Analytical Reconstruction Research Articles

Improving the accuracy of a digital spectral correlation-interferometric method of direction finding with analytical signal reconstruction for processing an incomplete spectrum of the signal

A method of correlation-interferometric direction finding has been improved, which effectively solves the problem of radio direction finding of radio emission sources under conditions of exposure to one or two masking interference. The problem was solved using the selection of an unmasked fragment of the spatial spectrum of the signal and the reconstruction of the missing samples of its signal group. As a result of the synthesis of the proposed method, estimates of signal samples were obtained as exact solutions to the proposed energy balance equations. The resulting solutions provide a significant increase in the signal-to-interference ratio and, accordingly, direction-finding accuracy without increasing the number of reception channels of the antenna array. As a result of the simulation, the dependences of the standard deviation of the bearing estimate on the signal-to-noise ratio in the presence of interference were built. Under the influence of one or two masking interferences and a signal-to-interference ratio of 0 dB, the use of the known direction-finding method without interference selection produces an anomalously large direction-finding error of more than 0.42 degrees, which is practically independent of the signal-to-noise ratio. The direction-finding method with selection of spectral signal samples masked by interference reduces the direction-finding error to 0.22 degrees when exposed to one interference and to 0.3 degrees when exposed to two interferences. This is due to the presence of power losses of the usable signal during the selection of its samples masked by interference. The proposed method of direction finding with reconstruction of signal samples provides a significant gain in accuracy by 3–30 times compared to the method of selection of masked samples in the range of changes in the signal-to-noise ratio (–20.5) dB. The direction-finding error of the proposed method decreases with increasing signal/noise according to a hyperbolic dependence. It is advisable to use the proposed direction-finding method when masking no more than two samples of the signal group

Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning-based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics-based learned reconstruction approach compared to commonly used approaches in terms of both runtime and image quality at highly compressive settings. Our source codes and dataset are made available at https://github.com/METU-SPACE-Lab/Efficient-Learned-3D-Near-Field-MIMO-Imaging upon publication to advance research in this field.

Slouching toward New Humanism

In the contemporary era, novel social and cultural patterns have emerged, prompting the social sciences to engage in timely reflections on current phenomena and the very essence of humanity. These models have undergone significant transformations, so much so that New Humanism appears to be a viable prospect. It is upon this premise that all the observations put forth in this study are centered, operating under the assumption that the evolution of knowledge is a continuous process, marked by perpetual paths of research and reflection. It is well established that Humanism represents a recurring theme in our daily lives, with its premises inscribed on the walls of cities and subways. In the modern era, the concept of Humanism is liberated from its “classical” meanings. As such, it becomes crucial to consider New Humanism as a supplement for the soul, capable of invigorating spirituality, restoring energy, and instilling ethics rooted in solidarity, recognition, and mutual respect among individuals. This mission serves as a formidable catalyst, motivating and mobilizing the consciences and intelligence of individuals, particularly those in the Western world. Furthermore, this pressing need necessitates the adoption of conceptualization and analytical reconstruction pathways, which are functional in actualizing the perspective of New Humanism, establishing it as a proposition of contemporary culture. It is often implicit in widespread individualism, which tends to manifest exaggerated and exasperating tones of hyper-individualism. It is thus essential not to overlook the doctrine espoused by classical Humanism, which suggests that it is possible to be disenchanted and yet remain builders of utopias, and realists who can see new lands and infinite ideal worlds. Indeed, human beings can transcend existing barriers, using tradition as a viaticum for the future, as evidenced by numerous disciplinary fields.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises. A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths. The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively. Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Embedding of octonion Fourier transform in geometric algebra of ℝ3 and polar representations of octonion analytic signals in detail

We show how the octonion Fourier transform can be embedded and studied in Clifford geometric algebra of three‐dimensional Euclidean space . We apply a new form of dimensionally minimal embedding of octonions in geometric algebra that expresses octonion multiplication nonassociativity with a sum of up to four (individually associative) geometric algebra product terms. This approach leads to new polar representations of octonion analytic signals and signal reconstruction formulas.

Single-slice rebinning reconstruction method for segmented helical computed tomography.

Recently, to easily extend the helical field-of-view (FOV), the segmented helical computed tomography (SHCT) method was proposed, as well as the corresponding generalized backprojection filtration (G-BPF) type algorithm. Similar to the geometric relationship between helical and circular CT, SHCT just becomes full-scan multiple source-translation CT (F-mSTCT) when the pitch is zero and the number of scan cycles is one. The strategy of G-BPF follows the idea of the generalized Feldkamp approximate cone-beam algorithm for helical CT, i.e., using the F-mSTCT cone-beam BPF algorithm to approximately perform reconstruction for SHCT. The image quality is limited by the pitch size, which implies that satisfactory quality could only be obtained under the conditions of small pitches. To extend the analytical reconstruction for SHCT, an effective single-slice rebinning (SSRB) method for SHCT is investigated here. Transforming the SHCT cone-beam reconstruction into the virtual F-mSTCT fan-beam stack reconstruction task with low computational complexity, and then some techniques are developed to address the challenges involved. By using the basic BPF reconstruction with derivating along the detector (D-BPF), our experiments demonstrate that SSRB has fewer interlayer artifacts, higher z-resolution, more uniform in-plane resolution, and higher reconstruction efficiency compared to G-BPF. SSRB could promote the effective application of deep learning in SHCT reconstruction.

Segmented Reconstruction of Low-Acceleration Orbital Maneuvers

This paper considers the problem of inferring the acceleration acting on a spacecraft in a near-circular orbit around a large planetary body when the acceleration is known to act in the along-track direction. A segmented approach is analyzed where the acceleration profile over a given period of time is approximated as a piecewise-constant profile. Noisy measurements of the spacecraft’s position are collected throughout the acceleration profile, from which the constant acceleration for each segment of the piecewise-constant profile can be inferred. The primary contribution of this work is an analytical approximation for the sensitivity of each measurement with respect to the constant acceleration of each segment. The resulting analytically approximated sensitivity matrix enables an analytical reconstruction of the acceleration profile given a numerically simulated reference trajectory. Applications of the segmented approach for thrust inference for both discontinuous and continuous thrust profiles are demonstrated. In particular, the reconstruction of a simulated thruster degradation profile over the course of 60 h is shown.

Bilateral extended FDK: An improved weighting method for static CT imaging.

Improving imaging speed has always been the focus of research in CT technology, which is related to the radiation dose and imaging quality of moving organs, including heart and blood vessels. However, it is difficult to achieve further improvement by increasing the rotation speed of the gantry due to its structural strength limitation. Differing from the conventional CTs, the static CT employs dozens of ray sources to acquire projection data from different angular ranges, and each source only needs to be rotated in a small range to finish a full 360° scan, thus greatly increasing the scanningspeed. As sources of static CT need to be evenly distributed over 360°, the sources and detectors have to be arranged on two parallel rings independently. Such a geometry can be considered as a special case of CT systems with a significantly large cone angle, that is, a part of the detector is missing in the vicinity of the mid-plane. Due to restriction of upper and lower bounds of the cone angle of the static CT, there are uneven projection data varying in each portion of the reconstruction volume, the conventional analytical or iterative reconstruction methods may introduce artifacts in the reconstructedoutcomes. Following the weighting approach extended FDK (xFDK) by Grimmer etal., we propose an improved bilateral xFDK algorithm (bixFDK), which focuses on the reconstruction of the expanded volume. With the same philosophy as xFDK in terms of weighting function design, bixFDK takes the longitudinal offset of the detector with respect to the source into consideration, making our method applicable to a wide range of CT geometries, especially for the static CT. Based on the proposed bixFDK, a new iterative scheme bixFDK-IR is also constructed to extend the applications to a wide range of scan protocols such as sparse-viewscan. The proposed method has been validated with the simulated phantom data and the actual clinical data of the static CT, and demonstrates that it can ensure good image quality and enlarge the reconstruction volume in z-direction of the staticCT. The bixFDK algorithm is an ideal reconstruction approach for static CT geometry, and the iterative scheme of bixFDK-IR is applicable to a wide range of CT geometries and scan protocols, thus providing a wide range of applicationscenarios.

Wealth inequality in the prehispanic northern US Southwest: from Malthus to Tyche.

Persistent differences in wealth and power among prehispanic Pueblo societies are visible from the late AD 800s through the late 1200s, after which large portions of the northern US Southwest were depopulated. In this paper we measure these differences in wealth using Gini coefficients based on house size, and show that high Ginis (large wealth differences) are positively related to persistence in settlements and inversely related to an annual measure of the size of the unoccupied dry-farming niche. We argue that wealth inequality in this record is due first to processes inherent in village life which have internally different distributions of the most productive maize fields, exacerbated by the dynamics of systems of balanced reciprocity; and second to decreasing ability to escape village life owing to shrinking availability of unoccupied places within the maize dry-farming niche as villages get enmeshed in regional systems of tribute or taxation. We embed this analytical reconstruction in the model of an 'Abrupt imposition of Malthusian equilibrium in a natural-fertility, agrarian society' proposed by Puleston et al. (Puleston C, Tuljapurkar S, Winterhalder B. 2014 PLoS ONE 9, e87541 (doi:10.1371/journal.pone.0087541)), but show that the transition to Malthusian dynamics in this area is not abrupt but extends over centuries This article is part of the theme issue 'Evolutionary ecology of inequality'.

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data.

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains. We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times. Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems. The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectors

We present our study on the reconstruction of photoelectron tracks in gas pixel detectors used for astrophysical X-ray polarimetry. Our work aims to maximize the performance of convolutional neural networks (CNNs) to predict the impact point of incoming X-rays from the image of the photoelectron track. A very high precision in the reconstruction of the impact point position is achieved thanks to the introduction of an artificial sharpening process of the images. We find that providing the CNN-predicted impact point as input to the state-of-the-art analytic analysis improves the modulation factor (~1% at 3 keV and ~6% at 6 keV) and naturally mitigates a subtle effect appearing in polarization measurements of bright extended sources known as “polarization leakage”.

Fermi pencil beams and off-axis laser detection.

This paper concerns the analytic reconstruction of properties for narrow laser beams propagating in turbulent atmospheres. We consider the setting of off-axis measurements based on wide-angle single scattering of light detected away from the main path of the beam. Light propagation in the beam itself is modeled by macroscopic approximations of radiative transfer equations that take the form of Fermi pencil beam or fractional Fermi pencil beam equations. This allows for a simplified reconstruction procedure of the beam's constitutive parameters, in particular, its direction of propagation and the location of the emitting source.

Giovanni Gentile's standing in the United States (1922–1938)

In his preface to The Social Philosophy of Giovanni Gentile, HS Harris wrote that “[Gentile's] association with Fascism, of which he was an ardent supporter from 1923 until his own death during the last throes of the Fascist Social Republic, prejudiced the issue. Critical evaluation of his political and social thought has been almost impossible for the Italians themselves because of the general climate of political opinion both before and after his death; and almost everyone outside Italy followed the lead given by Croce and dismissed Gentile's practical philosophy as unworthy of serious critical examination.” This statement undoubtedly points at an effective historiographical question. The present article aims to shed light on this issue, taking into consideration specifically the writings published about Gentile in the United States in the 1920s and 1930s. Some research has already been undertaken in this field, but it seems ripe for being further developed. Interest in the work of Gentile came especially from the intellectual circles of Columbia University and his work has usually been interpreted by historians as resulting from pro-fascist leanings. Yet, a closer examination of sources and consequently a more analytical reconstruction of the context suggests that the reception of the educational thought of Gentile and the knowledge of his political activity during the Fascist regime took place in the United States quite independently from political sympathies.

Fast Obstacle Avoidance Based on Real-Time Sensing

Humans excel at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance which is adept at partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance (FOA). The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. Thus, we can ensure that the agent does not become stuck when a feasible path exists between obstacles. Our algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in shared-control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller took 1 ms to evaluate over 30000 data points.

Convergence on inflation and divergence on price control among post Keynesian pioneers: insights from Galbraith and Lerner

This article proposes a historical and analytical reconstruction of a debate that never happened between John Kenneth Galbraith and Abba Lerner over the issue of price controls. While they adopted a similar analysis of underemployment inflation, shared by many post Keynesians, Lerner and Galbraith remained fundamentally opposed as to the effectiveness of price controls. Indeed, while both agreed on the relevance of price controls in the specific context of World War II, they disagreed over including price controls within the conventional framework of economic policies, as illustrated by their respective stances in the debate surrounding the stagflation of the 1970s. Throughout the paper, we provide the rationales behind their divergence on price controls by investigating its theoretical, epistemological, and normative roots. Finally, we put into perspective the contemporary debates about price control in the context of resurgent inflationary pressures with some salient points drawn from our reconstruction of the debate that opposed these two pioneering post Keynesians economists.

Vectorised Neutrino Reconstruction by Computing Graphs

Many particle physics analyses are adopting the concept of vectorised computing, often making them increasingly performant and resource-efficient. While a variety of computing steps can be vectorised directly, some calculations are challenging to implement. One of these is the analytical neutrino reconstruction which involves fitting that naturally varies between events. We show a vectorised implementation of the analytical neutrino reconstruction using a graph computing model. It uses established deep learning software libraries and is natively portable to local and external hardware accelerators such as GPUs. Using the example of ttH events with a semi-leptonic final state, we present performance studies for our implementation.

Algebraic geometry and p-adic numbers for scattering amplitude ansätze

Scattering amplitudes in perturbative quantum field theory exhibit a rich structure of zeros, poles and branch cuts which are best understood in complexified momentum space. It has been recently shown that by leveraging this information one can significantly simplify both analytical reconstruction and final expressions for the rational coefficients of transcendental functions appearing in phenomenologically-relevant scattering amplitudes. Inspired by these observations, we present a new algorithmic approach to the reconstruction problem based on p-adic numbers and computational algebraic geometry. For the first time, we systematically identify and classify the relevant irreducible surfaces in spinor space with five-point kinematics, and thanks to p-adic numbers – analogous to finite fields, but with a richer structure to their absolute value – we stably perform numerical evaluations close to these singular surfaces, thus completely avoiding the use of floating-point numbers. Then, we use the data thus acquired to build ansätze which respect the vanishing behavior of the numerator polynomials on the irreducible surfaces. These ansätze have fewer free parameters, and therefore reduced numerical sampling requirements. We envisage future applications to novel two-loop amplitudes.

Artificial Intelligence-Based Image Reconstruction for Computed Tomography: A Survey

Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction speed. In particular, conventional reconstruction methods usually optimize one aspect, while AI-based reconstruction has finally managed to attain all goals in one shot. However, there are limitations such as the requirements on large datasets, unstable performance, and weak generalizability in AI-based reconstruction methods. This work presents the review and discussion on the classification, the commercial use, the advantages, and the limitations of AI-based image reconstruction methods in CT.

ЛОГИКО-КОГНИТИВНЫЙ АНАЛИЗ ИСПОЛЬЗОВАНИЯ ВОПРОСА-ПРЕФОРМЫ В АРГУМЕНТАТИВНОЙ ДИСКУССИИ

The article presents the results of the study of the discussion snippets in the form of question-answering structures using the “argumentative discussion” construct; the study was based on the scripts of video panel discussions on the YouTube platform. Involving the methodology of the pragma-dialectical theory of argumentation in part of the analytical reconstruction of argumentative discourse, the author does not take as a basis the concept of persuading a rational addressee, which is common in argumentative theory, but takes the idea of verifying the point of view of cognitive agents of communication. It allows to consider the dialogic structure as a multi-subject procedure for checking the objectivity of knowledge of the participants in the discussion. The purpose of the article is to explore the question utterance in the position of the preform in the question-answering unity. The article contains scientific data from the field of erothetic logic, which shows that the question can be interpreted as a fragment of reasoning. For the reconstruction of cue-question, the concept of symptosymmetric proportionality of the components of dialogical unities is used. We analyzed all speech units in dialogic syntagmas in the form of Argumentative Step. Such an analysis showed that the basis of the question contains elements that perform argumentative functions. The focal meaning of these elements is found in response cues. As the result, the symptosymmetric models Preform-thesis ↔ refutation by facts, Preform-thesis ↔ antithesis, Preform-clarification ↔ correction were obtained. The interim result of the study is that the cue-question in the position of the preform is also a marker of a violation of the logical-categorical norms of reasoning and ad hoc errors.

Quantitative comparison of planar coded aperture imaging reconstruction methods

Imaging distributions of radioactive sources plays a substantial role in nuclear medicine as well as in monitoring nuclear waste and its deposit. Coded Aperture Imaging (CAI) has been proposed as an alternative to parallel or pinhole collimators, but requires image reconstruction as an extra step. Multiple reconstruction methods with varying run time and computational complexity have been proposed. Yet, no quantitative comparison between the different reconstruction methods has been carried out so far.This paper focuses on a comparison based on three sets of hot-rod phantom images captured with an experimental γ-camera consisting of a Tungsten-based MURA mask with a 2 mm thick 256 × 256 pixelated CdTe semiconductor detector coupled to a Timepix© readout circuit.Analytical reconstruction methods, MURA Decoding, Wiener Filter and a convolutional Maximum Likelihood Expectation Maximization (MLEM) algorithm were compared to data-driven Convolutional Encoder-Decoder (CED) approaches. The comparison is based on the contrast-to-noise ratio as it has been previously used to assess reconstruction quality. For the given set-up, MURA Decoding, the most commonly used CAI reconstruction method, provides robust reconstructions despite the assumption of a linear model. For single image reconstruction, however, MLEM performed best of all analytical reconstruction methods, but took on average 45 times longer than MURA Decoding. The fastest reconstruction method is the Wiener Filter with a run time 4.3 times faster compared to MURA Decoding and a mediocre quality. The CED with a specifically tailored training set was able to succeed the most commonly used MURA decoding on average by a factor between 1.37 and 2.60 and an equal run time.