Analytical Reconstruction Research Articles

Gravitational Bremsstrahlung in black-hole scattering at OG3: linear-in-spin effects

We compute the far-field time-domain waveform of the gravitational waves produced in the scattering of two spinning massive objects. The results include linear-in-spin (S) couplings and first-order gravitational corrections (G3), and are valid for encounters in the weak-field regime. Employing a field-theory framework based on the scattering of massive scalar and vector particles coupled to Einstein-Hilbert gravity, we derive results for leading and the next-to-leading spectral waveforms. We provide analytic expressions for the required scattering data, which include trees, one-loop amplitudes and their cuts. The expressions are extracted from numerical amplitude evaluations with the Caravel program, using analytic reconstruction techniques applied in the classical limit. We confirm a recent prediction for infrared physics of the classical observable, and observe the surprising appearance of a ultraviolet singularity, which drops out in the far-field waveform.

Analytic sparse-view CBCT reconstruction using a novel sinogram restoration method based on higher-order Fourier harmonic peaks

This study presents an analytic sparse-view cone-beam computed tomography (CBCT) reconstruction method using a novel sinogram restoration technique based on higher-order Fourier harmonic peaks to obtain CBCT images of reasonable quality with a reduced radiation dose. The proposed method involves four main steps: (1) acquiring sparse-view (P144) sinogram from a CBCT system, (2) upsampling (P720) the original sparse-view sinogram with equally spaced zero padding, (3) separating higher-order Fourier harmonic peaks of the upsampled sinogram using a band-pass filter, and (4) restoring the sinogram of the higher-order harmonic peaks by taking its magnitude, followed by computationally cost-efficient filtered-backprojection (FBP)-based CBCT reconstruction and denoising processes. The notation P144 indicates that the number of projections is 144. To verify the efficacy of the proposed method, an experiment was performed on a chest phantom using a prototype CBCT system comprising an X-ray tube (90 kV p and 40 mA), a large-area flat-panel detector (388 μm pixel size), and a rotational gantry. The quality of the restored sparse-view CBCT images was quantitatively evaluated in terms of the image intensity profile, universal quality index (UQI), and peak signal-to-noise ratio (PSNR). The results indicate that the proposed sinogram restoration method effectively recovered zero-padded areas in the original upsampled sinogram with image intensities nearly identical to those of the reference dense-view (P720) sinogram, which demonstrates the efficacy of the proposed sinogram restoration method. The quality of the sparse-view CBCT image restored with higher-order harmonic peaks was close to that of the reference dense-view CBCT image, maintaining the image quality. The UQI and PSNR values of the restored sparse-view CBCT image were 0.93 and 34.84, respectively, which are approximately 1.3 and 1.2 times greater than those of the original sparse-view CBCT image, respectively, improving the image quality. Consequently, the proposed method has the potential to resolve the issues caused by sparse-view sampling in FBP-based CBCT.

Analytic amplitudes for a pair of Higgs bosons in association with three partons

The pair production of Higgs bosons at the LHC can give information about the triple Higgs boson coupling. We perform an analytic one-loop calculation of the amplitudes for a pair of Higgs bosons in association with three partons, retaining the exact dependence on the quark mass circulating in the loop. These amplitudes constitute the real radiation corrections in the calculation of Higgs boson pair production at next-to-leading order in the strong coupling. The results of an analytic generalised-unitarity computation are simplified via analytic reconstruction in spinor variables. Compact ansätze for kinematic pole residues are iteratively fitted via p-adic evaluations near said poles and subtracted until no pole remains. A new ansatz construction is introduced to minimally parametrise coefficients of amplitudes with multiple massive external legs. The simplified expressions are faster to evaluate than automatic codes and can lead to more stable results near singular regions.

Room Temperature Magnetic Skyrmions in Gradient-Composition Engineered CoPt Single Layers.

Topologically protected magnetic skyrmions in magnetic materials are stabilized by an interfacial or bulk Dzyaloshinskii-Moriya interaction (DMI). Interfacial DMI decays with an increase of the magnetic layer thickness in just a few nanometers, and bulk DMI typically stabilizes magnetic skyrmions at low temperatures. Consequently, more flexibility in the manipulation of DMI is required for utilizing nanoscale skyrmions in energy-efficient memory and logic devices at room temperature (RT). Here, we demonstrate the observation of RT skyrmions stabilized by gradient DMI (g-DMI) in composition gradient-engineered CoPt single-layer films by employing the topological Hall effect, magnetic force microscopy, and nitrogen-vacancy scanning magnetometry. Skyrmions remain stable over a wide range of applied magnetic fields and are confirmed to be nearly Bloch-type from micromagnetic simulation and analytical magnetization reconstruction. Furthermore, we observe skyrmion pairs, which may be explained by skyrmion-antiskyrmion interactions. Our findings expand the family of magnetic materials hosting RT magnetic skyrmions by tuning g-DMI via gradient polarity and a choice of magnetic elements.

Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images


Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

Dose Reconstruction for Epidemiological Studies among Ukrainian Chernobyl Cleanup Workers.

The present paper provides an overview of the methods and summarizes the results of estimating radiation doses and their uncertainties for Ukrainian-American epidemiological studies among the Chernobyl (Chornobyl) cleanup workers. After the Chernobyl accident occurred on April 26, 1986, more than 300,000 Ukrainian cleanup workers took part between 1986 and 1990 in decontamination and recovery activities at the site of the Chernobyl Nuclear Power Plant. The U.S. National Cancer Institute in collaboration with the Ukrainian National Research Center for Radiation Medicine conducted several epidemiological studies in this population. An important part of these studies was the reconstruction of the study participants' radiation doses and the assessment of uncertainties in doses. A method called realistic analytical dose reconstruction with uncertainty estimation (RADRUE) was used to calculate the doses from external irradiation during cleanup missions, which was the main exposure pathway for most study participants. At the initial phase of the accident during the atmospheric releases of radioactivity from the destroyed reactor, the cleanup workers also received doses from inhalation of radionuclides. In addition, study participants received doses at their places of residence, especially those who lived in highly contaminated areas. The radiation doses estimated for 2,048 male cleanup workers included in the Ukrainian-American epidemiological studies varied widely: (i) bone-marrow doses from external irradiation in the case-control study of leukemia of 1,000 cleanup workers ranged from 3.7 × 10-5 mGy to 3.3 Gy (mean = 92 mGy); (ii) thyroid doses in the case-control study of thyroid cancer in 607 persons from all exposure pathways combined were from 0.15 mGy to 9.0 Gy (mean = 199 mGy); (iii) gonadal doses in 183 cleanup workers from all exposure pathways combined in the study of germline mutations in the offspring after parental irradiation (trio study) ranged from 0.58 mGy to 4.1 Gy (mean = 392 mGy); (iv) thyroid doses in the human factor uncertainties study among 47 persons were from 20 mGy to 2.1 Gy (mean = 295 mGy); and (v) lung doses in the study of germline genetic variants associated with host susceptibility to COVID-19 estimated for 211 cleanup workers were from 0.024 mGy to 2.5 Gy (mean = 249 mGy). Doses of female cleanup workers were much lower than those of male cleanup workers: the mean doses for female cleanup workers were 27 mGy for 34 women included in the trio study and 56 mGy for 48 women participated in the study of germline genetic variants associated with host susceptibility to COVID-19. Uncertainties in dose estimates included two components: (i) inherent uncertainties arising from the stochastic random variability of the parameters used in exposure assessment and from a lack of knowledge about the true values of the parameters; and (ii) human factor uncertainties due to poor memory recall resulting in incomplete, inaccurate, or missing responses during personal interviews with cleanup workers conducted long after exposure. This paper also discusses possible developments and improvements in the methods to assess the radiation doses and associated uncertainties for cleanup workers.

STREAMLINING DIGITAL CORRELATION-INTERFEROMETRIC DIRECTION FINDING WITH SPATIAL ANALYTICAL SIGNAL

This study investigated a search-free digital method for radio direction-finding that utilizes spatial analytical signal reconstruction. The research focused on optimizing the method's accuracy for estimating the direction of a radio source. The analysis identified the separation distance and number of chosen antenna elements for signal reconstruction as the key parameters influencing accuracy. Analytical optimization and simulations were employed to determine the optimal values for these parameters. The research successfully modeled the impact of antenna element selection on direction-finding errors under specific signal-to-noise conditions. This model can be used to guide further development and optimization of this radio direction-finding method.

Wavefront sensing with optical differentiation powered by deep learning.

We report the experimental demonstration of an optical differentiation wavefront sensor (ODWS) based on binary pixelated linear and nonlinear amplitude filtering in the far-field. We trained and tested a convolutional neural network that reconstructs the spatial phase map from nonlinear-filter-based ODWS data for which an analytic reconstruction algorithm is not available. It shows accurate zonal retrieval over different magnitudes of wavefronts and on randomly shaped wavefronts. This work paves the way for the implementation of simultaneously sensitive, high dynamic range, and high-resolution wavefront sensing.

Sumolewomart's Existence: Kejapanan, Small Medium Enterprise Prosperity

Creative industries in Kejapanan that are based on the wisdom and attributes of local culture must be developed, one of which is the work of handicraft and culinary products from Kejapanan. Sumolewomart product based on local culture is an important part of people’s identity in Kejapanan and can help facilitate the industrial development in the area. Bumdes Sumolewo Mart is a community business forum owned by the village Kejapanan that sells several commodities needed by the village community, at a relatively cheap price compared to other shops. Apart from that, Sumolewo BUMDes also sells other kinds of products such as snacks, instant noodles, drinks, toiletries, cigarettes, groceries, stationery, etc. Products sold at BUMDes are based on community needs. In this study, researchers used a new institutional economics paradigm, interpretative qualitative paradigm, narrative study, and rapid rural appraisal (RRA) for reconstructing the existence of local cultural attributes in the design of Kejapanan. Based on the results of the analytical reconstruction, it can be concluded that the existence of the antecedents of the creative ethnicity of Bumdes products contributes significantly to the development of entrepreneurship in the people of Kejapanan. Keywords: Ethnic Creative, Sumolewo Mart, Kejapanan economic stability

Noninvasive Quantification of Glucose Metabolism in Mice Myocardium Using the Spline Reconstruction Technique.

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (MRGlu) in seven male C57BL/6 mice (n=7). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of MRGlu obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies.

Iterative reconstruction for limited-angle CT using implicit neural representation

Objective. Limited-angle computed tomography (CT) presents a challenge due to its ill-posed nature. In such scenarios, analytical reconstruction methods often exhibit severe artifacts. To tackle this inverse problem, several supervised deep learning-based approaches have been proposed. However, they are constrained by limitations such as generalization issue and the difficulty of acquiring a large amount of paired CT images. Approach. In this work, we propose an iterative neural reconstruction framework designed for limited-angle CT. By leveraging a coordinate-based neural representation, we formulate tomographic reconstruction as a convex optimization problem involving a deep neural network. We then employ differentiable projection layer to optimize this network by minimizing the discrepancy between the predicted and measured projection data. In addition, we introduce a prior-based weight initialization method to ensure the network starts optimization with an informed initial guess. This strategic initialization significantly improves the quality of iterative reconstruction by stabilizing the divergent behavior in ill-posed neural fields. Our method operates in a self-supervised manner, thereby eliminating the need for extensive data. Main results. The proposed method outperforms other iterative and learning-based methods. Experimental results on XCAT and Mayo Clinic datasets demonstrate the effectiveness of our approach in restoring anatomical features as well as structures. This finding was substantiated by visual inspections and quantitative evaluations using NRMSE, PSNR, and SSIM. Moreover, we conduct a comprehensive investigation into the divergent behavior of iterative neural reconstruction, thus revealing its suboptimal convergence when starting from scratch. In contrast, our method consistently produced accurate images by incorporating an initial estimate as informed initialization. Significance. This work showcases the feasibility to reconstruct high-fidelity CT images from limited-angle x-ray projections. The proposed methodology introduces a novel data-free approach to enhance medical imaging, holding promise across various clinical applications.

Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction

Objective. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the inadequate number of projection views, an analytic reconstruction method utilizing filtered backprojection results in severe streaking artifacts. Recently, deep learning (DL) strategies employing image-domain networks have demonstrated remarkable performance in eliminating the streaking artifact caused by analytic reconstruction methods with sparse projection views. Nevertheless, it is difficult to clarify the theoretical justification for applying DL to sparse view computed tomography (CT) reconstruction, and it has been understood as restoration by removing image artifacts, not reconstruction. Approach. By leveraging the theory of deep convolutional framelets (DCF) and the hierarchical decomposition of measurement, this research reveals the constraints of conventional image and projection-domain DL methodologies, subsequently, the research proposes a novel dual-domain DL framework utilizing hierarchical decomposed measurements. Specifically, the research elucidates how the performance of the projection-domain network can be enhanced through a low-rank property of DCF and a bowtie support of hierarchical decomposed measurement in the Fourier domain. Main results. This study demonstrated performance improvement of the proposed framework based on the low-rank property, resulting in superior reconstruction performance compared to conventional analytic and DL methods. Significance. By providing a theoretically justified DL approach for sparse-view CT reconstruction, this study not only offers a superior alternative to existing methods but also opens new avenues for research in medical imaging. It highlights the potential of dual-domain DL frameworks to achieve high-quality reconstructions with lower radiation doses, thereby advancing the field towards safer and more efficient diagnostic techniques. The code is available at https://github.com/hanyoseob/HDD-DL-for-SVCT.

Searching for local features in primordial power spectrum using genetic algorithms

ABSTRACTWe present a novel methodology for exploring local features directly in the primordial power spectrum using a genetic algorithm pipeline coupled with a Boltzmann solver and Cosmic Microwave Background data (CMB). After testing the robustness of our pipeline using mock data, we apply it to the latest CMB data, including Planck 2018 and CamSpec PR4. Our model-independent approach provides an analytical reconstruction of the power spectra that best fits the data, with the unsupervised machine learning algorithm exploring a functional space built off simple ‘grammar’ functions. We find significant improvements upon the simple power-law behaviour, by Δχ2 ≲ −21, consistently with more traditional model-based approaches. These best-fits always address both the low-ℓ anomaly in the TT spectrum and the residual high-ℓ oscillations in the TT, TE, and EE spectra. The proposed pipeline provides an adaptable tool for exploring features in the primordial power spectrum in a model-independent way, providing valuable hints to theorists for constructing viable inflationary models that are consistent with the current and upcoming CMB surveys.

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons.

Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities. In this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter. Oxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i)a typical homogeneous reconstruction and (ii)a theoretical layered reconstruction. Results suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude). We have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.

Propagation of uncertainty for an epipole-dependent model for convergent stereovision structure computation

An analytic model incorporating stereo epipoles is proposed for structure computation using a convergent stereovision setup. The developed model is predicated on the image parameters of both CCD camera sensors, together with two extrinsic parameters, namely the stereo baseline distance and the stereo projection angle of the scene point of interest. In the model, the points on the image planes are measured relative to the principal points, stereo epipoles are featured, and only focal length-normalized camera sensor coordinates are required for structure computation. The reconstruction model could be employed in active vision-based metrology in which the stereo imaging cameras are systematically rotated about their vertical axes relative to each other. The performance of the model is studied, and its accuracy tested by comparing the 3-space coordinates it predicted to the those obtained by a gold standard triangulation and to the ground truth results. In terms of execution speed the proposed reconstruction model exhibited a computation time of 0.6 ms compared to 6.2 ms and 9.9 ms recorded for the direct linear transformation and gold standard triangulation algorithms respectively. The coordinate measurement uncertainties determined by experimental methods are subsequently compared with those obtained by a theoretical approach based on the analytic reconstruction model. Strong correlations were found to exist between the two sets of uncertainty values obtained.

A full reconstruction of two galaxy clusters intra-cluster medium with strong gravitational lensing

ABSTRACT Whilst X-rays and Sunyaev–Zel’dovich observations allow to study the properties of the intra-cluster medium (ICM) of galaxy clusters, their gravitational potential may be constrained using strong gravitational lensing. Although being physically related, these two components are often described with different physical models. Here, we present a unified technique to derive the ICM properties from strong lensing for clusters in hydrostatic equilibrium. In order to derive this model, we present a new universal and self-similar polytropic temperature profile, which we fit using the X-COP sample of clusters. We subsequently derive an analytical model for the electron density, which we apply to strong lensing clusters MACS J0242.5-2132 and MACS J0949.8+1708. We confront the inferred ICM reconstructions to XMM-Newton and ACT observations. We contrast our analytical electron density reconstructions with the best canonical β-model. The ICM reconstructions obtained prove to be compatible with observations. However they appear to be very sensitive to various dark matter halo parameters constrained through strong lensing (such as the core radius), and to the halo scale radius (fixed in the lensing optimizations). With respect to the important baryonic effects, we make the sensitivity on the scale radius of the reconstruction an asset, and use the inferred potential to constrain the dark matter density profile using ICM observations. The technique here developed should allow to take a new, and more holistic path to constrain the content of galaxy clusters.

An attenuation field network for dedicated cone beam breast CT with short scan and offset detector geometry

The feasibility of full-scan, offset-detector geometry cone-beam CT has been demonstrated for several clinical applications. For full-scan acquisition with offset-detector geometry, data redundancy from complementary views can be exploited during image reconstruction. Envisioning an upright breast CT system, we propose to acquire short-scan data in conjunction with offset-detector geometry. To tackle the resulting incomplete data, we have developed a self-supervised attenuation field network (AFN). AFN leverages the inherent redundancy of cone-beam CT data through coordinate-based representation and known imaging physics. A trained AFN can query attenuation coefficients using their respective coordinates or synthesize projection data including the missing projections. The AFN was evaluated using clinical cone-beam breast CT datasets (n = 50). While conventional analytical and iterative reconstruction methods failed to reconstruct the incomplete data, AFN reconstruction was not statistically different from the reference reconstruction obtained using full-scan, full-detector data in terms of image noise, image contrast, and the full width at half maximum of calcifications. This study indicates the feasibility of a simultaneous short-scan and offset-detector geometry for dedicated breast CT imaging. The proposed AFN technique can potentially be expanded to other cone-beam CT applications.

Direct Filter Learning From Iterative Reconstructed Images for High-Quality Analytical CBCT Reconstruction Using FDK-Based Neural Network

Direct Filter Learning From Iterative Reconstructed Images for High-Quality Analytical CBCT Reconstruction Using FDK-Based Neural Network

Machine learning for precise hit position reconstruction in Resistive Silicon Detectors

RSDs are LGAD silicon sensors with 100% fill factor, based on the principle of AC-coupled resistive read-out. Signal sharing and internal charge multiplication are the RSD key features to achieve picosecond-level time resolution and micron-level spatial resolution, thus making these sensors promising candidates as 4D-trackers for future experiments. This paper describes the use of a neural network to reconstruct the hit position of ionizing particles, an approach that can boost the performance of the RSD with respect to analytical models. The neural network has been trained in the laboratory and then validated on test beam data. The device-under-test in this work is a 450 μm-pitch matrix from the FBK RSD2 production, which achieved a resolution of about 65 μm at the DESY Test Beam Facility, a 50% improvement compared to a simple analytical reconstruction method, and a factor two better than the resolution of a standard pixel sensor of equal pitch size with binary read-out. The test beam result is compatible with the laboratory ones obtained during the neural network training, confirming the ability of the machine learning model to provide accurate predictions even in environments very different from the training one. Prospects for future improvements are also discussed.

Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views.

This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed backprojection filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF (their differences are different derivate directions along the detector and source, respectively), make some errors and artifacts in the reconstructed images due to a backprojection weighting factor and the half-scan mode, which deviates from the intention of mSTCT imaging. In this paper, to achieve reconstruction with as little error as possible under the extremely extended FOV, we combine the full-scan mSTCT (F-mSTCT) geometry with the previous BPF algorithms to study the performance and derive a suitable redundancy-weighted function for F-mSTCT. The experimental results indicate FS-BPF can get high-quality, stable images under the extremely extended FOV of imaging a large object, though it requires more projections than FD-BPF. Finally, for different practical requirements in extending FOV imaging, we give suggestions on algorithm selection.