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.

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.

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.

Comparing the effectiveness of different insecticide application orders for suppressing Drosophila suzukii Matsumura (Diptera: Drosophilidae) infestation: experimental and modeling approaches.

Drosophila suzukii Matsumura (Diptera: Drosophilidae) is a key pest of soft-skinned fruit such as blackberry and blueberry. Differing seasonal spray regimes are expected to have variable effects on D. suzukii populations. Semi-field cage trials were performed at three locations in the United States (Georgia, Oregon, and North Carolina) on blueberry and blackberry crops to evaluate this hypothesis. Insecticides with different efficacy rates (ZC - zeta-cypermethrin, SPI - spinetoram, CYAN - cyantraniliprole) were applied during field experiments conducted within large cages. Treatment schedules consisted of two insecticide applications which performed over three weeks. Seasonal treatment schedules were applied in the following order: ZC-CYAN and CYAN-ZC in rabbiteye and highbush blueberry with the addition of a ZC-SPI treatment applied in blackberry. In addition, a population model was applied to simulate the relative efficacy of the insecticide schedules in Oregon on D. suzukii population model based on previously published efficacy, biological, and weather parameters. Overall, all schedules resulted in reduced D. suzukii infestation compared to untreated control (UTC) treatments, with statistical differences in all three locations. The numerically lower infestation was found in some cases in ZC-CYAN schedule. Population modeling conducted exclusively for blueberry, and the simulations indicated no discernible differences between the two respective schedules (ZC-CYAN vs CYAN-ZC). The present study demonstrates that seasonal infestation of D. suzukii could be suppressed irrespective of application order. Additional research is required to assess the optimal timing and sequence of insecticide applications for controlling seasonal populations of D. suzukii in fruit crops. Such information could be invaluable for growers who are seeking to strategize their insecticide applications.

The Mechanism of miR-21-5p/TSP-1-Mediating Exercise on the Function of Endothelial Progenitor Cells in Aged Rats.

(1) Background: The declined function of peripheral circulating endothelial progenitor cells (EPCs) in aging individuals resulted in decreased endothelial cell regeneration and vascular endothelial function. Improving EPCs function in aging individuals plays an important role in preventing cardiovascular diseases. (2) Methods: Thirty aged (18-month-old) male Sprague-Dawley rats were randomly divided into control and exercise groups. An aerobic exercise intervention was performed 5 days/week for 8 weeks. EPCs functions, miR-21-5p, and TSP-1 expressions were detected after the intervention. The senescence rate, proliferation, and migration of EPCs were examined after overexpression of miR-21-5p and inhibition of TSP-1 expression. (3) Results: The senescence rate, proliferation, and migration of EPCs in exercise groups were significantly improved after exercise intervention. The miR-21-5p expression was increased and the TSP-1 mRNA expression was decreased in the EPCs after the intervention. miR-21-5p overexpression can improve EPCs function and inhibit TSP-1 expression but has no effect on senescence rate. Inhibition of TSP-1 expression could improve the function and reduce the senescence rate. (4) Conclusions: Our results indicate that long-term aerobic exercise can improve the functions of EPCs in aging individuals by downregulating TSP-1 expression via miR-21-5p, which reveals the mechanism of exercise in improving cardiovascular function.

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.

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.

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.

Electron diffraction of CS2 nanoclusters embedded in superfluid helium droplets.

We report experimental results from electron diffraction of CS2 nanoclusters embedded in superfluid helium droplets. From detailed measurements of the sizes of doped droplets, we can model the doping statistics under different experimental conditions, thereby obtaining the range of cluster sizes of CS2. Using a least squares fitting procedure, we can then determine the structures and contributions of dimers, trimers, and tetramers embedded in small droplets. While dimers prefer a stable gas phase structure, trimers and tetramers seem to forgo the highly symmetric gas phase structures and prefer compact cuts from the crystalline structure of CS2. In larger droplets containing more than 12 CS2 monomers, the diffraction profile is consistent with a three-dimensional nanostructure of bulk CS2. This work demonstrates the feasibility of electron diffraction for in situ monitoring of nanocluster formation in superfluid helium droplets.

Electron Diffraction of Pyrene Nanoclusters Embedded in Superfluid Helium Droplets.

We report electron diffraction of pyrene nanoclusters embedded in superfluid helium droplets. Using a least-squares fitting procedure, we have been able to separate the contribution of helium from those of the pyrene nanoclusters and determine the most likely structures for dimers and trimers. We confirm that pyrene dimers form a parallel double-layer structure with an interlayer distance of 3.5 Šand suggest that pyrene trimers form a sandwich structure but that the molecular planes are not completely parallel. The relative contributions of the dimers and trimers are ∼6:1. This work is an extension of our effort of solving structures of biological molecules using serial single-molecule electron diffraction imaging. The success of electron diffraction from an all-light-atom sample embedded in helium droplets offers reassuring evidence of the feasibility of this approach.

Canopy spray deposition and related mortality impacts of commonly used insecticides on Drosophila suzukii Matsumura (Diptera: Drosophilidae) populations in blueberry.

BACKGROUND: Insecticide applications in blueberry production systems play a crucial role in the control of Drosophila suzukii populations. Here, quantitative spray deposition patterns were obtained under replicated field experiments in blueberry during two field seasons with three sprayers, i.e. cannon, electrostatic, and air-blast. Seven insecticides were tested (at 6 hours using a Potter spray tower) to determine the mortality data for adult D. suzukii. Spray deposition and mortality data for adult D. suzukii were used to create model simulations for insect populations. Model simulations included field deposition rates of sprayers and insecticide mortality as factors. Simulations were applied in different combinations with five applications over a 6-week period. RESULTS: Relative deposition rates for the cannon sprayer were elevated in the upper zones of the canopy, whereas for the air-blast sprayer, deposition was greater in the bottom zones. Electrostatic spray deposition was relatively uniform within the six canopy zones. Clear trends in D. suzukii laboratory mortality were found with lowest to highest mortality recorded for phosmet, spinetoram, spinosad, malathion, cyantraniliprole, zeta-cypermethrin, and methomyl respectively. Maximum D. suzukii population impacts, as shown by model outputs, were observed with air-blast sprayers together with zeta-cypermethrin. CONCLUSION: The electrostatic sprayer had the least variable canopy deposition among the three types of spray equipment, and the air-blast sprayer had the highest overall deposition rates. This study provides new hypotheses that can be used for field verification with these spray technologies and insecticides as key factors. © 2019 Society of Chemical Industry.

Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.

Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity of transverse data truncation, researchers have discovered many solutions to accurately reconstruct the image. However, if these conditions are violated, accurate image reconstruction from line integrals remains an intellectual challenge. In this paper, a deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy. Particularly, accurate reconstructions were achieved for the case when the sparse view reconstruction problem (i.e., compressed sensing problem) is entangled with the classical interior tomographic problems.