Image Reconstruction Techniques Research Articles

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.

Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product

Abstract. The EarthCARE satellite mission's objective is to retrieve profiles of aerosol and cloud physical and optical properties using the combination of cloud-profiling radar (CPR), high-spectral-resolution UV lidar (ATLID) and passive multi-spectral imager (MSI) data. Based on synergistic retrievals using data from these instruments, the 3D atmospheric cloud–aerosol state is estimated and then used to model the top-of-atmosphere (TOA) broadband radiances, which may then be compared to co-incident EarthCARE broadband radiometer (BBR) measurements. A high-spectral-resolution lidar enables the independent retrieval of extinction and backscatter but, being space based, suffers from relatively low signal-to-noise ratio (SNR) levels. The ATLID FeatureMask (A-FM) product provides a feature detection mask for the existence of atmospheric features within the lidar profiles based on a number of (statistical) image reconstruction techniques. Next to this, it also identifies those regions where the lidar beam has been fully attenuated and where the surface backscatter has impacted the measured lidar backscatter signals directly above the surface. From the pixels assigned as clear sky (with no features present above), the clear-sky-averaged profiles for the three ATLID channels, the co-polar Mie channel, the total cross channel and the co-polar Rayleigh channel are created. These feature-free or clear-sky profiles are useful for e.g., assessing the quality of the ATLID Level-1 (L1) attenuated backscatters. An important goal of the A-FM product is to guide smoothing strategies within downstream processors e.g., the ATLID profile retrieval (A-PRO) algorithm which directly follows A-FM within the EarthCARE Level-2 (L2) processing chain. Within the A-PRO algorithm, profiles of extinction, backscatter and linear depolarization ratio are retrieved. However, smoothing of the ATLID L1 attenuated backscatter is necessary since the SNR levels present at the ATLID native resolution are generally not sufficient for meaningful retrievals to be conducted. At the same time, to prevent biased retrievals, any smoothing procedure must respect the cloud–aerosol structure and avoid mixing strong features, e.g., clouds, and weak features, e.g., aerosol regions, together. The A-FM product provides the A-PRO algorithm with important information that is used to guide various smoothing procedures. To enable the processing of the large datasets from observation up to L2 retrievals, each EarthCARE orbit is separated into eight frames, divided at latitudes of 22.5∘ N and 22.5∘ S and 62.5∘ N and 62.5∘ S. As a secondary product, A-FM outputs can be used to conduct a frame-by-frame evaluation of the ATLID L1 cross-talk calibration, where an EarthCARE frame is one-eighth of a full orbit. This evaluation can be performed by comparing the retrieved clear-sky profiles to the expected channel profiles. The A-FM product has been applied to both synthetic data from the EarthCARE end-to-end simulator (ECSIM) and the L1 data from the Aeolus wind lidar mission. Comparisons against the ECSIM model truth indicate that A-FM has a percentage correctness &gt; 90 % and is capable of reliably detecting aerosol and cloud regions within extinctions (&gt; 10−5 m−1).

3D image reconstruction of architectural model based on 3D printing technology

ABSTRACT The goal of this study was to develop a 3D image reconstruction technique for an architectural model based on 3D printing technology with the objective to enhance the reconstruction effect of the architectural model. This method first analyzes the basic process of 3D printing architectural models and 3D reconstruction technology. On this basis, a 3D architectural model is obtained by sorting out the materials, drawing, printing the models, and removing the supporting materials. According to the 3D image data of the architectural model obtained after camera calibration, radial correction, noise reduction, and multi-view camera motion recovery are carried out on the image data. After the experiment, it was found that the absolute error of the location value of the feature points in the X, Y and Z directions of the given model by the method proposed in this study was 0.15, 0.065 and 0.017 respectively, while the absolute error of the corresponding location in the references [6] and [8] was 1.48 and 1.46, 1.601 and 0.995, 0.839 and 0.94 respectively. It is clear that the method developed in this study has a substantially greater reconstruction accuracy and that it accurately extracts 3D data from the architectural model. The degree of correspondence between the reconstruction outcomes and the architectural model has greatly increased.

A retrospective evaluation of Bayesian-penalized likelihood reconstruction for [15O]H2O myocardial perfusion imaging

BackgroundNew Block-Sequential-Regularized-Expectation-Maximization (BSREM) image reconstruction technique has been introduced for clinical use mainly for oncologic use. Accurate and quantitative image reconstruction is essential in myocardial perfusion imaging with positron emission tomography (PET) as it utilizes absolute quantitation of myocardial blood flow (MBF). The aim of the study was to evaluate BSREM reconstruction for quantitation in patients with suspected coronary artery disease (CAD). Methods and ResultsWe analyzed cardiac [15O]H2O PET studies of 177 patients evaluated for CAD. Differences between BSREM and Ordered-Subset-Expectation-Maximization with Time-Of-Flight (TOF) and Point-Spread-Function (PSF) modeling (OSEM-TOF-PSF) in terms of MBF, perfusable tissue fraction, and vascular volume fraction were measured. Classification of ischemia was assessed between the algorithms. OSEM-TOF-PSF and BSREM provided similar global stress MBF in patients with ischemia (1.84 ± 0.21 g⋅ml−1⋅min−1 vs 1.86 ± 0.21 g⋅ml−1⋅min−1) and no ischemia (3.26 ± 0.34 g⋅ml−1⋅min−1 vs 3.28 ± 0.34 g⋅ml−1⋅min−1). Global resting MBF was also similar (0.97 ± 0.12 g⋅ml−1⋅min−1 and 1.12 ± 0.06 g⋅ml−1⋅min−1). The largest mean relative difference in MBF values was 7%. Presence of myocardial ischemia was classified concordantly in 99% of patients using OSEM-TOF-PSF and BSREM reconstructions ConclusionOSEM-TOF-PSF and BSREM image reconstructions produce similar MBF values and diagnosis of myocardial ischemia in patients undergoing [15O]H2O PET due to suspected obstructive coronary artery disease

Wearable Microwave Medical Sensing for Stroke Classification and Localization: A Space-Division-Based Decision-Tree Learning Method

Microwave medical imaging systems have shown a competitive advantage in stroke detection due to their cost-effectiveness, non-ionization, and portability. However, these systems often rely on time-consuming image reconstruction techniques, which are disadvantageous for timely stroke treatment. In this paper, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (i.e., the head). The space division is enabled by the generalized scattering matrix theory and incorporates the brain anatomy. Then a novel decision tree learning method is proposed, which facilitates efficient stroke feature identification for classification. The spatial information acquired from the decision tree also results in rapid stroke localization. In order to verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial haemorrhage stroke and an ischemic stroke with a wearable MMS system. Both numerical and experimental results are obtained. Compared to the traditional method, the classification rates for simulation and experimental results are improved by 14.1% and 19.2%, respectively. Furthermore, by utilizing the a priori information, the localization time is reduced by 21.1%. Finally, the localization accuracy is higher than 0.90 in both simulation and experimental studies. The classification accuracy and localization efficiency are shown to be greatly improved compared to the traditional method, which has great significance for wearable devices. This study proposes an efficient space-division-based detection method to localize the brain stroke without imaging.