B1-MRF: Large dynamic range MRF-based absolute B 1 + mapping in the human body at 7T.

PURPOSE: This study aims to map the transmit magnetic field ( B 1 + $$ {B}_1^{+} $$ ) in the human body at 7T using MR fingerprinting (MRF), with a focus on achieving high accuracy and precision across a large dynamic range, particularly at low flip angles (FAs). METHODS: A FLASH-based MRF sequence (B1-MRF) with high B 1 + $$ {B}_1^{+} $$ sensitivity was developed. Phantom and in vivo abdominal imaging were performed at 7T, and the results were compared with established reference methods, including a slow but precise preparation-based method (PEX), saturated TurboFLASH (satTFL), actual flip angle imaging (AFI) and Bloch-Siegert shift (BSS). RESULTS: The MRF signal curve was highly sensitive to B 1 + $$ {B}_1^{+} $$ , while T1 sensitivity was comparatively low. The phantom experiment showed good agreement of B 1 + $$ {B}_1^{+} $$ to PEX for a T1 range of 204-1691 ms evaluated at FAs from 0° to 70°. Compared to the references, a dynamic range increase larger than a factor of two was determined experimentally. In vivo liver scans showed a strong correlation between B1-MRF, satTFL, and RPE-AFI in a low body mass index (BMI) subject (18.1 kg/m2). However, in larger BMI subjects (≥25.5 kg/m2), inconsistencies were observed in low B 1 + $$ {B}_1^{+} $$ regions for satTFL and RPE-AFI, while B1-MRF still provided consistent results in these regions. CONCLUSION: B1-MRF provides accurate and precise B 1 + $$ {B}_1^{+} $$ maps over a wide range of FAs, surpassing the capabilities of existing methods in the FA range < 60°. Its enhanced sensitivity at low FAs is advantageous for various applications requiring precise B 1 + $$ {B}_1^{+} $$ estimates, potentially advancing the frontiers of ultra-high field (UHF) body imaging at 7T and beyond.

Toward accurate and fast velocity quantification with 3D ultrashort TE phase-contrast imaging.

PURPOSE: Traditional phase-contrast MRI is affected by displacement artifacts caused by non-synchronized spatial- and velocity-encoding time points. The resulting inaccurate velocity maps can affect the accuracy of derived hemodynamic parameters. This study proposes and characterizes a 3D radial phase-contrast UTE (PC-UTE) sequence to reduce displacement artifacts. Furthermore, it investigates the displacement of a standard Cartesian flow sequence by utilizing a displacement-free synchronized-single-point-imaging MR sequence (SYNC-SPI) that requires clinically prohibitively long acquisition times. METHODS: 3D flow data was acquired at 3T at three different constant flow rates and varying spatial resolutions in a stenotic aorta phantom using the proposed PC-UTE, a Cartesian flow sequence, and a SYNC-SPI sequence as reference. Expected displacement artifacts were calculated from gradient timing waveforms and compared to displacement values measured in the in vitro flow experiments. RESULTS: The PC-UTE sequence reduces displacement and intravoxel dephasing, leading to decreased geometric distortions and signal cancellations in magnitude images, and more spatially accurate velocity quantification compared to the Cartesian flow acquisitions; errors increase with velocity and higher spatial resolution. CONCLUSION: PC-UTE MRI can measure velocity vector fields with greater accuracy than Cartesian acquisitions (although pulsatile fields were not studied) and shorter scan times than SYNC-SPI. As such, this approach is superior to traditional Cartesian 3D and 4D flow MRI when spatial misrepresentations cannot be tolerated, for example, when computational fluid dynamics simulations are compared to or combined with in vitro or in vivo measurements, or regional parameters such as wall shear stress are of interest.

Respiratory motion-corrected T1 mapping of the abdomen.

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

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

Objective. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks.Approach. Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4-8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic cardiac T1 maps. However, they were limited to the ventricles. The proposed approach acquires rotated stacks in long-axis orientation with high in-plane resolution but low through-plane resolution. This results in radially overlapping stacks from which high-resolution T1 maps of the whole heart are reconstructed using a k-space-based SRR framework considering the complete acquisition model. Cardiac and residual respiratory motion between different breath holds is estimated and incorporated into the reconstruction. The proposed approach was evaluated in simulations and phantom experiments and successfully applied to ten healthy subjects.Main results. 3D T1 maps of the whole heart were obtained in the same acquisition time as previous methods covering only the ventricles. T1 measurements were possible even for small structures, such as the atrial wall. The proposed approach provided accurate (P> 0.4;R2> 0.99) and precise T1 values (SD of 64.32 ± 22.77 ms in the proposed approach, 44.73 ± 31.9 ms in the reference). The edge sharpness of the T1 maps was increased by 6.20% and 4.73% in simulation and phantom experiments, respectively. Contrast-to-noise ratios between the septum and blood pool increased by 14.50% inin vivomeasurements with a k-space compared to an image-space-based SRR.Significance. The proposed approach provided whole-heart high-resolution 1.3 mm isotropic T1 maps in an overall acquisition time of approximately three minutes. Small structures, such as the atrial and right ventricular walls, could be visualized in the T1 maps.

Joint B0 and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

OBJECTIVE: We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0  âˆ¼  50mT) MRI. METHODS: Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the B0-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees. RESULTS: We compare our model to different model-based approaches at distinct noise levels and various B0-field distributions. Our results show that our physics-informed neural network approach outperforms the purely model-based methods by improving the PSNR up to 11.7% and the RMSE up to 86.3%. CONCLUSION: Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and B0-field maps in the low-field regime. SIGNIFICANCE: low-field MRI is becoming increasingly more popular as it enables access to MR in challenging situations such as intensive care units or resource poor areas. Our method allows for fast and accurate image reconstruction in such low-field imaging with B0-inhomogeneity compensation under a wide range of various environmental conditions.

Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.

Purpose: To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. Approach: The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI). Results: The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R2>0.9). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI. Conclusions: We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.

Post-hoc standardisation of parametric T1 maps in cardiovascular magnetic resonance imaging: a proof-of-concept.

BACKGROUND: In cardiovascular magnetic resonance imaging parametric T1 mapping lacks universally valid reference values. This limits its extensive use in the clinical routine. The aim of this work was the introduction of our self-developed Magnetic Resonance Imaging Software for Standardization (MARISSA) as a post-hoc standardisation approach. METHODS: Our standardisation approach minimises the bias of confounding parameters (CPs) on the base of regression models. 214 healthy subjects with 814 parametric T1 maps were used for training those models on the CPs: age, gender, scanner and sequence. The training dataset included both sex, eleven different scanners and eight different sequences. The regression model type and four other adjustable standardisation parameters were optimised among 240 tested settings to achieve the lowest coefficient of variation, as measure for the inter-subject variability, in the mean T1 value across the healthy test datasets (HTE, N = 40, 156 T1 maps). The HTE were then compared to 135 patients with left ventricular hypertrophy including hypertrophic cardiomyopathy (HCM, N = 112, 121 T1 maps) and amyloidosis (AMY, N = 24, 24 T1 maps) after applying the best performing standardisation pipeline (BPSP) to evaluate the diagnostic accuracy. FINDINGS: The BPSP reduced the COV of the HTE from 12.47% to 5.81%. Sensitivity and specificity reached 95.83% / 91.67% between HTE and AMY, 71.90% / 72.44% between HTE and HCM, and 87.50% / 98.35% between HCM and AMY. INTERPRETATION: Regarding the BPSP, MARISSA enabled the comparability of T1 maps independently of CPs while keeping the discrimination of healthy and patient groups as found in literature. FUNDING: This study was supported by the BMBF / DZHK.