Image Reconstruction Techniques Research Articles

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Right middle pulmonary vein classification by 3D-CT reconstruction and its clinical significance.

A profound understanding of the type of right middle lobe (RML) vein return is crucial for thoracic surgeons to ensure safe anatomic lung resection. In this study, the type of venous return in the RML and its clinical significance were analyzed using the 3D computed tomography (3D-CT) reconstruction technique. This retrospective study analyzed the patients who underwent anatomical lobectomy or anatomical lung segment resection with preoperative 3D-CT reconstruction at the Department of Thoracic Oncology Surgery, Fujian Cancer Hospital from January 2020 to October 2022. The exclusion criteria were as follows: (I) blurred CT images and inadequate IQQA 3D reconstruction for clear visualization of bronchial and vascular subsegmental branches; (II) patients with a previous history of right middle lobectomy; (III) incomplete medical records. This study included 608 eligible cases, 245 males (40.30%) and 363 females (59.70%), with an average age of 58.61 years. We identified four major types of RML venous return, including 407 cases (66.94%) of central vein type, 123 cases (20.23%) of isolated vein type, 11 cases (1.81%) of basal vein type, and 67 cases (11.02%) of combined vein type. Furthermore, the central venous type comprised four subtypes: 191 cases (27.3%) had one branch, 165 cases (27.14%) had two branches, 43 cases (7.07%) had three branches, and 8 cases (1.32%) had more than three branches. The combined venous type includes four subtypes: combination type A (n=37 cases, 6.09%), combination type B (n=7 cases, 1.15%), combination type C (n=21 cases, 3.45%), and combination type D (n=2 cases, 0.33%). Based on 3D-CT reconstruction, numerous types of venous return in the RML were found, highlighting the significance of 3D-CT reconstruction for thoracic surgeons. It plays a crucial role in understanding the type of RML venous return prior to surgery, providing invaluable information to ensure surgical safety and success.

Performance evaluation of cosmic ray muon trajectory estimation algorithms

Muons, being elementary particles with minimal interaction with nuclear materials and abundant at sea level, have sparked interest in utilizing them for imaging various applications, such as mining [Borselli et al., Sci. Rep. 12, 22329 (2022)], volcano imaging [Nagamine et al., Nucl. Instrum. Meth. A, 356, 585(1995)], and underground tunnel detection [Guardincerri et al., Pure Appl. Geophys. 174, 2133 (2017)]. Recently, their use in nuclear nonproliferation and safeguard verification has gained attention, particularly in cargo screening for nuclear waste smuggling [Baesso et al., J. Instrum. 9, C10041 (2014)], source localization [L. J. Schultz et al., Nucl. Instrum. Meth. A 519, 687 (2004)], and locating nuclear fuel debris in reactors [Borozdin et al., Phys. Rev. Let. 109, 152501 (2012)]. However, the resolution of muon image reconstruction techniques is limited due to multiple Coulomb scattering (MCS) within the target object. To achieve robust muon tomography, it is crucial to develop efficient and flexible physics-based algorithms that can model the MCS process accurately and estimate the most probable trajectory of muons as they pass through the target object. To address this limitation, in this study, a novel algorithmic approach utilizing the Bayesian probability theory and Gaussian approximation of MCS is chosen. Different energy levels, materials, and target sizes were considered in the evaluations. The results demonstrate that the Generalized Muon Trajectory Estimation (GMTE) algorithm offers significant improvements over currently used algorithms. Across all test scenarios, the GMTE algorithm demonstrated ∼50% and 38% increase in precision compared to Straight Line Path (SLP) and Point of Closest Approach (PoCA) algorithms, respectively. Furthermore, it exhibited 10%–35% and 10%–15% increases in muon flux utilization for high and medium Z materials, respectively, compared to the PoCA algorithm. In conclusion, the extensive simulations confirm the enhanced performance and efficiency of the GMTE algorithm, offering improved resolution and reduced measurement time for cosmic ray muon imaging compared to the current SLP and PoCA algorithms.

High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-resolution spiral real-time cine imaging, including 2D balanced steady-state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U-Net-based image reconstruction network and 2D U-Net-based image segmentation network were proposed and evaluated. Low-rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal-to-noise ratio, normalized root-mean-square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p > 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL-based image reconstruction and quantification techniques enabled 1-min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high-resolution spiral real-time cine imaging with excellent performance.

Field recovery from digital inline holographic images of composite propellant combustion base on denoising diffusion model.

Digital inline holography has gained extensive application in the optical diagnosis of solid propellant combustion. However, this method confronts several challenges. Firstly, the calculation time required for reconstruction and depth of field extension is excessively long. Secondly, the excessive smoke, airflow, and flame during combustion cause significant interference and poor reconstruction quality, which reduces the accuracy of particle identification. To address these issues, we have developed a holographic image reconstruction technique for aluminum particle combustion based on the Attention Mechanism, U-net, and Diffusion models. This approach enables end-to-end reconstruction of aluminum particle combustion holographic images, while effectively circumventing the interference of airflow combustion and flame.

A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images

High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study

BackgroundThe Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (β) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies.Methods18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (β value of 100–500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM.ResultsOSEM had 11.5–18.6% higher BV than Q.Clear using β value of 500. Conversely, RC from OSEM to Q.Clear using β value of 500 decreased by 3.3–7.7% for a sphere with a diameter of 10 mm and 2.5–5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a β value of 500 was 5.2–8.1% in the smallest spheres compared to OSEM. When the β value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions.ConclusionsImplementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.

Study on sound absorption characteristic of porous asphalt mixture based on macroscale and mesoscale analysis

To explore the sound absorption characteristics of porous asphalt mixture, the effects of macroscopic parameters of asphalt mixture on sound absorption coefficient (SAC) were studied by the sound absorption coefficient test, and the mesoscopic void structure was investigated by industrial computed tomography (CT) and three-dimensional (3D) reconstruction techniques. The results show that the SAC can be increased by increasing the porosity or thickness and reducing the NMAS. The increase of porosity and the decrease of pavement thickness can better absorb high frequency noise (above 1000 Hz). Through the grey correlation analysis, it can be seen that the SAC of porous asphalt mixture is most affected by the porosity. There is a strong linear relationship between the microscopic void characteristic of porous asphalt mixture and the peak and mean value of SAC.

Accuracy of Electron Density Mapping of a Novel kVCBCT System Designed for Planning

A recently developed advanced kVCBCT system, designed for simulation and planning, includes improvements to the imaging panel and reconstruction technique, enabling more accurate electron density mapping than a typical CBCT. This study investigates the accuracy, limitations, and dosimetric impact of this new system. CBCT images were taken of both large (pelvis) and small (head) electron density phantoms with inserts ranging from lung (0.29) to cortical bone (1.69). Images were taken with several pre-set protocols with energies of 125 kVp and 140 kVp. The effects of longitudinal buildup (for scatter due to the cone-beam geometry) and blade position were also investigated. The HU values of each insert were measured over a small region of interest. Several electron density curves were generated - pelvis protocol on the large phantom, head protocol on the small phantom, and average - and imported into a treatment planning system. Dose calculation was performed using each curve and differences were observed. HU to electron density mapping was sensitive to the use of longitudinal buildup and blade position, with differences in the curves observed at both low and high electron densities, due to imager saturation effects not found in typical clinical scenarios. Subsequent studies used both longitudinal buildup and fully opened blades. Under these conditions, mapping was not sensitive to technique or phantom size at low electron densities. At higher values, the curves diverged, with the head protocol showing higher HU values and the pelvis protocol showing lower. The average curve matched very closely with the curve from our standard CT simulator. Dose calculation showed little dependence on the curve chosen, with max point dose differences of 1.2% between the pelvis and average scans, though most of the plan was <0.5% different. Results for the head protocol were similar. The system provides reliable HU values comparable to a CT simulator, though it is important to consider the effects of imager saturation by choosing appropriate scatter and collimation. The calibration is slightly sensitive to the choice of phantom size and beam energy, though these differences led to negligible (<0.5%) differences in dose calculation accuracy.

ProActive DeepFake Detection using GAN-based Visible Watermarking

With the advances in generative adversarial networks (GAN), facial manipulations called DeepFakes have caused major security risks and raised severe societal concerns. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and fails in blocking the disinformation spread in advance. Alternatively, precautions such as adding perturbations to the real data for unnatural distorted DeepFake output easily spotted by the human eyes are introduced as proactive defenses. Recent studies suggest that these existing proactive defenses can be easily bypassed by employing simple image transformation and reconstruction techniques when applied to the perturbed real data and the distorted output, respectively. The aim of this paper is to propose a novel proactive DeepFake detection technique using GAN-based visible watermarking. To this front, we propose a reconstructive regularization added to the GAN’s loss function that embeds a unique watermark to the assigned location of the generated fake image. Thorough experiments on multiple datasets confirm the viability of the proposed approach as a proactive defense mechanism against DeepFakes from the perspective of detection by human eyes. Thus, our proposed watermark-based GANs prevent the abuse of the pretrained GANs and smartphone apps, available via online repositories, for DeepFake creation for malicious purposes. Further, the watermarked DeepFakes can also be detected by the SOTA DeepFake detectors. This is critical for applications where automatic DeepFake detectors are used for mass audits due to the huge cost associated with human observers examining a large amount of data manually.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

Extending function mixture network for improved spectral super-resolution

Hyperspectral Image reconstruction from RGB images is a low-cost and convenient alternative to acquiring hyperspectral images directly. The challenge in estimating the spectral response function and using it for generating the hyperspectral image data is addressed effectively by the use of convolutional neural networks for the task. The accuracy of the reconstruction techniques is improving and effective architectures are being adapted for the purpose. Specifically, in a recent work the authors propose the use of Function Mixture Network, to model the mapping from RGB to hyperspectral. This technique benefits from the use of an adaptive spatial receptive field, however, given that the goal is spectral superresolution additional use of adaptive spectral field is prudent. Accordingly, in this paper we propose an improved Function Mixture Network based reconstruction technique by additionally incorporating a variable spectral receptive field. The proposed updates have resulted in significant gains in performance as is demonstrated by the experiments conducted over multiple datasets. An additional update to the architecture is the use of non-convolutional branches in the function mixture network. The experiments of three standard datasets, clearly demonstrate the superiority of the proposed technique for hyperspectral image reconstruction based on RGB data.

Tomographic Reconstruction of Atmospheric Water Vapor Profiles Using Multi-GNSS Observations

Continuously operating reference stations (CORS) provide augmentation services for the highly accurate, cm-level GNSS positioning needs of land surveyors, agriculture, and even autonomous vehicles. These stations have accurate coordinates, thus they can be used to estimate the signal delay caused by the neutral atmosphere including the atmospheric water vapor. The estimated zenith wet delay (ZWD) is in a close correlation with the integrated water vapor in the atmospheric column. Since a ground station tracks several satellites at every epoch, one could also estimate the slant tropospheric delays, which can provide information on the spatial distribution of the atmospheric water vapor, too. This paper introduces a near real-time multi-GNSS processing approach to estimate slant wet tropospheric delays and a coupled tomographic reconstruction technique to estimate the 3D wet refractivity model that can be assimilated in numerical weather models. The estimated zenith tropospheric delays (ZTDs) and tropospheric gradients are used to restore the slant wet delays (SWD) affecting the observed satellite-receiver range. The SWDs are used as input for a tomographic reconstruction algorithm providing the wet refractivities in a pre-defined voxel model. The derived refractivity profiles have been validated with radiosonde observations. The results show that our GNSS tomography approach could reconstruct the refractivities with the uncertainty of 10 ppm below 3 km of altitude and of 0.3 ppm at the altitude of 10 km in terms of standard deviation.

A new projection correction based voting strategy for breast calcification artifact reduction

Objective. Digital Breast Tomosynthesis (DBT) is an imaging technique that combines traditional tomography with image processing and reconstruction techniques. In screening for breast cancer, high attenuation lesion will cause calcification hardening artifacts, which reduces the reconstructed image quality and limits diagnostic accuracy. We focus on the reconstruction artifacts that are caused by high-attenuation features in DBT, and aim to propose an efficient and accurate method to remove calcification artifacts and retain calcification information. Approach. The proposed method first introduces a new segmentation method, which can segment breast calcification accurately and effectively. Then an interpolation method is used to eliminate both the calcified area and artifact area in the projection images which are then used to reconstruct the image without artifacts and calcifications. Finally, the interpolated reconstructed image and the unprocessed reconstructed image are fused under the proposed voting strategy to obtain the DBT image with calcification artifacts removal. Main results. 18 groups of simulated projection data and 10 groups of real projection data collected by us are used to evaluate the proposed method. Experimental results show that our algorithm can effectively reduce the calcification artifact and preserve the effective information in the image as well. Significance. The proposed method utilizes a novel projection correction based voting fusion strategy for image fusion, and is advanced in reducing breast calcification artifacts compared with other state-of-the-art methods. Our work paves the way for more efficient and precise DBT breast cancer screening.

Hybrid spiral scanning in a double-clad fiber-based handheld confocal scanning light ophthalmoscope.

High-speed, accessible, and robust in vivo imaging of the human retina is critical for screening of retinal pathologies, such as diabetic retinopathy, age-related macular degeneration, and others. Scanning light ophthalmoscopy (SLO) is a retinal imaging modality that produces digital, en face images of the human retina with superior image gradability rates when compared to the current standard of care in screening for these diseases, namely the flood-illumination handheld fundus camera (HFC). However, current-generation commercial SLO systems are mostly tabletop devices, limiting their accessibility and utility in screening applications. Moreover, most existing SLO systems use raster scan patterns, which are both inefficient and lead to undesired subject gaze drift when used with visible or pseudo-visible illumination. Non-raster scan patterns, especially spiral scanning as described herein, promise advantages in both scan efficiency and reduced subject eye motion. In this work, we introduce a novel "hybrid spiral" scan pattern and the associated hardware design and real-time image reconstruction techniques necessary for its implementation in an SLO system. Building upon this core hybrid spiral scanning SLO (HSS-SLO) technology, we go on to present a complete handheld HSS-SLO system, featuring a fiber-coupled portable patient interface which leverages a dual-clad fiber (DCF) to form a single-path optical topology, thus ensuring mechanically robust co-alignment of illumination and collection apertures, a necessity for a handheld system. The feasibility of HSS-SLO for handheld, in vivo imaging is demonstrated by imaging eight human volunteers.

Newly-designed adaptive non-blind deconvolution with structural similarity index in single-photon emission computed tomography

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.

2D-supervised fast neural fluid reconstruction technique for time-resolved volumetric flame reconstruction

The volumetric tomography reconstruction technique (VTRT) can calculate and restore the three-dimensional physical field based on the multi-view projection information of the flame and similar flow fields, which plays an important role in the research of volumetric targets. However, the reconstruction of three-dimensional scalar/vector fields representing the physical information of the flow field (temperature, density, chemical composition, etc.) requires a significant amount of computational resources and time, especially in the turbulent flame flow field, which requires high spatial and temporal resolutions. Based on the said background, in this work, we proposed a Fast Neural Fluid Reconstruction Technique (Fast-NFRT) based on deep learning. In this investigation, we first test the reconstruction accuracy, speed, and noise robustness of Fast-NFRT using numerically simulated flames. Then Fast-NFRT is used to reconstruct the experimental turbulent jet flame under two different conditions. Finally, with the camera settings preserved, the Fast-NFRT model was tested for transfer learning between the numerical simulation flame and the two experimental jet flames to examine the generalization performance of the reconstruction. It is found that the proposed Fast-NFRT model can achieve a temporal resolution of 50-500 fps with a similar reconstruction fidelity to traditional algebraic reconstruction methods, which demonstrates the capacity of the Fast-NFRT model and its potential in real-time reconstruction applications and dynamic analysis for complex flow dynamics.

Prosedur Pemeriksaan CT-Scan Angiography (CTA) Upper Ekstremity Pada Kasus Pseudoaneurisma Arteri Subclavia Kanan Di Rumah Sakit Primaya Tangerang

Upper Extremity CT-Scan Angiography (CTA) Examination Procedure in Right Subclavian Artery Pseudoaneurysm Cases at Primaya Hospital Tangerang. Upper Extremity CT-Scan Angiography (CTA) is described as a technique for non-invasively evaluating vascular structures via peripheral contrast injection. Upper Extremity CTA is carried out starting from patient preparation, tools and materials used, examination protocols, and image reconstruction techniques. In addition, the contrast infusion technique must also be appropriate to provide an informative image. The purpose of this study was to determine the procedure for examining the Upper Extremity CTA in cases of subclavian artery pseudoaneurysm with excess of the Upper Extremity CTA. This research is a qualitative research with a case study method using 1 patient. Data were collected using observation, documentation and radiographer interview methods. The results of the data collected can be concluded with the Upper Extremity CTA procedure using Scanning Scannogram, pre-contrast, &amp; post-contrast arterial phase, and venous phase, then image reconstruction from the MPR image and 3D VR image. The advantages of the CTA procedure using the smart prep technique image resolution quality could be higher.

Single-pixel image reconstruction using coherent nonlinear optics.

We propose and experimentally demonstrate a novel, to the best of our knowledge, hybrid optoelectronic system that utilizes mode-selective frequency upconversion, single-pixel detection, and a deep neural network to achieve the reliable reconstruction of two-dimensional (2D) images from a noise-contaminated database of handwritten digits. Our system is designed to maximize the multi-scale structural similarity index measure (MS-SSIM) and minimize the mean absolute error (MAE) during the training process. Through extensive evaluation, we have observed that the reconstructed images exhibit high-quality results, with a peak signal-to-noise ratio (PSNR) reaching approximately 20 dB and a structural similarity index measure (SSIM) of around 0.85. These impressive metrics demonstrate the effectiveness and fidelity of our image reconstruction technique. The versatility of our approach allows its application in various fields, including Lidar, compressive imaging, volumetric reconstruction, and so on.