T2 , and proton density (PD) maps at 1.15-mm3 isotropic voxels in 3 min.

METHODS

Wave-CAIPI readouts were embedded in the standard 3D-QALAS encoding scheme, enabling full-brain quantitative parameter maps (T1 , T2 , and PD) at acceleration factors of R = 3 × 2 with minimum SNR loss due to g-factor penalties. The quantitative parameter maps were estimated using a dictionary-based mapping algorithm incorporating inversion efficiency and B1 -field inhomogeneity effects. The parameter maps using the accelerated protocol were quantitatively compared with those obtained from the conventional 3D-QALAS sequence using GRAPPA acceleration of R = 2 in the ISMRM/NIST phantom, and in 10 healthy volunteers.

RESULTS

When tested in both the ISMRM/NIST phantom and 10 healthy volunteers, the quantitative maps using the accelerated protocol showed excellent agreement against those obtained from conventional 3D-QALAS at RGRAPPA  = 2.

CONCLUSION

Three-dimensional QALAS enhanced with wave-CAIPI readouts enables time-efficient, full-brain quantitative T1 , T2 , and PD mapping at 1.15 mm3 in 3 min at R = 3 × 2 acceleration. The quantitative maps obtained from the accelerated wave-CAIPI 3D-QALAS protocol showed very similar values to those from the standard 3D-QALAS (R = 2) protocol, alluding to the robustness and reliability of the proposed method.

Rapid whole brain 3D T2 mapping respiratory-resolved Double-Echo Steady State (DESS) sequence with improved repeatability

PURPOSE

To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation.

METHODS

A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T.

RESULTS

The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping.

CONCLUSION

The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.

Magnetic resonance coherence pathway unraveling

Efficiently acquiring multi-contrast magnetic resonance imaging data is crucial for patient comfort and clinical throughput. Developing scan acceleration methods tailored for specific applications drastically improves the value of an MRI examination. Here, we propose a novel method to control the aliasing of simultaneously acquired images of multiple spin echo coherence pathways with the goal of producing high quality multi-contrast images from a single acquisition. Modulating the radiofrequency phase of several pulses applied in brief succession also uniquely modulates the phase of spin echo coherence pathways. A method, termed magnetic resonance coherence pathway unraveling (MR-CPU), to control the aliasing of simultaneously acquired coherence pathway images is developed here along with parallel imaging-based reconstruction methods to separate them. MR-CPU was validated in phantom experiments and tested in vivo. High levels of correlation between reference pathway images and MR-CPU-derived coherence pathway images were found from the phantom experiments. Minimal artifacts arising from the separation of the overlapped coherence pathway images were observed in vivo. MR-CPU provides a novel mechanism through which to acquire and separate multiple overlapped coherence pathway images, thus adding to the diagnostic potential of an MRI exam without the penalty of additional scan time.

Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping

OBJECTIVE

The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction.

METHODS

Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals.

RESULTS

In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548).

CONCLUSION

The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction.

SIGNIFICANCE

Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.

Probing muscle recovery following downhill running using precise mapping of MRI T2 relaxation times

PURPOSE

Postexercise recovery rate is a vital component of designing personalized training protocols and rehabilitation plans. Tracking exercise-induced muscle damage and recovery requires sensitive tools that can probe the muscles' state and composition noninvasively.

METHODS

Twenty-four physically active males completed a running protocol consisting of a 60-min downhill run on a treadmill at -10% incline and 65% of maximal heart rate. Quantitative mapping of MRI T2 was performed using the echo-modulation-curve algorithm before exercise, and at two time points: 1 h and 48 h after exercise.

RESULTS

T2 values increased by 2%-4% following exercise in the primary mover muscles and exhibited further elevation of 1% after 48 h. For the antagonist muscles, T2 values increased only at the 48-h time point (2%-3%). Statistically significant decrease in the SD of T2 values was found following exercise for all tested muscles after 1 h (16%-21%), indicating a short-term decrease in the heterogeneity of the muscle tissue.

CONCLUSION

MRI T2 relaxation time constitutes a useful quantitative marker for microstructural muscle damage, enabling region-specific identification for short-term and long-term systemic processes, and sensitive assessment of muscle recovery following exercise-induced muscle damage. The variability in T2 changes across different muscle groups can be attributed to their different role during downhill running, with immediate T2 elevation occurring in primary movers, followed by delayed elevation in both primary and antagonist muscle groups, presumably due to secondary damage caused by systemic processes.

Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images

BACKGROUND

Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology.

PURPOSE

To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network.

STUDY TYPE

Prospective.

POPULATION

A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions.

FIELD STRENGTH/SEQUENCE

A 3.0 T, T₂ -weighted multi-echo spin-echo (MESE) sequence.

ASSESSMENT

MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T₂ values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T₂ ). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels.

STATISTICAL TESTS

Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant.

RESULTS

ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T₂ value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant.

DATA CONCLUSION

CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 3.

Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI

PURPOSE

To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model.

METHODS

Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints.

RESULTS

An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments.

CONCLUSION

Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.

P2T2: A physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI

Estimating T₂ relaxation time distributions from multi-echo T₂-weighted MRI (T₂W) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN) based methods have been proposed to address the complex inverse problem of estimating T₂ distributions from MRI data, but they are not yet robust enough for clinical data with low Signal-to-Noise ratio (SNR) and are highly sensitive to distribution shifts such as variations in echo-times (TE) used during acquisition. Consequently, their application is hindered in clinical practice and large-scale multi-institutional trials with heterogeneous acquisition protocols. We propose a physically-primed DNN approach, called P₂T₂, that incorporates the signal decay forward model in addition to the MRI signal into the DNN architecture to improve the accuracy and robustness of T₂ distribution estimation. We evaluated our P₂T₂ model in comparison to both DNN-based methods and classical methods for T₂ distribution estimation using 1D and 2D numerical simulations along with clinical data. Our model improved the baseline model's accuracy for low SNR levels (SNR<80) which are common in the clinical setting. Further, our model achieved a ∼35% improvement in robustness against distribution shifts in the acquisition process compared to previously proposed DNN models. Finally, Our P₂T₂ model produces the most detailed Myelin-Water fraction maps compared to baseline approaches when applied to real human MRI data. Our P₂T₂ model offers a reliable and precise means of estimating T₂ distributions from MRI data and shows promise for use in large-scale multi-institutional trials with heterogeneous acquisition protocols. Our source code is available at: https://github.com/Hben-atya/P2T2-Robust-T2-estimation.git.

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.

Signal-to-noise ratio penalties from a loss of stimulated echoes when using slab-selective excitation in three-dimensional fast spin echo imaging with long echo trains

Three-dimensional fast spin echo imaging with long echo trains combines high resolution with reasonable acquisition times and reduced specific absorption rate due to low refocusing flip angles. Typically, an entire volume is encoded (nonselective excitation) or localization can be performed with slab select excitation, which uses a long 90° pulse for precise localization, followed by a preliminary nonselective 180° pulse bounded by spoiler gradients to destroy signal outside of the volume of interest. Subsequent flip angles in the train are nonselective and identical between the two methods. The inclusion of the initial selective pulse and spoiler gradients results in a signal-to-noise ratio (SNR) penalty for slab selection, beyond the slice-averaging dependence, arising from a loss of stimulated echoes. SNR differences are explored using Bloch equation simulations of a T2-weighted 96 echo train sequence with varying parameters including T2, T1, and B1⁺ and compared with phantom and in vivo brain, neck, and knee experiments. In vivo SNR measurements in the three regions showed a maximum decrease of selective SNR by 29% (gastrocnemius muscle), 25% (pons), and 22% (globus pallidus), despite similar experimental parameters to nonselective experiments. Decreased SNR was compounded by B1⁺ variation affecting prescribed flip angles with further smaller reductions with T2 and T1 times. In conclusion, the elimination of coherences via the preliminary nominal 180° pulse and spoiler gradients in addition to the extended echo timing from the long excitation pulse resulted in a reduction in SNR compared with the nonselective case. Consideration of the required SNR and chosen anatomy as well as sequence restrictions should be weighed before choosing slab-selective excitation.

Fast and accurate T2 mapping using Bloch simulations and low-rank plus sparse matrix decomposition

PURPOSE

MRI's T2 relaxation time is one of the key contrast mechanisms for clinical diagnosis and prognosis of pathologies. Mapping this relaxation time, however, involves extensive scan times, which are needed to collect quantitative data, thereby impeding its integration into clinical routine. This study employs a low-rank plus sparse (L + S) signal decomposition approach in order to reconstruct accurate T2-maps from highly undersampled multi-echo spin-echo (MESE) MRI data.

METHODS

Two new algorithms are presented: the first uses standard L + S approach, where both L and S are iteratively updated. The second technique, dubbed SPArse and fixed RanK (SPARK), uses a fixed-rank L, under the assumption that most MESE information is found in the L component and that this rank can be pre-calculated. The utility of these new techniques is demonstrated on in vivo brain and calf data at x2 to x6 acceleration factors.

RESULTS

Accelerated T2 maps showed improved accuracy compared to fully sampled ground truth maps, when using L + S and SPARK techniques vis-à-vis standard GRAPPA acceleration.

CONCLUSION

SPARK provides accurate T2 maps with increased robustness to the selection of reconstruction parameters making it suitable to a wide range of applications and facilitating the use of quantitative T2 information in clinical settings.

Estimation of subvoxel fat infiltration in neurodegenerative muscle disorders using quantitative multi-T2 analysis

MRI's T₂ relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T₂ relaxation time. In this proof-of-concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T₂ values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary-based technique that offers accurate and reproducible mapping of T₂ relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T₂ and proton-density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T₂ value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.

Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks

This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

Introduction

Spatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that don't match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements.

Methods

Deep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction.

Results

Deli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes.

Conclusion

Deli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.

Inline dual-echo T2 quantification in brain using a fast mapping reconstruction technique

T2 mapping from 2D proton density and T2-weighted images (PD-T2) using Bloch equation simulations can be time consuming and introduces a latency between image acquisition and T2 map production. A fast T2 mapping reconstruction method is investigated and compared with a previous modeling approach to reduce computation time and allow inline T2 maps on the MRI console. Brain PD-T2 images from five multiple sclerosis patients were used to compare T2 map reconstruction times between the new subtraction method and the Euclidean norm minimization technique. Bloch equation simulations were used to create the lookup table for decay curve matching in both cases. Agreement of the two techniques used Bland-Altman analysis for investigating individual subsets of data and all image points in the five volumes (meta-analysis). The subtraction method resulted in an average reduction of computation time for single slices from 134 s (minimization method) to 0.44 s. Comparing T2 values between the subtraction and minimization methods resulted in a confidence interval ranging from -0.06 to 0.06 ms (95% of values were within ± 0.06 ms between the techniques). Using identical reconstruction code based on the subtraction method, inline T2 maps were produced from PD-T2 images directly on the scanner console. The excellent agreement between the two methods permits the subtraction technique to be interchanged with the previous method, reducing computation time and allowing inline T2 map reconstruction based on Bloch simulations directly on the scanner.

Making the invisible visible-ultrashort echo time magnetic resonance imaging: Technical developments and applications

Magnetic resonance imaging (MRI) uses a large magnetic field and radio waves to generate images of tissues in the body. Conventional MRI techniques have been developed to image and quantify tissues and fluids with long transverse relaxation times (T2s), such as muscle, cartilage, liver, white matter, gray matter, spinal cord, and cerebrospinal fluid. However, the body also contains many tissues and tissue components such as the osteochondral junction, menisci, ligaments, tendons, bone, lung parenchyma, and myelin, which have short or ultrashort T2s. After radio frequency excitation, their transverse magnetizations typically decay to zero or near zero before the receiving mode is enabled for spatial encoding with conventional MR imaging. As a result, these tissues appear dark, and their MR properties are inaccessible. However, when ultrashort echo times (UTEs) are used, signals can be detected from these tissues before they decay to zero. This review summarizes recent technical developments in UTE MRI of tissues with short and ultrashort T2 relaxation times. A series of UTE MRI techniques for high-resolution morphological and quantitative imaging of these short-T2 tissues are discussed. Applications of UTE imaging in the musculoskeletal, nervous, respiratory, gastrointestinal, and cardiovascular systems of the body are included.

Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising

High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T₂ ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T₂ relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T₂ -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T₂ values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T₂ maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T₂ maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.

High-Resolution Single Tooth MRI With an Inductively Coupled Intraoral Coil-Can MRI Compete With CBCT?

OBJECTIVES

The aims of this study were to quantify T1/T2-relaxation times of the dental pulp, develop a realistic tooth model, and compare image quality between cone-beam computed tomography (CBCT) and high-resolution magnetic resonance imaging (MRI) of single teeth using a wireless inductively coupled intraoral coil.

METHODS

T1/T2-relaxometry was performed at 3 T in 10 healthy volunteers (283 teeth) to determine relaxation times of healthy dental pulp and develop a realistic tooth model using extracted human teeth. Eight MRI sequences (DESS, CISS, TrueFISP, FLASH, SPACE, TSE, MSVAT-SPACE, and UTE) were optimized for clinically applicable high-resolution imaging of the dental pulp. In model, image quality of all sequences was assessed quantitatively (contrast-to-noise ratio) and qualitatively (visibility of anatomical structures and extent of susceptibility artifacts using a 5-point scoring scale). Cone-beam computed tomography served as the reference modality for qualitative assessment. Statistical analysis was performed using 2-way analysis of variance, Fisher exact test, and Cohen κ.

RESULTS

In vivo, relaxometry of dental pulps revealed T1/T2 relaxation times at 3 T of 738 ± 100/171 ± 36 milliseconds. For all sequences, an isotropic resolution of (0.21 mm) 3 was achieved, with acquisition times ranging from 6:19 to 8:02 minutes. In model, the highest contrast-to-noise ratio values were observed for UTE, followed by TSE and CISS. The best image/artifact quality, however, was found for DESS (mean ± SD: 1.3 ± 0.3/2.2 ± 0.0), FLASH (1.5 ± 0.3/2.4 ± 0.1), and CISS (1.5 ± 0.4/2.5 ± 0.1), at a level comparable to CBCT (1.2 ± 0.3/2.1 ± 0.1).

CONCLUSIONS

Optimized MRI protocols using an intraoral coil at 3 T can achieve an image quality comparable to reference modality CBCT within clinically applicable acquisition times. Overall, DESS revealed the best results, followed by FLASH and CISS.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI

Objective

Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRef_F), femoral head/muscle (AutoRef_FH) and pelvic bone/muscle (AutoRef_PB).

Materials and methods

T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases.

Results

AutoRef_FH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRef_FH), 0.739 (AutoRef_F) and 0.726 (AutoRef_PB).

Discussion

All AutoRef versions reduced variation in the multicenter data. AutoRef_FH pseudo-T2s were closest to experimentally measured T2s.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-022-01003-9.

T 2 ' mapping of the brain from water-unsuppressed 1 H-MRSI and turbo spin-echo data

PURPOSE

To obtain high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps of brain tissues from water-unsuppressed magnetic resonance spectroscopic imaging (MRSI) and turbo spin-echo (TSE) data.

METHODS

T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ mapping can be achieved using T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping from water-unsuppressed MRSI data and T 2 $$ {\mathrm{T}}_2 $$ mapping from TSE data. However, T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping often suffers from signal dephasing and distortions caused by B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity; T 2 $$ {\mathrm{T}}_2 $$ measurements may be biased due to system imperfections, especially for T 2 $$ {\mathrm{T}}_2 $$ -weighted image with small number of TEs. In this work, we corrected the B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity effect on T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping using a subspace model-based method, incorporating pre-learned spectral basis functions of the water signals. T 2 $$ {\mathrm{T}}_2 $$ estimation bias was corrected using a TE-adjustment method, which modeled the deviation between measured and reference T 2 $$ {\mathrm{T}}_2 $$ decays as TE shifts.

RESULTS

In vivo experiments were performed to evaluate the performance of the proposed method. High-quality T 2 * $$ {\mathrm{T}}_2^{\ast } $$ maps were obtained in the presence of large field inhomogeneity in the prefrontal cortex. Bias in T 2 $$ {\mathrm{T}}_2 $$ measurements obtained from TSE data was effectively reduced. Based on the T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and T 2 $$ {\mathrm{T}}_2 $$ measurements produced by the proposed method, high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps were obtained, along with neurometabolite maps, from MRSI and TSE data that were acquired in about 9 min. The results obtained from acute stroke and glioma patients demonstrated the feasibility of the proposed method in the clinical setting.

CONCLUSIONS

High-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps can be obtained from water-unsuppressed ¹ H-MRSI and TSE data using the proposed method. With further development, this method may lay a foundation for simultaneously imaging oxygenation and neurometabolic alterations of brain disorders.

Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation

Purpose: Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

Exact Solutions of the Bloch Equations of a Two-Level Atom Driven by the Generalized Double Exponential Quotient Pulses with Dephasing

We theoretically investigate a two-level atom driven by a time-dependent external field with a generalized double exponential temporal shape, in the presence of dephasing. Therefore, we provide exact analytical solutions for the population inversion, the real and the imaginary parts of the coherence for a family of chirped and time-dependent laser waveforms. We demonstrate that the remaining atomic population inversion can be controlled by the manipulation of the pulse’s shape structure.

Correcting for imaging gradients-related bias of T2 relaxation times at high-resolution MRI

Purpose

High‐resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T₂ relaxation times (qT₂) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High‐resolution T₂ mapping, however, suffers from severe underestimation of T₂ values due to molecular diffusion. This affects both single‐echo and multi‐echo spin echo (SSE and MESE), on top of the well‐known contamination of MESE signals by stimulated echoes, and especially on high‐field and preclinical scanners in which high imaging gradients are used in comparison to clinical scanners.

Methods

Diffusion bias due to imaging gradients was analyzed by quantifying the effective b‐value for each coherence pathway in SSE and MESE protocols, and incorporating this information in a joint T₂‐diffusion reconstruction algorithm. Validation was done on phantoms and in vivo mouse brain using a 9.4T and a 7T MRI scanner.

Results

Underestimation of T₂ values due to strong imaging gradients can reach up to 70%, depending on scan parameters and on the sample's diffusion coefficient. The algorithm presented here produced T₂ values that agreed with reference spectroscopic measurements, were reproducible across scan settings, and reduced the average bias of T₂ values from −33.5 ± 20.5% to −0.1 ± 3.6%.

Conclusions

A new joint T₂‐diffusion reconstruction algorithm is able to negate imaging gradient–related underestimation of T₂ values, leading to reliable mapping of T₂ values at high resolutions.

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T2 Mapping Values in Postmeniscectomy Knee Articular Cartilage after Running: Early Signs of Osteoarthritis?

Loading on the joints during running may have a deleterious effect on post-partial meniscectomy knee cartilage, leading to osteoarthritis. Utilizing T2-mapping measurements before and after running may enable the observation of changes in the articular cartilage of the postmeniscectomy knees compared with healthy knees. After medial partial meniscectomy, 12 volunteers underwent magnetic resonance imaging (MRI) of the both knees, before and immediately after 30 minutes of running. Quantitative assessment of articular cartilage was performed using a T2-mapping technique. In the medial compartment of the operated knees, significantly lower T2 values were found in anterior tibial plateau (pre- vs. postrun: 33.85 vs. 30.45 ms; p = 0.003) and central tibial plateau (33.33 vs. 30.63 ms; p = 0.007). Similar differences were found in lateral regions of central femur (post- vs. prerun: 35.86 vs. 40.35 ms; p = 0.015), posterior femur (34.89 vs. 37.73 ms; p = 0.001), and anterior tibia (24.66 vs. 28.70 ms, p = 0.0004). In lateral compartment, postrun values were significantly lower in operated compared with healthy knees, in central femur (34.89 vs. 37.59 ms; p = 0.043), posterior femoral (36.88 vs. 39.36 ms; p = 0.017), anterior tibia (24.66 vs. 30.20 ms; p = 0.009), and posterior tibia (28.84 vs. 33.17 ms; p = 0.006). No statistical difference was found while comparing postrun to prerun healthy knees. Lower T2 values were found in operated knees after 30 minutes of running. These changes were seen in medial and lateral compartments. We suspect that running may subject the articular cartilage to excessive loads in the post-partial meniscectomy knee, loads that in healthy knee do not cause any changes.

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 (T_1ρ ), 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 T_1ρ -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.

An in vivo implementation of the MEX MRI for myelin fraction of mice brain

OBJECTIVE

Magnetization EXchange (MEX) sequence measures a signal linearly dependent on the myelin proton fraction by selective suppression of water magnetization and a recovery period. Varying the recovery period enables extraction of the percentile fraction of myelin bound protons. We aim to demonstrate the MEX sequence sensitivity to the fraction of protons associated with myelin in mice brain, in vivo.

METHODS

The cuprizone mouse model was used to manipulate the myelin content. Mice fed cuprizone (n = 15) and normal chow (n = 8) were imaged in vivo using MEX sequence. MR images were segmented into corpus callosum and internal capsule (white matter) and cortical gray matter, and fitted to the recovery equation. Results were analyzed with correlation to MWF and histopathology.

RESULTS

The extracted parameters show significant differences in the corpus callosum between the cuprizone and control groups. The cuprizone group exhibited reduced myelin fraction 26.5% (P < 0.01). The gray matter values were less affected, with 13.5% reduction (P < 0.05); no changes were detected in the internal capsule. Results were validated by MWF scans and good correlation to the histology analysis (R² = 0.685).

CONCLUSION

The results of this first in vivo implementation of the MEX sequence provide a quantitative measure of demyelination in brain white matter.

BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T1 , T2 , M0 , B0 , and B1 maps

PURPOSE

Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T₁ , T₂ , and proton density (M₀ ) parameter maps, along with B₀ and B₁ information from the acquired signals.

THEORY AND METHODS

An imaging sequence with three 90° RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B₀ inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed.

RESULTS

The proposed acquisition provided distortion-free T₁ , T₂ , relative proton density (M0), B₀ , and B₁ maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T₁ , T₂ , M₀ , B₀ , and B₁ maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels.

CONCLUSION

The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T₁ , T₂ , M₀ , B₀ , and B₁ maps at 1 × 1 × 5 mm³ resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.

Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold Learning and Regularized Pre-Imaging

In this study, we presented a novel method to reconstruct the MR parametric maps from highly undersampled k-space data. Specifically, we utilized a nonlinear model to sparsely represent the unknown MR parameter-weighted images in high-dimensional feature space. Each image at a specific time point is assumed to belong to a low-dimensional manifold which is learned from training images created based on the parametric model. The final reconstruction is carried out by venturing the sparse representation of the images in the feature space back to the input space, using the pre-imaging technique. Particularly, among an infinite number of solutions that satisfy the data consistency, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick, sparse coding, and split Bregman iteration algorithm. In addition, both spatial and temporal regularizations were utilized to further improve the reconstruction quality. The proposed method was validated on both phantom and in vivo human brain T2 mapping data. Results showed the proposed method was superior to the conventional linear model-based reconstruction methods, in terms of artifact removal and quantitative estimate accuracy. The proposed method could be potentially beneficial for quantitative MR applications.

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6−33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10−35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28−43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking

PURPOSE

To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T 2 ∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan.

METHODS

The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T 2 ∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined.

RESULTS

High quality, co-registered T1 , T2 , and T 2 ∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T 2 ∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images.

CONCLUSION

The MR Multitasking-based 3D brain mapping of T1 , T2 , T 2 ∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.

Three-dimensional high-resolution T1 and T2 mapping of whole macaque brain at 9.4 T using magnetic resonance fingerprinting

PURPOSE

Quantitative T₁ and T₂ mapping in non-human primates with whole-brain coverage is challenged by the requirement of sub-millimeter resolution and the inhomogeneity of the transmit magnetic field (B₁ ⁺ ) covering a large field of view. The goal of the current study is to develop a magnetic resonance fingerprinting (MRF) method for simultaneous T₁ and T₂ mapping of the entire macaque brain within feasible scan time.

METHODS

A three-dimensional (3D) MRF sequence with both inversion- and T₂ -preparation modules was developed and evaluated on a 9.4 T preclinical scanner. Data acquisition used a 3D stack-of-spirals trajectory, with undersampling along both the in-plane and the through-plane directions. The effect of B₁ ⁺ inhomogeneity was accounted for by matching the acquired fingerprint to a dictionary simulated with the B₁ ⁺ factors measured from a separate scan. In vitro and ex vivo studies were performed to evaluate the accuracy and the undersampling capacity of the MRF method. The application of the MRF method for in vivo, brain-wide T₁ and T₂ mapping was demonstrated on macaques at 4, 6, and 12 years of age.

RESULTS

The MRF method enabled highly repeatable T₁ and T₂ mapping at high spatial resolution (0.35 × 0.35 × 1 mm³ ) with an acceleration factor of 24. In vivo studies showed significant age-related T₂ reduction in deep gray nuclei including the globus pallidus, the putamen, and the caudate nucleus.

CONCLUSIONS

This study demonstrates the first MRF study for brain-wide, multi-parametric quantification in non-human primates with sub-millimeter resolution.

Comparison of T2 Quantification Strategies in the Abdominal-Pelvic Region for Clinical Use

OBJECTIVES

The aim of the study was to compare different magnetic resonance imaging (MRI) acquisition strategies appropriate for T2 quantification in the abdominal-pelvic area. The different techniques targeted in the study were chosen according to 2 main considerations: performing T2 measurement in an acceptable time for clinical use and preventing/correcting respiratory motion.

MATERIALS AND METHODS

Acquisitions were performed at 3 T. To select sequences for in vivo measurements, a phantom experiment was conducted, for which the T2 values obtained with the different techniques of interest were compared with the criterion standard (single-echo SE sequence, multiple acquisitions with varying echo time). Repeatability and temporal reproducibility studies for the different techniques were also conducted on the phantom. Finally, an in vivo study was conducted on 12 volunteers to compare the techniques that offer acceptable acquisition time for clinical use and either address or correct respiratory motion.

RESULTS

For the phantom study, the DESS and T2-preparation techniques presented the lowest precision (ρ2 = 0.9504 and ρ2 = 0.9849 respectively), and showed a poor repeatability/reproducibility compared with the other techniques. The strategy relying on SE-EPI showed the best precision and accuracy (ρ2 = 0.9994 and Cb = 0.9995). GRAPPATINI exhibited a very good precision (ρ2 = 0.9984). For the technique relying on radial TSE, the precision was not as good as GRAPPATINI (ρ2 = 0.9872). The in vivo study demonstrated good respiratory motion management for all of the selected techniques. It also showed that T2 estimate ranges were different from one method to another. For GRAPPATINI and radial TSE techniques, there were significant differences between all the different types of organs of interest.

CONCLUSIONS

To perform T2 measurement in the abdominal-pelvic region, one should favor a technique with acceptable acquisition time for clinical use, with proper respiratory motion management, with good repeatability, reproducibility, and precision. In this study, the techniques relying respectively on SE-EPI, radial TSE, and GRAPPATINI appeared as good candidates.

Accelerated reconstruction of dictionary-based T2 relaxation maps based on dictionary compression and gradient descent search algorithms

Background Quantitative T₂-relaxation-based contrast maps have shown to be highly beneficial for clinical diagnosis and follow-up. The generation of quantitative maps, however, is impaired by long acquisition times, and time-consuming post-processing schemes. The EMC platform is a dictionary-based technique, which involves simulating theoretical signal curves for different physical and experimental values, followed by matching the experimentally acquired signals to the set simulated ones. Purpose Although the EMC technique has shown to produce accurate T₂ maps, it involves computationally intensive post-processing procedures. In this work we present an approach for accelerating the reconstruction of T₂ relaxation maps. Methods This work presents two alternative post-processing approaches for accelerating the reconstruction of EMC-based T₂ relaxation maps. These are (a) Dictionary compression using principal component analysis (PCA) and (b) gradient-descent search algorithm. Additional acceleration was achieved by finding the optimal MATLAB C++ compiler. The utility of the two suggested approaches was examined by calculating the relative error, produced by each technique. Results Gradient descent method was in perfect agreement with the ground truth exhaustive search matching process. PCA based acceleration produced root mean square error (RMSE) of up to 4% compared to exhaustive matching process. Overall acceleration of x16 was achieved using gradient descent in addition to x7 acceleration by choosing the optimal MATLAB C++ compiler. Conclusions Postprocessing of EMC-based T₂ relaxation maps can be accelerated without impairing the accuracy of the ensuing T₂ values.

Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features

PURPOSE

Multicomponent analysis of MRI T₂ relaxation time (mcT₂ ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T₂ values. This voxel-based approach is challenging due to the large ambiguity in the multi-T₂ space and the low SNR of MRI signals. Herein, we present a data-driven mcT₂ analysis, which utilizes the statistical strength of identifying spatially global mcT₂ motifs in white matter segments before deconvolving the local signal at each voxel.

METHODS

Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT₂ motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps.

RESULTS

Validations are shown for computer-generated signals, uniquely designed subvoxel mcT₂ phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT₂ phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans.

CONCLUSION

We conclude that studying global tissue motifs prior to performing voxel-wise mcT₂ analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T₂ space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.

T2 quantification in brain using 3D fast spin-echo imaging with long echo trains

Purpose

Three‐dimensional fast spin‐echo (FSE) sequences commonly use very long echo trains (>64 echoes) and severely reduced refocusing angles. They are increasingly used in brain exams due to high, isotropic resolution and reasonable scan time when using long trains and short interecho spacing. In this study, T₂ quantification in 3D FSE is investigated to achieve increased resolution when comparing with established 2D (proton‐density dual‐echo and multi‐echo spin‐echo) methods.

Methods

The FSE sequence design was explored to use long echo trains while minimizing T₂ fitting error and maintaining typical proton density and T₂‐weighted contrasts. Constant and variable flip angle trains were investigated using extended phase graph and Bloch equation simulations. Optimized parameters were analyzed in phantom experiments and validated in vivo in comparison to 2D methods for eight regions of interest in brain, including deep gray‐matter structures and white‐matter tracts.

Results

Phantom and healthy in vivo brain T₂ measurements showed that optimized variable echo‐train 3D FSE performs similarly to previous 2D methods, while achieving three‐fold‐higher slice resolution, evident visually in the 3D T₂ maps. Optimization resulted in better T₂ fitting and compared well with standard multi‐echo spin echo (within the 8‐ms confidence limits defined based on Bland‐Altman analysis).

Conclusion

T₂ mapping using 3D FSE with long echo trains and variable refocusing angles provides T₂ accuracy in agreement with 2D methods with additional high‐resolution benefits, allowing isotropic views while avoiding incidental magnetization transfer effects. Consequently, optimized 3D sequences should be considered when choosing T₂ mapping methods for high anatomic detail.

Post-Run T2 Mapping Changes in Knees of Adolescent Basketball Players

OBJECTIVE

While articular cartilage defects are common incidental findings among adult athletes, the effect of running on the cartilage of adolescent athletes have rarely been assessed. This study aims to assess the variations in the articular cartilage of the knees in healthy adolescent basketball players using quantitative T₂ MRI (magnetic resonance imaging).

DESIGN

Fifteen adolescent basketball players were recruited (13.8 ± 0.5 years old). Girls were excluded to avoid potential gender-related confounding effects. Players underwent a pre-run MRI scan of both knees. All participants performed a 30-minute run on a treadmill. Within 15 minutes after completion of their run, players underwent a second, post-run MRI scan. Quantitative T₂ maps were generated using the echo modulation curve (EMC) algorithm. Pre-run scans and post-run scans were compared using paired t test.

RESULTS

Participants finished their 30-minute run with a mean running distance of 5.77 ± 0.42 km. Pre-run scans analysis found statistically significant (P < 0.05) changes in 3 regions of the knee lateral compartment representing the cartilaginous tissue. No differences were found in the knee medial compartment. Post-run analysis showed lower T₂ values in the medial compartment compared to the pre-run scans in several weight-bearing regions: femoral condyle central (pre/post mean values of 33.9/32.2 ms, P = 0.020); femoral condyle posterior (38.1/36.8 ms, P = 0.038); and tibial plateau posterior (34.1/31.0 ms, P < 0.001). The lateral regions did not show any significant changes.

CONCLUSIONS

Running leads to microstructural changes in the articular cartilage in several weight-bearing areas of the medial compartment, both in the femoral and the tibial cartilage.