ABSTRACT Purpose To develop, optimize, and characterize a respiratory motion‐resolved free‐running isotropic 3D carotid vessel wall T 1 mapping technique named CARISMAT1C. Methods An inversion‐recovery gradient‐echo free‐running pulse sequence with interleaved double flip‐angle (2FA) was implemented, and an extended‐phase‐graph dictionary was used to map the T 1 relaxation time. In a phantom, the T 1 accuracy was compared to that of a single‐flip‐angle (1FA) variant, the inversion‐recovery spin‐echo reference, and clinical routine MOLLI, while the T 1 precision of several phyllotaxis‐based radial trajectories was compared. The most precise sequence was used in 11 healthy volunteers (25 ± 3Y, 3F). A synthetic 3D gray‐blood image was reconstructed from the source images and used to delineate the internal, external, and common carotid arteries. T 1 values across artery sections were compared to those of MOLLI. Results In the phantom, the 2FA variant had higher T 1 agreement with the IR‐SE reference than 1FA or MOLLI. The top‐to‐bottom phyllotaxis trajectory with golden‐step shuffling was the most precise and was retained for in vivo scans. End‐expiratory carotid vessel wall T 1 values (1185 ± 42 ms) were similar to those obtained with MOLLI (1162 ± 84 ms, p = 0.37) and showed less intersubject variability and high interscan repeatability. No significant differences were found across arterial segments in the vessel wall T 1 values. Conclusion CARISMAT1C had high T 1 accuracy in phantoms and high T 1 precision in vivo in different sections of the carotid artery tree.
PURPOSE:To develop, optimize, and characterize a respiratory motion-resolved free-running isotropic 3D carotid vessel wall T1 mapping technique named CARISMAT1C. METHODS:An inversion-recovery gradient-echo free-running pulse sequence with interleaved double flip-angle (2FA) was implemented, and an extended-phase-graph dictionary was used to map the T1 relaxation time. In a phantom, the T1 accuracy was compared to that of a single-flip-angle (1FA) variant, the inversion-recovery spin-echo reference, and clinical routine MOLLI, while the T1 precision of several phyllotaxis-based radial trajectories was compared. The most precise sequence was used in 11 healthy volunteers (25 ± 3Y, 3F). A synthetic 3D gray-blood image was reconstructed from the source images and used to delineate the internal, external, and common carotid arteries. T1 values across artery sections were compared to those of MOLLI. RESULTS:In the phantom, the 2FA variant had higher T1 agreement with the IR-SE reference than 1FA or MOLLI. The top-to-bottom phyllotaxis trajectory with golden-step shuffling was the most precise and was retained for in vivo scans. End-expiratory carotid vessel wall T1 values (1185 ± 42 ms) were similar to those obtained with MOLLI (1162 ± 84 ms, p = 0.37) and showed less intersubject variability and high interscan repeatability. No significant differences were found across arterial segments in the vessel wall T1 values. CONCLUSION:CARISMAT1C had high T1 accuracy in phantoms and high T1 precision in vivo in different sections of the carotid artery tree.
PURPOSE:To develop an accurate and computationally efficient motion-corrected MRI reconstruction framework that incorporates hundreds to thousands of motion and δ B 0 estimates from high-temporal-resolution tracking. METHODS:We propose Mobile-GRAPPA, a k-space preprocessing approach that uses MLP-parameterized local GRAPPA operators to jointly correct trajectory perturbations, coil reweighting, and δ B 0 -induced phase changes before standard downstream reconstruction. Reconstruction accuracy, noise propagation, spatial resolution, and runtime were evaluated using 3D MPRAGE, multi-echo 3D GRE, and 3D EPTI. RESULTS:Experiments with discrete motion states demonstrated that Mobile-GRAPPA followed by standard SENSE achieved image quality comparable to Aligned-SENSE. In 3D GRE with 1620 tracked states and 3D EPTI with 544 tracked states, Mobile-GRAPPA incorporated all state estimates with minimal motion-correction overhead. Total reconstruction times were approximately 15 s for GRE and 20 min for EPTI, whereas full-state Aligned-SENSE was computationally prohibitive (approximately 10 h for GRE and multiple days for EPTI). Pseudo-replica and PSF analyses showed limited additional noise amplification and negligible spatial-resolution loss. CONCLUSION:Mobile-GRAPPA enables dense motion and δ B 0 information to be incorporated with minimal motion-correction overhead while preserving standard SENSE, subspace, and other downstream reconstruction pipelines.
PURPOSE:The feasibility and reproducibility of adapting the T2-Relaxation-Under-Spin-Tagging (TRUST) MRI sequence for noninvasive measurement of venous oxygenation in the upper arm were investigated, aiming to enable future in vivo validation of T2b-oxygenation calibration curves in sickle cell disease (SCD). METHODS:The TRUST sequence, originally developed for the brain, was optimized for the upper arm by comparing control, label and difference signal. Accuracy was assessed using a phantom experiment. Twelve healthy volunteers underwent repeated TRUST scans using two different protocols: Three-averages and One-average. Reproducibility and temporal resolution were investigated, and a handgrip exercise was used to assess sensitivity to physiological changes. Statistical analyses included coefficient of variation (CV), within-subject CV (wsCV), and Bland-Altman analysis. RESULTS:Phantom experiments demonstrated accurate T2 estimation at low expected velocities with deviations from reference less than 16 ms (11.2%). In vivo, the adapted sequence provided faster and reproducible T2b (86.0 ± 30.0 ms) and Yv measurements (66.1% ± 11.7%). Reducing the number of averages improved temporal resolution without compromising measurement reproducibility (bias: 16.3 to 7.6 ms, wsCV: 39.8% to 35.3%). During exercise, significant decreases in T2b and Yv were observed in both the superficial and deep vein: mean T2b decreased from 87.2 to 58.9 ms (p < 0.001) in the cephalic vein, and from 93.3 to 71.3 ms (p < 0.001) in the brachial vein. CONCLUSION:The adapted TRUST protocol enables reproducible assessment of venous oxygenation in the upper arm, providing a technique for future validation and recalibration of T2b-oxygenation curves in SCD.
PURPOSE:To determine human blood longitudinal relaxation time (T1) ex vivo at 5 T using modified Look-Locker inversion recovery (MOLLI), and to evaluate its dependence on hematocrit (Hct) and oxygenation (Y). METHODS:A MOLLI 5(3)3 protocol was simulated across T1 values of 1000-3000 ms to optimize the sampling interval. Aqueous gadolinium phantoms were scanned using optimized MOLLI and multi-inversion-time recovery spin echo (IR-SE) acquisitions to derive a calibration equation. Arm venous blood from 27 healthy volunteers (mean age: 23.7 ± 4.3 years; 10 males) was measured using optimized MOLLI, with three repetitions per sample. Hct and Y were measured by blood gas analysis, and their relationships with calibrated T1 were evaluated. RESULTS:The minimum root mean square error (RMSE) was obtained at a MOLLI sampling interval of 2000 ms. In phantoms, MOLLI and IR-SE T1 showed a strong linear relationship: T1,IR-SE = 1.185 × T1,MOLLI-93.746 (R2 = 0.998, p < 0.0001). In blood, MOLLI showed high repeatability, with an overall repeatability RMSE of 1.80 ms and a mean coefficient of variation of 0.10% ± 0.08%. Calibrated blood T1 was 1933.6 ± 114.2 ms, range 1727.0-2128.0 ms. Blood 1/T1 dependence on Hct and Y: 1/T1 = 0.447-0.193 × Y + 0.447 × Hct + 0.150 (Y × Hct) (R2 = 0.855, p < 0.0001), yielding an arterial blood T1 of 1971 ms at Y = 0.98 and Hct = 0.42. CONCLUSION:Optimized and phantom calibrated MOLLI enabled rapid measurement of human ex vivo blood T1 at 5 T. Blood T1 at 5 T was higher than commonly reported adult values at 3 T and varied with both Hct and Y.
PURPOSE:Recent updates to the diagnostic criteria of multiple sclerosis (MS) require whole-brain T2*-weighted (T2*w) imaging with submillimeter resolution to detect novel diagnostic biomarkers such as the central vein sign. However, to achieve the needed submillimeter spatial resolution, conventional T2*w 3D gradient-echo scans sequences are limited by prohibitively long scan times for clinical use. Here, we evaluated a different approach based on a segmented 3D echo planar imaging (3D-EPI) sequence, accelerated with 2D Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA) undersampling and denoised with a deep learning-based network. METHODS:Fifty-two research participants were imaged at 3T using the 3D-EPI sequence acquired at different CAIPIRINHA acceleration factors (R = 2, 3, and 4) and denoised using a dedicated denoising convolutional neural network (DnCNN). Quantitative assessment of the accelerated T2*w 3D-EPI scans, before and after denoising, was performed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and tissue contrasts. A neuroradiologist separately assessed image quality in a blinded manner using predetermined scoring criteria. RESULTS:T2*w 3D-EPI with CAIPIRINHA acceleration enabled fast submillimeter isotropic (0.65 mm) imaging of the entire brain with scan times ranging between 3 min 22 s (R = 2) down to 1 min 56 s (R = 4). Even for the fastest scan (R = 4), accelerated T2*w 3D-EPI images denoised with DnCNN exhibited superior PSNR (3 dB increase), SSIM (13% increase) and lesion-to-vein tissue contrast (9% increase) compared to the non-denoised images. CONCLUSIONS:The 3D-EPI sequence combined with CAIPIRINHA and deep learning denoising enables fast submillimeter whole-brain T2*w imaging at 3T.
PURPOSE:Real-time phase-contrast MRI (RT-PC) enables continuous quantification of physiological flow dynamics across multiple temporal frequencies, but its broader application is limited by complex and fragmented post-processing workflows. We developed Flow 2.0, a standardized toolbox for RT-PC designed to integrate segmentation, flow quantification, visualization, and multi-frequency signal analysis within a unified environment. THEORY AND METHODS:Flow 2.0 integrates automated ROI segmentation, background phase correction, velocity aliasing correction, interactive waveform visualization, cardiac cycle reconstruction, and Hermitian-preserving frequency-domain processing. A phase-resolved respiratory modulation module enables systematic evaluation of respiratory influences through temporal phase shifting and cycle-based multi-parameter analysis. The framework was demonstrated using representative RT-PC datasets acquired under free and sustained deep breathing conditions. RESULTS:Automated processing of 500-frame RT-PC datasets was completed within seconds while preserving signal phase integrity. Cardiac cycle reconstruction and respiratory phase classification were consistently applied across acquisition conditions. The phase-resolved analysis module enabled visualization and quantification of respiratory modulation across multiple flow-derived parameters, including mean flow, stroke volume, and cardiac-related metrics. CONCLUSION:Flow 2.0 provides a standardized and extensible toolbox for RT-PC MRI, enabling reproducible, phase-resolved, and multi-parametric analysis of neurofluid dynamics within a unified processing environment.
PURPOSE:Ultra-low-field (ULF) MRI offers a promising path to accessible neuroimaging, with potential to address global healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio (SNR), reduced spatial resolution, and altered tissue contrasts relative to conventional high-field (HF) scans are significant barriers to ULF analysis and interpretation. While deep learning (DL) approaches have been proposed to enhance ULF image quality, many rely on synthetic training data due to the lack of available subject-matched ULF and HF scans, introducing potential "domain shift" errors when applied to real acquisitions. Here, we present a DL framework trained on real ULF and HF-MRIs to address these limitations and improve ULF-derived brain volume analysis. METHODS:A CycleGAN framework was developed for image-to-image translation across field strengths, while mitigating the need for large subject-matched ULF- and HF-MRIs. This approach enabled pretraining on large open-access MRI datasets followed by fine-tuning on real ULF scans. Model performance was evaluated through downstream brain volumetric analysis, assessing volumetric agreement with HF-derived measurements and test-retest reproducibility. We additionally explored a framework to reduce input acquisition requirements, improving scan protocol efficiency while preserving enhanced performance. RESULTS:The proposed methods significantly improved hippocampal volumetric agreement and brain segmentation accuracy between ULF- and HF-MRI compared with existing strategies. Test-retest reproducibility for DL-enhanced images was superior to that of direct segmentation on ULF scans. CONCLUSION:The proposed framework substantially improved the accuracy and reliability of ULF-derived brain volume measurements, particularly for subcortical structures such as the hippocampus.
Pulseq enables vendor-neutral description of MRI pulse sequences in human-readable .seq files and is widely used for sequence development and sharing. During design and debugging, rapid evaluation of sequence timing across RF pulses, gradient waveforms, and ADC events is essential. This is particularly challenging for non-Cartesian acquisitions, where waveform timing does not readily reveal the underlying k-space trajectory. Existing tools, however, provide limited interactive capabilities for this task. Here, we introduce SeqEyes, an interactive viewer for Pulseq .seq files tailored for efficient sequence analysis. The software parses .seq files to provide synchronized visualization of RF, gradient, and ADC waveforms alongside their corresponding k-space trajectories. This establishes a direct link between sequence structure and sampling behavior within a single interface. The tool supports interactive navigation across time, TRs, and sequence blocks, as well as event-level timing verification, visualization of Pulseq extensions, and diagnostic overlays. SeqEyes features a graphical user interface, complemented by command-line, Python, and MATLAB interfaces for integration into existing research workflows. By unifying waveform and trajectory visualizations, this open-source toolbox optimizes the routine validation of Pulseq sequences, ultimately reducing the barrier to developing and sharing complex MRI acquisitions.
PURPOSE:To derive a quantitative relationship between the mean Kärger model (KM) water exchange rate and the mean intercellular water transition rate applicable to brain and other complex biological tissues. THEORY AND METHODS:The mean KM water exchange rate applies to any KM, accommodates an arbitrary number of compartments, and can be estimated from the time dependence of the diffusional kurtosis. The mean intercellular water transition rate for a tissue is the average rate at which water leaves all cellular compartments and enters the extracellular space. It is shown that these two quantities are proportional to each other provided the exchange dynamics are uniform throughout an imaging voxel and the compartmental diffusivities and residence times are not correlated, with a proportionality coefficient depending on the diffusivities and compartmental water fractions. This relationship is illustrated for several white matter regions having highly aligned axons using diffusional kurtosis imaging (DKI) data from four healthy volunteers. RESULTS:For parallel axons, the proportionality coefficient simply reduces to the extracellular water fraction, which allows estimates for the KM exchange rates obtained with DKI to be translated into estimates for the axonal water transition rate. The axonal transition rate is found to vary from 0.83 s-1 in the posterior limb of the internal capsule to 2.00 s-1 in the body of the corpus callosum. CONCLUSION:Under mild assumptions, the mean KM water exchange rate is proportional to the mean intercellular water transition rate. This provides a practical method for estimating intercellular water transition rates from DKI.
PURPOSE:To extend localized quadratic (LQ) RF encoded spin-echo imaging with acquisition and reconstruction strategies that improve efficiency and artifact robustness, positioning it as a practical alternative to 3D FSE for high-resolution volumetric brain MRI. METHODS:The framework integrates (1) additional gradient-echo readouts for simultaneous T2*w or PDw with T2w without prolonging scan time, (2) an in-plane sampling scheme that distributes arms across shot/trajectory types to maximize k-space coverage and render phase inconsistencies as incoherent residue, (3) sliding-slice encoding to disperse through-plane artifacts, (4) a novel loop-ordering to avoid repeated startup cycles and improve motion robustness, and (5) a hybrid 2D/3D Physics-based Reconstruction with Iterative Model-based Enhancement (spiral-PRIME) performing deblurring, fat-water separation, and 3D wavelet denoising. Healthy-volunteer imaging (3 T) was compared with fully sampled LQ and conventional 3D FSE using peak signal-to-noise ratio, structural similarity index measure, pseudo-replica SNR gain, and qualitative review. RESULTS:Sliding-slice with loop-ordering reduced through-plane artifacts and improved temporal efficiency, while in-plane sampling dispersed trajectory inconsistencies as incoherent noise. Spiral-PRIME suppressed undersampling artifacts and noise while preserving fine structure. Relative to spiral-SENSE, spiral-PRIME achieved consistent T2w SNR gains of ∼30%-40% in WM/GM and substantially higher gains for T2*w, with CNR improvements most pronounced for GM-WM. Despite R ≈ 2.33 undersampling, reconstructions closely matched fully sampled references and delivered quality comparable to or exceeding 3D FSE. CONCLUSION:LQ spin-echo with spiral-PRIME enables efficient, multi-contrast volumetric brain imaging with robust artifact suppression and clinically meaningful SNR/CNR gains, supporting its potential as a practical alternative to 3D FSE.
PURPOSE:Most sites develop and use custom coils for 7 T body MRI since no standard pTx body coil exists, leading to diverse coil designs differing in transmit element type, layout, and number of receive channels. This study investigates and compares eight existing coils, including one remote body coil array and seven local arrays, regarding their transmit and receive performance. METHODS:Phantom measurements were conducted with all coils on the same 7 T scanner, using the same phantom and imaging protocol. Transmit performance was compared in terms of B1 + efficiency and coverage. Receive performance was compared in terms of SNR, coverage, noise correlation, and g-factors. RESULTS:Mean B1 + efficiency ranged from 2.02 to 4.33 μT/√kW across configurations, lowest for the remote array and highest for an 8Tx8Rx local array. Central SNR ranged from 278 to 1072, increasing with receive element count and peaking for an 8Tx32Rx configuration. HF-excitation-coverage ranged from 34 to 384 mm, and HF-receive-coverage from 135 to 384 mm, with the remote array combined with a local 32Rx array achieving highest coverage. Acceleration performance improved with increasing receive element count in the corresponding direction. An 8Tx16Rx and an 8Tx32Rx arrays performed best in LR-/AP-direction and the 32Rx array in HF-direction. CONCLUSION:No existing local pTx coil provides universally optimal performance, as each design offers different advantages in either B1 + efficiency, coverage, SNR, or acceleration. Among the local arrays, the 8Tx32Rx array (C5) might represent a reasonable compromise with respect to the parameters evaluated in this study, as it exhibits the highest central SNR, high coverage, and acceleration.
PURPOSE:The aim of this work is to investigate if q-space trajectory imaging (QTI) waveforms can be designed to probe QTI metrics at a single centroid frequency under realistic experimental conditions for in vivo human brain imaging. METHODS:Realistic diffusion encoding waveforms based on double-rotation gradient waveform and magic-angle spinning of the q-vector with varying encoding bandwidth, alignment of encoding spectra across encodings (tuning) and across axes (spectral isotropy) were designed to investigate the accuracy of the centroid frequency approximation. QTI metrics were computed for idealized as well as realistic oscillating and pulsed gradient waveforms using analytical diffusion spectra with 1D and 2D short-range disorder along and perpendicular to axons. For in vivo validation, the waveforms were integrated into a multiband spiral spin-echo sequence. RESULTS:The simulations demonstrate frequency-dependent QTI metrics. The combination of tuning and spectral isotropy led to metrics close to the simulated ground-truth at the centroid frequency of the encoding spectrum. Realistic waveforms with broader bandwidth compared to idealized waveforms introduce little additional error in QTI metrics due to truncation of the cumulant expansion. In agreement with literature, omitting tuning and spectral isotropy reduces isotropic variance and increases microscopic fractional anisotropy when LTE contains lower frequencies, and vice versa for PTE. This observation is confirmed qualitatively by the in vivo measurements. CONCLUSION:The accuracy of the centroid frequency approximation depends on the combination of encoding spectra and investigated tissue. Using similar encoding spectra for tuning and spectral isotropy leads to metrics, characteristic to the centroid frequency.
PURPOSE:To study the impact of mesoscopic magnetic susceptibility heterogeneity on chemical shift-encoded (CSE) proton density fat-fraction (PDFF) quantification in muscular dystrophies, a subgroup of neuromuscular disorders. THEORY AND METHODS:In MRI, extramyocellular lipid deposits induce orientation-dependent Larmor frequency variations due to microstructural anisotropy, resulting in spatially varying frequency shifts between fat and water and increased transverse relaxation rates. A newly developed PDFF quantification method accounting for resonance shifts and dual R2* rates was applied on standard 6-point CSE acquisitions of Duchenne (n = 15), Becker (n = 31), and facioscapulohumeral (n = 30) muscular dystrophy patients, and control subjects (n = 40). The impact of frequency shifts, decay functions, and lipid models on PDFF estimation was systematically assessed. RESULTS:Accounting for resonance shifts resulted in large PDFF quantification differences compared to a reference method (-3.8% [-14.8%, 7.2%]), significantly improved fitting quality (Bayesian Information Criterion (BIC) difference ≥ 10), and reduced fat/water separation artifacts, confirming predictions by numerical simulations. Bias and variability due to the lipid model were reduced to less than 1%. Fitting quality in high R2* regions was further improved using a dual relaxation model with linear/quadratic decay (BIC difference ≥ 2). Sensitivity to change was improved on the tested cohorts (SRM increased by 0.18). DTI-estimated angular dependencies reflected theoretical and numerical predictions for elongated axially symmetric lipid deposits. CONCLUSION:The proposed approach improvements could enhance the PDFF quantification reliability in neuromuscular disorders studies and support more accurate monitoring of myosteatosis.
We present p-Brain, a modular, open-source framework for reproducible, automated quantitative DCE-MRI at scale. Rather than a fixed pipeline, p-Brain is built from interchangeable stages (ingestion, T 1 / M 0 fitting, vascular and tissue ROI extraction, signal-to-concentration conversion, kinetic modeling, and quality control), each selected and configured through a single file-based interface, so any stage can be swapped or extended without modifying the surrounding code. In its default configuration, p-Brain converts signal to gadolinium concentration, derives arterial and venous input functions using convolutional neural network (CNN) slice selection and ROI segmentation, and produces voxelwise, regional, and whole-brain maps. It implements Patlak graphical analysis for the blood-brain barrier influx constant ( K i ) and blood volume ( v b ), and model-free Tikhonov-regularised residue deconvolution for cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), with structured metadata and stage-level quality-control artifacts for auditability. We validate p-Brain against an established reference workflow in two ways: On identical inputs its estimators reproduce the reference algorithms to machine precision, and as a fully automated pipeline it agrees with the reference voxelwise ( r > 0 . 96 , ICC > 0 . 96 ) across all five maps (CBF, CBV, MTT, K i , v b ) in 12 healthy controls. p-Brain runs on Linux, macOS, and Windows as a Python package and command-line tool, and is open and extensible to additional segmentation tools, input-function providers, and kinetic models.
PURPOSE:To adapt and validate a linear binning technique, developed for hyper-polarized 129Xe MRI, for functional lung MRI with matrix-pencil decomposition (MP)-MRI. METHODS:First, a dedicated normalization was applied to the perfusion-weighted and the ventilation-weighted map histograms. Then, using 154 MP-MRI scans of healthy children, reference bins were defined around the peak of the averaged histogram as normal (±1 SD), high (> 1 SD), low (between -1 SD and -2 SD), and defect (< -2 SD), and subsequently applied to a validation dataset comprising healthy children (HC, N = 41) and children with cystic fibrosis (CF, N = 30) to evaluate the accuracy of the classification and its discriminatory power. Furthermore, a third dataset comprising children with CF pre and post elexacaftor/tezacaftor/ivacaftor (ETI) therapy (N = 24) was binned to evaluate the method's sensitivity to treatment effects. Standard outcome parameters, computed with a median threshold, served as a comparison. RESULTS:The adapted linear binning resulted in significantly higher defect and low percentages for perfusion and ventilation between children with CF and HC (p values < 0.0001) with high discrimination (AUCs > 0.85). This was comparable to the standard median-threshold method. Two illustrative cases were included to demonstrate the complementary granularity of the linear binning method. Although standard thresholding indicated improvement in both ventilation and perfusion defects pre and post ETI, linear binning showed that perfusion improvement was restricted to the low category. CONCLUSION:A linear binning method for MP-MRI was developed and validated, providing a healthy reference based on a large dataset and advancing functional lung image processing.
PURPOSE:Assess the impact of incomplete spoiling and spatial saturation on T 1 mapping using the variable flip angle (VFA) spoiled gradient recalled echo sequence (SPGR). Develop a correction for incomplete spoiling that results in an accurate estimate of T 1 , essential for quantitative MRI applications. METHODS:A correction factor, derived from extended phase graph simulations, was applied to the measured SPGR signal to account for deviations from the theoretical steady-state signal. An iterative fitting process was used, refining the incomplete spoiling correction factor at each step based on the updated T 1 estimate. The incomplete spoiling correction method was validated against inversion recovery spin echo in phantom experiments and applied to in vivo data at 3T. Additionally, the hypothesis that spatial saturation disrupts the steady-state was also tested and validated in a phantom. RESULTS:Without a correction, the median T 1 error was +7.4% (IQR [4.4, 9.6]%) in both simulations and phantom experiments. After applying the correction, the median T 1 error decreased to -0.3% (IQR [-2.5, 2.1]%). In vivo, incomplete spoiling caused T 1 overestimation of 10-50 ms depending on B 1 + inhomogeneity. Spatial saturation induced on average a -10% T 1 bias. CONCLUSION:A correction for incomplete spoiling is essential to achieve an accurate measure of T 1 using the VFA SPGR method, addressing systematic T 1 overestimation when using this T 1 mapping technique. Furthermore, other mechanisms that may disrupt the steady-state signal, such as spatial saturation, should be considered.
