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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.

Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces. Methods: Building on model-based nonlinear and linear subspace techniques that enable reconstruction of signal dynamics, we train auto-encoders on dictionaries of simulated signal evolution to learn more compact, non-linear, 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 inserting the decoder into the forward model improves reconstructions in comparison to subspace constraints. Results: An auto-encoder with one real latent variable represents FSE, EPTI, and MPRAGE signal evolution as well as linear subspaces characterized by four basis vectors. In simulated/in-vivo T2-shuffling and in-vivo EPTI experiments, the proposed framework achieves consistent quantitative NRMSE and qualitative 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 non-linear latent representations of signal evolution improves time-resolved MRI reconstructions through reduced degrees of freedom.

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.

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.

PURPOSE: To demonstrate, through numerical simulations, novel designs of spatially-selective 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 ΔB(0) 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 ΔB(0) fields, which is produced by a 64-channel multi-coil ΔB(0) 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 2ms for the restricted slice excitation and 6ms for the inner-volume excitation. RESULTS: Excitation profiles were achieved for both the restricted slice excitation task (one-minus-minimum magnitude, 8%) within the 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 ΔB(0) fields achieves the target excitation profiles with short RF pulse durations, and demonstrate the potential to enhance fetal MRI with multi-channel body shim arrays.

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.

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.

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 In submission to IEEE Transactions on Medical Imaging (TMI)

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 to 10 minutes while breathing room air (21% O2) and for 5 to 10 minutes while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 minutes in total with an effective temporal resolution of approximately 3 seconds. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network (CNN) previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors 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) impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.

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.

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.

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.

© 2020 Elsevier Ltd 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.

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 (

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 B(0) spatial nonuniformity and lipid artifacts from tissues surrounding the brain. Array coils that multiplex RF-receive and B(0)-shim electrical currents (AC/DC mixing) over the same conductive loops provide many degrees of freedom to improve B(0) uniformity and reduce lipid artifacts. AC/DC coils are highly efficient due to compact design, requiring low shim currents (

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 [Formula: see text] 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 [Formula: see text] (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 [Formula: see text] and SAR metrics was found for five of the seven models. Optimizing only [Formula: see text] metrics improved [Formula: see text] efficiency across models by 15% on average and 28% for the best-case model. [Formula: see text] 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 [Formula: see text] and SAR metrics improve, showed an average [Formula: see text] efficiency improvement of 6% on average across models and 19% for the best-case model. [Formula: see text] 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.

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.

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.

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 T(1) and T(2) 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 T(1) of 92 ms (4.8%), but T(2) was less accurate at 16 ms (13.6%). During normoxia, placental T(1)=1825±141 ms (avg±standard deviation) and T(2)=60±16 ms (gestational age range 24.3-36.7, median 32.6 weeks). In the statistical model, placental T(2) rose and T(1) remained contant after hyperoxia, and no GA dependency was observed for T(1) or T(2). DISCUSSION: Well-characterized, motion-robust MRF was used to acquire T(1) and T(2) 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 T(2) accuracy.

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.

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., Comment: 19 pages, 10 figures, 2 tables, fetal MRI, head-pose detection, MSER, scan automation, scan prescription, slice positioning, final published version

We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques. In addition to avoiding over-smoothing that these techniques often suffer from, we also obviate the need for parameter selection. 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., National Institutes of Health (U.S.) (Grants R01EB020613, R01EB019437, P41EB015896, U01EB025162, S10RR023401, S10RR019307, S10RR019254, S10RR023043))

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 B

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.

Fetal motion is the dominant challenge to reliable performance and diagnostic quality of fetal magnetic resonance imaging (MRI). The fetus can move unpredictably and rapidly, leading to severe image artifacts. Consequently, MR acquisitions are largely limited to so-called single-shot techniques in an attempt to “freeze” fetal motion through fast imaging, while the problem due to motion occur between slices still exists. In this work, we propose a deep learning method for fetal pose estimation from MR volumes using the paradigm of conditional generative adversarial network which consists of two networks, a generator and a discriminator. The generator is responsible for estimating keypoint heatmaps from input MRI and the discriminator tries to learn the features of plausible fetal pose and distinguish ground-truth heatmaps from generated ones. With this adversarial training scheme, the generator can robustly produce realistic heatmaps for fetal pose inference. Besides, we use adaptive variance to model the difference in intensity of motion of different keypoints. Evaluation shows that the proposed method can improve the performance of pose estimation in 3D MRI, achieving quantitatively an average error of 2.64 mm and 98.31% accuracy (with error less than 10 mm). The proposed method can process volumes with latency less than 300 ms, potentially enabling low-latency online tracking of fetal pose during MR scans.

© 2017 International Society for Magnetic Resonance in Medicine 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.

© 2017 Informa UK Limited, trading as Taylor & Francis Group. 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.