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.

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.

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.

MR-oximetry with fat DESPOT.

PURPOSE: The R1 relaxation rate of fat is a promising marker of tissue oxygenation. Existing techniques to map fat R1 in MR-oximetry offer limited spatial coverage, require long scan times, or pulse sequences that are not readily available on clinical scanners. This work addresses these limitations with a 3D voxel-wise fat R1 mapping technique for MR-oximetry based on a variable flip angle (VFA) approach at 3 T. METHODS: Varying levels of dissolved oxygen (O2) were generated in a phantom consisting of vials of safflower oil emulsion, used to approximate human fat. Joint voxel-wise mapping of fat and water R1 was performed with a two-compartment VFA model fitted to multi-echo gradient-echo magnitude data acquired at four flip angles, referred to as Fat DESPOT. Global R1 was also calculated. Variations of fat, water, and global R1 were investigated as a function of the partial pressure of O2 (pO2). Inversion-prepared stimulated echo magnetic resonance spectroscopy was used as the reference technique for R1 measurements. RESULTS: Fat R1 from Fat DESPOT was more sensitive than water R1 and global R1 to variations in pO2, consistent with previous studies performed with different R1 mapping techniques. Fat R1 sensitivity to pO2 variations with Fat DESPOT (median O2 relaxivity r1, O2 = 1.57× 10-3 s-1 mmHg-1) was comparable to spectroscopy-based measurements for methylene, the main fat resonance (median r1, O2= 1.80 × 10-3 s-1 mmHg-1). CONCLUSION: Fat and water R1 can be measured on a voxel-wise basis using a two-component fit to multi-echo 3D VFA magnitude data in a clinically acceptable scan time. Fat and water R1 measured with Fat DESPOT were sensitive to variations in pO2. These observations suggest an approach to 3D in vivo MR oximetry.

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.

New developments in MRI: System characterization, technical advances and radiotherapy applications.

A Special Issue of Physica Medica - European Journal of Medical Physics, focused on some important points of contact between the world of magnetic resonance and that of medical physics, was published during 2021. This Editorial describes and comments on the content of this Focus Issue, which contains articles from leading groups invited by the Guest Editors.

A role for magnetic susceptibility in synthetic computed tomography.

PURPOSE: Radiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping. METHODS: Volumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans. RESULTS: The skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU. CONCLUSION: Susceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.

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.

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer.

BACKGROUND AND PURPOSE: A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. MATERIALS AND METHODS: Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. RESULTS: The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; %RDC = 12%), -0.007 to 0.029 × 10-3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. CONCLUSION: We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.

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.

Modeling the primary source intensity distribution: reconstruction and inter-comparison of six Varian TrueBeam sources.

The primary source size is one of the most important beam model parameters in small photon fields. In this work we apply a recently suggested reconstruction technique to characterize the primary source of 6 Varian TrueBeam (TB) linacs. A series of photon fluence profile measurements were performed on 6 Varian TB linacs in the crossplane and inplane orientation using radiochromic film in air and a 2 mm Pb foil as a build-up layer. An image reconstruction algorithm was then applied, based on the maximum likelihood expectation-maximization (MLEM) algorithm, to estimate the source distribution. The method iteratively ray-traces photons from the source plane to the measurement plane to extract source profile corrections. The technique was first benchmarked using a Monte Carlo (MC) model of a Varian TrueBeam with known input Gaussian source sizes. The robustness of the suggested technique was also tested by randomly sampling different combinations of source and field size values and repeating the reconstruction. At the MC benchmarking stage the MLEM reconstruction algorithm was capable of reproducing the Gaussian shape with a RMSE less than 4.0%, while the reconstructed source size (FWHM) and field size were determined with an accuracy level of 0.14 mm and 0.10 mm respectively. Experimentally, the reconstructed TB sources presented FWHM values between 1.02-1.5 mm ([Formula: see text]-0.18 mm) and 1.08-1.42 mm ([Formula: see text]-0.13 mm) in the crossplane and inplane orientations respectively. All TB sources studied in this work can be considered symmetric within uncertainties with the exception of one. The source distribution presented systematic deviations from a Gaussian distribution mostly in the lower tail region. Multi-parameter functional forms, such as Pearson VII or double Gaussian presented improvements in modeling the source in this region, but increase the model complexity. The reconstructed sources measured in this work can serve as reference values for commissioning beam models in small fields and set upper and lower thresholds values of the expected source size for a TB linac.

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.

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.

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.

Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities.

BACKGROUND AND PURPOSE: In this work, we validate a texture-based model computed from positron emission tomography (PET) and magnetic resonance imaging (MRI) for the prediction of lung metastases in soft-tissue sarcomas (STS). We explore functional imaging at different treatment time points and evaluate the feasibility of radiotherapy dose painting as a potential treatment strategy for patients with higher metastatic risk. MATERIALS AND METHODS: We acquired fluorodeoxyglucose (FDG)-PET, fluoromisonidazole (FMISO)-PET, diffusion weighting (DW)-MRI and dynamic contrast enhanced (DCE)-MRI data for 18 patients with extremity STS before, during, and after pre-operative radiotherapy. We tested the lung metastases prediction model using pre-treatment images. We evaluated the feasibility of dose painting using volumetric arc therapy (VMAT) via treatment re-planning with a prescription of 50 Gy to the planning target volume (PTV50Gy) and boost doses of 60 Gy to the FDG hypermetabolic gross tumour volume (GTV60Gy) and 65 Gy to the low-perfusion DCE-MRI hypoxic GTV contained within the GTV60Gy (GTV65Gy). RESULTS: The texture-based model for lung metastases prediction reached an area under the curve (AUC), sensitivity, specificity and accuracy of 0.71, 0.75, 0.85 and 0.82, respectively. Dose painting resulted in adequate coverage and homogeneity in the re-planned treatments: D95% to the PTV50Gy, GTV60Gy and GTV65Gy were 50.0 Gy, 60.3 Gy and 65.4 Gy, respectively. CONCLUSIONS: Textural biomarkers extracted from FDG-PET and MRI could be useful to identify STS patients that might benefit from dose escalation. The feasibility of treatment planning with double boost levels to intratumoural GTV functional sub-volumes was established.

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.

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.