Approaches, advantages, and challenges to photon counting detector and multi-energy CT.

Photon counting detector CT (PCD-CT) is the newest major development in CT technology and has been commercially available since 2021. It offers major technological advantages over current standard-of-care energy integrating detector CT (EID-CT) including improved spatial resolution, improved iodine contrast to noise ratio, multi-energy imaging, and reduced noise. This article serves as a foundational basis to the technical approaches and concepts of PCD-CT technology with primary emphasis on detector technology in direct comparison to EID-CT. The article also addresses current technological challenges to PCD-CT with particular attention to cross talk and its causes (e.g., Compton scattering, fluorescence, charge sharing, K-escape) as well as pile-up.

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

BACKGROUND: Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. PURPOSE: To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT) acquisition. METHODS: A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance cov ( x i , x j ) $\text{cov}({\bf x}_i,{\bf x}_j)$ between any two image pixel/voxel locations x i ${\bf x}_i$ and x j ${\bf x_j}$ . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location x i ${\bf x}_i$ . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensemble averaging. RESULTS: The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given local ROI. CONCLUSION: A new method was developed to enable local NPS from a single PCD-CT acquisition.

Noise power spectrum (NPS) in computed tomography: Enabling local NPS measurement without stationarity and ergodicity assumptions.

BACKGROUND: Conventional methods for estimating the noise power spectrum (NPS) often necessitate multiple computed tomography (CT) data acquisitions and are required to satisfy stringent stationarity and ergodicity conditions, which prove challenging in CT imaging systems. PURPOSE: The aim was to revisit the conventional NPS estimation method, leading to a new framework that estimates local NPS without relying on stationarity or ergodicity, thus facilitating experimental NPS estimations. METHODS: The scientific foundation of the conventional CT NPS measurement method, based on the Wiener-Khintchine theorem, was reexamined, emphasizing the critical conditions of stationarity and ergodicity. This work proposes an alternative framework, characterized by its independence from stationarity and ergodicity, and its ability to facilitate local NPS estimations. A spatial average of local NPS over a Region of Interest (ROI) yields the conventional NPS for that ROI. The connections and differences between the proposed alternative method and the conventional method are discussed. Experimental studies were conducted to validate the new method. RESULTS: (1) The NPS estimated using the conventional method was demonstrated to correspond to the spatial average of pointwise NPS from the proposed NPS estimation framework. (2) The NPS estimated over an ROI with the conventional method was shown to be the sum of the NPS estimated from the proposed method and a contribution from measurement uncertainty. (3) Local NPS estimations from the proposed method in this work elucidate the impact of surrounding image content on local NPS variations. CONCLUSION: The NPS estimation method proposed in this work allows for the estimation of local NPS without relying on stationarity and ergodicity conditions, offering local NPS estimations with significantly improved precision.

Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

BACKGROUND: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied to address the interior reconstruction problems for centered regions of interest (ROIs) with fixed sizes. Developing a method to enable interior tomography with arbitrarily located ROIs with nearly arbitrary ROI sizes inside a scanning field of view (FOV) remains an open question. PURPOSE: To develop a new pathway to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes using a single trained deep neural network model. METHODS: The method consists of two steps. First, an analytical weighted backprojection reconstruction algorithm was developed to perform domain transform from divergent fan-beam projection data to an intermediate image feature space, B ( x ⃗ ) $B(\vec{x})$ , for an arbitrary size ROI at an arbitrary location inside the FOV. Second, a supervised learning technique was developed to train a deep neural network architecture to perform deconvolution to obtain the true image f ( x ⃗ ) $f(\vec{x})$ from the new feature space B ( x ⃗ ) $B(\vec{x})$ . This two-step method is referred to as Deep-Interior for convenience. Both numerical simulations and experimental studies were performed to validate the proposed Deep-Interior method. RESULTS: The results showed that ROIs as small as a diameter of 5 cm could be accurately reconstructed (similarity index 0.985 ± 0.018 on internal testing data and 0.940 ± 0.025 on external testing data) at arbitrary locations within an imaging object covering a wide variety of anatomical structures of different body parts. Besides, ROIs of arbitrary size can be reconstructed by stitching small ROIs without additional training. CONCLUSION: The developed Deep-Interior framework can enable interior tomographic reconstruction from divergent fan-beam projections for short-scan and super-short-scan acquisitions for small ROIs (with a diameter larger than 5 cm) at an arbitrary location inside the scanning FOV with high quantitative reconstruction accuracy.

Ethyl ferulate suppresses post-myocardial infarction myocardial fibrosis by inhibiting transforming growth factor receptor 1.

BACKGROUND: With an increasing number of myocardial infarction (MI) patients, myocardial fibrosis is becoming a widespread health concern. It's becoming more and more urgent to conduct additional research and investigations into efficient treatments. Ethyl ferulate (EF) is a naturally occurring substance with cardioprotective properties. However, the extent of its impact and the underlying mechanism of its treatment for myocardial fibrosis after MI remain unknown. PURPOSE: The goal of this study was to look into how EF affected the signaling of the TGF-receptor 1 (TGFBR1) in myocardial fibrosis after MI. METHODS: Echocardiography, hematoxylin-eosin (HE) and Masson trichrome staining were employed to assess the impact of EF on heart structure and function in MI-affected mice in vivo. Cell proliferation assay (MTS), 5-Ethynyl-2'-deoxyuridine (EdU), and western blot techniques were employed to examine the influence of EF on native cardiac fibroblast (CFs) proliferation and collagen deposition. Molecular simulation and surface plasmon resonance imaging (SPRi) were utilized to explore TGFBR1 and EF interaction. Cardiac-specific Tgfbr1 knockout mice (Tgfbr1ΔMCK) were utilized to testify to the impact of EF. RESULTS: In vivo experiments revealed that EF alleviated myocardial fibrosis, improved cardiac dysfunction after MI and downregulated the TGFBR1 signaling in a dose-dependent manner. Moreover, in vitro experiments revealed that EF significantly inhibited CFs proliferation, collagen deposition and TGFBR1 signaling followed by TGF-ß1 stimulation. More specifically, molecular simulation, molecular dynamics, and SPRi collectively showed that EF directly targeted TGFBR1. Lastly, knocking down of Tgfbr1 partially reversed the inhibitory activity of EF on myocardial fibrosis in MI mice. CONCLUSION: EF attenuated myocardial fibrosis post-MI by directly suppressing TGFBR1 and its downstream signaling pathway.

Ferrostatin-1 suppresses cardiomyocyte ferroptosis after myocardial infarction by activating Nrf2 signaling.

OBJECTIVES: Ferroptosis, a new regulated cell death pathway, plays a crucial part in the development of cardiovascular disease. However, the precise underlying mechanism remains unclear. Therefore, this study aimed to elucidate this. METHODS: Herein, an erastin-induced H9C2 cell ferroptosis in vitro model and a myocardial infarction murine model, which was created by ligating the left anterior descending coronary artery, were established. Ferroptosis-related indicators, myocardial injury-related indicators, and Nrf2 signaling-related proteins expression were analyzed to explore the potential mechanism underlying cardiomyocyte ferroptosis-mediated cardiovascular disease development. RESULTS: We demonstrated that Nrf2 downregulation in myocardial tissue, accompanied by ferroptotic events and changes in xCT and GPX4 expressions, induced cardiomyocyte ferroptosis and myocardial injury after myocardial infarction. These events, including ferroptosis and changes in Nrf2, xCT, and GPX4 expressions, were improved by ferrostatin-1 in vivo and in vitro. Besides, Nrf2 deficiency or inhibition aggravated myocardial infarction-induced cardiomyocyte ferroptosis by decreasing xCT and GPX4 expressions in vivo and in vitro. Moreover, ferrostatin-1 directly targeted Nrf2, as evidenced by surface plasmon resonance analysis. CONCLUSIONS: These results indicated that myocardial infarction is accompanied by cardiomyocyte ferroptosis and that Nrf2 signaling plays a crucial part in regulating cardiomyocyte ferroptosis after myocardial infarction.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.

Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging.

BACKGROUND: Due to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD-CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD-CT systems, users' access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications. PURPOSE: To develop methods to correct statistical biases in PCD-CT without requiring access to raw PCD counts. METHODS: (1) The sample variance of air-only post-log sinograms was used to estimate air-only detector counts, N ¯ 0 $\bar{N}_0$ . (2) If the post-log sinogram data, y, is available, then N of each detector pixel was estimated using N = N ¯ 0 e - y $N = \bar{N}_0 \, \mathrm{e}^{-y}$ . Once N was estimated, a closed-form analytical bias correction was applied to the sinogram. (3) If a patient's post-log sinogram data are not archived, a forward projection of the bias-contaminated CT image was used to perform a first-order bias correction. Both the proposed sinogram domain- and image domain-based bias correction methods were validated using experimental PCD-CT data. RESULTS: Experimental results demonstrated that both sinogram domain- and image domain-based bias correction methods enabled reduced-dose PCD-CT images to match the CT numbers of reference-standard images within [-5, 5] HU. In contrast, uncorrected reduced-dose PCD-CT images demonstrated biases ranging from -25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods. CONCLUSIONS: CT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD-CT, allowing PCD-CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.

Unbiased zero-count correction method in low-dose high-resolution photon counting detector CT.

Objective.To address the zero-count problem in low-dose, high-spatial-resolution photon counting detector CT (PCD-CT) without introducing statistical biases or degrading spatial resolution.Approach.The classical approach to generate the sinogram projection data for estimating the line integrals of the linear attenuation coefficients of the image object is to take a log transform of detector counts, which requires zero counts to be replaced by positive numbers. Both the log transform and the zero-count replacement introduce biases. After analyzing the statistical properties of the zero-count replaced pre-log and post-log data, a formula for the statistical sinogram bias was derived, based on which a new sinogram estimator was empirically constructed to cancel the statistical biases. Dose- and object-independent free parameters in the proposed estimator were learned from simulated data, and then the estimator was applied to experimental low-dose PCD-CT data of physical phantoms for validation and generalizability testing. Both bias and noise performances of the proposed method were evaluated and compared with those of previous zero-count correction methods, including zero-weighting, zero-replacement, and adaptive filtration-based methods. The impact of these correction methods on spatial resolution was also quantified using line-pair patterns.Main Results.For all objects and reduced-dose levels, the proposed method reduces the statistical CT number biases to be within ± 10 HU, which is significantly lower than the biases given by the classical zero-count correction methods. The Bland-Altman analysis demonstrated that the proposed correction led to negligible sinogram biases at all attenuation levels, whereas the other correction methods did not. Additionally, the proposed method was found to have no discernible impact on image noise and spatial resolution.Significance.The proposed zero-count correction scheme allows the CT numbers of low-dose, high-spatial-resolution PCD-CT images to match those of standard-dose and standard-resolution PCD-CT images.

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis. PURPOSE: To develop a quality-checked and physics-constrained deep learning (DL) method to estimate material basis images from single-kV CT data without resorting to dual-energy CT acquisition schemes. METHODS: Single-kV CT images are decomposed into two material basis images using a deep neural network. The role of this network is to generate a feature space with 64 template features with the same matrix dimensions of the input single-kV CT image. These 64 template image features are then combined to generate the desired material basis images with different sets of combination coefficients, one for each material basis image. Dual-energy CT image acquisitions with two separate kVs were curated to generate paired training data between a single-kV CT image and the corresponding two material basis images. To ensure the obtained two material basis images are consistent with the encoded spectral information in the actual projection data, two physics constraints, that is, (1) effective energy of each measured projection datum that characterizes the beam hardening in data acquisitions and (2) physical factors of scanners such as detector and tube characteristics, are incorporated into the end-to-end training. The entire architecture is referred to as Deep-En-Chroma in this paper. In the application stage, the generated material basis images are sent to a deep quality check (Deep-QC) network to assess the quality of estimated images and to report the pixel-wise estimation errors for users. The models were developed using 5592 training and validation pairs generated from 48 clinical cases. Additional 1526 CT images from another 13 patients were used to evaluate the quantitative accuracy of water and iodine basis images estimated by Deep-En-Chroma. RESULTS: For the iodine basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is -0.25 mg/mL, and the agreement limits are [-0.75 mg/mL, +0.24 mg/mL]. For the water basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is 0.0 g/mL, and the agreement limits are [-0.01 g/mL, 0.01 g/mL]. Across the test cohort, the median [25th, 75th percentiles] root mean square errors between the Deep-En-Chroma and dual-energy material images are 14 [12, 16] mg/mL for the water images and 0.73 [0.64, 0.80] mg/mL for the iodine images. When significant errors are present in the estimated material basis images, Deep-QC can capture these errors and provide pixel-wise error maps to inform users whether the DL results are trustworthy. CONCLUSIONS: The Deep-En-Chroma network provides a new pathway to estimating the clinically relevant material basis images from single-kV CT data and the Deep-QC module to inform end-users of the accuracy of the DL material basis images in practice.

Calycosin protects against oxidative stress-induced cardiomyocyte apoptosis by activating aldehyde dehydrogenase 2.

Myocardial infarction (MI) is the leading cause of death worldwide, and oxidative stress is part of the process that causes MI. Calycosin, a naturally occurring substance with cardioprotective properties, is one of the major active constituents in Radix Astragali. In this study, effect of Calycosin was investigated in vivo and in vitro to determine whether it could alleviate oxidative stress and oxidative stress-induced cardiac apoptosis in neonatal cardiomyocytes (NCMs) via activation of aldehyde dehydrogenase 2 (ALDH2). Calycosin protected against oxidative stress and oxidative stress-induced apoptosis in NCMs. Molecular docking revealed that the ALDH2-Calycosin complex had a binding energy of -9.885 kcal/mol. In addition, molecular docking simulations demonstrated that the ALDH2-Calycosin complex was stable. Using BLI assays, we confirmed that Calycosin could interact with ALDH2 (KD  = 1.9 × 10-4 M). Furthermore, an ALDH2 kinase activity test revealed that Calycosin increased ALDH2 activity, exhibiting an EC50 of 91.79 µM. Pre-incubation with ALDH2 inhibitor (CVT-10216 or disulfiram) reduced the cardio-protective properties Calycosin. In mice with MI, Calycosin therapy substantially reduced myocardial apoptosis, oxidative stress, and activated ALDH2. Collectively, our findings clearly suggest that Calycosin reduces oxidative stress and oxidative stress-induced apoptosis via the regulation of ALDH2 signaling, which supports potential therapeutic use in MI.

Corrigendum to "Ginsenoside Ro, an oleanolic saponin of Panax ginseng, exerts anti-inflammatory effect by direct inhibiting toll like receptor 4 signaling pathway" [J Ginseng Res 46 (2022) 156-166].

[This corrects the article DOI: 10.1016/j.jgr.2021.05.011.].

Ginsenoside Ro, an oleanolic saponin of Panax ginseng, exerts anti-inflammatory effect by direct inhibiting toll like receptor 4 signaling pathway.

BACKGROUND: Panax ginseng Meyer (P. ginseng), a herb distributed in Korea, China and Japan, exerts benefits on diverse inflammatory conditions. However, the underlying mechanism and active ingredients remains largely unclear. Herein, we aimed to explore the active ingredients of P. ginseng against inflammation and elucidate underlying mechanisms. METHODS: Inflammation model was constructed by lipopolysaccharide (LPS) in C57BL/6 mice and RAW264.7 macrophages. Molecular docking, molecular dynamics, surface plasmon resonance imaging (SPRi) and immunofluorescence were utilized to predict active component. RESULTS: P. ginseng significantly inhibited LPS-induced lung injury and the expression of pro-inflammatory factors, including TNF-α, IL-6 and IL-1ß. Additionally, P. ginseng blocked fluorescence-labeled LPS (LPS488) binding to the membranes of RAW264.7 macrophages, the phosphorylation of nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs). Furthermore, molecular docking demonstrated that ginsenoside Ro (GRo) docked into the LPS binding site of toll like receptor 4 (TLR4)/myeloid differentiation factor 2 (MD2) complex. Molecular dynamic simulations showed that the MD2-GRo binding conformation was stable. SPRi demonstrated an excellent interaction between TLR4/MD2 complex and GRo (KD value of 1.16 × 10-9 M). GRo significantly inhibited LPS488 binding to cell membranes. Further studies showed that GRo markedly suppressed LPS-triggered lung injury, the transcription and secretion levels of TNF-α, IL-6 and IL-1ß. Moreover, the phosphorylation of NF-κB and MAPKs as well as the p65 subunit nuclear translocation were inhibited by GRo dose-dependently. CONCLUSION: Our results suggest that GRo exerts anti-inflammation actions by direct inhibition of TLR4 signaling pathway.

Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.

BACKGROUND: A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation coefficients. These patient models do not account for the detailed anatomical variations of human subjects, and thus, may limit the accuracy of intraview or organ-specific dose modulations in emerging CT technologies. PURPOSE: The purpose of this work was to show that 3D tomographic patient models can be generated from two-view scout images using deep learning strategies, and the reconstructed 3D patient models indeed enable accurate prescriptions of fluence-field modulated or organ-specific dose delivery in the subsequent CT scans. METHODS: CT images and the corresponding two-view scout images were retrospectively collected from 4214 individual CT exams. The collected data were curated for the training of a deep neural network architecture termed ScoutCT-NET to generate 3D tomographic attenuation models from two-view scout images. The trained network was validated using a cohort of 55 136 images from 212 individual patients. To evaluate the accuracy of the reconstructed 3D patient models, radiation delivery plans were generated using ScoutCT-NET 3D patient models and compared with plans prescribed based on true CT images (gold standard) for both fluence-field-modulated CT and organ-specific CT. Radiation dose distributions were estimated using Monte Carlo simulations and were quantitatively evaluated using the Gamma analysis method. Modulated dose profiles were compared against state-of-the-art tube current modulation schemes. Impacts of ScoutCT-NET patient model-based dose modulation schemes on universal-purpose CT acquisitions and organ-specific acquisitions were also compared in terms of overall image appearance, noise magnitude, and noise uniformity. RESULTS: The results demonstrate that (1) The end-to-end trained ScoutCT-NET can be used to generate 3D patient attenuation models and demonstrate empirical generalizability. (2) The 3D patient models can be used to accurately estimate the spatial distribution of radiation dose delivered by standard helical CTs prior to the actual CT acquisition; compared to the gold-standard dose distribution, 95.0% of the voxels in the ScoutCT-NET based dose maps have acceptable gamma values for 5 mm distance-to-agreement and 10% dose difference. (3) The 3D patient models also enabled accurate prescription of fluence-field modulated CT to generate a more uniform noise distribution across the patient body compared to tube current-modulated CT. (4) ScoutCT-NET 3D patient models enabled accurate prescription of organ-specific CT to boost image quality for a given body region-of-interest under a given radiation dose constraint. CONCLUSION: 3D tomographic attenuation models generated by ScoutCT-NET from two-view scout images can be used to prescribe fluence-field-modulated or organ-specific CT scans with high accuracy for the overall objective of radiation dose reduction or image quality improvement for a given imaging task.

Quantitative lung perfusion blood volume using dual energy CT-based effective atomic number (Zeff ) imaging.

BACKGROUND: Iodine material images (aka iodine basis images) generated from dual energy computed tomography (DECT) have been used to assess potential perfusion defects in the pulmonary parenchyma. However, iodine material images do not provide the needed absolute quantification of the pulmonary blood pool, as materials with effective atomic numbers (Zeff ) different from those of basis materials may also contribute to iodine material images, thus confounding the quantification of perfusion defects. PURPOSE: (i) To demonstrate the limitations of iodine material images in pulmonary perfusion defect quantification and (ii) to develop and validate a new quantitative biomarker using effective atomic numbers derived from DECT images. METHODS: The quantitative relationship between the perfusion blood volume (PBV) in pulmonary parenchyma and the effective atomic number (Zeff ) spatial distribution was studied to show that the desired quantitative PBV maps are determined by the spatial maps of Zeff as PB V Z eff ( x ) = a Z eff ß ( x ) + b , where a, b, and ß are three constants. Namely, quantitative PB V Z eff is determined by Zeff images instead of the iodine basis images. Perfusion maps were generated for four human subjects to demonstrate the differences between conventional iodine material image-based PBV (PBViodine ) derived from two-material decompositions and the proposed PB V Z eff method. RESULTS: Among patients with pulmonary emboli, the proposed PB V Z eff maps clearly show the perfusion defects while the PBViodine maps do not. Additionally, when there are no perfusion defects present in the derived PBV maps, no pulmonary emboli were diagnosed by an experienced thoracic radiologist. CONCLUSION: Effective atomic number-based quantitative PBV maps provide the needed sensitive and specific biomarker to quantify pulmonary perfusion defects.

Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical cohorts. PURPOSE: The purpose of this work is to address the previously mentioned challenges in current deep learning methods. METHODS: A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse-view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to reconstruct the image. If the noise level from the PICCS reconstruction is not satisfactory, another light duty deep neural network can then be used to reduce noise level. Both extensive numerical simulation data and human subject data have been used to quantitatively and qualitatively assess the performance of the proposed DL-PICCS method in terms of reconstruction accuracy and generalizability. RESULTS: Extensive evaluation studies have demonstrated that: (1) quantitative reconstruction accuracy of DL-PICCS for individual patient is improved when it is compared with the deep learning methods and CS-based methods; (2) the false-positive lesion-like structures and false negative missing anatomical structures in the deep learning approaches can be effectively eliminated in the DL-PICCS reconstructed images; and (3) DL-PICCS enables a deep learning scheme to relax its working conditions to enhance its generalizability. CONCLUSIONS: DL-PICCS offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability.

Tanshinone I Inhibits Oxidative Stress-Induced Cardiomyocyte Injury by Modulating Nrf2 Signaling.

Cardiovascular disease, a disease caused by many pathogenic factors, is one of the most common causes of death worldwide, and oxidative stress plays a major role in its pathophysiology. Tanshinone I (Tan I), a natural compound with cardiovascular protective effects, is one of the main active compounds extracted from Salvia miltiorrhiza. Here, we investigated whether Tan I could attenuate oxidative stress and oxidative stress-induced cardiomyocyte apoptosis through Nrf2/MAPK signaling in vivo and in vitro. We found that Tan I treatment protected cardiomyocytes against oxidative stress and oxidative stress-induced apoptosis, based on the detection of relevant oxidation indexes such as reactive oxygen species, superoxide dismutase, malondialdehyde, and apoptosis, including cell viability and apoptosis-related protein expression. We further examined the mechanisms underlying these effects, determining that Tan I activated nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) transcription into the nucleus and dose-dependently promoted the expression of Nrf2, while inhibiting MAPK signaling activation, including P38 MAPK, SAPK/JNK, and ERK1/2. Nrf2 inhibitors in H9C2 cells and Nrf2 knockout mice demonstrated aggravated oxidative stress and oxidative stress-induced cardiomyocyte injury; Tan I treatment suppressed these effects in H9C2 cells; however, its protective effect was inhibited in Nrf2 knockout mice. Additionally, the analysis of surface plasmon resonance demonstrated that Tan I could directly target Nrf2 and act as a potential Nrf2 agonist. Collectively, these data strongly indicated that Tan I might inhibit oxidative stress and oxidative stress-induced cardiomyocyte injury through modulation of Nrf2 signaling, thus supporting the potential therapeutic application of Tan I for oxidative stress-induced CVDs.

High Pitch Helical CT Reconstruction.

To avoid severe limited-view artifacts in reconstructed CT images, current multi-row detector CT (MDCT) scanners with a single x-ray source-detector assembly need to limit table translation speeds such that the pitch p (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When , it remains an open question whether one can reconstruct clinically useful helical CT images without severe artifacts. In this work, we show that a synergistic use of advanced techniques in conventional helical filtered backprojection, compressed sensing, and more recent deep learning methods can be properly integrated to enable accurate reconstruction up to p=4 without significant artifacts for single source MDCT scans.

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.

Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non-COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019-related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.