Acute nicotinamide riboside supplementation increases human cerebral NAD+ levels in vivo.

PURPOSE: The purpose of this study was to determine the effect of acute nicotinamide riboside (NR) supplementation on cerebral nicotinamide adenine dinucleotide (NAD+) levels in the human brain in vivo by means of downfield proton MRS (DF 1H MRS). METHODS: DF 1H MRS was performed on 10 healthy volunteers in a 7.0 T MRI scanner with spectrally selective excitation and spatially selective localization to determine cerebral NAD+ levels on two back-to-back days: once after an overnight fast (baseline) and once 4 h after oral ingestion of nicotinamide riboside (900 mg). Additionally, two more baseline scans were performed following the same paradigm to assess test-retest reliability of the NAD+ levels in the absence of NR. RESULTS: NR supplementation increased mean NAD+ concentration compared to the baseline (0.458 ± 0.053 vs. 0.392 ± 0.058 mM; p < 0.001). The additional two baseline scans demonstrated no differences in mean NAD+ concentrations (0.425 ± 0.118 vs. 0.405 ± 0.082 mM; p = 0.45), and no difference from the first baseline scan (F(2, 16) = 0.907; p = 0.424). CONCLUSION: These preliminary results confirm that acute NR supplementation increases cerebral NAD+ levels in healthy human volunteers and shows the promise of DF 1H MRS utility for robust detection of NAD+ in humans in vivo.

CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank.

The study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen-Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41-50 year old cohort) and a change of - 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81-100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51-60 year old cohort) and a change of - 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81-100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of - 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype-phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.

Three-dimensional aortic geometry: clinical correlates, prognostic value and genetic architecture.

Aortic structure and function impact cardiovascular health through multiple mechanisms. Aortic structural degeneration increases left ventricular afterload, pulse pressure and promotes target organ damage. Despite the impact of aortic structure on cardiovascular health, aortic 3D-geometry has yet to be comprehensively assessed. Using a convolutional neural network (U-Net) combined with morphological operations, we quantified aortic 3D-geometric phenotypes (AGPs) from 53,612 participants in the UK Biobank and 8,066 participants in the Penn Medicine Biobank. AGPs reflective of structural aortic degeneration, characterized by arch unfolding, descending aortic lengthening and luminal dilation exhibited cross-sectional associations with hypertension and cardiac diseases, and were predictive for new-onset hypertension, heart failure, cardiomyopathy, and atrial fibrillation. We identified 237 novel genetic loci associated with 3D-AGPs. Fibrillin-2 gene polymorphisms were identified as key determinants of aortic arch-3D structure. Mendelian randomization identified putative causal effects of aortic geometry on the risk of chronic kidney disease and stroke.

Optimization of 1H MR spectroscopy methods for large volume acquisition of low concentration downfield resonances at 3T and 7T.

Purpose: This goal of this study was to optimize spectrally selective 1H MRS methods for large volume acquisition of low concentration metabolites with downfield resonances at 7T and 3T, with particular attention paid to detection of nicotinamide adenine dinucleotide (NAD+) and tryptophan. Methods: Spectrally selective excitation was used to avoid magnetization transfer effects with water, and various sinc pulses were compared to a pure-phase E-BURP pulse. Localization using a single slice selective pulse was compared to voxel-based localization that used three orthogonal refocusing pulses, and low bandwidth refocusing pulses were used to take advantage of the chemical shift displacement of water. A technique for water sideband removal was added, and a method of coil channel combination for large volumes was introduced. Results: Proposed methods were compared qualitatively to previously-reported techniques at 7T. Sinc pulses resulted in reduced water signal excitation and improved spectral quality, with a symmetric, low bandwidth-time product pulse performing best. Single slice localization allowed shorter TEs with large volumes, enhancing signal, while low bandwidth slice selective localization greatly reduced the observed water signal. Gradient cycling helped remove water sidebands, and frequency aligning and pruning individual channels narrowed spectral linewidths. High quality brain spectra of NAD+ and tryptophan are shown in four subjects at 3T. Conclusion: Improved spectral quality with higher downfield signal, shorter TE, lower nuisance signal, reduced artifacts, and narrower peaks was realized at 7T. These methodological improvements allowed for previously unachievable detection of NAD+ and tryptophan in human brain at 3T in under five minutes.

Quantification of NAD + T 1 and T 2 relaxation times using downfield 1 H MRS at 7 T in human brain in vivo.

Introduction: The purpose of this study was to use a single-slice spectrally-selective sequence to measure T 1 and T 2 relaxation times of NAD + proton resonances in the downfield 1 H MRS spectrum in human brain at 7 T in vivo and assess the propagation of relaxation time uncertainty in NAD + quantification. Methods: Downfield spectra from 7 healthy volunteers were acquired at multiple echo times in all subjects to measure T 2 relaxation, and saturation recovery data were to measure T 1 relaxation. The downfield acquisition used a spectrally-selective 90° sinc pulse for excitation centered at 9.1 ppm with a bandwidth of 2 ppm, followed by a 180° spatially-selective Shinnar-Le Roux refocusing pulse for localization. For the multiple echo experiment, spectra were collected with echo times ranging from 13 to 33 ms. For the saturation recovery experiment, saturation was performed prior to excitation using the same spectrally-selective sinc pulse as was used for excitation. Saturation delay times (TS) ranged from 100 to 600 ms. Uncertainty propagation analysis was performed analytically and with Monte Carlo simulation. Results: The mean ± standard deviation of T 1 relaxation times of the H2, H6, and H4 protons were 152.7 ± 16.6, 163.6 ± 22.3, and 169.9 ± 11.2 ms, respectively. The mean ± standard deviation of T 2 relaxation times of the H2, H6, and H4 protons were 32.5 ± 7.0, 27.4 ± 5.2, and 38.1 ± 11.7 ms, respectively. The mean R 2 of the H2 and H6 T 1 fits were 0.98. The mean R 2 of the H4 proton T 1 fit was 0.96. The mean R 2 of the T 2 fits of the H2 and H4 proton resonances were 0.98, while the mean R 2 of the T 2 fits of the H4 proton was 0.93. The relative uncertainty in NAD + concentration due to relaxation time uncertainty was 8.5%-11%. Conclusion: Using downfield spectrally-selective spectroscopy with single-slice localization, we found NAD + T 1 and T 2 relaxation times to be approximately 162 ms and 32 ms respectively in the human brain in vivo at 7 T.

Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.

BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-photon emission computed tomography (SPECT)/computed tomography (CT). METHODS: We identified a primary cohort of 77 subjects at Brigham and Women's Hospital and a validation cohort of 93 consecutive patients imaged at the University of Pennsylvania who underwent SPECT/CT with PYP and HDP, respectively, for evaluation of ATTR-CM. Global heart regions of interest (ROIs) were traced on CT axial slices from the apex of the ventricle to the carina. Myocardial images were visually scored as grade 0 (no uptake), 1 (uptakeribs). A 2D U-net architecture was used to develop whole-heart segmentations for CT scans. Uptake was determined by calculating a heart-to-blood pool (HBP) ratio between the maximal counts value of the total heart region and the maximal counts value of the most superior ROI. RESULTS: Deep learning and ground truth segmentations were comparable (p=0.63). A total of 42 (55%) patients had abnormal myocardial uptake on visual assessment. Automated quantification of the mean HBP ratio in the primary cohort was 3.1±1.4 versus 1.4±0.2 (p<0.01) for patients with positive and negative cardiac uptake, respectively. The model had 100% accuracy in the primary cohort and 98% in the validation cohort. CONCLUSION: We have developed a highly accurate diagnostic tool for automatically segmenting and identifying myocardial uptake suggestive of ATTR-CM.

Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.

The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.

Relationships between body fat distribution and metabolic syndrome traits and outcomes: A mendelian randomization study.

BACKGROUND: Obesity is a complex, multifactorial disease associated with substantial morbidity and mortality worldwide. Although it is frequently assessed using BMI, many epidemiological studies have shown links between body fat distribution and obesity-related outcomes. This study examined the relationships between body fat distribution and metabolic syndrome traits using Mendelian Randomization (MR). METHODS/FINDINGS: Genetic variants associated with visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and gluteofemoral adipose tissue (GFAT), as well as their relative ratios, were identified from a genome wide association study (GWAS) performed with the United Kingdom BioBank. GWAS summary statistics for traits and outcomes related to metabolic syndrome were obtained from the IEU Open GWAS Project. Two-sample MR and BMI-controlled multivariable MR (MVMR) were performed to examine relationships between each body fat measure and ratio with the outcomes. Increases in absolute GFAT were associated with a protective cardiometabolic profile, including lower low density lipoprotein cholesterol (ß: -0.19, [95% CI: -0.28, -0.10], p < 0.001), higher high density lipoprotein cholesterol (ß: 0.23, [95% CI: 0.03, 0.43], p = 0.025), lower triglycerides (ß: -0.28, [95% CI: -0.45, -0.10], p = 0.0021), and decreased systolic (ß: -1.65, [95% CI: -2.69, -0.61], p = 0.0019) and diastolic blood pressures (ß: -0.95, [95% CI: -1.65, -0.25], p = 0.0075). These relationships were largely maintained in BMI-controlled MVMR analyses. Decreases in relative GFAT were linked with a worse cardiometabolic profile, with higher levels of detrimental lipids and increases in systolic and diastolic blood pressures. CONCLUSION: A MR analysis of ASAT, GFAT, and VAT depots and their relative ratios with metabolic syndrome related traits and outcomes revealed that increased absolute and relative GFAT were associated with a favorable cardiometabolic profile independently of BMI. These associations highlight the importance of body fat distribution in obesity and more precise means to categorize obesity beyond BMI.

Noninvasive Assessment of Lipomatous Metaplasia as a Substrate for Ventricular Tachycardia in Chronic Infarct.

Myocardial lipomatous metaplasia (LM) has been increasingly reported in patients with prior myocardial infarction. Cardiac magnetic resonance and cardiac contrast-enhanced computed tomography have been used to noninvasively detect and quantify myocardial LM in postinfarct patients, and may provide useful information for understanding cardiac mechanics, arrhythmia susceptibility, and prognosis. This review aims to summarize the advantages and disadvantages, clinical applications, and imaging features of different cardiac magnetic resonance sequences and cardiac contrast-enhanced computed tomography for LM detection and quantification. We also briefly summarize LM prevalence in different cohorts of postinfarct patients and review the clinical utility of cardiac imaging in exploring myocardial LM as an arrhythmogenic substrate in patients with prior myocardial infarction.

Magnetic resonance myocardial T1ρ mapping : Technical overview, challenges, emerging developments, and clinical applications.

The potential of cardiac magnetic resonance to improve cardiovascular care and patient management is considerable. Myocardial T1-rho (T1ρ) mapping, in particular, has emerged as a promising biomarker for quantifying myocardial injuries without exogenous contrast agents. Its potential as a contrast-agent-free ("needle-free") and cost-effective diagnostic marker promises high impact both in terms of clinical outcomes and patient comfort. However, myocardial T1ρ mapping is still at a nascent stage of development and the evidence supporting its diagnostic performance and clinical effectiveness is scant, though likely to change with technological improvements. The present review aims at providing a primer on the essentials of myocardial T1ρ mapping, and to describe the current range of clinical applications of the technique to detect and quantify myocardial injuries. We also delineate the important limitations and challenges for clinical deployment, including the urgent need for standardization, the evaluation of bias, and the critical importance of clinical testing. We conclude by outlining technical developments to be expected in the future. If needle-free myocardial T1ρ mapping is shown to improve patient diagnosis and prognosis, and can be effectively integrated in cardiovascular practice, it will fulfill its potential as an essential component of a cardiac magnetic resonance examination.

Identification of new resonances in downfield 1 H MRS of human calf muscle in vivo: Potentially metabolite precursors for skeletal muscle NAD.

PURPOSE: The purpose of this study was to identify and characterize newly discovered resonances appearing in the downfield proton MR spectrum (DF 1 H MRS) of the human calf muscle in vivo at 7T. METHODS: Downfield 1 H MRS was performed on the calf muscle of five healthy volunteers at 7T. A spectrally selective 90° E-BURP RF pulse with an excitation center frequency at 10.3 ppm and an excitation bandwidth of 2 ppm was used for DF 1 H MRS acquisition. RESULTS: In all participants, we observed new resonances at 9.7, 10.1, 10.3, and 10.9 ppm in the DF 1 H MRS. Phantom experiments at 37°C strongly suggest the new resonance at 9.7 ppm could be from H2-proton of the nicotinamide rings in nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN) while the resonance at 10.1 ppm could be attributed to the indole -NH proton of L-tryptophan. We observed that the resonances at 10.1 and 10.9 ppm are significantly suppressed when the water resonance is saturated, indicating that these peaks have either 1 H chemical exchange or cross-relaxation with water. Conversely, the resonances at 9.7 and 10.3 ppm exhibit moderate signal reduction in the presence of water saturation. CONCLUSION: We have identified new proton resonances in vivo in human calf muscle occurring at chemical shifts of 9.7, 10.1, 10.3, and 10.9 ppm. These preliminary results are promising for investigating the role of NR/NMN and L-tryptophan metabolism in understanding the de novo and salvage pathways of NAD+ synthesis in skeletal muscle.

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times. PURPOSE: To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity. METHODS: In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosity techniques based on calibration samples. RESULTS: The deep learning model provided accurate labeling (Dice score 0.93, intersection-over-union 0.88) and similar results to manual segmentation in quantifying cortical porosity (R2 ≥ 0.97, ICC ≥ 0.98) and geometry (R2 ≥ 0.82, ICC ≥ 0.75) parameters in vivo. Furthermore, the Suppression Ratio was validated compared to established porosity protocols (R2 ≥ 0.78). Automated parameters detected age- and osteoporosis-related impairments in cortical bone porosity (P ≤ .002) and geometry (P values ranging from <0.001 to 0.08). Finally, automated porosity markers showed strong, inverse Pearson's correlations with BMD measured by pQCT (|R| ≥ 0.88) and DXA (|R| ≥ 0.76) in postmenopausal women, confirming that lower mineral density corresponds to greater porosity. CONCLUSION: This study demonstrated feasibility of a simple, automated, and ionizing-radiation-free protocol for quantifying cortical bone porosity and geometry in vivo from UTE MRI and deep learning.

Peripheral and Central Iron Measures in Alcohol Use Disorder and Aging: A Quantitative Susceptibility Mapping Pilot Study.

Chronic excessive alcohol use has neurotoxic effects, which may contribute to cognitive decline and the risk of early-onset dementia. Elevated peripheral iron levels have been reported in individuals with alcohol use disorder (AUD), but its association with brain iron loading has not been explored. We evaluated whether (1) serum and brain iron loading are higher in individuals with AUD than non-dependent healthy controls and (2) serum and brain iron loading increase with age. A fasting serum iron panel was obtained and a magnetic resonance imaging scan with quantitative susceptibility mapping (QSM) was used to quantify brain iron concentrations. Although serum ferritin levels were higher in the AUD group than in controls, whole-brain iron susceptibility did not differ between groups. Voxel-wise QSM analyses revealed higher susceptibility in a cluster in the left globus pallidus in individuals with AUD than controls. Whole-brain iron increased with age and voxel-wise QSM indicated higher susceptibility with age in various brain areas including the basal ganglia. This is the first study to analyze both serum and brain iron loading in individuals with AUD. Larger studies are needed to examine the effects of alcohol use on iron loading and its associations with alcohol use severity, structural and functional brain changes, and alcohol-induced cognitive impairments.

Quantification of cross-relaxation in downfield 1 H MRS at 7 T in human calf muscle.

PURPOSE: The purpose of this study was to characterize the 1 H downfield MR spectrum from 8.0 to 10.0 ppm of human skeletal muscle at 7 T and determine the T1 and cross-relaxation rates of observed resonances. METHODS: We performed downfield MRS in the calf muscle of 7 healthy volunteers. Single-voxel downfield MRS was collected using alternately selective or broadband inversion-recovery sequences and spectrally selective 90° E-BURP RF pulse excitation centered at 9.0 ppm with bandwidth = 600 Hz (2.0 ppm). MRS was collected using TIs of 50-2500 ms. We modeled recovery of the longitudinal magnetization of three observable resonances using two models: (1) a three-parameter model accounting for the apparent T1 recovery and (2) a Solomon model explicitly including cross-relaxation effects. RESULTS: Three resonances were observed in human calf muscle at 7 T at 8.0, 8.2, and 8.5 ppm. We found broadband (broad) and selective (sel) inversion recovery T1 = mean ± SD (ms): T1-broad,8.0ppm = 2108.2 ± 664.5, T1-sel,8.0ppm = 753.6 ± 141.0 (p = 0.003); T1-broad,8.2ppm = 2033.5 ± 338.4, T1-sel,8.2ppm = 135.3 ± 35.3 (p < 0.0001); and T1-broad,8.5ppm = 1395.4 ± 75.4, T1-sel,8.5ppm = 107.1 ± 40.0 (p < 0.0001). Using the Solomon model, we found T1 = mean ± SD (ms): T1-8.0ppm = 1595.6 ± 491.1, T1-8.2ppm = 1737.2 ± 963.7, and T1-8.5ppm = 849.8 ± 282.0 (p = 0.04). Post hoc tests corrected for multiple comparisons showed no significant difference in T1 between peaks. The cross-relaxation rate σAB = mean ± SD (Hz) of each peak was σAB,8.0ppm = 0.76 ± 0.20, σAB,8.2ppm = 5.31 ± 2.27, and σAB,8.5ppm = 7.90 ± 2.74 (p < 0.0001); post hoc t-tests revealed the cross-relaxation rate of the 8.0 ppm peak was significantly slower than the peaks at 8.2 ppm (p = 0.0018) and 8.5 ppm (p = 0.0005). CONCLUSION: We found significant differences in effective T1 and cross-relaxation rates of 1 H resonances between 8.0 and 8.5 ppm in the healthy human calf muscle at 7 T.

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification.

Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As the time available for clinical office visits shortens and medical imaging data become more widely available, patient image data could be used to opportunistically identify patients for additional T2DM diagnostic workup by physicians. We investigated whether image-derived phenotypic data could be leveraged in tabular learning classifier models to predict T2DM risk in an automated fashion to flag high-risk patients without the need for additional blood laboratory measurements. In contrast to traditional binary classifiers, we leverage neural networks and decision tree models to represent patient data as 'SynthA1c' latent variables, which mimic blood hemoglobin A1c empirical lab measurements, that achieve sensitivities as high as 87.6%. To evaluate how SynthA1c models may generalize to other patient populations, we introduce a novel generalizable metric that uses vanilla data augmentation techniques to predict model performance on input out-of-domain covariates. We show that image-derived phenotypes and physical examination data together can accurately predict diabetes risk as a means of opportunistic risk stratification enabled by artificial intelligence and medical imaging. Our code is available at https://github.com/allisonjchae/DMT2RiskAssessment.

Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers.

Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using alternating direction method of multiplier framework and applied collaborative filtering denoisers. We apply the technique to the 2016 QSM Challenge and in 10 glioblastoma multiforme datasets. We compared its performance with four published QSM techniques and a multi-orientation QSM method. We analyzed magnetic susceptibility accuracy using brain region-of-interest measurements, and image quality using global error metrics. Reconstructions on glioblastoma data were analyzed using ranked and semiquantitative image grading by three neuroradiologist observers to assess image quality (IQ) and sharpness (IS). PnP-BM4D QSM showed good correlation (ß = 0.84, R2 = 0.98, p < 0.05) with COSMOS and no significant bias (bias = 0.007 ± 0.012). PnP-BM4D QSM achieved excellent quality when assessed using structural similarity index metric (SSIM = 0.860), high frequency error norm (HFEN = 58.5), cross correlation (CC = 0.804), and mutual information (MI = 0.475) and also maintained good conspicuity of fine features. In glioblastoma datasets, PnP-BM4D QSM showed higher performance (IQGrade = 2.4 ± 0.4, ISGrade = 2.7 ± 0.3, IQRank = 3.7 ± 0.3, ISRank = 3.9 ± 0.3) compared to MEDI (IQGrade = 2.1 ± 0.5, ISGrade = 2.1 ± 0.6, IQRank = 2.4 ± 0.6, ISRank = 2.9 ± 0.2) and FANSI-TGV (IQGrade = 2.2 ± 0.6, ISGrade = 2.1 ± 0.6, IQRank = 2.7 ± 0.3, ISRank = 2.2 ± 0.2). We illustrated the modularity of PnP QSM by interchanging two additional patch-based denoisers. PnP QSM reconstruction was feasible, and its flexibility was shown using several patch-based denoisers. This technique may allow rapid prototyping and validation of new denoisers for QSM reconstruction for an array of useful clinical applications.

Exome-wide association analysis of CT imaging-derived hepatic fat in a medical biobank.

Nonalcoholic fatty liver disease is common and highly heritable. Genetic studies of hepatic fat have not sufficiently addressed non-European and rare variants. In a medical biobank, we quantitate hepatic fat from clinical computed tomography (CT) scans via deep learning in 10,283 participants with whole-exome sequences available. We conduct exome-wide associations of single variants and rare predicted loss-of-function (pLOF) variants with CT-based hepatic fat and perform cross-modality replication in the UK Biobank (UKB) by linking whole-exome sequences to MRI-based hepatic fat. We confirm single variants previously associated with hepatic fat and identify several additional variants, including two (FGD5 H600Y and CITED2 S198_G199del) that replicated in UKB. A burden of rare pLOF variants in LMF2 is associated with increased hepatic fat and replicates in UKB. Quantitative phenotypes generated from clinical imaging studies and intersected with genomic data in medical biobanks have the potential to identify molecular pathways associated with human traits and disease.

Feasibility of Vascular Parameter Estimation for Assessing Hypertensive Pregnancy Disorders.

Hypertensive pregnancy disorders (HPDs), such as pre-eclampsia, are leading sources of both maternal and fetal morbidity in pregnancy. Noninvasive imaging, such as ultrasound (US) and magnetic resonance imaging (MRI), is an important tool for predicting and monitoring these high risk pregnancies. While imaging can measure hemodynamic parameters, such as uterine artery pulsatility and resistivity indices (PI and RI), the interpretation of such metrics for disease assessment relies on ad hoc standards, which provide limited insight to the physical mechanisms underlying the emergence of hypertensive pregnancy disorders. To provide meaningful interpretation of measured hemodynamic data in patients, advances in computational fluid dynamics can be brought to bear. In this work, we develop a patient-specific computational framework that combines Bayesian inference with a reduced-order fluid dynamics model to infer parameters, such as vascular resistance, compliance, and vessel cross-sectional area, known to be related to the development of hypertension. The proposed framework enables the prediction of hemodynamic quantities of interest, such as pressure and velocity, directly from sparse and noisy MRI measurements. We illustrate the effectiveness of this approach in two systemic arterial network geometries: an aorta with branching carotid artery and a maternal pelvic arterial network. For both cases, the model can reconstruct the provided measurements and infer parameters of interest. In the case of the maternal pelvic arteries, the model can make a distinction between the pregnancies destined to develop hypertension and those that remain normotensive, expressed through the value range of the predicted absolute pressure.

Identification of l-Tryptophan by down-field 1 H MRS: A precursor for brain NAD+ and serotonin syntheses.

PURPOSE: To explore the presence of new resonances beyond 9.4 ppm from the human brain, down-field proton MRS was performed in vivo in the human brain on 6 healthy volunteers at 7 T. METHODS: To maximize the SNR, a large voxel was placed within the brain to cover the maximal area in such a way that sinus cavities were avoided. A spectrally selective 90° E-BURP pulse with an excitation bandwidth of 2 ppm was used to probe the spectral chemical shift range between 9.1 and 10.5 ppm. The E-BURP pulse was integrated with PRESS spatial localization to obtain non-water-suppressed proton MR spectra from the desired spectral region. RESULTS: In the down-field proton MRS obtained from all of the volunteers scanned, we identified a new peak consistently resonating at 10.1 ppm. Protons associated with this resonance are in cross-relaxation with the bulk water, as demonstrated by the water saturation and deuterium exchange experiments. CONCLUSION: Based on the chemical shift, this new peak was identified as the indole (-NH) proton of l-tryptophan (l-TRP) and was further confirmed from phantom experiments on l-TRP. These promising preliminary results potentially pave the way to investigate the role of cerebral metabolism of l-TRP in healthy and disease conditions.