Image Reconstruction Techniques Research Articles

High-contrast, High-angular-resolution Optical Speckle Imaging: Uncovering Hidden Stellar Companions

We explore the possibility of detecting very faint, very close-in stellar companions using large aperture ground-based telescopes and the technique of optical speckle imaging. We examine the state of high-angular-resolution speckle imaging and contrast levels being achieved using current speckle cameras on the Gemini 8 m telescope. We then explore the use of the modern image reconstruction technique—multiframe blind deconvolution (MFBD)—applied to speckle imaging from the Gemini 8 m telescope. We show that MFBD allows us to measure the flux ratio of the imaged stars to high accuracy and the reconstructed images yield higher precision astrometry. Both of these advances provide a large refinement in the derived astrophysical parameters compared with current Fourier techniques. MFBD image reconstructions reach contrast levels of ∼5 × 10−3, near the diffraction limit, to ∼10−4 about 1.″0 away. At these deep contrast levels with angular limits starting near the 8 m diffraction limit (∼20 mas), most stellar companions to a solar-like stars can be imaged in the optical to near-IR bandpass (320–1000 nm). “To Xanadu we go...”—adapted from S. T. Coleridge.

Exploring the Potential of a Novel Iodine-Based Material as an Alternative Contrast Agent in X-ray Imaging Studies.

Contrast-enhanced mammography is one of the new emerging imaging techniques used for detecting breast tissue lesions. Optimization of imaging protocols and reconstruction techniques for this modality, however, requires the involvement of physical phantoms. Their development is related to the use of radiocontrast agents. This study assesses the X-ray properties of a novel contrast material in clinical settings. This material is intended for experimental use with physical phantoms, offering an alternative to commonly available radiocontrast agents. The water-soluble sodium salt of the newly synthesized diiodine-substituted natural eudesmic acid, Sodium 2,6-DiIodo-3,4,5-TriMethoxyBenzoate [NaDITMB], has been investigated with respect to one of the most commonly applied radiocontrast medium in medical practice-Omnipaque®. For this purpose, simulation and experimental studies were carried out with a computational phantom and a physical counterpart, respectively. Synthetic and experimental X-ray images were subsequently produced under varying beam kilovoltage peaks (kVps), and the proposed contrast material was evaluated. Simulation results revealed equivalent absorptions between the two simulated radiocontrast agents. Experimental findings supported these simulations, showing a maximum deviation of 3.7% between the image gray values of contrast materials for NaDITMB and Omnipaque solutions for a 46 kVp X-ray beam. Higher kVp X-ray beams show even smaller deviations in the mean grey values of the imaged contrast agents, with the NaDITMB solution demonstrating less than a 2% deviation compared to Omnipaque. The proposed contrast agent is a suitable candidate for use in experimental work related to contrast-enhanced imaging by utilizing phantoms. It boasts the advantages of easy synthesis and is recognized for its safety, ensuring a secure environment for both the experimenter and the environment.

The "hidden noise" problem in MR image reconstruction.

The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared-error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the "hidden noise" present in reference data can substantially confound standard approaches for ranking different image reconstruction results. Using both experimental and simulated k-space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained with higher-quality reference data. For conventional performance metrics, the reconstructions that matched best with the higher-quality reference data were substantially different from the reconstructions that matched best with typical noisy reference data. This leads to suboptimal reconstruction results if the performance with respect to noisy reference data is used to select which reconstruction methods/parameters to employ. These issues were reduced when employing alternative error metrics that better account for noise. Reference data containing hidden noise can substantially mislead the ranking of image reconstruction methods when using conventional error metrics, but this issue can be mitigated with alternative error metrics.

Study of the pellet ablation cloud using the tomography technique for two-directional simultaneous photography in GAMMA 10/PDX

The pellet ablation mechanism is an interesting subject for plasma fuelling in fusion plasmas. In GAMMA 10/PDX, pellet injection experiments for higher density plasma production are planned to conduct detached plasma experiments in the higher density plasma condition. We measured the pellet ablation cloud by using the two-directional simultaneous photography system in GAMMA 10/PDX. The tomography reconstruction technique was used for considering the pellet trajectory in the plasma and pellet ablation. The three-dimensional pellet trajectory and pellet ablation images in the plasma were clearly obtained for the first time, to the best of our knowledge.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP>DCNN-Weak>IR>DCNN-Medium>DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

DEVELOPMENT OF IMAGE RECONSTRUCTION FOR DETECTING STATIC OIL-GAS REGIMES USING INVASIVE ELECTRICAL CAPACITANCE TOMOGRAPHY IN STEEL PIPE APPLICATION – AN INITIAL STUDY An Initial Study

Electrical Capacitance Tomography (ECT) is a well-known technique for identifying two-phase regimes when a non-conducting medium is the main medium inside a pipe. However, the common non-invasive techniques of ECT are not suitable for monitoring non-conducting systems that use steel pipes. This is because the placement of the common ECT sensor is outside of the pipe and thus cannot penetrate the conducting pipe. Therefore, this paper presents the development of image reconstruction techniques for invasive Electrical Capacitance Tomography (ECT) in steel pipe applications. The study uses eight electrodes for invasive ECT that are independently designed for easy replacement. The shield guard of each electrode is individually designed, unlike the common sensor of ECT. Moreover, it produces a sensitivity map in the forward problem by modeling the geometry to mimic the real hardware of invasive ECT in Comsol Multiphysics livelink with MATLAB. The data from real hardware is exported offline and a linear back projection algorithm is used as the inverse problem. The phantom of gas-oil with different sizes, positions, and multiple phantoms from the range of 20mm, 25 mm and 33mm are tested. The tomograms of the region of interest can be obtained as a result. The paper concludes that the developed image reconstruction techniques for invasive ECT in steel pipe applications can provide acceptable and reliable results. This study can be useful for researchers and practitioners in the field of ECT and steel pipe applications.

A new frontier in electronics manufacturing: Optimized deep learning techniques for PCB image reconstruction

In the rapidly evolving electronics manufacturing sector, maintaining quality control and conducting failure analysis of Printed Circuit Boards (PCBs) are critical yet challenging tasks. This study presents a groundbreaking self-supervised learning framework to address existing gaps in the reconstruction of encoded or blurred Printed Circuit Board images. By leveraging a customized DeepLabV3+ architecture with depth-wise separable convolutions, our model is engineered to autonomously learn intrinsic Printed Circuit Board features, eliminating the need for manual data labeling. This not only alleviates computational burden but also ensures robust performance. Augmented by feature quantization and channel reduction techniques, our model stands out as both lightweight and resilient, making it highly adaptable for Printed Circuit board imaging. To validate the framework, a tailored dataset comprising raw and encoded Printed Circuit board images from diverse sources was assembled and further refined to match real-world industrial standards. Our model demonstrates unparalleled efficacy in Printed Circuit board image reconstruction, establishing a new benchmark for the field.

Unexpected object detection based on class correlation in semantic segmentation for automatic driving scenes

The detection of out-of-distribution (OoD) samples in semantic segmentation is crucial for autonomous driving, as deep learning models are typically trained under the assumption of a closed environment, whereas the real world presents an open and diverse set of scenarios. Existing methods employ uncertainty estimation, image reconstruction, and other techniques for OoD sample detection. We have observed that different classes may exhibit connections and associations in varying contexts. For example, objects encountered by autonomous vehicles differ in rural road scenes compared to urban environments, and the likelihood of encountering novel objects varies. This aspect is missing in current anomaly detection methods and is vital for OoD sample detection. Existing approaches solely consider the relative significance of each prediction class, overlooking the inter-object correlation. Although prediction scores (e.g., max logits) obtained from the segmentation network are applicable for OoD sample detection, the same problem persists, particularly for OoD objects. To address this issue, we propose the utilization of the Mahalanobis distance of max logits to evaluate the final predicted score. By calculating the Mahalanobis distance, the paper aims to uncover correlations between different classes, thus enhancing the effectiveness of OoD detection. To this end, we also extend the state-of-the-art segmentation model, DeepLabV3+, to enable OoD sample detection in this paper. Specifically, this paper proposes a novel backbone network, SOD-ResNet101, for extracting contextual and multi-scale semantic information, leveraging the class correlation feature of the Mahalanobis distance to enhance the detection performance of out-of-distribution objects. Notably, our approach eliminates the need for external datasets or separate network training, making it highly applicable to existing pretraining segmentation models.

Combining range-general and range-specific techniques for late-time focused image reconstructiona).

Synthetic aperture sonar (SAS) is an acoustic method for detecting objects in an environment. Conventional SAS image reconstruction techniques invert a forward model based on geometric scattering and straight-line propagation. Acoustic features that do not fit this model, such as multiple scattering and late-time returns, appear out of focus. This paper describes an image reconstruction technique that selectively applies range-general and range-specific methods to improve the focus of late-time returns while maintaining image quality away from the focal plane. The technique is demonstrated on experimental data and compared with a range-specific algorithm.

Investigation of spatial resolution of electrical capacitance tomography based on the electromagnetic momentum (ECT-EMM)

Electrical capacitance tomography (ECT) is a permittivity imaging method widely used in industrial inspection. The equations described by the ECT technique are nonlinear and ill-posed, which results in low image resolution. ECT can be considered an imaging method based on the Green’s reciprocity theorem, an energetic reciprocity theorem. ECT detects scalars, i.e. capacitances. Electromagnetic fields have both ‘energy’ and ‘momentum.’ In recent years, the electromagnetic momentum reciprocity theorem has enriched the electromagnetic reciprocity theorem. The electromagnetic momentum reciprocity theorem is an imaging method that detects vectors, i.e. capacitance gradients. Vectors contain richer information than scalars; thus, electrical capacitance tomography based on electromagnetic momentum (ECT-EMM) methods is expected to improve the resolution of permittivity imaging. This paper briefly describes the principle of the ECT-EMM technique for image reconstruction using sensitivity matrix gradient and capacitance gradient. Tikhonov regularisation algorithm is applied. The two methods, with and without capacitance measurements, are used to evaluate imaging resolution. Under different numbers of pixels and electrodes, typical permittivity distributions are used for reconstruction, and correlation coefficients are calculated. Simulations and experiments show that the ECT-EMM technique recognises object boundaries more clearly with high noise immunity. Five quality measures are used to evaluate the performance of the point spread function without capacitance measurements. Compared to ECT, the ECT-EMM technique is more sensitive to the central region away from the electrodes, recognises smaller minimum objects, and has smaller shape deformation.

Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality

IntroductionLow back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence.Material and methodsThis retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale.ResultsAccelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging.ConclusionIn conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers

Understanding the interactions between small, submicrometer-sized colloidal particles is crucial for numerous scientific disciplines and technological applications. In this study, we employ optical tweezers as a powerful tool to investigate these interactions. We utilize a full image reconstruction technique to achieve high precision in characterizing particle pairs that enable nanometer-scale measurement of their positions. This approach captures intricate details and provides a comprehensive understanding of the spatial arrangement between particles, overcoming previous limitations in resolution. Moreover, our research demonstrates that properly accounting for optical binding forces to determine the intrinsic interaction potential is vital. We employ a discrete dipole approximation approach to calculate optical binding potentials and achieve a good agreement between the calculated and observed binding forces. We incorporate the findings from these simulations into the assessment of the intrinsic interaction potentials and validate our methodology by using short-range depletion attraction induced by micelles as an example.

Noisy image enhancements using deep learning techniques

This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.

Development of a Low-Cost and Compact Medical Image Reconstruction Platform Based on EIT Technique

Electrical impedance tomography (EIT) is an important imaging modality in non-invasive and radiation-free image reconstruction techniques. With the advantages of real-time imaging capabilities, portability, and safety features, EIT has acquired substantial significance as a valuable technique for lung patients during the COVID-19 pandemic. Nevertheless, a significant challenge arises when many COVID-19 patients need to utilize EIT devices for monitoring and treatment. To address this problem, a low-cost and compact wireless communication EIT platform was developed for impedance image reconstruction and real-time monitoring of human lung operations. Since the system was structured as a platform, it can be readily replicated and scaled up in emergencies. The proposed system consisted of 16 electrodes placed on the chest of the lung patients. A stable current was injected into the object through a pair of adjacent electrodes, while the voltage was measured on the remaining electrode pairs using a lock-in amplifier. The electrical impedance between each electrode pair was determined based on Ohm’s law. Experimental results showed that the system could reconstruct the shape and the position of objects inside the medium with high accuracy. The average blur radius and percentage of the location error of the reconstructed image were 1.01 ± 0.28 and 5.18 ± 1.35%, respectively. Furthermore, images showing the operation of the human lung were also observed by the proposed system, confirming the possibility of using the system in clinical applications. The proposed EIT system with wireless communication is a potential study for large-scale implementation, particularly in response to the COVID-19 pandemic.

Implementation and improvement of image reconstruction techniques using muons from cosmic rays

In this article, two image reconstruction techniques for radiography and tomography using cosmic ray muons are presented. The simulation is carried out using the Geant4 package, simulating a multiple coincidence system of four RPCs (Resistive Plate Chambers). The reconstruction techniques presented are based on particle trajectory reconstruction and localization of a single center of dispersion. Two functional cases are displayed for each technique.

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.

Convolutional sparse coding for compressed sensing photoacoustic CT reconstruction with partially known support.

In photoacoustic tomography (PAT), imaging speed is an essential metric that is restricted by the pulse laser repetition rate and the number of channels on the data acquisition card (DAQ). Reconstructing the initial sound pressure distribution with fewer elements can significantly reduce hardware costs and back-end acquisition pressure. However, undersampling will result in artefacts in the photoacoustic image, degrading its quality. Dictionary learning (DL) has been utilised for various image reconstruction techniques, but they disregard the uniformity of pixels in overlapping blocks. Therefore, we propose a compressive sensing (CS) reconstruction algorithm for circular array PAT based on gradient domain convolutional sparse coding (CSCGR). A small number of non-zero signal positions in the sparsely encoded feature map are used as partially known support (PKS) in the reconstruction procedure. The CS-CSCGR-PKS-based reconstruction algorithm can use fewer ultrasound transducers for signal acquisition while maintaining image fidelity. We demonstrated the effectiveness of this algorithm in sparse imaging through imaging experiments on the mouse torso, brain, and human fingers. Reducing the number of array elements while ensuring imaging quality effectively reduces equipment hardware costs and improves imaging speed.

Five-view three-dimensional reconstruction for ultrafast dynamic imaging of pulsed radiation sources

Multiaxial neutron/x-ray imaging and three-dimensional (3D) reconstruction techniques play a crucial role in gaining valuable insights into the generation and evolution mechanisms of pulsed radiation sources. Owing to the short emission time (∼200 ns) and drastic changes of the pulsed radiation source, it is necessary to acquire projection data within a few nanoseconds in order to achieve clear computed tomography 3D imaging. As a consequence, projection data that can be used for computed tomography image reconstruction at a certain moment are often available for only a few angles. Traditional algorithms employed in the process of reconstructing 3D images with extremely incomplete data may introduce significant distortions and artifacts into the final image. In this paper, we propose an iterative image reconstruction method using cylindrical harmonic decomposition and a self-supervised denoising network algorithm based on the deep image prior method. We augment the prior information with a 2D total variation prior and a 3D deep image prior. Single-wire Z-pinch imaging experiments have been carried out at Qin-1 facility in five views and four frames, with a time resolution of 3 ns for each frame and a time interval of 40 ns between adjacent frames. Both numerical simulations and experiments verify that our proposed algorithm can achieve high-quality reconstruction results and obtain the 3D intensity distribution and evolution of extreme ultraviolet and soft x-ray emission from plasma.

Inter-scanner Aβ-PET harmonization using barrel phantom spatial resolution matching.

The standardized uptake value ratio (SUVR) is used to measure amyloid beta-positron emission tomography (Aβ-PET) uptake in the brainDifferences in PET scanner technologies and image reconstruction techniques can lead to variability in PET images across scanners. This poses a challenge for Aβ-PET studies conducted in multiple centers. The aim of harmonization is to achieve consistent Aβ-PET measurements across different scanners. In this study, we proposean Aβ-PET harmonization methodof matching spatial resolution, as measured viaabarrel phantom, across PET scanners. Our approach wasvalidated using paired subject data, for whichpatients were imaged on multiple scanners. In this study, three different PET scanners were evaluated: the Siemens Biograph Vision 600, Siemens Biograph molecular computed tomography (mCT), and Philips Gemini TF64. A total of five, eight, and five subjects were each scanned twice with [18F]-NAV4694 across Vision-mCT, mCT-Philips, and Vision-Philips scanner pairs. The Vision and mCT scans were reconstructed using various iterations, subsets, and post-reconstruction Gaussian smoothing, whereas only one reconstruction configuration was used for the Philips scans. The full-width at half-maximum (FWHM) of each reconstruction configuration was calculated using [18F]-filled barrel phantom scans with the Society of Nuclear Medicine and Molecular Imaging (SNMMI) phantom analysis toolkit. Regional SUVRs were calculated from 72 brain regions using the automated anatomical labelling atlas 3 (AAL3) atlas for each subject and reconstruction configuration. Statistical similarity between SUVRs was assessed using paired (within subject) t-tests for each pair of reconstructions across scanners; the higher the p-value, the greater the similarity between the SUVRs. Vision-mCT harmonization: Vision reconstruction with FWHM=4.10mm and mCT reconstruction with FWHM=4.30mm gave the maximal statistical similarity (maximum p-value) between regional SUVRs. Philips-mCT harmonization: The FWHM of the Philips reconstruction was 8.2mm and the mCT reconstruction with the FWHM of 9.35mm, which gave the maximal statistical similarity between regional SUVRs. Philips-Vision harmonization: The Vision reconstruction with an FWHM of 9.1mm gave the maximal statistical similarity between regional SUVRs when compared with the Philips reconstruction of 8.2mm and were selected as the harmonized for each scanner pair. Based on data obtained from three sets of participants, each scanned on a pair of PET scanners, it has been verified that using reconstruction configurations that produce matched-barrel, phantom spatial resolutions results in maximally harmonized Aβ-PET quantitation between scanner pairs. This finding is encouraging for the use of PET scanners in multi-center trials or updates during longitudinal studies. Question: Does the process of matching the barrel phantom-derived spatial resolution between scanners harmonize amyloid beta-standardized uptake value ratio (Aβ-SUVR) quantitation? Pertinent findings: It has been validated that reconstruction pairs with matched barrel phantom-derived spatial resolution maximize the similarity between subjects paired Aβ-PET (positron emission tomography) SUVR values recorded on two scanners. Implications for patient care: Harmonization between scanners in multi-center trials and PET camera updates in longitudinal studies can be achieved using a simple and efficient phantom measurement procedure, beneficial for the validity of Aβ-PET quantitation measurements.

Image reconstruction techniques using deep learning quality segmentation

Translational CT (TCT), in developing nations, a low-end computed tomography (CT) technology are relatively common. The limited-angle TCT scanning mode is often used with large-angle scanning to scan items within a narrow angular range, reduce X-ray radiation, scan long objects, and prevent detector discrepancies.. However, this scanning mode greatly reduces the picture quality and diagnostic accuracy due to the added noise and limited-angle distortions. A U-net convolutional neural network-based approach for limited-angle TCT image reconstruction has been created to reconstruct a high-quality image for the limited-angle TCT scanning mode (CNN). The limited-angle TCT projection data are first examined using the SART method, and the resulting picture is then fed into a trained CNN that can reduce artifacts and maintain structures to provide a better reconstructed image. Simulated studies are used to demonstrate the effectiveness of the algorithm designed for the limitedangle TCT scanning mode. In contrast to certain modern techniques, the developed algorithm considerably lowers noise and limited-angle artifacts while maintaining image structures.