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.

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.

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.

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.

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.

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.

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.

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.

Next-Generation Sequencing and Applications to the Diagnosis and Treatment of Lung Cancer.

Cancer is a genetic disease characterized by uncontrolled growth of abnormal cells. Over time, somatic mutations accumulate in the cells of an individual due to replication errors, chromosome segregation errors, or DNA damage. When not caught by traditional mechanisms, these somatic mutations can lead to cellular proliferation, the hallmark of cancer. Lung cancer is the leading cause of cancer-related mortality in the United States, accounting for approximately 160,000 deaths annually. Five year survival rates for lung cancer remain low (<50 %) for all stages, with even worse prognosis (<15 %) in late stage cases. Technological advances, including advances in next-generation sequencing (NGS), offer the vision of personalized medicine or precision oncology, wherein an individual's treatment can be based on his or her individual molecular profile, rather than on historical population-based medicine. Towards this end, NGS has already been used to identify new biomarker candidates for the early diagnosis of lung cancer and is increasingly used to guide personalized treatment decisions. In this review we will provide a high-level overview of NGS technology and summarize its application to the diagnosis and treatment of lung cancer. We will also describe how NGS can drive advances that bring us closer to precision oncology and discuss some of the technical challenges that will need to be overcome in order to realize this ultimate goal.

A modern understanding of the traditional and nontraditional biological functions of angiotensin-converting enzyme.

Angiotensin-converting enzyme (ACE) is a zinc-dependent peptidase responsible for converting angiotensin I into the vasoconstrictor angiotensin II. However, ACE is a relatively nonspecific peptidase that is capable of cleaving a wide range of substrates. Because of this, ACE and its peptide substrates and products affect many physiologic processes, including blood pressure control, hematopoiesis, reproduction, renal development, renal function, and the immune response. The defining feature of ACE is that it is composed of two homologous and independently catalytic domains, the result of an ancient gene duplication, and ACE-like genes are widely distributed in nature. The two ACE catalytic domains contribute to the wide substrate diversity of ACE and, by extension, the physiologic impact of the enzyme. Several studies suggest that the two catalytic domains have different biologic functions. Recently, the X-ray crystal structure of ACE has elucidated some of the structural differences between the two ACE domains. This is important now that ACE domain-specific inhibitors have been synthesized and characterized. Once widely available, these reagents will undoubtedly be powerful tools for probing the physiologic actions of each ACE domain. In turn, this knowledge should allow clinicians to envision new therapies for diseases not currently treated with ACE inhibitors.

Robust 4D flow denoising using divergence-free wavelet transform.

PURPOSE: To investigate four-dimensional flow denoising using the divergence-free wavelet (DFW) transform and compare its performance with existing techniques. THEORY AND METHODS: DFW is a vector-wavelet that provides a sparse representation of flow in a generally divergence-free field and can be used to enforce "soft" divergence-free conditions when discretization and partial voluming result in numerical nondivergence-free components. Efficient denoising is achieved by appropriate shrinkage of divergence-free wavelet and nondivergence-free coefficients. SureShrink and cycle spinning are investigated to further improve denoising performance. RESULTS: DFW denoising was compared with existing methods on simulated and phantom data and was shown to yield better noise reduction overall while being robust to segmentation errors. The processing was applied to in vivo data and was demonstrated to improve visualization while preserving quantifications of flow data. CONCLUSION: DFW denoising of four-dimensional flow data was shown to reduce noise levels in flow data both quantitatively and visually.

Myeloid expression of angiotensin-converting enzyme facilitates myeloid maturation and inhibits the development of myeloid-derived suppressor cells.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells which accumulate in cancer, infection and chronic inflammation. These cells suppress T-cell function and the immune response. Angiotensin-converting enzyme (ACE) is a peptidase that is now known to regulate aspects of myelopoiesis. Here, we show that ACE expression correlates with myeloid maturation in vitro. Forced ACE overexpression in monocytic cells reduces the generation of MDSCs. In vivo, mice with a genetic change resulting in myeloid cell ACE overexpression have reduced numbers of blood and splenic MDSCs in a tumor model and in a model of chronic inflammation induced by complete Freund's adjuvant. In contrast, ACE-null mice produce large numbers of MDSCs during chronic inflammation. Macrophages from mice with myeloid ACE overexpressing are more pro-inflammatory and have more tumor-killing activity than cells from wild-type mice. Thus, manipulating myeloid ACE activity can interfere with MDSC development and the maturation of myeloid cells.

Translational utility of next-generation sequencing.

The development of next-generation sequencing (NGS) technology has made DNA sequencing not only rapid and cost-effective, but also highly accurate and reproducible. The translational utility of genomic sequencing is clear, from understanding of human genetic variation and its association with disease risk and individual response to treatment, to the interpretation and translation of the data for clinical decision making. It will be a critical technology for disease characterization and monitoring in molecular pathology and is expected to become a central piece of routine healthcare management which will result in accurate and reliable reporting, a prerequisite for physicians to practice genomic medicine.

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.

Identification of prolyl carboxypeptidase as an alternative enzyme for processing of renal angiotensin II using mass spectrometry.

Angiotensin-converting enzyme 2 (ACE2) catalyzes conversion of ANG II to ANG-(1-7). The present study uses newly established proteomic approaches and genetic mouse models to examine the contribution of alternative renal peptidases to ACE2-independent formation of ANG-(1-7). In situ and in vitro mass spectrometric characterization showed that substrate concentration and pH control renal ANG II processing. At pH ≥6, ANG-(1-7) formation was significantly reduced in ACE2 knockout (KO) mice. However, at pH <6, formation of ANG-(1-7) in ACE2 KO mice was similar to that in wild-type (WT) mice, suggesting alternative peptidases for renal ANG II processing. Furthermore, the dual prolyl carboxypeptidase (PCP)-prolyl endopeptidase (PEP) inhibitor Z-prolyl-prolinal reduced ANG-(1-7) formation in ACE2 KO mice, while the ACE2 inhibitor MLN-4760 had no effect. Unlike the ACE2 KO mice, ANG-(1-7) formation from ANG II in PEP KO mice was not different from that in WT mice at any tested pH. However, at pH 5, this reaction was significantly reduced in kidneys and urine of PCP-depleted mice. In conclusion, results suggest that ACE2 metabolizes ANG II in the kidney at neutral and basic pH, while PCP catalyzes the same reaction at acidic pH. This is the first report demonstrating that renal ANG-(1-7) formation from ANG II is independent of ACE2. Elucidation of ACE2-independent ANG-(1-7) production pathways may have clinically important implications in patients with metabolic and renal disease.

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.