Accurate T1 Research Articles

Whole-brain T2 mapping with radial sampling and retrospective motion correction at 3T.

Several barriers prevent the use of whole-brain T2 mapping in routine use despite increasing interest in this parameter. One of the main barriers is the long scan time resulting in patient discomfort and motion corrupted data. To address this challenge, a method for accurate whole-brain T2 mapping with a limited acquisition time and motion correction capabilities is investigated. A 3D radial multi-echo spin-echo sequence was implemented with optimized sampling trajectory enabling the estimation of intra-scan motion, subsequently used to correct the raw data. Motion corrected echo images are then reconstructed with linear subspace constrained reconstruction. Experiments were carried out on phantom and volunteers at 3T to evaluate the accuracy of the T2 estimation, the sensitivity to lesions and the efficiency of the correction on motion corrupted data. Whole-brain T2 mapping acquired in less than 7 min enabled the depiction of lesions in the white matter with longer T2. Data retrospectively corrupted with typical motion traces of pediatric patients highly benefited from the motion correction by reducing the error in T2 estimates within the lesions. All datasets acquired on seven volunteers, with deliberate motion, also showed that motion corrupted T2 maps could be improved with the retrospective motion correction both at the voxel level and the structure level. A whole-brain T2 mapping sequence with retrospective intra-scan motion correction and reasonable acquisition time is proposed. The method necessitates advanced iterative reconstruction strategies but no additional navigator, external device, or increased scan time is required.

3D MR fingerprinting for dynamic contrast-enhanced imaging of whole mouse brain.

Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n = 3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) in the whole mouse brain was demonstrated (n = 5). Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor of up to 48. Dynamic contrast-enhancedMRF scans achieved a spatial resolution of 192 × 192 × 500 μm3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 × 192 × 250 μm3) in 30 min. We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.

Optimization and validation of low-field MP2RAGE T1 mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers.

Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T1 mapping of hypoxia biomarkers show promise, T1-to-noise ratio (T1NR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T1-weighted contrast images with simultaneous T1 mapping. To optimize MP2RAGE for low-field T1 mapping; conduct experimental validation in a ground-truth phantom; establish feasibility and reproducibility of low-field MP2RAGE acquisition and T1 mapping in healthy volunteers. The MP2RAGE optimization was performed to maximize the contrast-to-noise ratio (CNR) of T1 values in white matter (WM) and gray matter (GM) brain tissues at 0.35T. Low-field MP2RAGE images were acquired on a 0.35T MR-Linac (ViewRay MRIdian) using a multi-channel head coil. Validation of T1 mapping was performed with a ground-truth Eurospin phantom, containing inserts of known T1 values (400-850ms), with one and two average (1A and 2A) MP2RAGE scans across four acquisition sessions, resulting in eight T1 maps. Mean (±SD) T1 relative error, T1NR, and intersession coefficient of variation (CV) were determined. Whole-brain MP2RAGE scans were acquired in 5 healthy volunteers across two sessions (A and B) and T1 maps were generated. Mean (±SD) T1 values for WM and GM were determined. Whole-brain T1 histogram analysis was performed, and reproducibility was determined with the CV between sessions. Voxel-by-voxel T1 difference maps were generated to evaluate 3D spatial variation. Low-field MP2RAGE optimization resulted in parameters: MP2RAGETR of 3250ms, inversion times (TI1/TI2) of 500/1200ms, and flip angles (α1/α2) of 7/5°. Eurospin T1 maps exhibited a mean (±SD) relative error of 3.45%±1.30%, T1NR of 20.13±5.31, and CV of 2.22%±0.67% across all inserts. Whole-brain MP2RAGE images showed high anatomical quality with clear tissue differentiation, resulting in mean (±SD) T1 values: 435.36±10.01ms for WM and 623.29±14.64ms for GM across subjects, showing excellent concordance with literature. Whole-brain T1 histograms showed high intrapatient and intersession reproducibility with characteristic intensity peaks consistent with voxel-level WM and GM T1 values. Reproducibility analysis revealed a CV of 0.46%±0.31% and 0.35%±0.18% for WM and GM, respectively. Voxel-by-voxel T1 difference maps show a normal 3D spatial distribution of noise in WM and GM. Low-field MP2RAGE proved effective in generating accurate, reliable, and reproducible T1 maps with high T1NR in phantom studies and in vivo feasibility established in healthy volunteers. While current work is focused on refining the MP2RAGE protocol to enable clinically efficient OE-MRI, this study establishes a foundation for TOLD T1 mapping for hypoxia biomarkers. This advancement holds the potential to facilitate a paradigm shift toward MR-guided biological adaptation and dose painting by leveraging 3D hypoxic spatial distributions and improving outcomes in conventionally challenging-to-treat cancers.

Respiratory motion-corrected T1 mapping of the abdomen.

The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency. Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times. For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8s. The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.

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.

Ultrafast T2 and T2* mapping using single-shot spatiotemporally encoded MRI with reduced field of view and spiral out-in-out-in trajectory.

T2 and T2* mapping are crucial components of quantitative magnetic resonance imaging, offering valuable insights into tissue characteristics and pathology. Single-shot methods can achieve ultrafast T2 or T2* mapping by acquiring multiple readout echo trains. However, the extended echo trains pose challenges, such as compromised image quality and diminished quantification accuracy. In this study, we develop a single-shot method for ultrafast T2 and T2* mapping with reduced echo train length. The proposed method is based on ultrafast single-shot spatiotemporally encoded (SPEN) MRI combined with reduced field of view (FOV) and spiral out-in-out-in (OIOI) trajectory. Specifically, a biaxial SPEN excitation scheme was employed to excite the spin signal into the spatiotemporal encoding domain. The OIOI trajectory with high acquisition efficiency was employed to acquire signals within targeted reduced FOV. Through non-Cartesian super-resolved (SR) reconstruction, 12 aliasing-free images with different echo times were obtained within 150ms. These images were subsequently fitted to generate T2 or T2* mapping simultaneously using a derived model. Accurate and co-registered T2 and T2* maps were generated, closely resembling the reference maps. Numerical simulations demonstrated substantial consistency (R2>0.99) with the ground truth values. A mean difference of 0.6% and 1.7% was observed in T2 and T2*, respectively, in in vivo rat brain experiments compared to the reference. Moreover, the proposed method successfully obtained T2 and T2* mappings of rat kidney in free-breathing mode, demonstrating its superiority over multishot methods lacking respiratory navigation. The results suggest that the proposed method can achieve ultrafast and accurate T2 and T2* mapping, potentially facilitating the application of T2 and T2* mapping in scenarios requiring high temporal resolution.

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T1 mapping.

Magnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed-sensing MP2RAGE (csMP2RAGE) T1 mapping for accelerating MTsat imaging. VFA, MP2RAGE, and csMP2RAGE were compared against inversion-recovery T1 in an aqueous phantom at 3 T. The same 1-mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T1 and MTsat. Bloch-McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability. The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T1 times (< 5% difference) in the phantom, but T1 accuracy was more impacted by T2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization-transfer effects. In the test-retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T1 mapping and 7.8% for MTsat imaging. We demonstrated that MP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.

3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks

Objective. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks. Approach. Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4–8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic cardiac T1 maps. However, they were limited to the ventricles. The proposed approach acquires rotated stacks in long-axis orientation with high in-plane resolution but low through-plane resolution. This results in radially overlapping stacks from which high-resolution T1 maps of the whole heart are reconstructed using a k-space-based SRR framework considering the complete acquisition model. Cardiac and residual respiratory motion between different breath holds is estimated and incorporated into the reconstruction. The proposed approach was evaluated in simulations and phantom experiments and successfully applied to ten healthy subjects.Main results. 3D T1 maps of the whole heart were obtained in the same acquisition time as previous methods covering only the ventricles. T1 measurements were possible even for small structures, such as the atrial wall. The proposed approach provided accurate (P > 0.4; R 2 > 0.99) and precise T1 values (SD of 64.32 ± 22.77 ms in the proposed approach, 44.73 ± 31.9 ms in the reference). The edge sharpness of the T1 maps was increased by 6.20% and 4.73% in simulation and phantom experiments, respectively. Contrast-to-noise ratios between the septum and blood pool increased by 14.50% in in vivo measurements with a k-space compared to an image-space-based SRR. Significance. The proposed approach provided whole-heart high-resolution 1.3 mm isotropic T1 maps in an overall acquisition time of approximately three minutes. Small structures, such as the atrial and right ventricular walls, could be visualized in the T1 maps.

A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.

This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping.

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS.

To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling. A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold. Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time. The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.

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.

STEAM-SASHA: a novel approach for blood- and fat-suppressed native T1 measurement in the right ventricular myocardium

ObjectiveThe excellent blood and fat suppression of stimulated echo acquisition mode (STEAM) can be combined with saturation recovery single-shot acquisition (SASHA) in a novel STEAM-SASHA sequence for right ventricular (RV) native T1 mapping.Materials and methodsSTEAM-SASHA splits magnetization preparation over two cardiac cycles, nulling blood signal and allowing fat signal to decay. Breath-hold T1 mapping was performed in a T1 phantom and twice in 10 volunteers using STEAM-SASHA and a modified Look-Locker sequence at peak systole at 3T. T1 was measured in 3 RV regions, the septum and left ventricle (LV).ResultsIn phantoms, MOLLI under-estimated while STEAM-SASHA over-estimated T1, on average by 3.0% and 7.0% respectively, although at typical 3T myocardial T1 (T1 > 1200 ms) STEAM-SASHA was more accurate. In volunteers, T1 was higher using STEAM-SASHA than MOLLI in the LV and septum (p = 0.03, p = 0.006, respectively), but lower in RV regions (p > 0.05). Inter-study, inter-observer and intra-observer coefficients of variation in all regions were < 15%. Blood suppression was excellent with STEAM-SASHA and noise floor effects were minimal.DiscussionSTEAM-SASHA provides accurate and reproducible T1 in the RV with excellent blood and fat suppression. STEAM-SASHA has potential to provide new insights into pathological changes in the RV in future studies.

Black-Blood Magnetization Prepared 2 Rapid Acquisition Gradient Echoes: A Fast and Three-Dimensional MR Black-Blood T1 Mapping Technique for Quantitative Assessment of Atherosclerosis and Venous Thrombosis.

Blood flow signals may be a confounder in quantifying T1 values of plaque or thrombus and how to realize black-blood T1 mapping remains a challenge task. To develop a fast and three-dimensional black-blood T1 mapping technique for quantitative assessment of atherosclerosis and venous thrombosis. Sequence development and optimization via phantoms and volunteers as well as pilot prospective. Numerical simulations, a standard phantom, 8 healthy volunteers (mean age, 22 ± 1 years; 5 males), and 19 patients (mean age, 57 ± 14 years; 13 males) with atherosclerosis or venous thrombosis. 3T/inversion recovery spin-echo sequence (IR-SE), magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE), and black-blood prepared MP2RAGE (BB-MP2RAGE). The black-blood preparation (i.e., delay alternating with nutation for tailored excitation, DANTE) was incorporated into MP2RAGE for black-blood T1 mapping. The BB-MP2RAGE was optimized numerically based on the Bloch equation, and then the phantom study was performed to verify the accuracy of T1 mapping by BB-MP2RAGE against IR-SE and MP2RAGE. Preliminary clinical validation was prospectively performed to assess the flow suppression effect and its potential application in plaque and thrombosis identification. Pearson correlation test, Bland-Altman analysis, paired t-test, and intraclass correlation coefficient. A P value <0.05 indicates a statistically significant difference. Phantom experiments showed comparable accuracy of T1 maps by BB-MP2RAGE with IR-SE and MP2RAGE (all r2 > 0.99); Compared to MP2RAGE, BB-MP2RAGE effectively nulled the blood flow signals, and had a significant improvement in contrast-to-noise ratio between static tissue and blood (250.5 ± 66.6 vs. 91.9 ± 35.9). BB-MP2RAGE can quantify plaque or thrombus T1 relaxation time with blood flow signal suppression. Accurate T1 mapping with sufficient blood flow suppression was achieved by BB-MP2RAGE. BB-MP2RAGE has the potential to quantitatively characterize atherosclerosis and venous thrombosis. 1 TECHNICAL EFFICACY: Stage 1.

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

Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data

We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric T2 model. We synthetically generate a wide variety of high quality signals and corresponding parameters from a wide range of naturally occurring prior parameter values. To capture spatial variation, the generative signal decay model is combined with a generative spatial model conditioned on generic tissue segmentations. Synthesized 3D datasets can be used to train any convolutional neural network (CNN) based architecture for MWF estimation. Our source code is available at: https://github.com/quin-med-harvard-edu/synthmapReduction of acquisition time at the expense of lower SNR, as well as accuracy and repeatability of MWF estimation techniques, are key factors that affect the adoption of MWF mapping in clinical practice. We demonstrate that the synthetically trained CNN provides superior accuracy over the competing methods under the constraints of naturally occurring noise levels as well as on the synthetically generated images at low SNR levels. Normalized root mean squared error (nRMSE) is less than 7% on synthetic data, which is significantly lower than competing methods. Additionally, the proposed method yields a coefficient of variation (CoV) that is at least 4x better than the competing method on intra-session test-retest reference dataset.

A deep learning approach for accurate and robust T1 estimation from cardiac magnetic resonance MOLLI sequences

Abstract Funding Acknowledgements Type of funding sources: None. Background T1 parametric maps of the myocardium are a recent development in cardiac Magnetic Resonance Imaging (MRI). They can be used to characterize the composition of the heart and identify alterations that can be indicative of a pathology. A common method of T1 estimation involves pixel-wise non-linear curve fitting from Modified Look-Locker Inversion Recovery (MOLLI) series [1]. Whilst relatively accurate, this method presents several drawbacks. Firstly, it assumes magnetization recovery to be a purely exponential process dependent on T1, thus neglecting other parameters such as T2 and B1 that can interfere in the process. Secondly, it does not consider that each acquisition during an inversion recovery experiment inverts the magnetization vector by a given angle and thus interferes with the recovery leading to the measurement of a shorter T1* rather than the actual T1 [2]. Additionally, since it considers each pixel individually, neglecting spatial information, it is sensitive to image quality [3]. Finally, since non-linear curve fitting is an iterative algorithm, it is computationally expensive and time-consuming. Methods To address these issues a convolutional neural network, DeepBLESS [4], and two spatially aware variants of it, were trained to predict T1, T2, and B1 with a dataset of MOLLI series simulated from experimental data. The simulation was performed using Bloch simulations and parameters extracted from a dataset composed of 27 single-slice MOLLI series acquired with the 5(3s)3 protocol using a b-SSFP readout at a main magnetic field strength of 3 Tesla. Thus, a dataset could be generated without relying on exponential fitting for ground truth generation. Results The exponential fitting of T1 was shown to consistently underestimate true T1 values by 138 +- 90 ms. In comparison, the spatially naïve DeepBLESS approach showed a mean absolute error of 14 +- 9 ms and the spatially aware DeepBLESS approach (Median-DeepBLESS) showed a mean absolute error of 13 +- 9 ms. As can be seen in Figure 1, Median-DeepBLESS also showed considerably more resilience to noise than exponential fitting and the spatially naïve version of DeepBLESS. Additionally, the deep learning approaches showcased promising results with regards to the estimation of T2 and B1 from MOLLI sequences (See Figure 2). Conclusions It was shown that the Deep Learning approaches eliminated the underestimation bias of exponential fitting with the spatially aware network Median-DeepBLESS being more robust to noise than both exponential fitting and the standard DeepBLESS network for myocardial T1 estimation. Relatively accurate estimation of T2 and B1 from MOLLI sequences was also accomplished through the Deep Learning approach, which may lead to simultaneous T1, T2, and B1 estimation in common clinical practice in the future.

Open-source myocardial T1 mapping with simultaneous multi-slice acceleration: Combining an auto-calibrated blipped-bSSFP readout with VERSE-MB pulses.

Enabling fast and accessible myocardial T1 mapping is crucial for extending its clinical application. We introduce Open-MOLLI-SMS combining simultaneous multi-slice (SMS) with auto-calibration and variable-rate selective excitation (VERSE)-multiband pulses to obtain all slices in a fast single-shot T1 mapping sequence. Open-MOLLI-SMS was developed by integrating SMS with the open-source method Open-MOLLI previously implemented in Pulseq. Three methods were integrated for Open-MOLLI-SMS: (1) auto-calibration blip patterns to ensure consistency between the data and coil information; (2) a blipped-balanced SSFP (bSSFP) readout to induce controlled aliasing in parallel imaging shifts without disturbing the bSSFP frequency response; and (3) a VERSE-multiband pulse for minimizing the achievable TR and the specific absortion rate (SAR) impact of SMS. Two (SMS2) or three (SMS3) slices were excited simultaneously and encoded with an in-plane acceleration factor of 2. Experiments were performed in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom and five healthy volunteers. Phantom results show accurate T1 estimates for reference values between 400 to 2200 ms. Artifacts were visible for Open-MOLLI-SMS3 but not replicated in vivo. In vivo Open-MOLLI-SMS (T1 SMS2 = 993 ± 10 ms; T1 SMS3 = 1031 ± 17 ms) provided similar values to mean T1 single-band Open-MOLLI estimates (T1 Open-MOLLI = 1005 ± 47 ms). Open-MOLLI-SMS2 provided the closest estimates to the reference. This proof-of-principle implementation study demonstrates the feasibility of speeding up T1 -mapping acquisitions and increasing coverage by combining auto-calibration strategies with a blipped-bSFFP readout and VERSE multiband RF excitation pulses. The proposed methodology was built on the Open-MOLLI mapping sequence, which provides a fast means for prototyping and enables open-source sharing of the method.

Robust Assessment of Macromolecular Fraction (MMF) in Muscle with Differing Fat Fraction Using Ultrashort Echo Time (UTE) Magnetization Transfer Modeling with Measured T1.

Magnetic resonance imaging (MRI) is widely regarded as the most comprehensive imaging modality to assess skeletal muscle quality and quantity. Magnetization transfer (MT) imaging can be used to estimate the fraction of water and macromolecular proton pools, with the latter including the myofibrillar proteins and collagen, which are related to the muscle quality and its ability to generate force. MT modeling combined with ultrashort echo time (UTE-MT modeling) may improve the evaluation of the myotendinous junction and regions with fibrotic tissues in the skeletal muscles, which possess short T2 values and higher bound-water concentration. The fat present in muscle has always been a source of concern in macromolecular fraction (MMF) calculation. This study aimed to investigate the impact of fat fraction (FF) on the estimated MMF in bovine skeletal muscle phantoms embedded in pure fat. MMF was calculated for several regions of interest (ROIs) with differing FFs using UTE-MT modeling with and without T1 measurement and B1 correction. Calculated MMF using measured T1 showed a robust trend, particularly with a negligible error (<3%) for FF < 20%. Around 5% MMF reduction occurred for FF > 30%. However, MMF estimation using a constant T1 was robust only for regions with FF < 10%. The MTR and T1 values were also robust for only FF < 10%. This study highlights the potential of the UTE-MT modeling with accurate T1 measurement for robust muscle assessment while remaining insensitive to fat infiltration up to moderate levels.

Feasibility of joint mapping of triglyceride saturation and water longitudinal relaxation in a single breath hold applied to high fat-fraction adipose depots in the periclavicular anatomy

IntroductionSimultaneous mapping of triglyceride (TAG) saturation and tissue water relaxation may improve the characterization of the structure and function of anatomies with significant adipose tissue. While several groups have demonstrated in vivo TAG saturation imaging using MRI, joint mapping of relaxation and TAG saturation is understudied. Such mappings may avoid bias from physiological motion, if they can be done within a single breath-hold, and also account for static and applied magnetic field heterogeneity. MethodsWe propose a transient-state/MR fingerprinting single breath-hold sequence at 3 T, a low-rank reconstruction, and a parameter estimation pipeline that jointly estimates the number of double bonds (NDB), number of methylene interrupted double bonds (NMIDB), and tissue water T1, while accounting for non-ideal radiofrequency transmit scaling and off-resonance effects. We test the proposed method in simulations, in phantom against MR spectroscopy (MRS), and in vivo regions in and around high fat fraction (FF) periclavicular adipose tissue. Partial volume and multi-peak transverse relaxation effects are explored. ResultsThe simulation results demonstrate accurate NDB, NMIDB, and water T1 estimates across a range of NDB, NMIDB, and T1 values. In phantoms, the proposed method's estimates of NDB and NMIDB correlate with those from MR spectroscopy (Pearson correlation ≥0.98), while the water T1 estimates are concordant with a standard phantom. The NDB and NMIDB are sensitive to partial volumes of water, showing increasing bias at FF < 40%. This bias is found to be due to noise and transverse relaxation effects. The in vivo periclavicular adipose tissue has high FF (>90%). The adipose tissue NDB and NMIDB, and muscle T1 estimates are comparable to those reported in the literature. ConclusionRobust estimation of NDB, NMIDB at high FF and water T1 across a broad range of FFs are feasible using the proposed methods. Further reduction of noise and model bias are needed to employ the proposed technique in low FF anatomies and pathologies.