Elucidating thoracic aortic dissection pathogenesis: The interplay of m1A-related gene expressions and miR-16-5p/YTHDC1 Axis in NLRP3-dependent pyroptosis.

The underlying molecular mechanisms of thoracic aortic dissection (TAD) remain incompletely understood. Recent insights into RNA methylation and microRNA-mediated gene regulation offer new avenues for exploring how these processes contribute to the pathophysiology of TAD, particularly through the modulation of pyroptosis and smooth muscle cell viability. This research aimed to elucidate the interplay of m1A-related gene expressions and miR-16-5p/YTHDC1 Axis in NLRP3-dependent pyroptosis, a mechanism implicated in the pathogenesis of TAD. We collected tissue samples from 28 human TAD patients and 8 healthy aortic group, as well as utilized a mouse model to replicate the disease. A combination of computational, in vitro, and in vivo methods was applied, including CIBERSORTx analysis, Pearson correlation, gene transfection using antagomiR-16-5p, siRNA, and several staining as well as cell culture techniques. Our analysis indicated two differentially expressed genes, ALKBH2 and YTHDC1. We found significant upregulation of has-miR-16-5p and downregulation of YTHDC1 at mRNA level in AD samples. Immune cell infiltration in TAD samples was examined using the CIBERSORTx database. We confirmed that YTHDC1 was a target of miR-16-5p, as evidenced by an inhibitory effect on luciferase activity. Inhibition of miR-16-5p enhanced SMC proliferation and promoted cell viability whilst downregulating NLRP3-pyroptosis. YTHDC1 expression was increased, and NLRP3-pyroptosis expressions were inhibited, suggesting miR-16-5p/YTHDC1 axis may involve the NLRP3-pyroptosis of the SMC. In vivo analyses confirmed the prevention of NLRP3-pyroptosis in middle layer of the thoracic aorta, implying that the miR-16-5p/YTHDC1 axis regulated SMC proliferation via NLRP3-pyroptosis signaling. Our findings underscored the anti-pyroptotic role of miR-16-5p/YTHDC1 axis in the pathogenesis of TAD, suggesting a potential therapeutic strategy via targeting YTHDC1 and suppressing miR-16-5p to inhibit NLRP3-dependent pyroptosis. Although further investigation is needed, these results relating to SMC proliferation are a significant step forward in understanding TAD.

Social Determinants of Cardiovascular Risk Factors Among Asian American Subgroups.

BACKGROUND: Social determinants of health (SDOH) play a significant role in the development of cardiovascular risk factors. We investigated SDOH associations with cardiovascular risk factors among Asian American subgroups. METHODS AND RESULTS: We utilized the National Health Interview Survey, a nationally representative survey of US adults, years 2013 to 2018. SDOH variables were categorized into economic stability, neighborhood and social cohesion, food security, education, and health care utilization. SDOH score was created by categorizing 27 SDOH variables as 0 (favorable) or 1 (unfavorable). Self-reported cardiovascular risk factors included diabetes, high cholesterol, high blood pressure, obesity, insufficient physical activity, suboptimal sleep, and nicotine exposure. Among 6395 Asian adults aged ≥18 years, 22.1% self-identified as Filipino, 21.6% as Asian Indian, 21.0% as Chinese, and 35.3% as other Asian. From multivariable-adjusted logistic regression models, each SD increment of SDOH score was associated with higher odds of diabetes among Chinese (odds ratio [OR], 1.45; 95% CI, 1.04-2.03) and Filipino (OR, 1.24; 95% CI, 1.02-1.51) adults; high blood pressure among Filipino adults (OR, 1.28; 95% CI, 1.03-1.60); insufficient physical activity among Asian Indian (OR, 1.42; 95% CI, 1.22-1.65), Chinese (OR, 1.58; 95% CI, 1.33-1.88), and Filipino (OR, 1.24; 95% CI, 1.06-1.46) adults; suboptimal sleep among Asian Indian adults (OR, 1.20; 95% CI, 1.01-1.42); and nicotine exposure among Chinese (OR, 1.56; 95% CI, 1.15-2.11) and Filipino (OR, 1.50; 95% CI, 1.14-1.97) adults. CONCLUSIONS: Unfavorable SDOH are associated with higher odds of cardiovascular risk factors in Asian American subgroups. Culturally specific interventions addressing SDOH may help improve cardiovascular health among Asian Americans.

Association of Acculturation with Cardiovascular Risk Factors in Asian-American Subgroups.

Objective: This cross-sectional study aims to better understand the heterogeneous associations of acculturation level on CV risk factors among disaggregated Asian subgroups. We hypothesize that the association between acculturation level and CV risk factors will differ significantly by Asian subgroup. Methods: We used the National Health Interview Survey (NHIS), a nationally representative US survey, years 2014-18. Acculturation was defined using: (a) years in the US, (b) US citizenship status, and (c) level of English proficiency. We created an acculturation index, categorized into low vs. high (scores of 0-3 and 4, respectively). Self-reported CV risk factors included diabetes, high cholesterol, hypertension, obesity, tobacco use, and sufficient physical activity. Rao-Scott Chi Square was used to compare age-standardized, weighted prevalence of CV risk factors between Asian subgroups. We used logistic regression analysis to assess associations between acculturation and CV risk factors, stratified by Asian subgroup. Results: The study sample consisted of 6,051 adults ≥ 18 years of age (53.9% female; mean age 46.6 [SE 0.33]). The distribution by race/ethnicity was Asian Indian 26.9%, Chinese 22.8%, Filipino 18.1%, and other Asian 32.3%. The association between acculturation and CV risk factors differed by Asian subgroups. From multivariable adjusted models, high vs. low acculturation was associated with: high cholesterol amongst Asian Indian (OR=1.57, 95% CI: 1.11, 2.37) and other Asian (OR=1.48, 95% CI: 1.10, 2.01) adults, obesity amongst Filipino adults (OR= 1.62, 95% CI: 1.07, 2.45), and sufficient physical activity amongst Chinese (OR= 1.54, 95% CI: 1.09, 2.19) and Filipino adults (OR=1.58, 95% CI: 1.10, 2.27). Conclusion: This study demonstrates that acculturation is heterogeneously associated with higher prevalence of CV risk factors among Asian subgroups. More studies are needed to better understand these differences that can help to inform targeted, culturally specific interventions.

Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan.

OBJECTIVES: To evaluate the performance of an engineered machine learning algorithm to identify kidney stones and measure stone characteristics without the need for human input. METHODS: We performed a cross-sectional study of 94 children and adults who had kidney stones identified on non-contrast CT. A previously developed deep learning algorithm was trained to segment renal anatomy and kidney stones and to measure stone features. The performance and speed of the algorithm to measure renal anatomy and kidney stone features were compared to the current gold standard of human measurement performed by 3 independent reviewers. RESULTS: The algorithm was 100% sensitive and 100% specific in detecting individual kidney stones. The mean stone volume segmented by the algorithm was smaller than that of human reviewers and had moderate overlap (Dice score: 0.66). There was substantial variation between human reviewers in total segmented stone volume (Jaccard score: 0.17) and volume of the single largest stone (Jaccard score: 0.33). Stone segmentations performed by the machine learning algorithm more precisely approximated stone borders than those performed by human reviewers on qualitative assessment. CONCLUSION: An engineered machine learning algorithm can identify and characterize stones more accurately and reliably than humans, which has the potential to improve the precision and efficiency of assessing kidney stone burden.

Can virtual events achieve co-benefits for climate, participation, and satisfaction? Comparative evidence from five international Agriculture, Nutrition and Health Academy Week conferences.

The advancement of science and evidence-based solutions for planetary health increasingly require interdisciplinary and international learning and sharing. Yet aviation travel to academic conferences is carbon-intensive and expensive, thus perpetuating planetary health and equity challenges. Using data from five annual international Agriculture, Nutrition and Health Academy Week conferences from 2016 to 2020, we explore whether moving to virtual conferencing produced co-benefits for climate, participation, attendee interaction, and satisfaction. We report on: absolute number of attendees, proportion of attendees from countries of different income levels, number of participants at social events, aviation CO2 emissions, and overall ratings of the event by participants. Transitioning online resulted in large reductions in travel-related aviation CO2 emissions, alongside increased attendance-including among attendees from low-income and middle-income countries. This was achieved without a major change in the participant rating of the event. However, the online format resulted in lower participation in conference social events. The urgency of reducing CO2 emissions in pursuit of planetary health and improving equity in scientific exchange requires new modalities of academic conferencing. This study indicates that co-benefits can be achieved when transitioning online. Challenges exist for virtual events, such as emulating the intangible facets of in-person interactions, overcoming time-zone limitations, and digital divides.

Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation.

Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-sharing convolutional neural networks (CNNs) to k-space data without taking into consideration the k-space data's spatial frequency properties, leading to ineffective learning of the image reconstruction models. Moreover, complementary information of spatially adjacent slices is often ignored in existing deep learning methods. To overcome such limitations, we have developed a deep learning algorithm, referred to as adaptive convolutional neural networks for k-space data interpolation (ACNN-k-Space), which adopts a residual Encoder-Decoder network architecture to interpolate the undersampled k-space data by integrating spatially contiguous slices as multi-channel input, along with k-space data from multiple coils if available. The network is enhanced by self-attention layers to adaptively focus on k-space data at different spatial frequencies and channels. We have evaluated our method on two public datasets and compared it with state-of-the-art existing methods. Ablation studies and experimental results demonstrate that our method effectively reconstructs images from undersampled k-space data and achieves significantly better image reconstruction performance than current state-of-the-art techniques. Source code of the method is available at https://gitlab.com/qgpmztmf/acnn-k-space.

Fabrication of 3D-Printed Ceramic Structures for Portable Solar Desalination Devices.

This paper proposes the fabrication process of the first fully 3D-printed ceramic core structures for portable solar desalination devices optimized to tackle water scarcity from an energy and sustainability perspective. Robocasting, a 3D printing technique, is utilized to fabricate a fully ceramic structure of an integrated solar absorber/thermal insulator/water transporter based on the two-layered structure of modified graphene on silica (MG@Silica) and the porous silica structure. Robocasting has demonstrated its flexibility in tailoring structural designs, combining nanopores and microchannels that exhibit uniform water transport delivery and thermal insulation. This portable device can be used immediately to collect fresh drinking water without an additional setup. It possesses a water evaporation rate of 2.4 kg m-2 h-1 with a drinking water production capacity of 0.5 L m-2 h-1. This novel device has shown excellent ion rejection ability, with the collected water meeting the World Health Organization (WHO) drinking water standards.

Efficacy of alanyl glutamine in nutritional support therapy for patients with sepsis: A protocol for systematic review and meta-analysis.

BACKGROUND: Sepsis is a systemic inflammatory response caused by infection, which is a common complication after severe infection, trauma, shock, and surgery, and is also an important factor in inducing septic shock and multiple organ dysfunction syndrome (MODS), and has become one of the important causes of death in critically ill patients. Septic patients with gastrointestinal transport function weakened, are prone to malnutrition, resulting in decreased immune function, thereby affecting the therapeutic effect. Clinical practice shows that the nutritional metabolism and immune response of patients with sepsis can be effectively improved by giving alanyl glutamine nutritional support treatment, but there is no evidence of evidence-based medicine. The study carried out in this protocol aims to evaluate the effectiveness of alanyl glutamine in nutritional support therapy for patients with sepsis. METHODS: The Cochrane Library, PubMed, Embase, Web of Science, WHO International Clinical Trials Registry Platform, CNKI, CBM, VIP, and Wanfang databases were searched by computer, to retrieve all randomized controlled trials (RCTs) on nutritional support for the treatment of sepsis with alanyl glutamine from the date of database establishment to December 2020. Two researchers independently selected the study, extracted and managed the data. RevMan5.3 software was used to analyze the included literature. RESULTS: This study observed the changes of serum albumin (ALB), prealbumin (PAB), hemoglobin (Hb), C-reactive protein (CRP), immunoglobulin (IgG, IgA, and IgM), APACHE II score before and after treatment to evaluate the efficacy of alanyl glutamine in nutritional support therapy for patients with sepsis. CONCLUSION: This study will provide reliable evidence for the application of alanyl glutamine in nutritional support therapy for patients with sepsis. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/VRZPJ.

ACEnet: Anatomical context-encoding network for neuroanatomy segmentation.

Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation. In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans, consisting of 1) an anatomical context encoding module to incorporate anatomical information in 2D CNNs and 2) a spatial context encoding module to integrate 3D image information in 2D CNNs. In addition, a skull stripping module is adopted to guide the 2D CNNs to attend to the brain. Extensive experiments on three benchmark datasets have demonstrated that our method achieves promising performance compared with state-of-the-art alternative methods for brain structure segmentation in terms of both computational efficiency and segmentation accuracy.

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection.

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule detection methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure in conjunction with a region proposal network. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the false positive rate and combat the sample imbalance problem associated with nodule detection. We adopt the squeeze-and-excitation structure to learn effective image features and utilize inter-dependency information of different feature maps. We have validated our method based on publicly available CT scans with manually labelled ground-truth obtained from LIDC/IDRI dataset and its subset LUNA16 with thinner slices. Ablation studies and experimental results have demonstrated that our method could outperform state-of-the-art nodule detection methods by a large margin.

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.

We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was ∼ 50 -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100 mL/min-100 g and a tube current-time product of 100 mAs (50% of nominal), the MBF estimates were 101 ± 12 , 94 ± 56 , and 54 ± 24 mL / min - 100 g for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( 103 ± 23 mL / min - 100 g ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics.

A facile oxidation-dehydration reaction-driven robust porous copper oxide nanobelt coating on copper foam for an energy-saving and low-cost urea oxidization reaction.

We fabricated a robust porous copper oxide nanobelt coating on copper foam by a facile oxidation-dehydration reaction, which is firstly reported as a low-cost pure copper-based urea oxidization catalyst. This catalyst has enriched electrochemically active surface area, abudant nanopores and micropores for gas and electrolyte diffusion, and high conductivity from copper foam for electron transfer and herein shows superior UOR performance, outperforming noble metal catalysts or most of the as-reported nonprecious metal UOR catalysts especially at high current density.

Room temperature thiosulfate ion redox reaction-driven synthesis of a robust porous copper-cobalt-sulfur-oxygen nanowire coating on copper foam for highly-efficient and low-cost oxygen evolution reaction.

A robust porous copper-cobalt-sulfur-oxygen nanowire coating (Cu-Co-S-O NWC) was fabricated for the first time on copper foam using a mild thiosulfate ion redox reaction-driven chemical bath synthesis (CBS) strategy. Cu-Co-S-O NWC has a large ECAS, enriched tunnels for gas and electrolyte diffusion and good electron transfer performance from the highly conductive copper foam substrate, and herein, shows improved overall OER activity, outperforming the precious IrO2 catalyst and almost all the as-reported Cu-based or Co-based catalysts, especially at high current density.

Anti-aging effects exerted by Tetramethylpyrazine enhances self-renewal and neuronal differentiation of rat bMSCs by suppressing NF-kB signaling.

In order to improve the therapeutic effects of mesenchymal stem cell (MSC)-based therapies for a number of intractable neurological disorders, a more favorable strategy to regulate the outcome of bone marrow MSCs (bMSCs) was examined in the present study. In view of the wide range of neurotrophic and neuroprotective effects, Tetramethylpyrazine (TMP), a biologically active alkaloid isolated from the herbal medicine Ligusticum wallichii, was used. It was revealed that treatment with 30-50 mg/l TMP for 4 days significantly increased cell viability, alleviated senescence by suppressing NF-κB signaling, and promoted bMSC proliferation by regulating the cell cycle. In addition, 40-50 mg/l TMP treatment may facilitate the neuronal differentiation of bMSCs, verified in the present study by presentation of neuronal morphology and expression of neuronal markers: microtubule-associated protein 2 (MAP-2) and neuron-specific enolase (NSE). The quantitative real-time polymerase chain reaction (qRT-PCR) revealed that TMP treatment may promote the expression of neurogenin 1 (Ngn1), neuronal differentiation 1 (NeuroD) and mammalian achaete-scute homolog 1 (Mash1). In conclusion, 4 days of 40-50 mg/l TMP treatment may significantly delay bMSC senescence by suppressing NF-κB signaling, and enhancing the self-renewal ability of bMSCs, and their potential for neuronal differentiation.

Robust and superwetting island-shaped phytate bimetallic oxyhydroxide porous nanoclusters via a mild self-assembly-etching-catching-electrochemical oxidization strategy for an enhanced oxygen evolution reaction.

Robust and superwetting island-shaped phytate bimetallic oxyhydroxide (PBMO) porous nanoclusters were fabricated by a mild self-assembly-etching-catching-electrochemical oxidization strategy, which show enhanced water oxidation catalytic activity, outperforming the benchmark noble metal IrO2 catalyst and most of the organic metal or NiFe-based catalysts especially at high current density.

Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.

Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study (e.g. difference in patients) for learning discriminative low dimensional representations. To achieve feature dimensionality reduction with improved discriminative power and robustness to noisy radiomic features, we develop an adaptive sparsity regularization based collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively. Our method is built on adaptive sparsity regularized matrix tri-factorization for simultaneous feature denoising and dimension reduction so that the noise is adaptively isolated from the features, and grouping information of patients with distinctive features provides latent supervision information to guide feature dimension reduction. The sparsity regularization is grounded on distribution modeling of transform-domain coefficients in a Bayesian framework. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and empirical results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT.

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1 > RPM2 > Fermi > SCM > LSVD > MUP [Formula: see text] other methods. In terms of bias, the models ranked best-to-worst are: SCM > RPM2 > RPM1 > PTU > LSVD [Formula: see text] other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.

SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification.

There are several computational methods for estimating myocardial blood flow (MBF) using CT myocardial perfusion imaging (CT-MPI). Previous work has shown that model-based deconvolution methods are more accurate and precise than model-independent methods such as singular value decomposition and max-upslope. However, iterative optimization is computationally expensive and models are sensitive to image noise, thus limiting the utility of low x-ray dose acquisitions. We propose a new processing method, SLICR, which segments the myocardium into super-voxels using a modified simple linear iterative clustering (SLIC) algorithm and quantifies MBF via a robust physiologic model (RPM). We compared SLICR against voxel-wise SVD and voxel-wise model-based deconvolution methods (RPM, single-compartment and Johnson-Wilson). We used image data from a digital CT-MPI phantom to evaluate robustness of processing methods to noise at reduced x-ray dose. We validate SLICR in a porcine model with and without partial occlusion of the LAD coronary artery with known pressure-wire fractional flow reserve. SLICR was ~50 times faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show all clinically interesting features (e.g., a flow deficit in the endocardial wall). SLICR showed much better precision and accuracy than the other methods. For example, at simulated MBF=100 mL/min/100g and 100 mAs exposure (50% of nominal dose) in the digital simulator, MBF estimates were 101 ± 12 mL/min/100g, 160 ± 54 mL/min/100g, and 122 ± 99 mL/min/100g for SLICR, SVD, and Johnson-Wilson, respectively. SLICR even gave excellent results (103 ± 23 ml/min/100g) at 50 mAs, corresponding to 25% nominal dose.