Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.

PURPOSE: T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

R2* Impact on Hepatic Fat Quantification With a Commercial Single Voxel Technique at 1.5 and 3.0 T.

Rationale and Objectives: Fat quantification accuracy using a commercial single-voxel high speed T2-corrected multi-echo (HISTO) technique and its robustness to R2* variations at 3.0 T, such as those introduced by iron in liver, has not been fully established. This study evaluated HISTO at 3.0 T and sought to reproduce results at 1.5 T. Methods: Phantoms were prepared with a range of fat content and R2*. Data were acquired at 1.5 T and 3.0 T, using HISTO and a Dixon technique. Fat quantification accuracy was evaluated as a function of R2*. The patient study included 239 consecutive patients. Data were acquired at 1.5 T or 3.0 T, using HISTO and Dixon techniques. The techniques were compared using Bland-Altman plots. Bias significance was evaluated using a one-sample t-test. Results: In phantoms, HISTO was accurate within 10% up to a R2* of 100 s-1 at both field strengths, while Dixon was accurate within 10% where R2* was accurately quantified (up to 350 s-1 at 1.5 T, and 550 s-1 at 3.0 T). In patients, where R2* was <100 s-1, fat quantification from both techniques agreed at 1.5 T (P = .71), but not at 3.0 T (P = .007), with a bias <1%. Conclusion: Results suggest that HISTO is reliable when R2* is <100 s-1, corresponding to patients with at most mild liver iron overload, and that it should be used with caution when R2* is >100 s-1. Dixon should be preferred for hepatic fat quantification due to its robustness to R2* variations.

Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

Longitudinal relaxation in fat-water mixtures and its dependence on fat content at 3 T.

Longitudinal (T1 ) relaxation of triglyceride molecules and water is of interest for fat-water separation and fat quantification. A better understanding of T1 relaxation could benefit modeling for applications in fat quantification and relaxation mapping. This work investigated T1 relaxation of spectral resonances of triglyceride molecules and water in liquid fat-water mixtures and its dependence on the fat fraction. Dairy cream and a safflower oil emulsion were used. These were diluted with distilled water to produce a variety of fat mass fractions (4.4% to 35% in dairy cream and 6.3% to 52.3% in safflower oil emulsion). T1 was measured at room temperature at 3 T using an inversion recovery STimulated Echo Acquisition Mode (STEAM) MR spectroscopy method with a series of inversion times. T1 variations as a function of fat fraction were investigated for various resonances. A two-component model was developed to describe the relaxation in a fat-water mixture as a function of the fat fraction. The T1 of water and of all fat resonances studied in this work decreased as the fat fraction increased. The relative variation in T1 was different for each fat resonance. The T1 of the methylene resonance showed the least variation as a function of the fat fraction. The proposed two-component model closely fits the observed T1 variations. In conclusion, this work clarifies how the T1 of major and minor fat resonances and of the water resonance varies as a function of the fat fraction in fat-water mixtures. Knowledge of these variations could serve modeling, analysis of MRI measurements in fat-water mixtures, and phantom preparation.

A simulation study of cell size and volume fraction mapping for tissue with two underlying cell populations using diffusion-weighted MRI.

PURPOSE: To propose a method for voxel-wise estimation of cell radii and volume fractions of two cell populations when they coexist in the same MR voxel using the combination of diffusion-weighted MRI and microstructural modeling. METHOD: Microstructure models were investigated using diffusion data simulated with the matrix method for a range of microstructures mimicking tumor tissue with two cell populations, using acquisition parameters available on preclinical scanners. The effect of noise was investigated for a subset of these microstructures. The accuracy and precision of the estimated radii and volume fractions for large and small cells Rl,Rs,vin,l,vin,s were evaluated by comparing the estimates to their true values. The stability of model fitting was characterized by the percentage of accepted fits. RESULTS: The estimation accuracy and precision, and thus the ability to robustly distinguish the two cell populations, depended on the microstructural properties and SNR. For a SNR of 50, a minimum difference of 3 µm between the radius of the large and small cell populations was required for differentiation. Proposed modifications to the two cell population microstructure model, including constrained fits, improved the stability of fits. CONCLUSIONS: This proof-of-concept study proposed a diffusion MRI-based method for voxel-wise estimation of cell radii and volume fractions of two cell populations when they coexist in the same MR voxel. The ability to reliably characterize tissue with two cell populations opens exciting avenues of potential applications in both tumor diagnosis and treatment monitoring.

Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail.

PURPOSE: Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. METHODS: RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail. RESULTS: RRIFT estimated the transfer constant Ktrans and interstitial volume ve with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of Ktrans had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for Ktrans , 0.96 for ve , and 0.73 for the plasma volume vp using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for Ktrans , 0.93 for ve and 0.78 for vp . CONCLUSIONS: Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.

Impact of magnetic susceptibility anisotropy at 3 T and 7 T on T2*-based myelin water fraction imaging.

PURPOSE: Myelin Water Fraction (MWF) mapping can be achieved by fitting multi-gradient recalled echo (MGRE) magnitude images with a three-component model or a pseudo-continuous T2∗ distribution. Recent findings of compartment-specific orientation-dependent magnetic susceptibility shifts have spurred the inclusion of frequency offset (Δf) terms in the fitting models. In this work, we performed simulations to assess the impact of Δf's on the MWF, derived from three different fitting models, at two field strengths. THEORY AND METHODS: White matter MGRE signals were simulated using the Hollow Cylinder Fiber Model at 3 and 7 T, for a range of fiber orientations (θ), and analyzed using: 1) a multi-component T2∗ signal magnitude model (MCMT2∗); 2) a three-component T2∗ signal magnitude model (3CMT2∗); and, 3) a three-component complex T2∗ signal model (3CCT2∗). RESULTS: At 3 T, MCMT2∗ & 3CMT2∗ yielded accurate MWFs: (11.9±1.1)% and (11.7±1.0)% (mean± standard deviation across 1000 simulations, true MWF = 12%), respectively. 3CCT2∗ MWFs were less accurate and had the largest variability: (9.2±5.0)%. At 7 T, MCMT2∗ and 3CMT2∗ MWFs became less accurate as θ increased. This was remedied by 3CCT2∗, at the expense of accuracy for small θ. CONCLUSION: This work suggests that if no information regarding Δf is sought, MCMT2∗ and 3CMT2∗ are preferable at 3 T. At 7 T, Δf cannot be overlooked.

Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal.

PURPOSE: Phase processing impacts the accuracy of quantitative susceptibility mapping (QSM). Techniques for phase unwrapping and background removal have been proposed and demonstrated mostly in brain. In this work, phase processing was evaluated in the context of large susceptibility variations (Δχ) and negligible signal, in particular for susceptibility estimation using the iterative phase replacement (IPR) algorithm. METHODS: Continuous Laplacian, region-growing, and quality-guided unwrapping were evaluated. For background removal, Laplacian boundary value (LBV), projection onto dipole fields (PDF), sophisticated harmonic artifact reduction for phase data (SHARP), variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP), regularization enabled sophisticated harmonic artifact reduction for phase data (RESHARP), and 3D quadratic polynomial field removal were studied. Each algorithm was quantitatively evaluated in simulation and qualitatively in vivo. Additionally, IPR-QSM maps were produced to evaluate the impact of phase processing on the susceptibility in the context of large Δχ with negligible signal. RESULTS: Quality-guided unwrapping was the most accurate technique, whereas continuous Laplacian performed poorly in this context. All background removal algorithms tested resulted in important phase inaccuracies, suggesting that techniques used for brain do not translate well to situations where large Δχ and no or low signal are expected. LBV produced the smallest errors, followed closely by PDF. CONCLUSION: Results suggest that quality-guided unwrapping should be preferred, with PDF or LBV for background removal, for QSM in regions with large Δχ and negligible signal. This reduces the susceptibility inaccuracy introduced by phase processing. Accurate background removal remains an open question. Magn Reson Med 79:3103-3113, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Multi-gradient-echo myelin water fraction imaging: Comparison to the multi-echo-spin-echo technique.

PURPOSE: Myelin water fraction (MWF) mapping based on multi-gradient recalled-echo (MGRE) imaging has been proposed as an alternative to the conventional multi-echo-spin-echo (MESE) approach. In this work, we performed a comparative study of MESE and MGRE-derived MWFs in the same subject group. METHODS: MESE and MGRE data were acquired in 12 healthy volunteers at 3T. T2* decay curves were corrected for the effects of field inhomogeneities and multicomponent analysis of T2  and T2* signals was performed using non-negative least-squares fitting. RESULTS: When comparing MGRE and MESE-MWFs across volunteers, no significant differences were observed between average values in WM, deep GM (dGM), and cortical GM (cGM) that were (14 ± 3%), (6 ± 2%), and (8 ± 2%) for MGRE, and (13 ± 2%), (6 ± 1%), and (7 ± 1%), respectively, for MESE. The MGRE and MESE-MWFs showed a strong correlation (r2 = 0.84) and Bland-Altman analysis revealed a small positive bias of (0.8 ± 1.6%) (absolute difference) for the MGRE-MWF. CONCLUSION: Overall, we observed excellent agreement between the two techniques. The small positive bias of the MGRE-MWF is thought to be a consequence of its potentially reduced sensitivity to water exchange effects, compared to the MESE-MWF. This work suggests that with careful correction for the effects of field inhomogeneities, MGRE-MWF imaging is a promising alternative to the MESE approach. Magn Reson Med 79:1439-1446, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

An extended reference region model for DCE-MRI that accounts for plasma volume.

The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated Ktrans by over 10% at vp  = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible vp where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.

Probabilistic classification of tumour habitats in soft tissue sarcoma.

The purpose of this work is to propose a method to characterize tumour heterogeneity on MRI, using probabilistic classification based on a reference tissue. The method uses maps of the apparent diffusion coefficient (ADC), T2 relaxation, and a calculated map representing high-b-value diffusion-weighted MRI (denoted simDWI) to identify up to five habitats (i.e. sub-regions) of tumours. In this classification method, the parameter values (ADC, T2 , and simDWI) from each tumour voxel are compared against the corresponding parameter probability distributions in a reference tissue. The probability that a tumour voxel belongs to a specific habitat is the joint probability for all parameters. The classification can be visualized using a custom colour scheme. The proposed method was applied to data from seven patients with biopsy-confirmed soft tissue sarcoma, at three time-points over the course of pre-operative radiotherapy. Fast-spin-echo images with two different echo times and diffusion MRI with three b-values were obtained and used as inputs to the method. Imaging findings were compared with pathology reports from pre-radiotherapy biopsy and post-surgical resection. Regions of hypercellularity, high-T2 proteinaceous fluid, necrosis, collagenous stroma, and fibrosis were identified within soft tissue sarcoma. The classifications were qualitatively consistent with pathological observations. The percentage of necrosis on imaging correlated strongly with necrosis estimated from FDG-PET before radiotherapy (R2  = 0.97) and after radiotherapy (R2  = 0.96). The probabilistic classification method identifies realistic habitats and reflects the complex microenvironment of tumours, as demonstrated in soft tissue sarcoma.

Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches.

PURPOSE: Reference region models (RRMs) can quantify tumor perfusion in dynamic contrast-enhanced MRI without an arterial input function. Inspection of the RRM reveals that one of the free parameters in the fit is uniquely linked to the reference region and is common to all voxels. A two-step approach is proposed that takes this constraint into account. METHODS: Three constrained RRM (CRRM) approaches were devised and evaluated. Simulations were performed to compare their accuracy and precision over a range of noise and temporal resolutions. The CRRM was also applied on a virtual phantom that simulates different perfusion values. In vivo evaluation was performed on data from breast cancer and soft tissue sarcoma. RESULTS: In simulations, the CRRM consistently improved precision and had better accuracy at low signal-to-noise ratio (SNR). In virtual phantom, the CRRMs were able to fit voxels that had similar kinetics to the reference tissue, whereas the unconstrained models failed to accurately fit these voxels. In the in vivo data, the constrained approaches produced parameter maps that had less variability and were in better agreement with the Tofts model. CONCLUSION: These findings indicate that the two-step fitting approach of the CRRM can reduce the variability of perfusion estimates for quantifying perfusion with dynamic contrast-enhanced (DCE) MRI. Magn Reson Med 78:1547-1557, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Field inhomogeneity correction for gradient echo myelin water fraction imaging.

PURPOSE: Recently, the multi-echo gradient echo (MGRE) sequence has been proposed for multicomponent T2* (MC T2*) based myelin water fraction (MWF) mapping. This approach has appeal because it can provide fast whole-brain coverage, has low specific absorption rate, and short echo spacing. However, the MGRE signal requires correction for accurate MWF mapping, because of its sensitivity to magnetic field inhomogeneities (ΔB0 ). We propose a ΔB0 correction method for 2D MGRE data obtained for MWF mapping. THEORY AND METHODS: Latter-echo MGRE data were fit to estimate B0 gradients in the slice-select direction ( Gz). The decay signal was corrected for the effects of Gz, and MC T2* analysis was performed using nonnegative least-squares fitting. The method was evaluated using simulations and its performance demonstrated in healthy volunteers. RESULTS: Simulations showed that MWFs are significantly biased in the presence of Gz and that our correction method leads to accurate MWF estimates. In vivo MWF maps obtained from corrected data showed recovery of MWF estimates in areas of high ΔB0, and overall good agreement with literature values obtained with the reference MC T2-based method. CONCLUSION: A new algorithm was presented for ΔB0 correction of 2D MGRE echo data acquired for MWF imaging. Simulations and in vivo data showed an improvement in MWF estimates. Magn Reson Med 78:49-57, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Biexponential longitudinal relaxation in white matter: Characterization and impact on T1 mapping with IR-FSE and MP2RAGE.

PURPOSE: Magnetization transfer in white matter (WM) causes biexponential relaxation, but most quantitative T1 measurements fit data assuming monoexponential relaxation. The resulting monoexponential T1 estimate varies based on scan parameters and represents a source of variation between studies, especially at high fields. In this study, we characterized WM T1 relaxation and performed simulations to determine how to minimize this deviation. METHODS: To characterize biexponential relaxation, four volunteers were scanned at 3T and 7T using inversion recovery fast spin echo (IR-FSE) with 13 inversion times (TIs). Three volunteers were scanned with IR-FSE using TIs chosen by simulations to reduce T1 deviation, and with MP2RAGE. RESULTS: At 3T, the biexponential relaxation has a short component of T1 = 48 ms (9%) and a long component of T1 = 939 ms. At 7T the short component is T1 = 57 ms (11%) and the long component is 1349 ms (89%). For IR-FSE, acquiring four TIs with a minimum of 150 ms (3T) or 200 ms (7T) yielded monoexponential T1 estimates that match the long component to within 10 ms. For MP2RAGE, significant differences (90 ms at 3T, 125 ms at 7T) remain at all parameter values. CONCLUSION: Many T1 mapping sequences yield robust estimates of the long T1 component with suitable choice of TIs, allowing reproducible, sequence-independent T1 values to be measured. However, this is not true of MP2RAGE in its current implementation. Magn Reson Med 75:2265-2277, 2016. © 2015 Wiley Periodicals, Inc.

Accelerating parameter mapping with a locally low rank constraint.

PURPOSE: To accelerate MR parameter mapping using a locally low rank (LLR) constraint, and the combination of parallel imaging and the LLR constraint. THEORY AND METHODS: An LLR method is developed for MR parameter mapping and compared with a globally low rank method in a multiecho spin-echo T2 mapping experiment. For acquisition with coil arrays, a combined LLR and parallel imaging method is proposed. The proposed method is evaluated in a variable flip angle T1 mapping experiment and compared with the LLR method and parallel imaging alone. RESULTS: In the multiecho spin-echo T2 mapping experiment, the LLR method is more accurate than the globally low rank method for acceleration factors 2 and 3, especially for tissues with high T2 values. Variable flip angle T1 mapping is achieved by acquiring datasets with 10 flip angles, each dataset accelerated by a factor of 6, and reconstructed by the proposed method with a small normalized root mean square error of 0.025. CONCLUSIONS: The LLR method is likely superior to the globally low rank method for MR parameter mapping. The proposed combined LLR and parallel imaging method has better performance than the two methods alone, especially with highly accelerated acquisition.

MRI-based myelin water imaging: A technical review.

Multiexponential T2 relaxation time measurement in the central nervous system shows a component that originates from water trapped between the lipid bilayers of myelin. This myelin water component is of significant interest as it provides a myelin-specific MRI signal of value in assessing myelin changes in cerebral white matter in vivo. In this article, the various acquisition and analysis strategies proposed to date for myelin water imaging are reviewed and research conducted into their validity and clinical applicability is presented. Comparisons between the imaging methods are made with a discussion regarding potential difficulties and model limitations.

On the accuracy of T1 mapping: searching for common ground.

PURPOSE: There are many T1 mapping methods available, each of them validated in phantoms and reporting excellent agreement with literature. However, values in literature vary greatly, with T1 in white matter ranging from 690 to 1100 ms at 3 Tesla. This brings into question the accuracy of one of the most fundamental measurements in quantitative MRI. Our goal was to explain these variations and look into ways of mitigating them. THEORY AND METHODS: We evaluated the three most common T1 mapping methods (inversion recovery, Look-Locker, and variable flip angle) through Bloch simulations, a white matter phantom and the brains of 10 healthy subjects (single-slice). We pooled the T1 histograms of the subjects to determine whether there is a sequence-dependent bias and whether it is reproducible across subjects. RESULTS: We found good agreement between the three methods in phantoms, but poor agreement in vivo, with the white matter T1 histogram peak in healthy subjects varying by more than 30% depending on the method used. We also found that the pooled brain histograms displayed three distinct white matter peaks, with Look-Locker consistently underestimating, and variable flip angle overestimating the inversion recovery T1 values. The Bloch simulations indicated that incomplete spoiling and inaccurate B1 mapping could account for the observed differences. CONCLUSION: We conclude that the three most common T1 mapping protocols produce stable T1 values in phantoms, but not in vivo. To improve the accuracy of T1 mapping, we recommend that sites perform in vivo validation of their T1 mapping method against the inversion recovery reference method, as the first step toward developing a robust calibration scheme.

Visualization of intra-thalamic nuclei with optimized white-matter-nulled MPRAGE at 7T.

Novel MR image acquisition strategies have been investigated to elicit contrast within the thalamus, but direct visualization of individual thalamic nuclei remains a challenge because of their small size and the low intrinsic contrast between adjacent nuclei. We present a step-by-step specific optimization of the 3D MPRAGE pulse sequence at 7T to visualize the intra-thalamic nuclei. We first measured T1 values within different sub-regions of the thalamus at 7T in 5 individuals. We used these to perform simulations and sequential experimental measurements (n=17) to tune the parameters of the MPRAGE sequence. The optimal set of parameters was used to collect high-quality data in 6 additional volunteers. Delineation of thalamic nuclei was performed twice by one rater and MR-defined nuclei were compared to the classic Morel histological atlas. T1 values within the thalamus ranged from 1400ms to 1800ms for adjacent nuclei. Using these values for theoretical evaluations combined with in vivo measurements, we showed that a short inversion time (TI) close to the white matter null regime (TI=670ms) enhanced the contrast between the thalamus and the surrounding tissues, and best revealed intra-thalamic contrast. At this particular nulling regime, lengthening the time between successive inversion pulses (TS=6000ms) increased the thalamic signal and contrast and lengthening the α pulse train time (N*TR) further increased the thalamic signal. Finally, a low flip angle during the gradient echo acquisition (α=4°) was observed to mitigate the blur induced by the evolution of the magnetization along the α pulse train. This optimized set of parameters enabled the 3D delineation of 15 substructures in all 6 individuals; these substructures corresponded well with the known anatomical structures of the thalamus based on the classic Morel atlas. The mean Euclidean distance between the centers of mass of MR- and Morel atlas-defined nuclei was 2.67mm (±1.02mm). The reproducibility of the MR-defined nuclei was excellent with intraclass correlation coefficient measured at 0.997 and a mean Euclidean distance between corresponding centers of mass found at first versus second readings of 0.69mm (±0.38mm). This 7T strategy paves the way to better identification of thalamic nuclei for neurosurgical planning and investigation of regional changes in neurological disorders.

AAPM Task Group 334: A guidance document to using radiotherapy immobilization devices and accessories in an MR environment.

Use of magnetic resonance (MR) imaging in radiation therapy has increased substantially in recent years as more radiotherapy centers are having MR simulators installed, requesting more time on clinical diagnostic MR systems, or even treating with combination MR linear accelerator (MR-linac) systems. With this increased use, to ensure the most accurate integration of images into radiotherapy (RT), RT immobilization devices and accessories must be able to be used safely in the MR environment and produce minimal perturbations. The determination of the safety profile and considerations often falls to the medical physicist or other support staff members who at a minimum should be a Level 2 personnel as per the ACR. The purpose of this guidance document will be to help guide the user in making determinations on MR Safety labeling (i.e., MR Safe, Conditional, or Unsafe) including standard testing, and verification of image quality, when using RT immobilization devices and accessories in an MR environment.