Local SAR management strategies to use two-channel RF shimming for fetal MRI at 3 T

PURPOSE

This study evaluates the imaging performance of two-channel RF-shimming for fetal MRI at 3 T using four different local specific absorption rate (SAR) management strategies.

METHODS

Due to the ambiguity of safe local SAR levels for fetal MRI, local SAR limits for RF shimming were determined based on either each individual's own SAR levels in standard imaging mode (CP mode) or the maximum SAR level observed across seven pregnant body models in CP mode. Local SAR was constrained either indirectly by further constraining the whole-body SAR (wbSAR) or directly by using subject-specific local SAR models. Each strategy was evaluated by the improvement of the transmit field efficiency (average |B1 + |) and nonuniformity (|B1 + | variation) inside the fetus compared with CP mode for the same wbSAR.

RESULTS

Constraining wbSAR when using RF shimming decreases B1 + efficiency inside the fetus compared with CP mode (by 12%-30% on average), making it inefficient for SAR management. Using subject-specific models with SAR limits based on each individual's own CP mode SAR value, B1 + efficiency and nonuniformity are improved on average by 6% and 13% across seven pregnant models. In contrast, using SAR limits based on maximum CP mode SAR values across seven models, B1 + efficiency and nonuniformity are improved by 13% and 25%, compared with the best achievable improvement without SAR constraints: 15% and 26%.

CONCLUSION

Two-channel RF-shimming can safely and significantly improve the transmit field inside the fetus when subject-specific models are used with local SAR limits based on maximum CP mode SAR levels in the pregnant population.

Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB0 shim array fields

PURPOSE

Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB0 shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels.

METHODS

Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations. Requirements for refocusing pulse designs are derived from the extended phase graph formalism obviating time-consuming sub-voxel isochromatic simulations to model the effects of crusher gradients. To refine resultant slice-profiles because of voxelwise optimization functions, we propose an algorithm that stochastically offsets spatial points at which loss is computed during optimization.

RESULTS

We first applied our proposed design framework to standard slice-selective excitation and refocusing pulses in the absence of non-linear ΔB0 shim array fields and compared them against pulses designed with Shinnar-Le Roux algorithm. Next, we demonstrated our technique in a simulated setup of fetal brain imaging in pregnancy for restricted-slice excitation and refocusing of the fetal brain.

CONCLUSIONS

Our proposed framework for optimizing RF pulse and time-varying spatially non-linear ΔB0 shim array fields achieve high fidelity restricted-slice excitation and refocusing for fetal MRI, which could enable zoomed fast-spin-echo-MRI and other applications.

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.

MARIE 2.0: A Perturbation Matrix Based Patient-Specific MRI Field Simulator

High static field MR scanners can produce human tissue images of astounding clarity, but rely on high frequency electromagnetic radiation that generates complicated in-tissue field patterns that are patient-specific and potentially harmful. Many such scanners use parallel transmitters to better control field patterns, but then adjust the transmitters based on general guidelines rather than optimizing for the specific patient, mostly because computing patient-specific fields was presumed far too slow. It was recently demonstrated that the combination of fast low-resolution tissue mapping and fast voxel-based field simulation can be used to perform a patient-specific MR safety check in minutes. However, the field simulation required several of those minutes, making it too slow to perform the dozens of simulations that would be needed for patient-specific optimization. In this paper we describe a compressed-perturbation-matrix technique that nearly eliminates the computational cost of including complex coils (or coils and shields) in voxel-based field simulation of tissue, thereby reducing simulation time from minutes to seconds. The approach is demonstrated on a wide variety of head+coil and head+coil+shield configurations, using the implementation in MARIE 2.0, the latest version of the open-source MR field simulator MARIE.

Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI

PURPOSE

To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model.

METHODS

Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T₂ -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints.

RESULTS

An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T₂ -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments.

CONCLUSION

Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.

Cross-Sectional Observational Study of Typical in utero Fetal Movements Using Machine Learning

Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in utero. The aim of this prospective study was to use machine learning (ML) on in utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks [GW]) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5-10 min while breathing room air (21% O2) and for 5-10 min while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 min in total with effective temporal resolution approximately 3 s. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors were manually corrected, and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities <31 GW and longer AMT of lower extremities >35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) that impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.

Parallel transmission 2D RARE imaging at 7T with transmit field inhomogeneity mitigation and local SAR control

PURPOSE

We develop and test a parallel transmit (pTx) pulse design framework to mitigate transmit field inhomogeneity with control of local specific absorption rate (SAR) in 2D rapid acquisition with relaxation enhancement (RARE) imaging at 7T.

METHODS

We design large flip angle RF pulses with explicit local SAR constraints by numerical simulation of the Bloch equations. Parallel computation and analytical expressions for the Jacobian and the Hessian matrices are employed to reduce pulse design time. The refocusing-excitation "spokes" pulse pairs are designed to satisfy the Carr-Purcell-Meiboom-Gill (CPMG) condition using a combined magnitude least squares-least squares approach.

RESULTS

In a simulated dataset, the proposed approach reduced peak local SAR by up to 56% for the same level of refocusing uniformity error and reduced refocusing uniformity error by up to 59% (from 32% to 7%) for the same level of peak local SAR compared to the circularly polarized birdcage mode of the pTx array. Using explicit local SAR constraints also reduced peak local SAR by up to 46% compared to an RF peak power constrained design. The excitation and refocusing uniformity error were reduced from 20%-33% to 4%-6% in single slice phantom experiments. Phantom experiments demonstrated good agreement between the simulated excitation and refocusing uniformity profiles and experimental image shading.

CONCLUSION

PTx-designed excitation and refocusing CPMG pulse pairs can mitigate transmit field inhomogeneity in the 2D RARE sequence. Moreover, local SAR can be decreased significantly using pTx, potentially leading to better slice coverage, enabling larger flip angles or faster imaging.

Change in T2* measurements of placenta and fetal organs during Braxton Hicks contractions

Maternal-placental perfusion can be temporarily compromised by Braxton Hicks (BH) uterine contractions. Although prior studies have employed T2* changes to investigate the effect of BH contractions on placental oxygen, the effect of these contractions on the fetus has not been fully characterized. We investigated the effect of BH contractions on quantitative fetal organ T2* across gestation together with the birth information. We observed a slight but significant decrease in fetal brain and liver T2* during contractions.

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.

Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis

We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space data. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The proposed joint learning schemes enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures at every layer of the network. This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces. We demonstrate the advantages of joint convolutional learning for a variety of tasks, including motion correction, denoising, reconstruction from undersampled acquisitions, and combined undersampling and motion correction on simulated and real world multicoil MRI data. The joint models produce consistently high quality output images across all tasks and datasets. When integrated into a state of the art unrolled optimization network with physics-inspired data consistency constraints for undersampled reconstruction, the proposed architectures significantly improve the optimization landscape, which yields an order of magnitude reduction of training time. This result suggests that joint representations are particularly well suited for MRI signals in deep learning networks. Our code and pretrained models are publicly available at https://github.com/nalinimsingh/interlacer.

Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI

PURPOSE

To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data.

METHODS

Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded imaging.

RESULTS

SPARK yields SSIM improvement and 1.5 - 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-Cartesian, 2D and 3D wave-encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements.

CONCLUSION

SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.

A 31-channel integrated "AC/DC" B0 shim and radiofrequency receive array coil for improved 7T MRI

PURPOSE

To test an integrated "AC/DC" array approach at 7T, where B0 inhomogeneity poses an obstacle for functional imaging, diffusion-weighted MRI, MR spectroscopy, and other applications.

METHODS

A close-fitting 7T 31-channel (31-ch) brain array was constructed and tested using combined Rx and ΔB0 shim channels driven by a set of rapidly switchable current amplifiers. The coil was compared to a shape-matched 31-ch reference receive-only array for RF safety, signal-to-noise ratio (SNR), and inter-element noise correlation. We characterize the coil array's ability to provide global and dynamic (slice-optimized) shimming using ΔB0 field maps and echo planar imaging (EPI) acquisitions.

RESULTS

The SNR and average noise correlation were similar to the 31-ch reference array. Global and slice-optimized shimming provide 11% and 40% improvements respectively compared to baseline second-order spherical harmonic shimming. Birdcage transmit coil efficiency was similar for the reference and AC/DC array setups.

CONCLUSION

Adding ΔB0 shim capability to a 31-ch 7T receive array can significantly boost 7T brain B0 homogeneity without sacrificing the array's rdiofrequency performance, potentially improving ultra-high field neuroimaging applications that are vulnerable to off-resonance effects.

Improving D-2-hydroxyglutarate MR spectroscopic imaging in mutant isocitrate dehydrogenase glioma patients with multiplexed RF-receive/B0 -shim array coils at 3 T

MR spectroscopic imaging (MRSI) noninvasively maps the metabolism of human brains. In particular, the imaging of D-2-hydroxyglutarate (2HG) produced by glioma isocitrate dehydrogenase (IDH) mutations has become a key application in neuro-oncology. However, the performance of full field-of-view MRSI is limited by B0 spatial nonuniformity and lipid artifacts from tissues surrounding the brain. Array coils that multiplex RF-receive and B0 -shim electrical currents (AC/DC mixing) over the same conductive loops provide many degrees of freedom to improve B0 uniformity and reduce lipid artifacts. AC/DC coils are highly efficient due to compact design, requiring low shim currents (<2 A) that can be switched fast (0.5 ms) with high interscan reproducibility (10% coefficient of variation for repeat measurements). We measured four tumor patients and five volunteers at 3 T and show that using AC/DC coils in addition to the vendor-provided second-order spherical harmonics shim provides 19% narrower spectral linewidth, 6% higher SNR, and 23% less lipid content for unrestricted field-of-view MRSI, compared with the vendor-provided shim alone. We demonstrate that improvement in MRSI data quality led to 2HG maps with higher contrast-to-noise ratio for tumors that coincide better with the FLAIR-enhancing lesions in mutant IDH glioma patients. Smaller Cramér-Rao lower bounds for 2HG quantification are obtained in tumors by AC/DC shim, corroborating with simulations that predicted improved accuracy and precision for narrower linewidths. AC/DC coils can be used synergistically with optimized acquisition schemes to improve metabolic imaging for precision oncology of glioma patients. Furthermore, this methodology has broad applicability to other neurological disorders and neuroscience.

Selective RF excitation designs enabled by time-varying spatially non-linear ΔB0 fields with applications in fetal MRI

PURPOSE

To demonstrate, through numerical simulations, novel designs of spatially selective radiofrequency (RF) excitations of the fetal brain by both a restricted 2D slice and 3D inner-volume selection. These designs exploit a single-channel RF pulse, conventional gradient fields, and the spatially non-linear ΔB₀ fields of a multi-coil shim array, using an auto-differentiation optimization algorithm.

METHODS

The design algorithm jointly optimizes the RF pulse and the time-varying ΔB₀ fields, which is produced by a 64-channel multi-coil ΔB₀ body array to augment the RF and the linear gradient fields, using an auto-differentiation approach. Two design targets were specified, one a 4-mm thick slice with a limited in-slice extent in one dimension ("restricted slice"), and the other a 3D inner-volume selection encompassing the fetal brain ("inner volume"). The RF duration was limited to 2 ms for the restricted slice excitation and 6 ms for the inner-volume excitation.

RESULTS

Excitation profiles were achieved for both the restricted slice excitation task (one-minus-minimum magnitude, 8%) within the region of interest (ROI) and (maximum-minus-zero magnitude, 8%) in the suppressed regions and the fetal brain volume excitation task (13% and 9%, respectively).

CONCLUSIONS

The proposed joint design of RF and time-varying, spatially non-linear ΔB₀ fields achieves the target excitation profiles with short RF pulse durations and demonstrates the potential to enhance fetal MRI with multi-channel body shim arrays.

Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T

PURPOSE

Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction.

METHODS

A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired.

RESULTS

The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899.

CONCLUSION

The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.

Quantitative T1 and T2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia

INTRODUCTION

MR relaxometry has been used to assess placental exchange function, but methods to date are not sufficiently fast to be robust to placental motion. Magnetic resonance fingerprinting (MRF) permits rapid, voxel-wise, intrinsically co-registered T₁ and T₂ mapping. After characterizing measurement error, we scanned pregnant women during air and oxygen breathing to demonstrate MRF's ability to detect placental oxygenation changes.

METHODS

The accuracy of FISP-based, sliding-window reconstructed MRF was tested on phantoms. MRF scans in 9-s breath holds were acquired at 3T in 31 pregnant women during air and oxygen breathing. A mixed effects model was used to test for changes in placenta relaxation times between physiological states, to assess the dependency on gestational age (GA), and the impact of placental motion.

RESULTS

MRF estimates of known phantom relaxation times resulted in mean absolute errors for T₁ of 92 ms (4.8%), but T₂ was less accurate at 16 ms (13.6%). During normoxia, placental T₁ = 1825 ± 141 ms (avg ± standard deviation) and T₂ = 60 ± 16 ms (gestational age range 24.3-36.7, median 32.6 weeks). In the statistical model, placental T₂ rose and T₁ remained contant after hyperoxia, and no GA dependency was observed for T₁ or T₂.

DISCUSSION

Well-characterized, motion-robust MRF was used to acquire T₁ and T₂ maps of the placenta. Changes with hyperoxia are consistent with a net increase in oxygen saturation. Toward the goal of whole-placenta quantitative oxygenation imaging over time, we aim to implement 3D MRF with integrated motion correction to improve T₂ accuracy.

STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning

Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal function, is limited to fast imaging techniques with compromises in image quality and resolution. Super-resolution for dynamic fetal MRI is still a challenge, especially when multi-oriented stacks of image slices for oversampling are not available and high temporal resolution for recording the dynamics of the fetus or placenta is desired. Further, fetal motion makes it difficult to acquire high-resolution images for supervised learning methods. To address this problem, in this work, we propose STRESS (Spatio-Temporal Resolution Enhancement with Simulated Scans), a self-supervised super-resolution framework for dynamic fetal MRI with interleaved slice acquisitions. Our proposed method simulates an interleaved slice acquisition along the high-resolution axis on the originally acquired data to generate pairs of low- and high-resolution images. Then, it trains a super-resolution network by exploiting both spatial and temporal correlations in the MR time series, which is used to enhance the resolution of the original data. Evaluations on both simulated and in utero data show that our proposed method outperforms other self-supervised super-resolution methods and improves image quality, which is beneficial to other downstream tasks and evaluations.

Rapid head-pose detection for automated slice prescription of fetal-brain MRI

In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. Our ~2-second procedure automatically locates the fetal brain and eyes, which we derive from maximally stable extremal regions (MSERs). The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%. The pipeline may be used to automatically orient the anatomical sequence, removing the need to estimate the head pose from 2D views and reducing delays during which motion can occur.

Safety and imaging performance of two-channel RF shimming for fetal MRI at 3T

PURPOSE

This study investigates whether two-channel radiofrequency (RF) shimming can improve imaging without increasing specific absorption rate (SAR) for fetal MRI at 3T.

METHODS

Transmit field ( B 1 + ) average and variation in the fetus was simulated in seven numerical pregnant body models. Safety was quantified by maternal and fetal peak local SAR and fetal average SAR. The shim parameter space was divided into improved B 1 + (magnitude and homogeneity) and improved SAR regions, and an overlap where RF shimming improved both classes of metrics compared with birdcage mode was assessed. Additionally, the effect of fetal position, tissue detail, and dielectric properties on transmit field and SAR was studied.

RESULTS

A region of subject-specific RF shim parameter space improving both B 1 + and SAR metrics was found for five of the seven models. Optimizing only B 1 + metrics improved B 1 + efficiency across models by 15% on average and 28% for the best-case model. B 1 + variation improved by 26% on average and 49% for the best case. However, for these shim settings, fetal SAR increased by up to 106%. The overlap region, where both B 1 + and SAR metrics improve, showed an average B 1 + efficiency improvement of 6% on average across models and 19% for the best-case model. B 1 + variation improved by 13% on average and 40% for the best case. RFS could also decrease maternal/fetal SAR by up to 49%/58%.

CONCLUSION

RF shimming can improve imaging compared with birdcage mode without increasing fetal and maternal SAR when a patient-specific SAR model is incorporated into the shimming procedure.

Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)

High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so image volumes are degraded by inter- and intra-slice motion artifacts. Besides, manual annotation for fetal MR image quality assessment are usually time-consuming. Therefore, in this work, a semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed. Our method is based on the mean teacher model, where we not only enforce consistency between student and teacher models on the whole image, but also adopt an ROI consistency loss to guide the network to focus on the brain region. The proposed method is evaluated on a fetal brain MR dataset with 11,223 labeled images and more than 200,000 unlabeled images. Results show that compared with supervised learning, the proposed method can improve model accuracy by about 6% and outperform other state-of-the-art semi-supervised learning methods. The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy

Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a novel approach for fetal landmarks detection. In this task 15 agents are deployed to detect 15 landmarks simultaneously by DRL. The optimization is challenging, and here we propose an improved DRL that incorporates priors on physical structure of the fetal body. First, we use graph communication layers to improve the communication among agents based on a graph where each node represents a fetal-body landmark. Further, additional reward based on the distance between agents and physical structures such as the fetal limbs is used to fully exploit physical structure. Evaluation of this method on a repository of 3-mm resolution in vivo data demonstrates a mean accuracy of landmark estimation within 10 mm of ground truth as 87.3%, and a mean error of 6.9 mm. The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.

An integrated RF-receive/B0-shim array coil boosts performance of whole-brain MR spectroscopic imaging at 7 T

Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B0 field. Here, we show that an integrated RF-receive/B0-shim (AC/DC) array coil can be used to mitigate 7 T B0 inhomogeneity, which improves spectral quality and metabolite quantification over a whole-brain slab. Our results from simulations, phantoms, healthy and brain tumor human subjects indicate improvements of global B0 homogeneity by 55%, narrower spectral linewidth by 29%, higher signal-to-noise ratio by 31%, more precise metabolite quantification by 22%, and an increase by 21% of the brain volume that can be reliably analyzed. AC/DC shimming provide the highest correlation (R2 = 0.98, P = 0.001) with ground-truth values for metabolite concentration. Clinical translation of AC/DC and MRSI is demonstrated in a patient with mutant-IDH1 glioma where it enables imaging of D-2-hydroxyglutarate oncometabolite with a 2.8-fold increase in contrast-to-noise ratio at higher resolution and more brain coverage compared to previous 7 T studies. Hence, AC/DC technology may help ultra-high field MRSI become more feasible to take advantage of higher signal/contrast-to-noise in clinical applications.

Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging

PURPOSE

To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly.

METHODS

Data from our multi-contrast acquisition were embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R = 6 (2D) and R = 4 × 4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min.

RESULTS

Across all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary k-space sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplary slices and quantitative error metrics.

CONCLUSION

By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers, which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R = 16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams.

Placental MRI: Development of an MRI compatible ex vivo system for whole placenta dual perfusion

PURPOSE

Placental dysfunction plays a key role in diseases that affect the fetus in utero and after birth. Aiming to develop a platform for validating in vivo placental MRI and investigations into placental physiology, we designed and built a prototype MRI-compatible perfusion chamber with an integrated MRI receive coil for high SNR ex vivo placental imaging.

PRINCIPAL RESULTS

After optimizing placenta vascular clearing and perfusion protocols, we performed contrast enhanced MR angiography and MR relaxometry on eight carefully selected placentas while they were perfused via the umbilical arteries (UAs). Additionally, two of these placentas underwent maternal perfusion via the intervillous space (IVS). Despite striving for homogenous perfusion across the whole placenta, imaging results were highly heterogeneous for both UA and IVS perfused placentas. By histology, we observed blood congestion in the villi in regions that showed low UA perfusion during MRI. In two placentas prominent chorionic arteries followed by adjacent veins underwent contrast enhancement in the absence of villous capillary blush. The single placenta from a pregnancy affected by IUGR had the most homogeneous villous capillary perfusion.

MAJOR CONCLUSIONS

A dual perfusion system for ex vivo placentas compatible with MRI permitted assessment of UA and IVS placental perfusion. We observed spatial UA perfusion heterogeneity and evidence for arteriovenous shunting in placentas from normal pregnancies and deliveries, but relative villous capillary perfusion homogeneity in a single IUGR placenta. Future work will focus on system optimization, followed by physiological manipulation and validation of in vivo placental MRI.

Individualized SAR calculations using computer vision-based MR segmentation and a fast electromagnetic solver

PURPOSE

We propose a fast, patient-specific workflow for on-line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that individual. Our goal is an improved correspondence between the patient and model, reducing reliance on general anatomical body models.

METHODS

A 3D fat-water 3T acquisition (~2 minutes) is automatically segmented using a computer vision algorithm (~1 minute) into what we found to be the most important electromagnetic tissue classes: air, bone, fat, and soft tissues. We then compute the individual's EM field exposure and global and local SAR matrices using a fast electromagnetic integral equation solver. We assess the approach in 10 volunteers and compare to the SAR seen in a standard generic body model (Duke).

RESULTS

The on-the-table workflow averaged 7'44″. Simulation of the simplified Duke models confirmed that only air, bone, fat, and soft tissue classes are needed to estimate global and local SAR with an error of 6.7% and 2.7%, respectively, compared to the full model. In contrast, our volunteers showed a 16.0% and 20.3% population variability in global and local SAR, respectively, which was mostly underestimated by the Duke model.

CONCLUSION

Timely construction and deployment of a patient-specific model is computationally feasible. The benefit of resolving the population heterogeneity compared favorably to the modest modeling error incurred. This suggests that individualized SAR estimates can improve electromagnetic safety in MRI and possibly reduce conservative safety margins that account for patient-model mismatch, especially in non-standard patients.

Placental MRI: Effect of maternal position and uterine contractions on placental BOLD MRI measurements

INTRODUCTION

Before using blood-oxygen-level-dependent magnetic resonance imaging (BOLD MRI) during maternal hyperoxia as a method to detect individual placental dysfunction, it is necessary to understand spatiotemporal variations that represent normal placental function. We investigated the effect of maternal position and Braxton-Hicks contractions on estimates obtained from BOLD MRI of the placenta during maternal hyperoxia.

METHODS

For 24 uncomplicated singleton pregnancies (gestational age 27-36 weeks), two separate BOLD MRI datasets were acquired, one in the supine and one in the left lateral maternal position. The maternal oxygenation was adjusted as 5 min of room air (21% O₂), followed by 5 min of 100% FiO₂. After datasets were corrected for signal non-uniformities and motion, global and regional BOLD signal changes in R₂* and voxel-wise Time-To-Plateau (TTP) in the placenta were measured. The overall placental and uterine volume changes were determined across time to detect contractions.

RESULTS

In mothers without contractions, increases in global placental R₂* in the supine position were larger compared to the left lateral position with maternal hyperoxia. Maternal position did not alter global TTP but did result in regional changes in TTP. 57% of the subjects had Braxton-Hicks contractions and 58% of these had global placental R₂* decreases during the contraction.

CONCLUSION

Both maternal position and Braxton-Hicks contractions significantly affect global and regional changes in placental R₂* and regional TTP. This suggests that both factors must be taken into account in analyses when comparing placental BOLD signals over time within and between individuals.

Design and implementation of a low-cost, tabletop MRI scanner for education and research prototyping

While access to a laboratory MRI system is ideal for teaching MR physics as well as many aspects of signal processing, providing multiple MRI scanners can be prohibitively expensive for educational settings. To address this need, we developed a small, low-cost, open-interface tabletop MRI scanner for academic use. We constructed and tested 20 of these scanners for parallel use by teams of 2-3 students in a teaching laboratory. With simplification and down-scaling to a 1 cm FOV, fully-functional scanners were achieved within a budget of $10,000 USD each. The design was successful for teaching MR principles and basic signal processing skills and serves as an accessible testbed for more advanced MR research projects. Customizable GUIs, pulse sequences, and reconstruction code accessible to the students facilitated tailoring the scanner to the needs of laboratory exercise. The scanners have been used by >800 students in 6 different courses and all designs, schematics, sequences, GUIs, and reconstruction code is open-source.

Comparison of CBF Measured with Combined Velocity-Selective Arterial Spin-Labeling and Pulsed Arterial Spin-Labeling to Blood Flow Patterns Assessed by Conventional Angiography in Pediatric Moyamoya

BACKGROUND AND PURPOSE

Imaging CBF is important for managing pediatric moyamoya. Traditional arterial spin-labeling MR imaging detects delayed transit thorough diseased arteries but is inaccurate for measuring perfusion because of these delays. Velocity-selective arterial spin-labeling is insensitive to transit delay and well-suited for imaging Moyamoya perfusion. This study assesses the accuracy of a combined velocity-selective arterial spin-labeling and traditional pulsed arterial spin-labeling CBF approach in pediatric moyamoya, with comparison to blood flow patterns on conventional angiography.

MATERIALS AND METHODS

Twenty-two neurologically stable pediatric patients with moyamoya and 5 asymptomatic siblings without frank moyamoya were imaged with velocity-selective arterial spin-labeling, pulsed arterial spin-labeling, and DSA (patients). Qualitative comparison was performed, followed by a systematic comparison using ASPECTS-based scoring. Quantitative pulsed arterial spin-labeling CBF and velocity-selective arterial spin-labeling CBF for the middle cerebral artery, anterior cerebral artery, and posterior cerebral artery territories were also compared.

RESULTS

Qualitatively, velocity-selective arterial spin-labeling perfusion maps reflect the DSA parenchymal phase, regardless of postinjection timing. Conversely, pulsed arterial spin-labeling maps reflect the DSA appearance at postinjection times closer to the arterial spin-labeling postlabeling delay, regardless of vascular phase. ASPECTS comparison showed excellent agreement (88%, κ = 0.77, P < .001) between arterial spin-labeling and DSA, suggesting velocity-selective arterial spin-labeling and pulsed arterial spin-labeling capture key perfusion and transit delay information, respectively. CBF coefficient of variation, a marker of perfusion variability, was similar for velocity-selective arterial spin-labeling in patient regions of delayed-but-preserved perfusion compared to healthy asymptomatic sibling regions (coefficient of variation = 0.30 versus 0.26, respectively, Δcoefficient of variation = 0.04), but it was significantly different for pulsed arterial spin-labeling (coefficient of variation = 0.64 versus 0.34, Δcoefficient of variation = 0.30, P < .001).

CONCLUSIONS

Velocity-selective arterial spin-labeling offers a powerful approach to image perfusion in pediatric moyamoya due to transit delay insensitivity. Coupled with pulsed arterial spin-labeling for transit delay information, a volumetric MR imaging approach capturing key DSA information is introduced.

Individual variation in simulated fetal SAR assessed in multiple body models

PURPOSE

We generate 12 models from 4 pregnant individuals to evaluate individual differences in local specific absorption rate (SAR) for differing body habitus and fetal and maternal positions.

METHODS

Structural MR images from 4 pregnant subjects (including supine and left-lateral maternal positions) were manually segmented to create 12 body models by rotating the fetus, modifying the fat content, and altering the maternal arm position in 1 of the subjects. Electromagnetic simulations modeled at 3 Tesla determined the average and peak local SAR in the maternal trunk, fetus, fetal brain, and amniotic fluid.

RESULTS

We observed a significant range of fetal and maternal peak local SAR across the models (maternal trunk: 19.14-44.03 watts/kg, fetus: 9.93-18.79 watts/kg, fetal brain 3.36-10.3 watts/kg). We found that maternal body habitus changes introduced a significant variation in the maternal peak local SAR but not the fetal local SAR. However, the maternal position (either rotating the mother to left-lateral position or altering the arm position) introduced changes in fetal peak local SAR (range: 11.9-17.9 watts/kg). Rotating the fetus also introduced variation in the fetal and fetal brain peak local SAR.

CONCLUSION

The observed variation in SAR emphasizes the need for more anatomical models to enable better safety management of individuals during fetal MRI, including a wider range of gestational ages.

Placental MRI: Developing Accurate Quantitative Measures of Oxygenation

The Human Placenta Project has focused attention on the need for noninvasive magnetic resonance imaging (MRI)-based techniques to diagnose and monitor placental function throughout pregnancy. The hope is that the management of placenta-related pathologies would be improved if physicians had more direct, real-time measures of placental health to guide clinical decision making. As oxygen alters signal intensity on MRI and oxygen transport is a key function of the placenta, many of the MRI methods under development are focused on quantifying oxygen transport or oxygen content of the placenta. For example, measurements from blood oxygen level-dependent imaging of the placenta during maternal hyperoxia correspond to outcomes in twin pregnancies, suggesting that some aspects of placental oxygen transport can be monitored by MRI. Additional methods are being developed to accurately quantify baseline placental oxygenation by MRI relaxometry. However, direct validation of placental MRI methods is challenging and therefore animal studies and ex vivo studies of human placentas are needed. Here we provide an overview of the current state of the art of oxygen transport and quantification with MRI. We suggest that as these techniques are being developed, increased focus be placed on ensuring they are robust and reliable across individuals and standardized to enable predictive diagnostic models to be generated from the data. The field is still several years away from establishing the clinical benefit of monitoring placental function in real time with MRI, but the promise of individual personalized diagnosis and monitoring of placental disease in real time continues to motivate this effort.

Computer-Vision Techniques for Water-Fat Separation in Ultra High-Field MRI Local Specific Absorption Rate Estimation

OBJECTIVE

The purpose of this paper is to prove that computer-vision techniques allow synthesizing water-fat separation maps for local specific absorption rate (SAR) estimation, when patient-specific water-fat images are not available.

METHODS

We obtained ground truth head models by using patient-specific water-fat images. We obtained two different label-fusion water-fat models generating a water-fat multiatlas and applying the STAPLE and local-MAP-STAPLE label-fusion methods. We also obtained patch-based water-fat models applying a local group-wise weighted combination of the multiatlas. Electromagnetic (EM) simulations were performed, and B1+ magnitude and 10 g averaged SAR maps were generated.

RESULTS

We found local approaches provide a high DICE overlap (72.6 ± 10.2% fat and 91.6 ± 1.5% water in local-MAP-STAPLE, and 68.8 ± 8.2% fat and 91.1 ± 1.0% water in patch-based), low Hausdorff distances (18.6 ± 7.7 mm fat and 7.4 ± 11.2 mm water in local-MAP-STAPLE, and 16.4 ± 8.5 mm fat and 7.2 ± 11.8 mm water in patch-based) and a low error in volume estimation (15.6 ± 34.4% fat and 5.6 ± 4.1% water in the local-MAP-STAPLE, and 14.0 ± 17.7% fat and 4.7 ± 2.8% water in patch-based). The positions of the peak 10 g-averaged local SAR hotspots were the same for every model.

CONCLUSION

We have created patient-specific head models using three different computer-vision-based water-fat separation approaches and compared the predictions of B1+ field and SAR distributions generated by simulating these models. Our results prove that a computer-vision approach can be used for patient-specific water-fat separation, and utilized for local SAR estimation in high-field MRI.

SIGNIFICANCE

Computer-vision approaches can be used for patient-specific water-fat separation and for patient specific local SAR estimation, when water-fat images of the patient are not available.

Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T₁, T₂, and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.

Motion-robust sub-millimeter isotropic diffusion imaging through motion corrected generalized slice dithered enhanced resolution (MC-gSlider) acquisition

PURPOSE

To develop an efficient MR technique for ultra-high resolution diffusion MRI (dMRI) in the presence of motion.

METHODS

gSlider is an SNR-efficient high-resolution dMRI acquisition technique. However, subject motion is inevitable during a prolonged scan for high spatial resolution, leading to potential image artifacts and blurring. In this study, an integrated technique termed Motion Corrected gSlider (MC-gSlider) is proposed to obtain high-quality, high-resolution dMRI in the presence of large in-plane and through-plane motion. A motion-aware reconstruction with spatially adaptive regularization is developed to optimize the conditioning of the image reconstruction under difficult through-plane motion cases. In addition, an approach for intra-volume motion estimation and correction is proposed to achieve motion correction at high temporal resolution.

RESULTS

Theoretical SNR and resolution analysis validated the efficiency of MC-gSlider with regularization, and aided in selection of reconstruction parameters. Simulations and in vivo experiments further demonstrated the ability of MC-gSlider to mitigate motion artifacts and recover detailed brain structures for dMRI at 860 μm isotropic resolution in the presence of motion with various ranges.

CONCLUSION

MC-gSlider provides motion-robust, high-resolution dMRI with a temporal motion correction sensitivity of 2 s, allowing for the recovery of fine detailed brain structures in the presence of large subject movements.

Computation of ultimate SAR amplification factors for radiofrequency hyperthermia in non-uniform body models: impact of frequency and tumour location

PURPOSE

We introduce a method for calculation of the ultimate specific absorption rate (SAR) amplification factors (uSAF) in non-uniform body models. The uSAF is the greatest possible SAF achievable by any hyperthermia (HT) phased array for a given frequency, body model and target heating volume.

METHODS

First, we generate a basis-set of solutions to Maxwell's equations inside the body model. We place a large number of electric and magnetic dipoles around the body model and excite them with random amplitudes and phases. We then compute the electric fields created in the body model by these excitations using an ultra-fast volume integral solver called MARIE. We express the field pattern that maximises the SAF in the target tumour as a linear combination of these basis fields and optimise the combination weights so as to maximise SAF (concave problem). We compute the uSAFs in the Duke body models at 10 frequencies in the 20-900 MHz range and for twelve 3 cm-diameter tumours located at various depths in the head and neck.

RESULTS

For both shallow and deep tumours, the frequency yielding the greatest uSAF was ∼900 MHz. Since this is the greatest frequency that we simulated, we hypothesise that the globally optimal frequency is actually greater.

CONCLUSIONS

The uSAFs computed in this work are very large (40-100 for shallow tumours and 4-17 for deep tumours), indicating that there is a large room for improvement of the current state-of-the-art head and neck HT devices.

Functional oxygen extraction fraction (OEF) imaging with turbo gradient spin echo QUIXOTIC (Turbo QUIXOTIC)

PURPOSE

QUantitative Imaging of eXtraction of Oxygen and TIssue Consumption (QUIXOTIC) is a recent technique that measures voxel-wise oxygen extraction fraction (OEF) but suffers from long scan times, limiting its application. We implemented multiecho QUIXOTIC dubbed turbo QUIXOTIC (tQUIXOTIC) that reduces scan time eightfold and then applied it in functional MRI.

METHODS

tQUIXOTIC utilizes a novel turbo gradient spin echo readout enabling measurement of venular blood transverse relaxation rate in a single tag-control acquisition. Using tQUIXOTIC, we estimated cortical gray matter (GM) OEF, created voxel-by-voxel GM OEF maps, and quantified changes in visual cortex OEF during a blocked design flashing checkerboard visual stimulus. Contamination from cerebrospinal fluid partial volume averaging was estimated and corrected.

RESULTS

The average cortical GM OEF was estimated as 0.38 ± 0.06 (n = 8) using a 3.4-min acquisition. The average OEF in the visual cortex was estimated as 0.43 ± 0.04 at baseline and 0.35 ± 0.05 during activation, with an average %ΔOEF of -20%. These values are consistent with those of past studies.

CONCLUSION

tQUIXOTIC successfully estimated cortical GM OEF in clinical scan times and detected changes in OEF during blocked design visual stimulation. tQUIXOTIC will be useful to monitor regional OEF clinically and in blocked design or event-related functional MRI experiments. Magn Reson Med 79:2713-2723, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study

Fetal health is critically dependent on placental function, especially placental transport of oxygen from mother to fetus. When fetal growth is compromised, placental insufficiency must be distinguished from modest genetic growth potential. If placental insufficiency is present, the physician must trade off the risk of prolonged fetal exposure to placental insufficiency against the risks of preterm delivery. Current ultrasound methods to evaluate the placenta are indirect and insensitive. We propose to use Blood-Oxygenation-Level-Dependent (BOLD) MRI with maternal hyperoxia to quantitatively assess mismatch in placental function in seven monozygotic twin pairs naturally matched for genetic growth potential. In-utero BOLD MRI time series were acquired at 29 to 34 weeks gestational age. Maps of oxygen Time-To-Plateau (TTP) were obtained in the placentas by voxel-wise fitting of the time series. Fetal brain and liver volumes were measured based on structural MR images. After delivery, birth weights were obtained and placental pathological evaluations were performed. Mean placental TTP negatively correlated with fetal liver and brain volumes at the time of MRI as well as with birth weights. Mean placental TTP positively correlated with placental pathology. This study demonstrates the potential of BOLD MRI with maternal hyperoxia to quantify regional placental function in vivo.

Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling

PURPOSE

This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF).

THEORY AND METHODS

A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T₁ , T₂ , and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach.

RESULTS

The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short.

CONCLUSIONS

The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933-942, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Single-step quantitative susceptibility mapping with variational penalties

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd.

Assessing the effects of subject motion on T2 relaxation under spin tagging (TRUST) cerebral oxygenation measurements using volume navigators

PURPOSE

Subject motion may cause errors in estimates of blood T₂ when using the T₂ -relaxation under spin tagging (TRUST) technique on noncompliant subjects like neonates. By incorporating 3D volume navigators (vNavs) into the TRUST pulse sequence, independent measurements of motion during scanning permit evaluation of these errors.

METHODS

The effects of integrated vNavs on TRUST-based T₂ estimates were evaluated using simulations and in vivo subject data. Two subjects were scanned with the TRUST+vNav sequence during prescribed movements. Mean motion scores were derived from vNavs and TRUST images, along with a metric of exponential fit quality. Regression analysis was performed between T₂ estimates and mean motion scores. Also, motion scores were determined from independent neonatal scans.

RESULTS

vNavs negligibly affected venous blood T₂ estimates and better detected subject motion than fit quality metrics. Regression analysis showed that T₂ is biased upward by 4.1 ms per 1 mm of mean motion score. During neonatal scans, mean motion scores of 0.6 to 2.0 mm were detected.

CONCLUSION

Motion during TRUST causes an overestimate of T₂ , which suggests a cautious approach when comparing TRUST-based cerebral oxygenation measurements of noncompliant subjects. Magn Reson Med 78:2283-2289, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Spatiotemporal alignment of in utero BOLD-MRI series

PURPOSE

To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs.

MATERIALS AND METHODS

The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison.

RESULTS

The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P < 0.01) and volume overlap and distance between region boundaries measures were significantly improved (P < 0.01).

CONCLUSION

The proposed approach to align MRI time series enables more accurate quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:403-412.

Construction and modeling of a reconfigurable MRI coil for lowering SAR in patients with deep brain stimulation implants

Post-operative MRI of patients with deep brain simulation (DBS) implants is useful to assess complications and diagnose comorbidities, however more than one third of medical centers do not perform MRIs on this patient population due to stringent safety restrictions and liability risks. A new system of reconfigurable magnetic resonance imaging head coil composed of a rotatable linearly-polarized birdcage transmitter and a close-fitting 32-channel receive array is presented for low-SAR imaging of patients with DBS implants. The novel system works by generating a region with low electric field magnitude and steering it to coincide with the DBS lead trajectory. We demonstrate that the new coil system substantially reduces the SAR amplification around DBS electrodes compared to commercially available circularly polarized coils in a cohort of 9 patient-derived realistic DBS lead trajectories. We also show that the optimal coil configuration can be reliably identified from the image artifact on B₁⁺ field maps. Our preliminary results suggest that such a system may provide a viable solution for high-resolution imaging of DBS patients in the future. More data is needed to quantify safety limits and recommend imaging protocols before the novel coil system can be used on patients with DBS implants.

The ultimate signal-to-noise ratio in realistic body models

PURPOSE

We compute the ultimate signal-to-noise ratio (uSNR) and G-factor (uGF) in a realistic head model from 0.5 to 21 Tesla.

METHODS

We excite the head model and a uniform sphere with a large number of electric and magnetic dipoles placed at 3 cm from the object. The resulting electromagnetic fields are computed using an ultrafast volume integral solver, which are used as basis functions for the uSNR and uGF computations.

RESULTS

Our generalized uSNR calculation shows good convergence in the sphere and the head and is in close agreement with the dyadic Green's function approach in the uniform sphere. In both models, the uSNR versus B₀ trend was linear at shallow depths and supralinear at deeper locations. At equivalent positions, the rate of increase of the uSNR with B₀ was greater in the sphere than in the head model. The uGFs were lower in the realistic head than in the sphere for acceleration in the anterior-posterior direction, but similar for the left-right direction.

CONCLUSION

The uSNR and uGFs are computable in nonuniform body models and provide fundamental performance limits for human imaging with close-fitting MRI array coils. Magn Reson Med 78:1969-1980, 2017. © 2016 International Society for Magnetic Resonance in Medicine.