Characteristics of Abdominal Visceral Adipose Tissue, Metabolic Health and the Gut Microbiome in Adults.

CONTEXT: Compared with the relatively benign effects of increased subcutaneous adipose tissue (SAT), increased visceral adipose tissue (VAT) volume is a causal risk factor for hypertension, hyperlipidemia, type 2 diabetes, and cardiovascular disease. In rodents, increased VAT volume and triglyceride density and ectopic lipid accumulation in kidneys and liver have been induced by alterations in the gut microbiome. However, few studies have characterized these relationships in humans. OBJECTIVE: To evaluate the tissue triglyceride content of VAT and SAT, liver, kidneys, and pancreas in male and female adults and assess associations with markers of glucose tolerance, serum insulin, and lipids and characteristics of the gut microbiome. METHODS: Cross-sectional observational study of healthy human adults (n = 60) at a clinical research center. Body mass index (BMI), body composition, and oral glucose tolerance were assessed. Microbiome analysis was conducted on stool samples using 16S rRNA v3 amplicon sequencing. The triglyceride content of VAT, SAT, liver, kidney and pancreas were determined by assessing proton density fat fraction (PDFF) with magnetic resonance imaging (MRI). RESULTS: Higher VAT PDFF and the ratio of VAT to SAT PDFF were related to higher BMI, HbA1c, HOMA-IR, non-high-density lipoprotein cholesterol, plasma triglycerides, low-density lipoprotein (LDL) cholesterol, and lower high-density lipoprotein (HDL) cholesterol. A higher VAT PDFF and VAT to SAT PDFF ratio were associated with lower alpha diversity and altered beta diversity of the gut microbiome. Differences in VAT were associated with higher relative abundance of the phylum Firmicutes, lower relative abundance of the phylum Bacteroidetes, and enrichment of the bacterial genera Dorea, Streptococcus, and Solobacterium. CONCLUSION: VAT PDFF measured with MRI is related to impaired glucose homeostasis, dyslipidemia, and differences in the gut microbiome, independently of the total body fat percentage.

Motion-compensated low-rank reconstruction for simultaneous structural and functional UTE lung MRI.

PURPOSE: Three-dimensional UTE MRI has shown the ability to provide simultaneous structural and functional lung imaging, but it is limited by respiratory motion and relatively low lung parenchyma SNR. The purpose of this paper is to improve this imaging by using a respiratory phase-resolved reconstruction approach, named motion-compensated low-rank reconstruction (MoCoLoR), which directly incorporates motion compensation into a low-rank constrained reconstruction model for highly efficient use of the acquired data. THEORY AND METHODS: The MoCoLoR reconstruction is formulated as an optimization problem that includes a low-rank constraint using estimated motion fields to reduce the rank, optimizing over both the motion fields and reconstructed images. The proposed reconstruction along with XD and motion state-weighted motion-compensation (MostMoCo) methods were applied to 18 lung MRI scans of pediatric and young adult patients. The data sets were acquired under free-breathing and without sedation with 3D radial UTE sequences in approximately 5 min. After reconstruction, they went through ventilation analyses. Performance across reconstruction regularization and motion-state parameters were also investigated. RESULTS: The in vivo experiments results showed that MoCoLoR made efficient use of the data, provided higher apparent SNR compared with state-of-the-art XD reconstruction and MostMoCo reconstructions, and yielded high-quality respiratory phase-resolved images for ventilation mapping. The method was effective across the range of patients scanned. CONCLUSION: The motion-compensated low-rank regularized reconstruction approach makes efficient use of acquired data and can improve simultaneous structural and functional lung imaging with 3D-UTE MRI. It enables the scanning of pediatric patients under free-breathing and without sedation.

SLfRank: Shinnar-Le-Roux Pulse Design With Reduced Energy and Accurate Phase Profiles Using Rank Factorization.

The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (by as much as 26%) and more accurate phase profiles. Concretely, the SLR algorithm consists of two steps: (1) an invertible transform between frequency selective pulses and polynomial pairs that represent Cayley-Klein (CK) parameters and (2) the design of the CK polynomial pair to match the desired magnetization profiles. Because the CK polynomial pair is bi-linearly coupled, the original algorithm sequentially solves for each polynomial instead of jointly. This results in sub-optimal pulses. Instead, we leverage a convex relaxation technique, commonly used for low rank matrix recovery, to address the bi-linearity. Our numerical experiments show that the resulting pulses are almost always globally optimal in practice. For slice excitation, the proposed algorithm results in more accurate linear phase profiles. And in general the improved pulses have lower energy than the original SLR pulses.

Parent involvement in child anthropometric measurement.

BACKGROUND: Young children are often accompanied by their parent/caregiver when attending primary healthcare visits, where clinical procedures such as anthropometric measurements are conducted. Parents are not typically involved in their child's anthropometric measurement collection, and there are no recommendations for parental involvement during visits. The objective of this study was to describe parents' experiences with being involved in their child's anthropometric measurements. METHODS: A 10-question survey comprised of scaled and open-ended questions was self-administered to participants after child anthropometric measurement collection including length/height, weight, head, arm, and waist circumference. Survey data were analyzed using a general inductive approach and thematic analysis. Surveys were collected in participating TARGet Kids! primary care practice sites in Toronto, Canada. Survey respondents included 30 parents of children < 2 years of age, and 30 parents of children 2-5 years of age. RESULTS: 76% of parents with children aged < 2 years and 93% of those with children aged 2-5 years rated their overall experience in being involved in their child's anthropometric measurement as enjoyable or thoroughly enjoyable. Analysis of open-ended survey questions revealed five themes: [1] parent interest in child growth; [2] ease of anthropometric measurement; [3] extended clinic visit; [4] child discomfort; and [5] interest in participating in research. CONCLUSION: Parents reported a high degree of enjoyment in being involved in their child's anthropometric measurements. Parent participation in anthropometric measurement may improve parental satisfaction with children's primary healthcare. Future research may include assessing the reliability of measurements taken with the support of a parent/caregiver.

High-resolution, respiratory-resolved coronary MRA using a Phyllotaxis-reordered variable-density 3D cones trajectory.

PURPOSE: To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography (CMRA) using a 3D cones trajectory. METHODS: To achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases. RESULTS: In initial studies of six subjects, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists and quantitative evaluation with the image edge profile acutance metric indicate the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01). CONCLUSION: We have demonstrated a new method for free-breathing, high-resolution CMRA based on a variable-density 3D cones trajectory with modified phyllotaxis ordering and respiratory-resolved motion compensation with 3D iNAVs.

Randomized controlled trial evaluating a virtual parenting intervention for young children at risk of obesity: study protocol for Parenting Addressing Early Years Intervention with Coaching Visits in Toronto (PARENT) trial.

BACKGROUND: The prevalence of overweight (15%) and obesity (6%) in children under 5 years of age in Canada are high, and young children with overweight and obesity are at increased risk of the development of chronic disease(s) in adulthood. Prior research has demonstrated very few published trials on effective obesity prevention interventions in young children at risk of obesity, within primary healthcare settings. The aim of this study is to determine if 18-48-month-old children at risk for obesity, who are randomized to receive the Parents Together program (i.e., intervention group), have reduced body mass index z-score (zBMI), compared to those not receiving the intervention, at a 12-month follow-up. Secondary clinical outcomes between the intervention and control groups will be compared at 12 months. METHODS: A pragmatic, parallel group, 1:1, superiority, randomized control trial (RCT) through the TARGetKids! Practice Based Research Network will be conducted. Young children (ages 18-48 months) who are at increased risk for childhood obesity will be invited to participate. Parents who are enrolled in the intervention group will participate in eight weekly group sessions and 4-5 coaching visits, facilitated by a trained public health nurse. Children and parents who are enrolled in the control group will receive the usual health care. The primary outcome will be compared between intervention arms using an analysis of covariance (ANCOVA). Feasibility and acceptability will be assessed by parent focus groups and interviews, and fidelity to the intervention will be measured using nurse-completed checklists. A cost-effectiveness analysis (CEA) will be conducted. DISCUSSION: This study will aim to reflect the social, cultural, and geographic diversity of children in primary care in Toronto, Ontario, represented by an innovative collaboration among applied child health researchers, community health researchers, and primary care providers (i.e., pediatricians and family physicians in three different models of primary care). Clinical and implementation outcomes will be used to inform future research to test this intervention in a larger number, and diverse practices across diverse geographic settings in Ontario. TRIAL REGISTRATION: ClinicalTrials.gov NCT03219697. Registered on June 27, 2017.

Impaired Cold-Stimulated Supraclavicular Brown Adipose Tissue Activity in Young Boys With Obesity.

Childhood obesity is a growing worldwide problem. In adults, lower cold-induced brown adipose tissue (BAT) activity is linked to obesity and metabolic dysfunction; this relationship remains uncertain in children. In this cross-sectional study, we compared cold-induced supraclavicular (SCV) BAT activity (percent change in proton density fat fraction [PDFF]) within the SCV region after 1 h of whole-body cold exposure (18°C), using MRI in 26 boys aged 8-10 years: 13 with normal BMI and 13 with overweight/obesity. Anthropometry, body composition, hepatic fat, visceral adipose tissue (VAT), and pre- and postcold PDFF of the subcutaneous adipose tissue (SAT) in the posterior neck region and the abdomen were measured. Boys with overweight/obesity had lower cold-induced percent decline in SCV PDFF compared with those with normal BMI (1.6 ± 0.8 vs. 4.7 ± 1.2%, P = 0.044). SCV PDFF declined significantly in boys with normal BMI (2.7 ± 0.7%, P = 0.003) but not in boys with overweight/obesity (1.1 ± 0.5%, P = 0.053). No cold-induced changes in the PDFF of either neck SAT (-0.89 ± 0.7%, P = 0.250, vs. 0.37 ± 0.3%, P = 0.230) or abdominal SAT (-0.39 ± 0.5%, P = 0.409, and 0.25 ± 0.2%, P = 0.139, for normal BMI and overweight/obesity groups, respectively) were seen. The cold-induced percent decline in SCV PDFF was inversely related to BMI (r = -0.39, P = 0.047), waist circumference (r = -0.48, P = 0.014), and VAT (r = -0.47, P = 0.014). Thus, in young boys, as in adults, BAT activity is lower in those with overweight/obesity, suggesting that restoring activity may be important for improving metabolic health.

Lower brown adipose tissue activity is associated with non-alcoholic fatty liver disease but not changes in the gut microbiota.

In rodents, lower brown adipose tissue (BAT) activity is associated with greater liver steatosis and changes in the gut microbiome. However, little is known about these relationships in humans. In adults (n = 60), we assessed hepatic fat and cold-stimulated BAT activity using magnetic resonance imaging and the gut microbiota with 16S sequencing. We transplanted gnotobiotic mice with feces from humans to assess the transferability of BAT activity through the microbiota. Individuals with NAFLD (n = 29) have lower BAT activity than those without, and BAT activity is inversely related to hepatic fat content. BAT activity is not related to the characteristics of the fecal microbiota and is not transmissible through fecal transplantation to mice. Thus, low BAT activity is associated with higher hepatic fat accumulation in human adults, but this does not appear to have been mediated through the gut microbiota.

SURE-based automatic parameter selection for ESPIRiT calibration.

PURPOSE: ESPIRiT is a parallel imaging method that estimates coil sensitivity maps from the auto-calibration region (ACS). This requires choosing several parameters for the optimal map estimation. While fairly robust to these parameter choices, occasionally, poor selection can result in reduced performance. The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams. METHODS: By viewing ESPIRiT as a denoiser, Stein's unbiased risk estimate (SURE) is leveraged to automatically optimize parameter selection in a data-driven manner. The optimum parameters corresponding to the minimum true squared error, minimum SURE as derived from densely sampled, high-resolution, and non-accelerated data and minimum SURE as derived from ACS are compared using simulation experiments. To avoid optimizing the rank of ESPIRiT's auto-calibrating matrix (one of the parameters), a heuristic derived from SURE-based singular value thresholding is also proposed. RESULTS: Simulations show SURE derived from the densely sampled, high-resolution, and non-accelerated data to be an accurate estimator of the true mean squared error, enabling automatic parameter selection. The parameters that minimize SURE as derived from ACS correspond well to the optimal parameters. The soft-threshold heuristic improves computational efficiency while providing similar results to an exhaustive search. In-vivo experiments verify the reliability of this method. CONCLUSIONS: Using SURE to determine ESPIRiT parameters allows for automatic parameter selections. In-vivo results are consistent with simulation and theoretical results.

Rosette Trajectories Enable Ungated, Motion-Robust, Simultaneous Cardiac and Liver T2 * Iron Assessment.

BACKGROUND: Quantitative T2 * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T2 * estimates. PURPOSE: To develop and evaluate a motion-robust, simultaneous cardiac and liver T2 * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI. STUDY TYPE: Prospective. PHANTOM/POPULATION: Six ferumoxytol-containing phantoms (26-288 µg/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment. FIELD STRENGTH/SEQUENCE: 1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T2 * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T2 * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale. STATISTICAL TESTS: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis. RESULTS: Rosette R2* (1/T2 *) was linearly correlated with ferumoxytol concentration (r2 = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T2 * had lower spatial CoV (liver: 18.4 ± 9.3% Cartesian, 8.8% ± 3.4% rosette, P = 0.02, heart: 37.7% ± 14.3% Cartesian, 13.4% ± 1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001). DATA CONCLUSION: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T2 * mapping approach for iron quantification. J. MAGN. RESON. IMAGING 2020;52:1688-1698.

Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions.

PURPOSE: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging. THEORY AND METHODS: The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging. RESULTS: The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features. CONCLUSIONS: We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS: An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS: 3D iNAVs reconstructed using the DL model-based approach and conventional l1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.

Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging.

Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. These explicit and implicit signal priors can be synergistically combined in an inverse problem framework to recover sharp, multi-contrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, bio-physical parameters from the data. This article introduces physics-based modeling constraints in MRI and shows how they can be used in conjunction with compressed sensing for image reconstruction and quantitative imaging. We describe model-based quantitative MRI, as well as its linear subspace approximation. We also discuss approaches to selecting user-controllable scan parameters given knowledge of the physical model. We present several MRI applications that take advantage of this framework for the purpose of multi-contrast imaging and quantitative mapping.

Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning.

We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data. Existing methods either use sampling density compensations which sacrifice reconstruction accuracy, or circulant preconditioners which increase per-iteration computation. Our approach overcomes both shortcomings. Concretely, we show that viewing the reconstruction problem in the dual formulation allows us to precondition in k-space using density-compensation-like operations. Using the primal-dual hybrid gradient method, the proposed preconditioning method does not have inner loops and are competitive in accelerating convergence compared to existing algorithms. We derive l2 -optimized preconditioners, and demonstrate through experiments that the proposed method converges in about ten iterations in practice.

MRI Reveals Human Brown Adipose Tissue Is Rapidly Activated in Response to Cold.

CONTEXT: In rodents, cold exposure induces the activation of brown adipose tissue (BAT) and the induction of intracellular triacylglycerol (TAG) lipolysis. However, in humans, the kinetics of supraclavicular (SCV) BAT activation and the potential importance of TAG stores remain poorly defined. OBJECTIVE: To determine the time course of BAT activation and changes in intracellular TAG using MRI assessment of the SCV (i.e., BAT depot) and fat in the posterior neck region (i.e., non-BAT). DESIGN: Cross-sectional. SETTING: Clinical research center. PATIENTS OR OTHER PARTICIPANTS: Twelve healthy male volunteers aged 18 to 29 years [body mass index = 24.7 ± 2.8 kg/m2 and body fat percentage = 25.0% ± 7.4% (both, mean ± SD)]. INTERVENTIONS: Standardized whole-body cold exposure (180 minutes at 18°C) and immediate rewarming (30 minutes at 32°C). MAIN OUTCOME MEASURES: Proton density fat fraction (PDFF) and T2* of the SCV and posterior neck fat pads. Acquisitions occurred at 5- to 15-minute intervals during cooling and subsequent warming. RESULTS: SCV PDFF declined significantly after only 10 minutes of cold exposure [-1.6% (SE: 0.44%; P = 0.007)] and continued to decline until 35 minutes, after which time it remained stable until 180 minutes. A similar time course was also observed for SCV T2*. In the posterior neck fat (non-BAT), there were no cold-induced changes in PDFF or T2*. Rewarming did not result in a change in SCV PDFF or T2*. CONCLUSIONS: The rapid cold-induced decline in SCV PDFF suggests that in humans BAT is activated quickly in response to cold and that TAG is a primary substrate.

ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging.

Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI). Therefore, we report a novel calibrationless parallel imaging technique which simultaneously estimates coil profiles and image content in a relaxed forward model. Our method is robust against a wide class of data inconsistencies, minimizes imaging artifacts and is comparably fast, combining important advantages of many conceptually different state-of-the-art parallel imaging approaches. Depending on the experimental setting, data can be undersampled well below the Nyquist limit. Here, even high acceleration factors yield excellent imaging results while being robust to noise and the occurrence of phase singularities in the image domain, as we show on different data. Moreover, our method successfully reconstructs acquisitions with insufficient field-of-view. We further compare our approach to ESPIRiT and SAKE using spin-echo and gradient echo MRI data from the human head and knee. In addition, we show its applicability to non-Cartesian imaging on radial FLASH cardiac MRI data. Using theoretical considerations, we show that ENLIVE can be related to a low-rank formulation of blind multi-channel deconvolution, explaining why it inherently promotes low-rank solutions.

Local Kernels that Approximate Bayesian Regularization and Proximal Operators.

In this work, we broadly connect kernel-based filtering (e.g. approaches such as the bilateral filter and nonlocal means, but also many more) with general variational formulations of Bayesian regularized least squares, and the related concept of proximal operators. Variational/Bayesian/proximal formulations often result in optimization problems that do not have closed-form solutions, and therefore typically require global iterative solutions. Our main contribution here is to establish how one can approximate the solution of the resulting global optimization problems using locally adaptive filters with specific kernels. Our results are valid for small regularization strength (i.e. weak noise) but the approach is powerful enough to be useful for a wide range of applications because we expose how to derive a "kernelized" solution to these problems that approximates the global solution in one shot, using only local operations. As another side benefit in the reverse direction, given a local data-adaptive filter constructed with a particular choice of kernel, we enable the interpretation of such filters in the variational/Bayesian/proximal framework.

Recent advances in the detection of brown adipose tissue in adult humans: a review.

The activation of brown adipose tissue (BAT) is associated with reductions in circulating lipids and glucose in rodents and contributes to energy expenditure in humans indicating the potential therapeutic importance of targetting this tissue for the treatment of a variety of metabolic disorders. In order to evaluate the therapeutic potential of human BAT, a variety of methodologies for assessing the volume and metabolic activity of BAT are utilized. Cold exposure is often utilized to increase BAT activity but inconsistencies in the characteristics of the exposure protocols make it challenging to compare findings. The metabolic activity of BAT in response to cold exposure has most commonly been measured by static positron emission tomography of 18F-fluorodeoxyglucose in combination with computed tomography (18F-FDG PET-CT) imaging, but recent studies suggest that under some conditions this may not always reflect BAT thermogenic activity. Therefore, recent studies have used alternative positron emission tomography and computed tomography (PET-CT) imaging strategies and radiotracers that may offer important insights. In addition to PET-CT, there are numerous emerging techniques that may have utility for assessing BAT metabolic activity including magnetic resonance imaging (MRI), skin temperature measurements, near-infrared spectroscopy (NIRS) and contrast ultrasound (CU). In this review, we discuss and critically evaluate the various methodologies used to measure BAT metabolic activity in humans and provide a contemporary assessment of protocols which may be useful in interpreting research findings and guiding the development of future studies.

Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator.

PURPOSE: To achieve motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data. METHODS: Five-minute free-breathing scans were acquired with a 3D ultrashort echo time (UTE) sequence with 1.25 mm isotropic resolution. From this data, dynamic 3D self-navigating images were reconstructed under locally low rank (LLR) constraints and used for motion compensation with one of two methods: a soft-gating technique to penalize the respiratory motion induced data inconsistency, and a respiratory motion-resolved technique to provide images of all respiratory motion states. RESULTS: Respiratory motion estimation derived from the proposed dynamic 3D self-navigator of 7.5 mm isotropic reconstruction resolution and a temporal resolution of 300 ms was successful for estimating complex respiratory motion patterns. This estimation improved image quality compared to respiratory belt and DC-based navigators. Respiratory motion compensation with soft-gating and respiratory motion-resolved techniques provided good image quality from highly undersampled data in volunteers and clinical patients. CONCLUSION: An optimized 3D UTE sequence combined with the proposed reconstruction methods can provide high-resolution motion robust pulmonary MRI. Feasibility was shown in patients who had irregular breathing patterns in which our approach could depict clinically relevant pulmonary pathologies. Magn Reson Med 79:2954-2967, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

General phase regularized reconstruction using phase cycling.

PURPOSE: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging. THEORY AND METHODS: The problem of enforcing phase constraints in reconstruction was studied under a regularized inverse problem framework. A general phase regularized reconstruction algorithm was proposed to enable various joint reconstruction of partial Fourier imaging, water-fat imaging and flow imaging, along with parallel imaging (PI) and compressed sensing (CS). Since phase regularized reconstruction is inherently non-convex and sensitive to phase wraps in the initial solution, a reconstruction technique, named phase cycling, was proposed to render the overall algorithm invariant to phase wraps. The proposed method was applied to retrospectively under-sampled in vivo datasets and compared with state of the art reconstruction methods. RESULTS: Phase cycling reconstructions showed reduction of artifacts compared to reconstructions without phase cycling and achieved similar performances as state of the art results in partial Fourier, water-fat and divergence-free regularized flow reconstruction. Joint reconstruction of partial Fourier + water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized flow imaging + PI + CS were demonstrated. CONCLUSION: The proposed phase cycling reconstruction provides an alternative way to perform phase regularized reconstruction, without the need to perform phase unwrapping. It is robust to the choice of initial solutions and encourages the joint reconstruction of phase imaging applications. Magn Reson Med 80:112-125, 2018. © 2017 International Society for Magnetic Resonance in Medicine.