Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks

Purpose: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network. Methods: A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kVCT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV-CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). Results: The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. Conclusions: The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single-energy CT images

A New Insight into Electron Acceleration Properties from Theoretical Modeling of Double-Peaked Radio Light Curves in Core-Collapse Supernovae

It is recognized that some core-collapse supernovae (SNe) show a double-peaked radio light curve within a few years since the explosion. A shell of circumstellar medium (CSM) detached from the SN progenitor has been considered to play a viable role in characterizing such a re-brightening of radio emission. Here, we propose another mechanism that can give rise to the double-peaked radio light curve in core-collapse SNe. The key ingredient in the present work is to expand the model for the evolution of the synchrotron spectral energy distribution (SED) to a generic form, including fast and slow cooling regimes, as guided by the widely-accepted modeling scheme of gamma-ray burst afterglows. We show that even without introducing an additional CSM shell, the radio light curve would show a double-peaked morphology when the system becomes optically thin to synchrotron self-absorption at the observational frequency during the fast cooling regime. We can observe this double-peaked feature if the transition from fast cooling to slow cooling regime occurs during the typical observational timescale of SNe. This situation is realized when the minimum Lorentz factor of injected electrons is initially large enough for the non-thermal electrons' SED to be discrete from the thermal distribution. We propose SN 2007bg as a special case of double-peaked radio SNe that can be explained by the presented scenario. Our model can serve as a potential diagnostic for electron acceleration properties in SNe.Comment: 16 pages, 8 figures, submitted to Ap

Assessment of biological dosimetric margin for stereotactic body radiation therapy

Purpose: To develop a novel biological dosimetric margin (BDM) and to create a biological conversion factor (BCF) that compensates for the difference between physical dosimetric margin (PDM) and BDM, which provides a novel scheme of a direct estimation of the BDM from the physical dose (PD) distribution. Methods: The offset to isocenter was applied in 1‐mm steps along left‐right (LR), anterior‐posterior (AP), and cranio‐caudal (CC) directions for 10 treatment plans of lung stereotactic body radiation therapy (SBRT) with a prescribed dose of 48 Gy. These plans were recalculated to biological equivalent dose (BED) by the linearquadratic model for the dose per fraction (DPF) of d = 3–20 Gy/fr and α/β= 3 - 10. BDM and PDM were defined so that the region that satisfied that the dose covering 95% (or 98%) of the clinical target volume was greater than or equal to the 90% of the prescribed PD and BED, respectively. An empirical formula of the BCF was created as a function of the DPF. Results: There was no significant difference between LR and AP directions for neither the PDM nor BDM. On the other hand, BDM and PDM in the CC direction were significantly larger than in the other directions. BCFs of D95% and D98% were derived for the transverse (LR and AP) and longitudinal (CC) directions. Conclusions: A novel scheme to directly estimate the BDM using the BCF was developed. This technique is expected to enable the BED‐based SBRT treatment planning using PD‐based treatment planning systems

Synthesized effective atomic numbers for commercially available dual-energy CT

Purpose: The objective of this study was to assess synthesized effective atomic number (Zeff) values with a new developed tissue characteristic phantom and contrast material of varying iodine concentrations using single-source fast kilovoltage switching dual-energy CT (DECT) scanner. Methods: A newly developed multi energy tissue characterisation CT phantom and an acrylic phantom with various iodine concentrations of were scanned using single-source fast kilovoltage switching DECT (GE-DECT) scanner. The difference between the measured and theoretical values of Zeff were evaluated. Additionally, the difference and coefficient of variation (CV) values of the theoretical and measured values were compared with values obtained with the Canon-DECT scanner that was analysed in our previous study. Results: The average Zeff difference in the Multi-energy phantom was within 4.5%. The average difference of the theoretical and measured Zeff values for the acrylic phantom with variation of iodine concentration was within 3.3%. Compared to the results for the single-source Canon-DECT scanner used in our previous study, the average difference and CV of the theoretical and measured Zeff values obtained with the GE-DECT scanner were markedly smaller. Conclusions: The accuracy of the synthesized Zeff values with GE-DECT had a good agreement with the theoretical Zeff values for the Multi-Energy phantom. The GE-DECT could reduce the noise and the accuracy of the Zeff values than that with Canon-DECT for the varying iodine concentrations of contrast medium. Advances in knowledge: The accuracy and precision of the Zeff values of the contrast medium with the GE-DECT could be sufficient with human equivalent materials

Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine

We propose a novel method for solving optimal sensor placement problem for high-dimensional system using an annealing machine. The sensor points are calculated as a maximum clique problem of the graph, the edge weight of which is determined by the proper orthogonal decomposition (POD) mode obtained from data based on the fact that a high-dimensional system usually has a low-dimensional representation. Since the maximum clique problem is equivalent to the independent set problem of the complement graph, the independent set problem is solved using Fujitsu Digital Annealer. As a demonstration of the proposed method, the pressure distribution induced by the K\'arm\'an vortex street behind a square cylinder is reconstructed based on the pressure data at the calculated sensor points. The pressure distribution is measured by pressure-sensitive paint (PSP) technique, which is an optical flow diagnose method. The root mean square errors (RMSEs) between the pressure measured by pressure transducer and the reconstructed pressures (calculated from the proposed method and an existing greedy method) at the same place are compared. As the result, the similar RMSE is achieved by the proposed method using approximately 1/5 number of sensor points obtained by the existing method. This method is of great importance as a novel approach for optimal sensor placement problem and a new engineering application of an annealing machine

Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology

Time-series clustering serves as a powerful data mining technique for time-series data in the absence of prior knowledge about clusters. A large amount of time-series data with large size has been acquired and used in various research fields. Hence, clustering method with low computational cost is required. Given that a quantum-inspired computing technology, such as a simulated annealing machine, surpasses conventional computers in terms of fast and accurately solving combinatorial optimization problems, it holds promise for accomplishing clustering tasks that are challenging to achieve using existing methods. This study proposes a novel time-series clustering method that leverages an annealing machine. The proposed method facilitates an even classification of time-series data into clusters close to each other while maintaining robustness against outliers. Moreover, its applicability extends to time-series images. We compared the proposed method with a standard existing method for clustering an online distributed dataset. In the existing method, the distances between each data are calculated based on the Euclidean distance metric, and the clustering is performed using the k-means++ method. We found that both methods yielded comparable results. Furthermore, the proposed method was applied to a flow measurement image dataset containing noticeable noise with a signal-to-noise ratio of approximately 1. Despite a small signal variation of approximately 2%, the proposed method effectively classified the data without any overlap among the clusters. In contrast, the clustering results by the standard existing method and the conditional image sampling (CIS) method, a specialized technique for flow measurement data, displayed overlapping clusters. Consequently, the proposed method provides better results than the other two methods, demonstrating its potential as a superior clustering method.Comment: 13 pages, 4 figure

Auroral evidence of radial transport at Jupiter during January 2014

We present Jovian auroral observations from the 2014 January Hubble Space Telescope (HST) campaign and investigate the auroral signatures of radial transport in the magnetosphere alongside contemporaneous radio and Hisaki EUV data. HST FUV auroral observations on day 11 show, for the first time, a significantly superrotating polar spot poleward of the main emission on the dawnside. The spot transitions from the polar to main emission region in the presence of a locally broad, bright dawnside main emission feature and two large equatorward emission features. Such a configuration of the main emission region is also unreported to date. We interpret the signatures as part of a sequence of inward radial transport processes. Hot plasma inflows from tail reconnection are thought to flow planetward and could generate the superrotating spot. The main emission feature could be the result of flow shears from prior hot inflows. Equatorward emissions are observed. These are evidence of hot plasma injections in the inner magnetosphere. The images are thought to be part of a prolonged period of reconnection. Radio emissions measured by Wind suggest that hectometric (HOM) and non-Io decametric (DAM) signatures are associated with the sequence of auroral signatures, which implies a global magnetospheric disturbance. The reconnection and injection interval can continue for several hours