T2 Quantification Research Articles

Histopathological myocardial changes in patients with severe aortic stenosis referred for surgical valve replacement: a CMR correlation study

Abstract Background Myocardial fibrosis (MF) takes part in left ventricular (LV) remodeling in patients with aortic stenosis (AS), driving the transition from hypertrophy to heart failure. The structural events that occur in this transition are not fully enlightened. Aim To describe histopathology changes at endomyocardial biopsy (EMB) in patients with severe AS referred to surgical aortic valve replacement (AVR); to correlate them with LV tissue characterization from pre-operative cardiac magnetic resonance (CMR). Methods One-hundred-fifty-eight patients (73[68-77]years, 50%women) referred for surgical AVR because of severe symptomatic AS, with pre-operative CMR (n=143) with late gadolinium enhancement (LGE), T1, T2 mapping and extracellular volume fraction (ECV) quantification. Intra-operative septal EMB was obtained in 129 patients. MF was assessed through Masson ́s Trichrome histochemistry. Immunohistochemistry was performed for both inflammatory cells and extracellular matrix (ECM) characterization (Type I Collagen, Fibronectin, Tenascin C). Results Non-ischemic LGE was present in 106 patients (67.1%) (median fraction: 5.0% [2.0-9.7]). Native T1 was above normal: 1053ms[1024-1071] and T2 within normal range (39.3ms[37.3-42.0]). Median MF was 11.9%[6.54-19.97], with predominant type I collagen perivascular distribution (95.3%). Subendocardial cardiomyocyte ischemic-like changes were identified in 45% of EMB. There was no inflammation, despite ECM remodeling expression. MF quantification at EMB was correlated with LGE mass and indexed ECV (p=0.008; p=0.017, respectively). Conclusion Patients with severe symptomatic AS referred for surgical AVR have unspecific histological myocardial changes, including signs of cardiomyocyte ischemic insult. ECM remodeling is ongoing, with MF heterogeneity. These features may be recognized by comprehensive CMR involving LGE, T1 mapping derived indexes and their combination with LV morphofunctional characterization.

3D B1+ corrected simultaneous myocardial T1 and T1ρ mapping with subject-specific respiratory motion correction and water-fat separation.

To develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of respiratory motion, fat, and B1+ inhomogeneities and validate it for joint myocardial T1 and T1ρ mapping at 3T. An electrocardiogram-triggered sequence with dual-echo Dixon readout was developed, where nine cardiac cycles were repeatedly acquired with inversion recovery and T1ρ preparation pulses for T1 and T1ρ sensitization. A subject-specific respiratory motion model relating the 1D diaphragmatic navigator to the respiration-induced 3D translational motion of the heart was constructed followed by respiratory motion binning and intra-bin 3D translational and inter-bin non-rigid motion correction. Spin history B1+ inhomogeneities were corrected with optimized dual flip angle strategy. After water-fat separation, the water images were matched to the simulated dictionary for T1 and T1ρ quantification. Phantoms and 10 heathy subjects were imaged to validate the proposed technique. The proposed technique achieved strong correlation (T1: R2 = 0.99; T1ρ: R2 = 0.98) with the reference measurements in phantoms. 3D cardiac T1 and T1ρ maps with spatial resolution of 2 × 2 × 4 mm were obtained with scan time of 5.4 ± 0.5 min, demonstrating comparable T1 (1236 ± 59 ms) and T1ρ (50.2 ± 2.4 ms) measurements to 2D separate breath-hold mapping techniques. The estimated B1+ maps showed spatial variations across the left ventricle with the septal and inferior regions being 10%-25% lower than the anterior and septal regions. The proposed technique achieved efficient 3D joint myocardial T1 and T1ρ mapping at 3T with respiratory motion correction, spin history B1+ correction and water-fat separation.

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data.

High-resolution fMRI at 7T is challenged by suboptimal alignment quality between functional data and structural scans. This study aims to develop a rapid acquisition method that provides distortion-matched, artifact-mitigated structural reference data. We introduce an efficient sequence protocol termed T1234, which offers adjustable distortions. This approach involves a T1-weighted 2-inversion 3D-EPI sequence with four spatial encoding directions optimized for high-resolution fMRI. A forward Bloch model was used for T1 quantification and protocol optimization. Twenty participants were scanned at 7T using both structural and functional protocols to evaluate the utility of T1234. Results from two protocols are presented. A fast distortion-free protocol reliably produced whole-brain segmentations at 0.8mm isotropic resolution within 3:00-3:40 minutes. It demonstrates robustness across sessions, participants, and three different 7T SIEMENS scanners. For a protocol with geometric distortions that matched functional data, T1234 facilitates layer-specific fMRI signal analysis with enhanced laminar precision. This structural mapping approach enables precise registration with fMRI data. T1234 has been successfully implemented, validated, and tested, and is now available to users at our center and at over 50 centers worldwide.

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

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. 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. 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. 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.

Analysis of Intervertebral Disc Degeneration Induced by Endplate Drilling or Needle Puncture in Complement C6-Sufficient and C6-Deficient Rabbits.

Previous studies indicate an implication of the terminal complement complex (TCC) in disc degeneration (DD). To investigate the functional role of TCC in trauma-induced DD in vivo, the model of endplate (EP) drilling was first applied in rabbits using a C6-deficient rabbit strain in which no TCC formation was possible. In parallel the model of needle puncture was investigated. Using a minimally invasive surgical intervention, lumbar rabbit intervertebral discs (IVDs) were treated with EP drilling or needle puncture. Degenerative effects of both surgical interventions were assessed by Pfirrmann grading and T2 quantification of the IVDs based on high-resolution MRI (11.7 T), as well as radiographic determination of disc height index. Pfirrmann grading indicated significant degenerative effects after EP drilling. Contrary to our assumption, no evidence was found that the absence of TCC formation in C6-deficient rabbits reduces the development of DD compared to C6-sufficient animals. EP drilling was proven to be suitable for application in rabbits. However, results of the present study do not provide clear evidence of a central functional role of TCC within DD and suggest that TCC deposition in DD patients may be primarily considered as a marker of complement activation during DD progression.

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

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

Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain

IntroductionThis study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). MethodsData were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. ResultsTotal scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. ConclusionThe PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.

Robust T2 estimation with balanced steady state free precession.

To develop a novel signal representation for balanced steady state free precession (bSSFP) displaying its T2 independence on B1 and on magnetization transfer (MT) effects. A signal model for bSSFP is developed that shows only an explicit dependence (up to a scaling factor) on E2 (and, therefore, T2) and a novel parameter c (with implicit dependence on the flip angle and E1). Moreover, it is shown that MT effects, entering the bSSFP signal via a binary spin bath model, can be captured by a redefinition of T1 and, therefore, leading to modification of E1, resulting in the same signal model. Various sets of phase-cycled bSSFP brain scans (different flip angles, different TR, different RF pulse durations, and different number of phase cycles) were recorded at 3 T. The parameters T2 (E2) and c were estimated using a variable projection (VARPRO) method and Monte-Carlo simulations were performed to assess T2 estimation precision. Initial experiments confirmed the expected independence of T2 on various protocol settings, such as TR, the flip angle, B1 field inhomogeneity, and the RF pulse duration. Any variation (within the explored range) appears to directly affect the estimation of the parameter c only-in agreement with theory. BSSFP theory predicts an extraordinary feature that all MT and B1-related variational aspects do not enter T2 estimation, making it a potentially robust methodology for T2 quantification, pending validation against existing standards.

Histopathological myocardial changes in patients with severe aortic stenosis referred for surgical valve replacement: a cardiac magnetic resonance correlation study.

Myocardial fibrosis (MF) takes part in left ventricular (LV) remodelling in patients with aortic stenosis (AS), driving the transition from hypertrophy to heart failure. The structural changes that occur in this transition are not fully enlightened. The aim of this study was to describe histopathological changes at endomyocardial biopsy (EMB) in patients with severe AS referred to surgical aortic valve replacement (AVR) and to correlate them with LV tissue characterization from pre-operative cardiac magnetic resonance (CMR). One-hundred fifty-eight patients [73 (68-77) years, 50% women] were referred for surgical AVR because of severe symptomatic AS, with pre-operative CMR (n = 143) with late gadolinium enhancement (LGE), T1, T2 mapping, and extracellular volume fraction (ECV) quantification. Intra-operative septal EMB was obtained in 129 patients. MF was assessed through Masson's Trichrome histochemistry. Immunohistochemistry was performed for both inflammatory cells and extracellular matrix (ECM) characterization (Type I Collagen, Fibronectin, Tenascin C). Non-ischaemic LGE was present in 106 patients (67.1%) [median fraction: 5.0% (2.0-9.7)]. Native T1 was above normal [1053 ms (1024-1071)] and T2 within the normal range [39.3 ms (37.3-42.0)]. Median MF was 11.9% (6.54-19.97), with predominant type I collagen perivascular distribution (95.3%). Sub-endocardial cardiomyocyte ischaemic-like changes were identified in 45% of EMB. There was no inflammation, despite ECM remodelling expression. MF quantification at EMB was correlated with LGE mass (P = 0.008) but not with global ECV (P = 0.125). Patients with severe symptomatic AS referred for surgical AVR have unspecific histological myocardial changes, including signs of cardiomyocyte ischaemic insult. ECM remodelling is ongoing, with MF heterogeneity. These features may be recognized by comprehensive CMR protocols. However, no single CMR parameter captures the burden of MF and histological myocardial changes in this setting.

No-wait inversion—a novel model for T1 mapping from inversion recovery measurements without the waiting times

Introduction: Quantification of longitudinal relaxation time T1 gained interest as an important MR-inducible tissue property for tissue characterization. Standard inversion recovery (IR) measurements for T1 determination take a prohibitively long time, and signal models assume a perfect inversion. Acceleration is possible by using the Look–Locker (LL) technique or other accelerated, model-based algorithms. However, the calculation of real T1 values from LL acquisitions necessitates the knowledge of equilibrium magnetization M0. Thus, usually, a waiting time to allow for free relaxation between global inversion pulses must be implemented. This study aims to introduce a novel model-based fitting approach for T1 mapping without the need for such waiting times.Methods: Single-inversion spiral LL spoiled gradient echo acquisitions were performed in a phantom and eight healthy volunteers using a 1.5T magnetic resonance imaging (MRI) scanner. The measurements comprised two parts, one without magnetization preparation and a second featuring a global inversion pulse preparation before each of the 35 slices. Acquisition was performed without any waiting time in between slices, i.e., before the inversion pulses. T1 maps were calculated based on an iterative model-based reconstruction algorithm which combines the information from these two measurements, with and without inversion.Results: Accurate T1 maps were obtained in phantom and volunteer measurements. ROI-based mean T1 values differ by an average of 1.5% in the phantom and 5% in vivo between reference measurements and the proposed method. The combined fit benefits from both the information obtained in the inversion prepared and the unprepared measurements. The former provides a large dynamic range for accurate model-based fitting of the relaxation process, while the latter provides equilibrium magnetization M0, necessary to obtain accurate T1 values from a LL-like acquisition.Conclusion: The proposed model of a combined fit of an inversion-prepared and an unprepared measurement allows for robust fast T1 mapping, even in cases of imperfect inversion due to skipped waiting times for magnetization recovery. Thus, it can render long waiting times in between inversion pulses redundant.

Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging.

Due to hypoxic vasoconstriction, perfusion is interesting in the lungs. Magnetic Resonance Imaging (MRI) perfusion imaging based on Dynamic Contrast Enhancement (DCE) has been demonstrated in patients with Chronic Obstructive Pulmonary Diseases (COPD) using visual scores, and quantification methods were recently developed further. Inter-patient correlations of echo time-dependent observed T1 [T1(TE)] have been shown with perfusion scores, pulmonary function testing, and quantitative computed tomography. Here, we examined T1(TE) quantification and quantitative perfusion MRI together and investigated both inter-patient and local correlations between T1(TE) and quantitative perfusion. 22 patients (age 68.0 ± 6.2) with COPD were examined using morphological MRI, inversion recovery multi-echo 2D ultra-short TE (UTE) in 1-2 slices for T1(TE) mapping, and 4D Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE. T1(TE) maps were calculated from 2D UTE at five TEs from 70 to 2,300 μs. Pulmonary Blood Flow (PBF) and perfusion defect (QDP) maps were produced from DCE measurements. Lungs were automatically segmented on UTE images and morphological MRI and these segmentations registered to DCE images. DCE images were separately registered to UTE in corresponding slices and divided into corresponding subdivisions. Spearman's correlation coefficients were calculated for inter-patient correlations using the entire segmented slices and for local correlations separately using registered images and subdivisions for each TE. Median T1(TE) in normal and defect areas according to QDP maps were compared. Inter-patient correlations were strongest on average at TE2 = 500 μs, reaching up to |ρ| = 0.64 for T1 with PBF and |ρ| = 0.76 with QDP. Generally, local correlations of T1 with PBF were weaker at TE2 than at TE1 or TE3 and with maximum values of |ρ| = 0.66 (from registration) and |ρ| = 0.69 (from subdivision). In 18 patients, T1 was shorter in defect areas than in normal areas, with the relative difference smallest at TE2. The inter-patient correlations of T1 with PBF and QDP found show similar strength and TE-dependence as those previously reported for visual perfusion scores and quantitative computed tomography. The local correlations and median T1 suggest that not only base T1 but also the TE-dependence of observed T1 in normal areas is closer to that found previously in healthy volunteers than in defect areas.

Kiosk 7R-TC-07 - T2 Quantification in Acute Cardiac Rejection: A Comparison of Breath-hold and FBee-breathing Sequences

Kiosk 7R-TC-07 - T2 Quantification in Acute Cardiac Rejection: A Comparison of Breath-hold and FBee-breathing Sequences

Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification.

To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.

Multiparametric quantification of T1 and T2 relaxation time of bone metastasis in comparison with red or fatty bone marrow using magnetic resonance fingerprinting.

To assess the T1 and T2 values of bone marrow lesions in spine and pelvis derived from magnetic resonance fingerprinting (MRF) and to evaluate the differences in values among bone metastasis, red marrow and fatty marrow. Sixty patients who underwent lumbar spine and pelvic MRI with magnetic resonance fingerprinting were retrospectively included. Among eligible patients, those with bone metastasis, benign red marrow deposition and normal fatty marrow were identified. Two radiologists independently measured the T1 and T2 values from metastatic bone lesions, fatty marrow, and red marrow deposition on three-dimensional-magnetic resonance fingerprinting. Intergroup comparison and interobserver agreement were analyzed. T1 relaxation time was significantly higher in osteoblastic metastasis than in red marrow (1674.6 ± 436.3 vs 858.7 ± 319.5, p < .001). Intraclass correlation coefficients for T1 and T2 values were 0.96 (p < 0.001) and 0.83 (p < 0.001), respectively. T2 relaxation time of osteoblastic metastasis and red marrow deposition had no evidence of a difference (osteoblastic metastasis, 57.9 ± 25.0 vs red marrow, 58.0 ± 34.4, p = 0.45), as were the average T2 values of osteolytic metastasis and red marrow deposition (osteolytic metastasis, 45.3 ± 15.1 vs red marrow, 58.0 ± 34.4, p = 0.63). We report the feasibility of three-dimensional-magnetic resonance fingerprinting based quantification of bone marrow to differentiate bone metastasis from red marrow. Simultaneous T1 and T2 quantification of metastasis and red marrow deposition was possible in spine and pelvis and showed significant different values with excellent inter-reader agreement. T1 values from three-dimensional-magnetic resonance fingerprinting might be a useful quantifier for evaluating bone marrow lesions.

T1rho and T2rho based myocardial tissue characterisation in small animals at high-field MRI

Abstract Introduction Cardiac tissue characterisation using T2-based MRI promises non-invasive diagnosis or monitoring of inflammatory heart disease, toxic heart damage after chemotherapy and myocardial infarction or post-infarction patients. T2 relaxation is also an indicator for the detection of myocardial oedema or intramyocardial haemorrhage [1]. However, accurate T2 quantification is challenging due to motion, blood flow and field inhomogeneities, especially in high-field MRI [2]. In this work, we present an alternative to T2 imaging that uses spin-locking (SL) to provide a robust measurement of T2ρ relaxation. It is known from relaxation theory that T2ρ has a sensitivity almost identical to T2 [3]. In the small animal study presented here, cardiac T2ρ quantification is tested for the first time using a newly developed pulse sequence and compared with established methods. Methods All measurements were performed on a preclinical 7T MRI. T2 contrast was prepared using a CPMG (Carr-Purcell-Meiboom-Gill) sequence. T2ρ contrast was conventionally generated by a balanced-SL (BSL) preparation [4]. Furthermore, a new approach for T2ρ was developed based on a MLEV sequence (Malcolm-Levitt, [2]). In contrast to [2], all pulse delays were minimised here, producing pure T2ρ contrast (Fig. 1). The new method was first validated in phantom experiments and then tested in three mice with radial image acquisition accelerated for cardio-MRI. Results The results of the phantom study are shown in figure 2. No significant differences were found between BSL and MLEV for T2ρ quantification (mean deviation 0.74%). In vivo, significant artefacts were observed for T2 (Fig. 3), while both T2ρ methods showed improved image quality. For the evaluation of relaxation times in the left ventricle, the mean across all animals was: T2=22.9±2.6ms (R2=0.979), BSL-T2ρ=51.4±5.4ms (R2=0.983) and MLEV-T2ρ=60.0±3.4ms (R2&amp;gt;0.99). Thus, the MLEV-T2ρ method achieves the most robust quantification with a mean R2&amp;gt;0.99 and provides relaxation time maps with diagnostic image quality. Discussion: In the present study, myocardial T2ρ mapping was tested at 7T. While no significant differences between BSL and MLEV appeared in phantom experiments, significant difference was found in vivo. Here, only the MLEV technique could achieve robust quantification. One reason for the improved performance is the continuous refocusing and minimisation of free dephasing of the transverse magnetisation. The results also show that MLEV-T2ρ has improved image quality and more robust quantification compared to CPMG-T2. Conclusion Cardiac T2ρ quantification is a robust alternative to established T2 imaging and can be applied under high-field conditions. A combined measurement of T1ρ and T2ρ relaxation time could be used for native detection of myocardial fibrosis and oedema.Figure 1)Comparison of image qualityFigure 2)Results in a knockout model

Rapid Whole-Knee Quantification of Cartilage using T1, T2*, and TRAFF2 mapping with Magnetic Resonance Fingerprinting.

Quantitative Magnetic Resonance Imaging (MRI) holds great promise for the early detection of cartilage deterioration. Here, a Magnetic Resonance Fingerprinting (MRF) framework is proposed for comprehensive and rapid quantification of T1, T2*, and TRAFF2 with whole-knee coverage. A MRF framework was developed to achieve quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame of reference ( TRAFF2) along with T1 and T2*. The proposed sequence acquires 65 measurements of 25 high-resolution slices, interleaved with 7 inversion pulses and 40 RAFF2 trains, for whole-knee quantification in a total acquisition time of 3:25 min. Comparison with reference T1, T2*, and TRAFF2 methods was performed in phantom and in seven healthy subjects at 3T. Repeatability (test-retest) with and without repositioning was also assessed. Phantom measurements resulted in good agreement between MRF and the reference with mean biases of -54, 2, and 5 ms for T1, T2*, and TRAFF2, respectively. Complete characterization of the whole-knee cartilage was achieved for all subjects, and, for the femoral and tibial compartments, a good agreement between MRF and reference measurements was obtained. Across all subjects, the proposed MRF method yielded acceptable repeatability without repositioning ( R2 ≥ 0.94) and with repositioning ( R2 ≥ 0.57) for T1, T2*, and TRAFF2. The short scan time combined with the whole-knee coverage makes the proposed MRF framework a promising candidate for the early assessment of cartilage degeneration with quantitative MRI, but further research may be warranted to improve repeatability after repositioning and assess clinical value in patients.

Bi-component dictionary matching for MR fingerprinting for efficient quantification of fat fraction and water T1 in skeletal muscle.

To propose an efficient bi-component MR fingerprinting (MRF) fitting method using a Variable Projection (VARPRO) strategy, applied to the quantification of fat fraction (FF) and water T1 ( ) in skeletal muscle tissues. The MRF signals were analyzed in a two-step process by comparing them to the elements of separate water and fat dictionaries (bi-component dictionary matching). First, each pair of water and fat dictionary elements was fitted to the acquired signal to determine an optimal FF that was used to merge the fingerprints in a combined water/fat dictionary. Second, standard dictionary matching was applied to the combined dictionary for determining the remaining parameters. A clustering method was implemented to further accelerate the fitting. Accuracy, precision, and matching time of this approach were evaluated on both numerical and in vivo datasets, and compared to the reference dictionary-matching approach that includes FF as a dictionary parameter. In numerical phantoms, all MRF parameters showed high correlation with ground truth for the reference and the bi-component method (R2 > 0.98). In vivo, the estimated parameters from the proposed method were highly correlated with those from the reference approach (R2 > 0.997). The bi-component method achieved an acceleration factor of up to 360 compared to the reference dictionary matching. The proposed bi-component fitting approach enables a significant acceleration of the reconstruction of MRF parameter maps for fat-water imaging, while maintaining comparable precision and accuracy to the reference on FF and estimation.

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning.

To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

T1 mapping of myocardium in rats using self-gated golden-angle acquisition.

The aim of this study is to design a method of myocardial T1 quantification in small laboratory animals and to investigate the effects of spatiotemporal regularization and the needed acquisition duration. We propose a compressed-sensing approach to T1 quantification based on self-gated inversion-recovery radial two/three-dimensional (2D/3D) golden-angle stack-of-stars acquisition with image reconstruction performed using total-variation spatiotemporal regularization. The method was tested on a phantom and on a healthy rat, as well as on rats in a small myocardium-remodeling study. The results showed a good match of the T1 estimates with the results obtained using the ground-truth method on a phantom and with the literature values for rats myocardium. The proposed 2D and 3D methods showed significant differences between normal and remodeling myocardium groups for acquisition lengths down to approximately 5 and 15 min, respectively. A new 2D and 3D method for quantification of myocardial T1 in rats was proposed. We have shown the capability of both techniques to distinguish between normal and remodeling myocardial tissue. We have shown the effects of image-reconstruction regularization weights and acquisition length on the T1 estimates.

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

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