Search

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.

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.

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.

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.

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

Image denoising is of great importance for medical imaging system, since it can improve image quality for disease diagnosis and downstream image analyses. In a variety of applications, dynamic imaging techniques are utilized to capture the time-varying features of the subject, where multiple images are acquired for the same subject at different time points. Although signal-to-noise ratio of each time frame is usually limited by the short acquisition time, the correlation among different time frames can be exploited to improve denoising results with shared information across time frames. With the success of neural networks in computer vision, supervised deep learning methods show prominent performance in single-image denoising, which rely on large datasets with clean-vs-noisy image pairs. Recently, several self-supervised deep denoising models have been proposed, achieving promising results without needing the pairwise ground truth of clean images. In the field of multi-image denoising, however, very few works have been done on extracting correlated information from multiple slices for denoising using self-supervised deep learning methods. In this work, we propose Deformed2Self, an end-to-end self-supervised deep learning framework for dynamic imaging denoising. It combines single-image and multi-image denoising to improve image quality and use a spatial transformer network to model motion between different slices. Further, it only requires a single noisy image with a few auxiliary observations at different time frames for training and inference. Evaluations on phantom and in vivo data with different noise statistics show that our method has comparable performance to other state-of-the-art unsupervised or self-supervised denoising methods and outperforms under high noise levels.

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))

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.

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.

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., National Institutes of Health (Grants U01 HD087211, R01 EB017337)

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 was 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 three minutes. 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 exemplarily 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.

The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI observations of the gravid abdomen that are acquired at low spatial resolution but relatively high temporal resolution and over long durations (10–30 minutes). We estimate fetal pose per frame in MRI volumes of the pregnant abdomen via deep learning algorithms that detect key fetal landmarks. Evaluation of the proposed method shows that our framework achieves quantitatively an average error of 4.47 mm and 96.4% accuracy (with error less than 10 mm). Fetal pose estimation in MRI time series yields novel means of quantifying fetal movements in health and disease, and enables the learning of kinematic models that may enhance prospective mitigation of fetal motion artifacts during MRI acquisition.

© 2017 International Society for Magnetic Resonance in Medicine 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.

© 2018 International Society for Magnetic Resonance in Medicine 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.

Purpose A new framework for the design of parallel transmit (pTx) pulses is presented introducing constraints for local and global specific absorption rate (SAR) in the presence of errors in the radiofrequency (RF) transmit chain. Methods The first step is the design of a pTx RF pulse with explicit constraints for global and local SAR. Then, the worst possible SAR associated with that pulse due to RF transmission errors (“worst-case SAR”) is calculated. Finally, this information is used to re-calculate the pulse with lower SAR constraints, iterating this procedure until its worst-case SAR is within safety limits. Results Analysis of an actual pTx RF transmit chain revealed amplitude errors as high as 8% (20%) and phase errors above 3° (15°) for spokes (spiral) pulses. Simulations show that using the proposed framework, pulses can be designed with controlled “worst-case SAR” in the presence of errors of this magnitude at minor cost of the excitation profile quality. Conclusion Our worst-case SAR-constrained pTx design strategy yields pulses with local and global SAR within the safety limits even in the presence of RF transmission errors. This strategy is a natural way to incorporate SAR safety factors in the design of pTx pulses. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

PURPOSE: MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. METHODS: The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. RESULTS: The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. CONCLUSION: It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

The non-invasive assessment of (patho-)physiological parameters such as, perfusion and oxygenation, is of great importance for the characterization of pathologies e.g., tumors, which may be helpful to better predict treatment response and potential outcome. To better understand the influence of physiological parameters on the investigated oxygenation and perfusion sensitive MRI methods, MRI measurements were correlated with subsequent invasive micro probe measurements during free breathing conditions of air, air+10% CO2 and 100% O2 in healthy mice brain. MRI parameters were the irreversible (R2), reversible (R2') and effective (R2*) transverse relaxation rates, venous blood oxygenation level assessed by quantitative blood oxygenation level dependent (qBOLD) method and cerebral blood flow (CBF) assessed by arterial spin labeling (ASL) using a 7 T small animal MRI scanner. One to two days after MRI, tissue perfusion and pO2 were measured by Laser-Doppler flowmetry and fluorescence quenching micro probes, respectively. The tissue pO2 values were converted to blood oxygen saturation by using the Hill equation. The animals were anesthetized by intra peritoneal injection of ketamine-xylazine-acepromazine (10-2-0.3 mg/ml · kg). Results for normal/hypercapnia/hyperoxia conditions were: R2[s(∧)-1] = 20.7/20.4/20.1, R2*[s(∧)-1] = 31.6/29.6/25.9, R2'[s-(∧)1] = 10.9/9.2/5.7, qBOLD venous blood oxygenation level = 0.43/0.51/0.56, CBF[ml · min(∧)-1 · 100 g(∧)-1] = 70.6/105.5/81.8, Laser-Doppler flowmetry[a.u.] = 89.2/120.2/90.6 and pO2[mmHg] = 6.3/32.3/46.7. All parameters were statistically significantly different with P0.001 between all breathing conditions. All MRI and the corresponding micro probe measurements were also statistically significantly (P ≤ 0.03) correlated with each other. However, converting the tissue pO2 to blood oxygen saturation = 0.02/0.34/0.63, showed only very limited agreement with the qBOLD venous blood oxygenation level. We found good correlation between MRI and micro probe measurements. However, direct conversion of tissue pO2 to blood oxygen saturation by using the Hill equation is very limited. Furthermore, adverse effects of anesthesia and trauma due to micro probe insertion are strong confounding factors and need close attention for study planning and conduction of experiments. Investigation of the correlation of perfusion and oxygenation sensitive MRI methods with micro probe measurements in pathologic tissue such as tumors is now of compelling interest.

There is an unmet medical need for noninvasive imaging of regional brain oxygenation to manage stroke, tumor, and neurodegenerative diseases. Oxygenation imaging from magnetic susceptibility in MRI is a promising new technique to measure local venous oxygen extraction fraction (OEF) along the cerebral venous vasculature. However, this approach has not been tested in vivo at different levels of oxygenation. The primary goal of this study was to test whether susceptibility imaging of oxygenation can detect OEF changes induced by hypercapnia, via CO[subscript 2] inhalation, within selected a priori brain regions. Ten healthy subjects were scanned at 3 T with a 32-channel head coil. The end-tidal CO[subscript 2] (ETCO[subscript 2]) was monitored continuously and inspired gases were adjusted to achieve steady-state conditions of eucapnia (41 ± 3 mm Hg) and hypercapnia (50 ± 4 mm Hg). Gradient echo phase images and pseudo-continuous arterial spin labeling (pcASL) images were acquired to measure regional OEF and CBF respectively during eucapnia and hypercapnia. By assuming constant cerebral oxygen consumption throughout both gas states, regional CBF values were computed to predict the local change in OEF in each brain region. Hypercapnia induced a relative decrease in OEF of − 42.3% in the straight sinus, − 39.9% in the internal cerebral veins, and approximately − 50% in pial vessels draining each of the occipital, parietal, and frontal cortical areas. Across volunteers, regional changes in OEF correlated with changes in ETCO[subscript 2]. The reductions in regional OEF (via phase images) were significantly correlated (P < 0.05) with predicted reductions in OEF derived from CBF data (via pcASL images). These findings suggest that susceptibility imaging is a promising technique for OEF measurements, and may serve as a clinical biomarker for brain conditions with aberrant regional oxygenation.

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , 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., NIH (Grants R01-EB017219, F32-EB024381, R01-EB017337, R01-NS089212, P41-EB015896, U01-MH093765, and R24-MH106096)

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.

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.

© 2017 International Society for Magnetic Resonance in Medicine 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.

Purpose MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1, T2, proton density, and B0, the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction. Theory and Methods We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach. Results For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1–2% relative error). Conclusion The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources. Magn Reson Med 74:523–528, 2015. © 2014 Wiley Periodicals, Inc.

Purpose Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this study, we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants. Methods We performed electromagnetic simulations at 3T using a uniform phantom and a multitissue realistic head model with a generic DBS implant. Our strategy is based on using implant-friendly modes, which are defined as the modes of an array that reduce the local SAR around the DBS lead tip. These modes are used in a spokes pulse design algorithm in order to produce highly uniform magnitude least-squares flip angle excitations. Results Local SAR (1 g) at the lead tip is reduced below 0.1 W/kg compared with 31.2 W/kg, which is obtained by a simple quadrature birdcage excitation without any sort of SAR mitigation. For the multitissue realistic head model, peak 10 g local SAR and global SAR are obtained as 4.52 W/kg and 0.48 W/kg, respectively. A uniform axial flip angle is also obtained (NRMSE

Purpose Local specific absorption rate (SAR) limits many applications of parallel transmit (pTx) in ultra high-field imaging. In this Note, we introduce the use of an array element, which is intentionally inefficient at generating spin excitation (a “dark mode”) to attempt a partial cancellation of the electric field from those elements that do generate excitation. We show that adding dipole elements oriented orthogonal to their conventional orientation to a linear array of conventional loop elements can lower the local SAR hotspot in a C-spine array at 7 T. Methods We model electromagnetic fields in a head/torso model to calculate SAR and excitation B1+ patterns generated by conventional loop arrays and loop arrays with added electric dipole elements. We utilize the dark modes that are generated by the intentional and inefficient orientation of dipole elements in order to reduce peak 10g local SAR while maintaining excitation fidelity. Results For B1+ shimming in the spine, the addition of dipole elements did not significantly alter the B1+ spatial pattern but reduced local SAR by 36%. Conclusion The dipole elements provide a sufficiently complimentary B1+ and electric field pattern to the loop array that can be exploited by the radiofrequency shimming algorithm to reduce local SAR. Magn Reson Med 73:1533–1539, 2015. © 2014 Wiley Periodicals, Inc.

Purpose To enable fast reconstruction of quantitative susceptibility maps with total variation penalty and automatic regularization parameter selection. Methods l1-Regularized susceptibility mapping is accelerated by variable splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and fast Fourier transforms. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization. Results Compared with the nonlinear conjugate gradient (CG) solver, the proposed method is 20 times faster. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering, and l1-regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 min using MATLAB on a standard workstation compared with 22 min using the CG solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 min, which would have taken 4 h with the CG algorithm. The proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5 times faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional blood oxygen level–dependent susceptibility mapping, where processing of the massive time series dataset would otherwise be prohibitive with the CG solver. Conclusion Online reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion. Magn Reson Med 72:1444–1459, 2014. © 2013 Wiley Periodicals, Inc.

Athinoula A. Martinos Center for Biomedical Imaging (Advanced Multimodal Neuroimaging Training Program; Grant number: R90-DA023427)

Cerebral perfusion in neonates with congenital heart disease is a clinical concern. Combined measures of MRI and NIRS can provide complementary information to improve monitoring. We compare multimodal measures of cerebral hemodynamics in this group.

Diffusion spectrum imaging offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (∼1 h). Recent work by Menzel et al. demonstrated successful recovery of diffusion probability density functions from sub-Nyquist sampled q-space by imposing sparsity constraints on the probability density functions under wavelet and total variation transforms. As the performance of compressed sensing reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for diffusion probability density functions can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in diffusion spectrum imaging, whereby we reduce the scan time of whole brain diffusion spectrum imaging acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the 3T Connectome MRI. The root-mean-square error of the reconstructed “missing” diffusion images were calculated by comparing them to a gold standard dataset (obtained from acquiring 10 averages of diffusion images in these missing directions). The root-mean-square error from the proposed reconstruction method is up to two times lower than that of Menzel et al.'s method and is actually comparable to that of the fully-sampled 50 minute scan. Comparison of tractography solutions in 18 major white-matter pathways also indicated good agreement between the fully-sampled and 3-fold accelerated reconstructions. Further, we demonstrate that a dictionary trained using probability density functions from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from other subjects. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.

MRI-based bone segmentation is a challenging task because bone tissue and air both present low signal intensity on MR images, making it difficult to accurately delimit the bone boundaries. However, estimating bone from MRI images may allow decreasing patient ionization by removing the need of patient-specific CT acquisition in several applications. In this work, we propose a fast GPU-based pseudo-CT generation from a patient-specific MRI T1-weighted image using a group-wise patch-based approach and a limited MRI and CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel with the patches of all MR images in the database, which lie in a certain anatomical neighborhood. The pseudo-CT is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a GPU. The use of patch-based techniques allows a fast and accurate estimation of the pseudo-CT from MR T1-weighted images, with a similar accuracy as the patient-specific CT. The experimental normalized cross correlation reaches 0.9324±0.0048 for an atlas with 10 datasets. The high NCC values indicate how our method can accurately approximate the patient-specific CT. The GPU implementation led to a substantial decrease in computational time making the approach suitable for real applications.

In this work we introduce the concept of correlation chemical shift imaging (CCSI). Novel CCSI pulse sequences are demonstrated on clinical scanners for two-dimensional Correlation Spectroscopy (COSY) and Total Correlation Spectroscopy (TOCSY) imaging experiments. To date there has been limited progress reported towards a feasible and robust multivoxel 2D COSY. Localized 2D TOCSY imaging is shown for the first time in this work. Excitation with adiabatic GOIA-W(16,4) pulses (Gradient Offset Independent Adiabaticity Wurst modulation) provides minimal chemical shift displacement error, reduced lipid contamination from subcutaneous fat, uniform optimal flip angles, and efficient mixing for coupled spins, while enabling short repetition times due to low power requirements. Constant-density spiral readout trajectories are used to acquire simultaneously two spatial dimensions and f2 frequency dimension in (kx,ky,t2) space in order to speed up data collection, while f1 frequency dimension is encoded by consecutive time increments of t1 in (kx,ky,t1,t2) space. The efficient spiral sampling of the k-space enables the acquisition of a single-slice 2D COSY dataset with an 8 × 8 matrix in 8:32 min on 3 T clinical scanners, which makes it feasible for in vivo studies on human subjects. Here we present the first results obtained on phantoms, human volunteers and patients with brain tumors. The patient data obtained by us represent the first clinical demonstration of a feasible and robust multivoxel 2D COSY. Compared to the 2D J-resolved method, 2D COSY and TOCSY provide increased spectral dispersion which scales up with increasing main magnetic field strength and may have improved ability to unambiguously identify overlapping metabolites. It is expected that the new developments presented in this work will facilitate in vivo application of 2D chemical shift correlation MRS in basic science and clinical studies. Copyright © 2011 John Wiley & Sons, Ltd.

Venous oxygen saturation (Yv) in cerebral veins and the cerebral metabolic rate of oxygen (CMRO2) are important indicators for brain function and disease. Although MRI has been used for global measurements of these parameters, currently there is no recognized technique to quantify regional Yv and CMRO2 using noninvasive imaging. This article proposes a technique to quantify CMRO2 from independent MRI estimates of Yv and cerebral blood flow. The approach uses standard gradient-echo and arterial spin labeling acquisitions to make these measurements. Using MR susceptometry on gradient-echo phase images, Yv was quantified for candidate vein segments in gray matter that approximate a long cylinder parallel to the main magnetic field. Local cerebral blood flow for the identified vessel was determined from a corresponding region in the arterial spin labeling perfusion map. Fick's principle of arteriovenous difference was then used to quantify CMRO2 locally around each vessel. Application of this method in young, healthy subjects provided gray matter averages of 59.6% ± 2.3% for Yv, 51.7 ± 6.4 mL/100 g/min for cerebral blood flow, and 158 ± 18 μmol/100 g/min for CMRO2 (mean ± SD, n = 12), which is consistent with values previously reported by positron emission tomography and MRI. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.

Clinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k-space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M-FOCUSS. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein solving each of the inverse problems corresponds to finding the parameters (here, image gradient coefficients) associated with each of the images. The variance of image gradients across contrasts for a single volumetric spatial position is a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the k-space data belonging to each image are used independently to infer the image gradients. Thus, commonality of image spatial structure across contrasts is exploited without the problematic assumption of correlation across contrasts. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root-mean-square error) compared with previous compressed sensing algorithms and show the benefit of joint inversion under a hierarchical Bayesian model.

Diffusion tensor imaging (DTI) of the brain has become a mainstay in the study of normal aging of white matter, and only recently has attention turned to the use of DTI to examine aging effects in gray matter structures. Of the many changes in the brain that occur with advancing age is increased presence of iron, notable in selective deep gray matter structures. In vivo detection and measurement of iron deposition is possible with magnetic resonance imaging (MRI) because of iron's effect on signal intensity. In the process of a DTI study, a series of diffusion-weighted images (DWI) is collected, and while not normally considered as a major dependent variable in research studies, they are used clinically and they reveal striking conspicuity of the globus pallidus and putamen caused by signal loss in these structures, presumably due to iron accumulation with age. These iron deposits may in turn influence DTI metrics, especially of deep gray matter structures. The combined imaging modality approach has not been previously used in the study of normal aging. The present study used legacy DTI data collected in 10 younger (22–37 years) and 10 older (65–79 years) men and women at 3.0 T and fast spin-echo (FSE) data collected at 1.5 T and 3.0 T to derive an estimate of the field-dependent relaxation rate increase (the “FDRI estimate”) in the putamen, caudate nucleus, globus pallidus, thalamus, and a frontal white matter sample comparison region. The effect of age on the diffusion measures in the deep gray matter structures was distinctly different from that reported in white matter. In contrast to lower anisotropy and higher diffusivity typical in white matter of older relative to younger adults observed with DTI, both anisotropy and diffusivity were higher in the older than younger group in the caudate nucleus and putamen; the thalamus showed little effect of age on anisotropy or diffusivity. Signal intensity measured with DWI was lower in the putamen of elderly than young adults, whereas the opposite was observed for the white matter region and thalamus. As a retrospective study based on legacy data, the FDRI estimates were based on FSE sequences, which underestimated the classical FDRI index of brain iron. Nonetheless, the differential effects of age on DTI metrics in subcortical gray matter structures compared with white matter tracts appears to be related, at least in part, to local iron content, which in the elderly of the present study was prominent in the FDRI estimate of the putamen and visibly striking in the diffusion-weighted image of the basal ganglia structures., National Institutes of Health (U.S.) (Grant AG17919), National Institutes of Health (U.S.) (Grant AA05965), National Institutes of Health (U.S.) (Grant AA10723), National Institutes of Health (U.S.) (Grant AA12388)

Slice-selective RF waveforms that mitigate severe B inhomogeneity at 7 Tesla using parallel excitation were designed and validated in a water phantom and human studies on six subjects using a 16-element degenerate stripline array coil driven with a butler matrix to utilize the eight most favorable birdcage modes. The parallel RF waveform design applied magnitude least-squares (MLS) criteria with an optimized k-space excitation trajectory to significantly improve profile uniformity compared to conventional least-squares (LS) designs. Parallel excitation RF pulses designed to excite a uniform in-plane flip angle (FA) with slice selection in the z-direction were demonstrated and compared with conventional sinc-pulse excitation and RF shimming. In all cases, the parallel RF excitation significantly mitigated the effects of inhomogeneous B on the excitation FA. The optimized parallel RF pulses for human B mitigation were only 67% longer than a conventional sinc-based excitation, but significantly outperformed RF shimming. For example the standard deviations (SDs) of the in-plane FA (averaged over six human studies) were 16.7% for conventional sinc excitation, 13.3% for RF shimming, and 7.6% for parallel excitation. This work demonstrates that excitations with parallel RF systems can provide slice selection with spatially uniform FAs at high field strengths with only a small pulse-duration penalty. Magn Reson Med 60:1422–1432, 2008. © 2008 Wiley-Liss, Inc.

A novel radio-frequency (RF) pulse design algorithm is presented that generates fast slice-selective excitation pulses that mitigate B1+ inhomogeneity present in the human brain at high field. The method is provided an estimate of the B1+ field in an axial slice of the brain and then optimizes the placement of sinc-like “spokes” in kz via an L1-norm penalty on candidate (kx, ky) locations; an RF pulse and gradients are then designed based on these weighted points. Mitigation pulses are designed and demonstrated at 7T in a head-shaped water phantom and the brain; in each case, the pulses mitigate a significantly non-uniform transmit profile and produce nearly uniform flip angles across the field of excitation (FOX). The main contribution of this work, the sparsity-enforced spoke placement and pulse design algorithm, is derived for conventional single-channel excitation systems and applied in the brain at 7T, but readily extends to lower field systems, nonbrain applications, and multichannel parallel excitation arrays.

Clinical magnetic resonance imaging (MRI) protocols typically include multiple acquisitions of the same region of interest under different contrast settings. This paper presents an efficient algorithm to jointly reconstruct a set of undersampled images with different contrasts. The proposed method has faster reconstruction time and better quality as measured by the normalized root-mean-square error (RMSE) compared to the existing methods consisting of multi-contrast Fast Composite Splitting Algorithm (FCSA-MT), Multiple measurement vectors FOCal Underdetermined System Solver (M-FOCUSS), and total variation regularized compressed sensing (SparseMRI). To efficiently solve the £2, 1-regularized optimization problem, our proposed algorithm adopts the Split Bregman (SB) technique to divide the problem into sub-problems. We efficiently compute a closed-form solution to each of the sub-problems by implementing a 3D spatial gradient operator as element-wise multiplication in k-space. As demonstrated by the in vivo results, the proposed algorithm (SB-L21) offers 2x, 32x, and 66x faster reconstruction with lower RMSE averaged across all contrasts and slices compared to FCSA-MT, M-FOCUSS, and SparseMRI, respectively.

Alcoholism comorbidity is highly prevalent in individuals infected with human immunodeficiency virus (HIV). Each condition is known to affect brain structure, function, and metabolism, but the combined effects on the brain have only recently been considered. Single-voxel, proton MR spectroscopy (MRS) has yielded sensitive measures of early brain deterioration in the progression of HIV, but has limited coverage of neocortex, whereas MRS imaging (MRSI) can simultaneously interrogate large regions of cortex. Included were 15 men with HIV+alcoholism, nine men with HIV alone, eight men with alcoholism alone (abstinent for 3-17 months), and 23 controls. The two HIV groups were matched in T-cell count and were not demented; the two alcoholism groups were relatively matched in lifetime alcohol consumption. We used MRSI with a variable-density spiral sequence to quantify major proton metabolites--N-acetylaspartate (NAA), creatine (Cr), and choline (Cho)-in the superior parietal-occipital cortex. Metabolites were expressed in absolute units and as the NAA/Cr ratio. Significant group effects were present for NAA and Cr. Only the HIV+alcoholism group was significantly affected, exhibiting a 0.8 SD deficit in NAA and a 1.0 SD deficit in Cr. The deficits were not related to highly active antiretroviral therapy (HAART) status. Neither HIV infection nor alcoholism independently resulted in parietal-occipital cortical metabolite abnormalities, yet each disease carried a liability that put affected individuals at a heightened risk of neuronal compromise when the diseases were compounded. Further, the use of absolute measures revealed deficits in NAA and Cr that would have gone undetected if these metabolites were expressed as a ratio.

Purpose To evaluate within-scanner and between-scanner reliability of fractional anisotropy (FA) and trace (sum of the diagonal elements of the diffusion tensor) as measured by diffusion tensor imaging (DTI). Materials and Methods Ten young healthy adults were scanned on three separate days, on two different systems made by the same manufacturer. One scan was acquired at one site, and two scans were acquired on two different occasions on another scanner at another site. Three levels of analysis were used to compare the DTI metrics: 1) a voxel-by-voxel analysis of all supratentorial brain (gray matter + white matter + cerebrospinal fluid) and of supratentorial white matter; 2) a slice-by-slice analysis of supratentorial white matter; and 3) a single-region analysis of the corpus callosum. Results The voxel-by-voxel analysis of all supratentorial brain found that FA and trace measures and correlations were equivalently and significantly higher within than across scanners. For supratentorial white matter, FA was similar within and across scanners, whereas trace demonstrated across-scanner bias. A similar pattern was observed for the slice-by-slice comparison. For the single-region analysis of the corpus callosum, within-scanner FA and trace measures were highly reproducible for FA (CV = 1.9%) and trace (CV = 2.6%), but both DTI measures showed a systematic mean bias across scanners (CV = 4.5% for FA and CV = 7.5% for trace). Conclusion These estimates of measurement variation and scanner bias can be used to predict effect sizes for longitudinal and multisite studies using diffusion tensor imaging. J. Magn. Reson. Imaging 2003;18:427–433. © 2003 Wiley-Liss, Inc.

Quantitative oxygen extraction fraction (OEF) in cortical veins was studied in patients with multiple sclerosis (MS) and healthy subjects via magnetic resonance imaging (MRI) phase images at 7 Tesla (7 T). Flow-compensated, three-dimensional gradient-echo scans were acquired for absolute OEF quantification in 23 patients with MS and 14 age-matched controls. In patients, we collected T2∗-weighted images for characterization of white matter, deep gray matter, and cortical lesions, and also assessed cognitive function. Variability of OEF across readers and scan sessions was evaluated in a subset of volunteers. OEF was averaged from 2 to 3 pial veins in the sensorimotor, parietal, and prefrontal cortical regions for each subject (total of ∼10 vessels). We observed good reproducibility of mean OEF, with intraobserver coefficient of variation (COV)=2.1%, interobserver COV=5.2%, and scan—rescan COV=5.9%. Patients exhibited a 3.4% reduction in cortical OEF relative to controls ( P=0.0025), which was not different across brain regions. Although oxygenation did not relate with measures of structural tissue damage, mean OEF correlated with a global measure of information processing speed. These findings suggest that cortical OEF from 7-T MRI phase is a reproducible metabolic biomarker that may be sensitive to different pathologic processes than structural MRI in patients with MS.

Diffusion tensor imaging was used to measure regional differences in brain white matter microstructure (intravoxel coherence) and macrostructure (intervoxel coherence) and age-related differences between men and women. Neuropsychiatrically healthy men and women, spanning the adult age range, showed the same pattern of variation in regional white matter coherence. The greatest coherence measured was in corpus callosum, where commissural fibers have one primary orientation, lower in the centrum semiovale, where fibers cross from multiple axes, and lowest in pericallosal areas, where fibers weave and interstitial fluid commonly pools. Age-related declines in intravoxel coherence was equally strong and strikingly similar in men and women, with evidence for greater age-dependent deterioration in frontal than parietal regions. Degree of regional white matter coherence correlated with gait, balance, and interhemispheric transfer test scores.

Background: N-acetyl aspartate (NAA) is an amino acid present in high concentrations in neurons and is thus a putative neuronal marker. In vivo proton magnetic resonance spectroscopy (1H MRS) studies have shown lower NAA concentrations in patients with various neurodegenerative disorders, suggesting decreased neuronal number, size, or function. Dorsolateral prefrontal (DLPF) NAA has not been extensively assessed in bipolar disorder patients, but it could be decreased in view of consistent reports of decreased DLPF cerebral blood flow and metabolism in mood disorders. We measured DLPF NAA in patients with bipolar disorder and healthy control subjects using in vivo 1H MRS. Methods: We obtained ratios of NAA, choline, and myoinositol (mI) to creatine-phosphocreatine (Cr-PCr) in bilateral DLPF 8-mL voxels of 20 bipolar patients (10 Bipolar I, 10 Bipolar II) and 20 age- and gender-matched healthy control subjects using 1H MRS. Results: DLPF NAA/Cr-PCr ratios were lower on the right hemisphere(p < .03) and the left hemisphere (p < .003) in bipolar disorder patients compared with healthy control subjects. Conclusions: These preliminary data suggest that bipolar disorder patients have decreased DLPF NAA/Cr-PCr. This finding could represent decreased neuronal density or neuronal dysfunction in the DLPF region.

Echo planar (EP) diffusion tensor imaging (DTI) permits in vivo identification of the orientation and coherence of brain white matter tracts but suffers from field inhomogeneity-induced geometric distortion. To reduce spatial distortion, polynomial warping corrections were applied and the effects tested on measures of fractional anisotropy (FA) in the genu and splenium of corpus callosum. Implementation entailed spatially warping EP images obtained without diffusion weighting (b = 0) to long-echo T(2)-weighted fast spin echo images, collected for anatomical delineation, tissue segmentation, and coregistration with the diffusion images. Using the optimal warping procedure (third-order polynomial), the effects of age on FA and a quantitative measure of intervoxel coherence (C) in the genu, splenium, centrum semiovale, and frontal and parietal pericallosal white matter were examined in 31 healthy men (23-76 years). FA declined significantly with age in all regions except the splenium, whereas intervoxel coherence positively correlated with age in the genu. Magn Reson Med 44:259-268, 2000.

Density-weighted k-space sampling with spiral trajectories is used to reduce spatial side lobes in chemical-shift imaging (CSI). In this method, more time is spent collecting data at the center of k space and less time at the edges of k space in order to make the sampling density proportional to a given apodization function, subject to constraints imposed by gradient performance and Nyquist sampling. The efficient k-space coverage of spiral-based trajectories enables good control over the sampling density within practical in vivo scan times. The density-weighted acquisition is compared to a conventional, nonweighted spiral sampling without the application of a window function. For a fixed voxel size and imaging time, the noise variance is observed to be the same for both cases, while spatial side lobes are greatly reduced with the variable-density sampling. This method is demonstrated on a normal volunteer by imaging of brain metabolites at 1.5 T with both single slice CSI and volumetric CSI. Magn Reson Med 42:314-323, 1999.

A method is presented to collect spatially resolved two-dimensional spectra on a conventional clinical scanner. Time-varying gradients during the readout period rapidly sample data simultaneously for two spatial and two spectral dimensions. The k-space trajectories are based on spiral paths that make efficient use of the gradient hardware. A gridding algorithm is used for reconstruction. With the spiral-based readout gradients, current single-voxel two-dimensional techniques can be extended to spatially resolved volumetric acquisitions. The method is demonstrated with a two-dimensional J-resolved acquisition of a phantom with separate compartments of lactic acid and ethyl alcohol in water. Data were acquired with a spatial resolution of 18 x 18 pixels over a 24 cm field of view, 400 Hz spectral bandwidth in the chemical shift dimension with a 3.8 Hz resolution, and 50 Hz spectral bandwidth in the second frequency dimension with a 1.56 Hz resolution.