Image Reconstruction Method Research Articles

MCI-HyperNet: A multiple contextual information-based adaptive weight learning network for controllable image reconstruction

Contemporary deep learning methods for image reconstruction have shown promising results over classical methods. However, they are not robust to image alterations due to deviations in the imaging conditions at test time. The training process does not account for explicit variations in the contextual information about the imaging task, such as image control parameters and input settings that could hold a causal relationship to the visual content of the reconstructed image. In this work, we propose dynamic weight prediction (DWP) networks to learn the contextual settings and the relationships between them. This model-based meta-learning approach would offer two valuable capabilities in a single network - (i) scalability to multiple input settings and (ii) tunability to continuously varying control settings with the knowledge of a few settings. The proposed network, MCI-HyperNet, is a controllable image reconstruction network with DWP sub-networks conditioned on multiple contextual settings. The proposed approach strikes a balance between reliable context-adaptive weight learning and context-agnostic weight learning to obtain robust image reconstructions. We have extensively experimented with our network for accelerated MRI reconstruction considering the essential research directions provided in the fastMRI challenge results. The proposed method exhibits (i) superior reconstruction quality with improvement margins of ∼0.5 dB in PSNR and ∼0.01 in SSIM over adaptive reconstruction methods for cardiac MRI and multiple brain MRI contrasts, (ii) better scalability to multiple anatomies, contrasts, and under-sampling mask patterns with accuracy improvement margins of ∼0.2 dB in PSNR and ∼0.005 in SSIM over joint training and context-specific models for knee MRI, and (iii) generality to 40 arbitrary acceleration factors over joint training when trained on just five acceleration factors. Our model can learn multiple contrast sequences through continual learning without catastrophically forgetting old MRI sequences. Furthermore, our method is benchmarked for reconstruction quality against other MRI reconstruction architectures trained for a specific contextual setting. Besides, an interpretability analysis using a relational knowledge distillation measure shows that our model exhibits superior context-specific and relational knowledge across contexts over jointly trained models. Our source code is publicly available at https://github.com/sriprabhar/MCI-HyperNet.

Analysis for Improvement of Doppler Tomography Imaging of Objects Scattering Continuous Ultrasonic Waves

The paper describes an innovative ultrasound imaging method called Doppler Tomography (DT), otherwise known as Continuous Wave Ultrasonic Tomography (CWUT). Thanks to this method, it is possible to image the tissue cross-section in vivo using a simple two-transducer ultrasonic probe and using the Doppler effect. It should be noted that DT significantly differs from the conventional ultrasound Doppler method of measuring blood flow velocity. The main difference is that when measuring blood flow, we receive information with an image of the velocity distribution in a given blood vessel (Nowicki, 1995), while DT allows us to obtain a cross-sectional image of stationary tissue structure. In the conventional method, the probe remains stationary, while in the DT method, the probe moves and the examined tissue remains stationary. This paper presents a method of image reconstruction using the DT method. First, the basic principle of correlation of generated Doppler frequencies with the location of inclusions from which they originate is explained. Then the exact process and algorithm in this method are presented. Finally, the impact of several key parameters on imaging quality is examined. As a result, the conclusions of the research allow to improve the image reconstruction process using the DT method.

Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.

Synthetic Aperture Cardiac Imaging with Reduced Number of Acquisition Channels. A Feasibility Study

Commercially available cardiac scanners use 64–128 elements phased-array (PA) probes and classical delay-and-sum beamforming to reconstruct a sector B-mode image. For portable and hand-held scanners, which are the fastest growing market, channel count reduction can greatly decrease the total power and cost of devices. The introduction of ultra-fast imaging methods based on plane waves and diverging waves provides new insight into heart’s moving structures and enables the implementation of new myocardial assessment and advanced flow estimation methods, thanks to much higher frame rates. The goal of this study was to show the feasibility of reducing the channel count in the diverging wave synthetic aperture image reconstruction method for phased-arrays. The application of ultra-fast 32-channel subaperture imaging combined with spatial compounding allowed the frame rate of approximately 400 fps for 120 mm visualization to be achieved with image quality obtained on par with the classical 64-channel beamformer. Specifically, it was shown that the proposed method resulted in image quality metrics (lateral resolution, contrast and contrast-to-noise ratio), for a visualization depth not exceeding 50 mm, that were comparable with the classical PA beamforming. For larger visualization depths (80–100 mm) a slight degradation of the above parameters was observed. In conclusion, diverging wave phased-array imaging with reduced number of channels is a promising technology for low-cost, energy efficient hand-held cardiac scanners.

Pipeline visualization based on RIR-RepVGG network

Electrical Capacitance Tomography (ECT) is a process tomography technology based on capacitance-sensitive field, which is of vital importance to accelerate the development of industrial intelligence by measuring the required capacitance data through sensors to achieve the flow pattern detection of filled pipes and thus meet the controllability of pipe flow patterns. The traditional ECT flow pattern recognition method has low recognition rate and more complicated operation. In this paper, the detection method of filling pipeline is studied based on electrical capacitance tomography (ECT) technology. Aiming at the low imaging accuracy of traditional ECT reconstruction algorithms, an ECT image reconstruction method based on improved residual neural network was proposed. The RIR-RepVGG residual network structure is proposed and a nonlinear mapping network is designed. The improved residual network is used to establish the mapping relationship between the capacitance vector and the two-phase flow image. Through the static study by filling pipeline simulation experiments and static experiments, it is proved that the ECT image reconstruction method proposed in this paper can reduce the artifacts and deformation of the reconstructed image, improve the reconstruction accuracy, and has a better reconstruction effect for complex flow patterns. Application is of great significance.

Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm

Objective. Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction method, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function. Approach. A practical implementation of a fully 3D PET image reconstruction could not be performed at present because of a graphics processing unit memory limitation. Consequently, we modify the DIP optimization to a block iteration and sequential learning of an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term is added to the loss function to enhance the quantitative accuracy of the PET image. Main results. We evaluated our proposed method using Monte Carlo simulation with [18F]FDG PET data of a human brain and a preclinical study on monkey-brain [18F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximum a posteriori EM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that, compared with other algorithms, the proposed method improved the PET image quality by reducing statistical noise and better preserved the contrast of brain structures and inserted tumors. In the preclinical experiment, finer structures and better contrast recovery were obtained with the proposed method. Significance. The results indicated that the proposed method could produce high-quality images without a prior training dataset. Thus, the proposed method could be a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.

Comprehensive evaluation of artifact reduction and tissue recovery effects of metal artifact reduction technique based on full-reference metric

For the comprehensive evaluation of metal artifact reduction (MAR) technique, not only the removal of metal artifacts but also the evaluation of the area restored by MAR is required. We propose a method to comprehensively evaluate the effect by MAR in this study. We have conducted the computed tomography scan to acquire both the evaluation image and the reference image for the full-reference based evaluation. The evaluation image and reference image were reconstructed into 24 image sets according to the tube potentials, image reconstruction method, and use of the MAR technique. Images of two different positions were selected according to the distance from metal and material (bone, tissue) distribution, and bone and tissue were automatically segmented in both evaluation and reference images. The values of full width at half the maximum (FWHM) and centroid were extracted after Gaussian modeling of each segmented region. Then, we computed four evaluation metrics (FWHMNM: non-MAR to non-metal ratio of FWHM, FWHMM: MAR to non-metal ratio of FWHM, CENTNM: non-MAR to non-metal ratio of centroid, CENTM: MAR to non-metal ratio of centroid), and the MAR image and non-MAR image were compared. The overlap ratio automatically segmented from the evaluation image and reference image were position 1 (bone: 99.61%, tissue: 99.23%) with 80 kVp, position 1 (bone: 99.32%, tissue: 99.56%) with 120 kVp, position 2 (bone: 99.20%, tissue: 99.73%) with 80 kVp, and position 2 (bone: 99.23%, tissue: 99.67%) with 120 kVp. The FWHMNM showing the change of image pixel value by metal artifact was calculated as (bone: 1.32–1.46, tissue: 1.08–1.16) at 80 kVp and (bone: 1.19–1.27, tissue: 1.02–1.05) at 120 kVp. More metal artifacts occurred at 80 kVp tube potential. Regardless of the tube potential and image reconstruction method, the MAR showed an overall artifact reduction effect (1 < FWHMM < FWHMNM). However, distortion of pixel values occurred due to the MAR in regions where metal artifacts were high in proximity to metal (1 < FWHMNM < FWHMM). Overall, the average value of the medium was maintained (CENTM: 0.98–1.03) after MAR application, but there was a change of image value in region around the metal (CENTM: 0.97–1.11). In this study, we propose a new method to evaluate the effect of metal artifacts and MAR technique using full-reference based method. Metal artifacts, effect of MAR technique, and side-effect caused by MAR technique were quantitatively analyzed through proposed method. There are some limitations in applying it to clinical imaging since our method is a reference-based evaluation. However, our experimental results were important for understanding the effects of the MAR technique and its functional properties.

Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging

Objective. Here, we propose a dynamic residual Kaczmarz (DRK) method as an improved reconstruction method for magnetic particle imaging (MPI) to achieve a better reconstruction quality from high-noise signals. Approach. Based on the Kaczmarz (KZ) method, we introduced a residual vector to select parts of the low-noise equations for reconstruction. In each iteration, a low-noise subset was formulated based on the residual vector. Thus, the reconstruction converged to an accurate result with less noise. Main Results. To evaluate the performance of the proposed method, it was compared with classical Kaczmarz-type methods and state-of-the-art regularization models. The numerical simulation results demonstrate that the DRK method can achieve better reconstruction quality than all other comparison methods at similar noise levels. It can acquire a signal-to-background ratio (SBR) that is five times higher than that of classical Kaczmarz-type methods at a 5 dB noise level. Furthermore, the DRK method can acquire up to 0.7 structural similarity (SSIM) indicators at a 5 dB noise level when combined with the non-negative fused Least absolute shrinkage and selection operator (LASSO) regularization model. In addition, a real experiment based on the OpenMPI data set validated that the proposed DRK method can be applied to real data and perform well. Significance. The experimental results demonstrate that the proposed DRK method can significantly improve the reconstruction quality of MPI when the signals contain high noise. It has the potential to be applied to MPI instruments that contain high signal noise, such as human-sized MPI instruments. It is beneficial for expanding the biomedical applications of MPI technology.

Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism

Electrical impedance tomography (EIT) is an imaging technology for estimating the conductivity change for the measured field of the human body. However, because EIT image reconstruction is severely ill-posed and nonlinear inverse problem, the reconstructed image based on traditional algorithm is lower accurate with obscure boundaries and lots of artifacts. In order to improve the accuracy of reconstructed image, an EIT image reconstruction method based on RBF and attention mechanism is proposed in this paper. The proposed RCU-Net network architecture consists of two sub-networks: RBF and CBAM-UNet. The RBF neural network accomplishes the nonlinear mapping between the boundary voltages and conductivity distribution values. The CBAM-UNet combining attention mechanism CBAM with U-Net makes further processing for the resulting images to obtain more sharp and explicit images. RCU-Net was trained and tested on the simulated dataset created by EIDORS platform. The experiment reveals that the proposed method outperforms the traditional methods in distinguishing multi-target, generalization ability and anti-interference, and it has improved the reconstructed image quality which has an average reduction of 55.09% and 26.23% on RE (Relative Error) and an increase of 14.19% and 3.08% on CC (Correlation Coefficient) respectively compared with RBF-EIT and RBF-UNet methods. In addition, the experimental data from KIT4 system can be applied to the trained network. This successful transition also demonstrates the proposed method can reconstruct more explicit images with high fidelity.

Simultaneous quantitative imaging of two PET radiotracers via the detection of positron-electron annihilation and prompt gamma emissions.

In conventional positron emission tomography (PET), only one radiotracer can be imaged at a time, because all PET isotopes produce the same two 511 keV annihilation photons. Here we describe an image reconstruction method for the simultaneous in vivo imaging of two PET tracers and thereby the independent quantification of two molecular signals. This method of multiplexed PET imaging leverages the 350-700 keV range to maximize the capture of 511 keV annihilation photons and prompt γ-ray emission in the same energy window, hence eliminating the need for energy discrimination during reconstruction or for signal separation beforehand. We used multiplexed PET to track, in mice with subcutaneous tumours, the biodistributions of intravenously injected [124I]I-trametinib and 2-deoxy-2-[18F]fluoro-D-glucose, [124I]I-trametinib and its nanoparticle carrier [89Zr]Zr-ferumoxytol, and the prostate-specific membrane antigen (PSMA) and infused PSMA-targeted chimaeric antigen receptor T cells after the systemic administration of [68Ga]Ga-PSMA-11 and [124I]I. Multiplexed PET provides more information depth, gives new uses to prompt γ-ray-emitting isotopes, reduces radiation burden by omitting the need for an additional computed-tomography scan and can be implemented on preclinical and clinical systems without any modifications in hardware or image acquisition software.

Open-UST: An Open-Source Ultrasound Tomography Transducer Array System.

Fast imaging methods are needed to promote clinical adoption of ultrasound tomography (UST), and more widely available UST hardware could support the experimental validation of new measurement configurations. In this work, an open-source 256-element transducer ring array was developed (morganjroberts.github. io/open-UST) and manufactured using rapid prototyping, for only £2k. Novel manufacturing techniques were used, resulting in a 1.17° mean beam axis skew angle, a [Formula: see text] mean element position error, and a [Formula: see text] deviation in matching layer thickness. The nominal acoustic performance was measured using hydrophone scans and watershot data, and the 61.2 dB signal-to-noise ratio (SNR), 55.4° opening angle, 10.2 mm beamwidth, and 54% transmit-receive bandwidth (-12 dB) were found to be similar to existing systems and compatible with state-of-the-art full-waveform-inversion image reconstruction methods. The interelement variation in acoustic performance was typically < 10% without using normalization, meaning that the elements can be modeled identically during image reconstruction, removing the need for individual source definitions based on hydrophone measurements. Finally, data from a phantom experiment were successfully reconstructed. These results demonstrate that the open-UST system is accessible for users and is suitable for UST imaging research.

Image reconstruction method in grating interferometer with phase stepping using grating position and dose fluctuation correction

In this paper, we propose a method for reducing artifacts in X-ray and neutron grating interferometer imaging by optimizing the grating position and dose fluctuation. Grating interferometer can obtain additional information about the object, such as phase-contrast image and dark-field image, compared to traditional X-ray imaging. However, several errors such as inaccurate grating positions and non-uniform X-ray intensity can cause artifacts in the image and degrade image quality. The proposed method can reduce the artifacts by minimizing the value in the sample-free region in the transmission and dark-field images. We conducted experiments on both simulated and X-ray and neutron experimental data and demonstrated the effectiveness of our method.

Avoiding Optimal Mean Robust and Sparse BPCA with L1-norm Maximization

&lt;p&gt;Recently, the robust PCA/2DPCA methods have achieved great success in subspace learning. Nevertheless, most of them have a basic premise that the average of samples is zero and the optimal mean is the center of the data. Actually, this premise only applies to PCA/2DPCA methods based on L2-norm. The robust PCA/2DPCA method with L1-norm has an optimal mean deviate from zero, and the estimation of the optimal mean leads to an expensive calculation. Another shortcoming of PCA/2DPCA is that it does not pay enough attention to the instinct correlation within the part of data. To tackle these issues, we introduce the maximum variance of samples&amp;rsquo; difference into Block principal component analysis (BPCA) and propose a robust method for avoiding the optimal mean to extract orthonormal features. BPCA, which is generalized from PCA and 2DPCA, is a general PCA/2DPCA framework specialized in part learning, can makes better use of the partial correlation. However, projection features without sparsity not only have higher computational complexity, but also lack semantic properties. We integrate the elastic network into avoiding optimal mean robust BPCA to perform sparse constraints on projection features. These two BPCA methods (non-sparse and sparse) make the presumption of zero-mean data unnecessary and avoid optimal mean calculation. Experiments on reference benchmark databases indicate the usefulness of the proposed two methods in image classification and image reconstruction.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Image Quality Improvement in Sparse-View X-Ray Phase-Contrast Trimodal CBCT With Multifrequency Fringe Modulation and Iterative Methods

X-ray grating interferometer can simultaneously retrieve attenuation, refraction and scattering signals of the imaged sample, which shows its great potential in biomedical imaging. However, fast scan of gratings and high centrifugal forces during rotating in X-ray grating-based cone-beam computed tomography (CBCT) system may lead to phase-stepping errors and grating position fluctuations, which cause reconstruction failure with phase-stepping (PS) method. Whats more, the reconstructed slices usually suffer from severe artifacts under low dose conditions. Herein, we propose a multi-frequency model to simulate the mechanical instability of the system and put forward novel iterative image reconstruction methods named MFM-TLI and MLEM-FISTA-TV to improve image quality in sparse-view trimodal CBCT. Results reveal that the model and MFM-TLI is effective to retrieve high quality absorption, differential phase contrast (DPC) and dark-field (DF) signals under mechanical instability, significantly outperforming the PS method. Image quality reconstructed by MLEM-FISTA-TV is improved compared to other iterative and analytical algorithms with sparse-view data. To the best of our knowledge, this is the first time multi-frequency method is applied in X-ray grating interferometer and iterative MLEM-TV algorithm is evaluated on experimental sparse-view dark-field CBCT data. This work might provide support for future trimodal CBCT reconstruction.

Numerical analysis of aerospace plate damage via 3D electrical impedance tomography.

The carbon fiber reinforced plastic (CFRP) is widely used in the aerospace industry due to its high strength and lightweight characteristics, making it crucial to ensure the reliability of these materials. This has led to an increasing focus on research on the health monitoring technologies of aerospace materials. Electrical impedance tomography (EIT) is a non-invasive and cost-effective technology that has the potential to realize real-time health monitoring of materials by measuring changes in electrical parameters. This paper investigates the application of EIT for direct 3D reconstruction of damage in CFRP laminates with significant conductivity anisotropy distribution. Based on the corrected sensitivity matrix formula, the direct 3D image reconstruction method combined with the fast iterative shrinkage-thresholding algorithm (FISTA) is proposed to achieve damage imaging of CFRP laminates in the inverse problem. The fast convergence of the FISTA makes it possible to solve complex inverse problems. The numerical simulation results indicate that, compared with 2D EIT, the proposed method is more capable of providing damage information, especially in the depth direction. This research plays a constructive role in realizing 3D image reconstruction of CFRP material damage and has significant implications for improving the reliability and safety of CFRP materials in aerospace applications.

Extended aperture image reconstruction for plane-wave imaging

B-mode images undergo degradation in the boundary region because of the limited number of elements in the ultrasound probe. Herein, a deep learning-based extended aperture image reconstruction method is proposed to reconstruct a B-mode image with an enhanced boundary region. The proposed network can reconstruct an image using pre-beamformed raw data received from the half-aperture of the probe. To generate a high-quality training target without degradation in the boundary region, the target data were acquired using the full-aperture. Training data were acquired from an experimental study using a tissue-mimicking phantom, vascular phantom, and simulation of random point scatterers. Compared with plane-wave images from delay and sum beamforming, the proposed extended aperture image reconstruction method achieves improvement at the boundary region in terms of the multi-scale structure of similarity and peak signal-to-noise ratio by 8% and 4.10 dB in resolution evaluation phantom, 7% and 3.15 dB in contrast speckle phantom, and 5% and 3 dB in in vivo study of carotid artery imaging. The findings in this study prove the feasibility of a deep learning-based extended aperture image reconstruction method for boundary region improvement.

Motion Artifact Suppression Method for the Clinical Application of Otoscopic Spectral-Domain Optical Coherence Tomography

A compact OCT device and a method for image reconstruction are described. The proposed algorithm contains a novel procedure for motion artifact suppression based on a correction of the phase of the original interferometric signal due to the mutual correlation of adjacent A-scans. This procedure corrects distortions caused by unwanted displacements of the probe relative to the object in real time at a speed of up to 3 mm/s and an image acquisition rate of 20 B-scans per second. All processing is performed in real time using only the CPU, allowing the device to be controlled from a consumer-grade laptop or compact PC without the need for a discrete GPU. Due to its compact size, the device can be used in the conditions of an ENT examination room or operating room and can be freely moved to another room without the help of additional personnel, if necessary.

Data-efficient Bayesian learning for radial dynamic MR reconstruction.

Cardiac MRI has become the gold-standard imaging technique for assessing cardiovascular morphology and function. In spite of this, its slow data acquisition process presents imaging challenges due to the motion from heartbeats, respiration, and blood flow. In recent studies, deep learning (DL) algorithms have shown promising results for the task of image reconstruction. However, there have been instances where they have introduced artifacts that may be misinterpreted as pathologies or may obscure the detection of pathologies. Therefore, it is important to obtain a metric, such as the uncertainty of the network output, that identifies such artifacts. However, this can be quite challenging for large-scale image reconstruction problems such as dynamic multi-coil non-CartesianMRI. To efficiently quantify uncertainties of a physics-informed DL-based image reconstruction method for a large-scale accelerated 2D multi-coil dynamic radial MRI reconstruction problem, and demonstrate the benefits of physics-informed DL over model-agnostic DL in reducing uncertainties while at the same time improving imagequality. We extended a recently proposed physics-informed 2D U-Net that learns spatio-temporal slices (named XT-YT U-Net), and employed it for the task of uncertainty quantification (UQ) by using Monte Carlo dropout and a Gaussian negative log-likelihood loss function. Our data comprised 2D dynamic MR images acquired with a radial balanced steady-state free precession sequence. The XT-YT U-Net, which allows for training with a limited amount of data, was trained and validated on a dataset of 15 healthy volunteers, and further tested on data from four patients. An extensive comparison between physics-informed and model-agnostic neural networks (NNs) concerning the obtained image quality and uncertainty estimates was performed. Further, we employed calibration plots to assess the quality of theUQ. The inclusion of the MR-physics model of data acquisition as a building block in the NN architecture led to higher image quality (NRMSE: , PSNR: , and SSIM: ), lower uncertainties ( ), and, based on the calibration plots, an improved UQ compared to its model-agnostic counterpart. Furthermore, the UQ information can be used to differentiate between anatomical structures (e.g., coronary arteries, ventricle boundaries) andartifacts. Using an XT-YT U-Net, we were able to quantify uncertainties of a physics-informed NN for a high-dimensional and computationally demanding 2D multi-coil dynamic MR imaging problem. In addition to improving the image quality, embedding the acquisition model in the network architecture decreased the reconstruction uncertainties as well as quantitatively improved the UQ. The UQ provides additional information to assess the performance of different networkapproaches.

Investigating the effect of data acquisition geometry and image reconstruction method on neutron computed tomography images at Tehran Research Reactor

تصویربرداری مقطع‌نگاری نوترونی یکی از کاربردهای مدرن رآکتورهای تحقیقاتی در سرتاسر جهان به‌شمار می‌رود. تلفیق قابلیت نمایش سه‌بعدی تصویربرداری مقطع‌نگاری با ویژگی‌های منحصر به فرد برهم‌کنش نوترون با مواد، می‌تواند اطلاعات بسیار ارزشمندی از ساختار داخلی مواد و تجهیزات را در اختیار محققین قرار ‌دهد. در این تحقیق، تأثیر هندسه داده‌برداری تجربی و روش بازسازی تصویر بر کیفیت تصاویر مقطع‌نگاری نوترونی سامانه تصویربرداری رآکتور تحقیقاتی تهران براساس شاخص وضوح و میزان تولید نویز مورد مطالعه قرار گرفته است. در مرحله داده‌برداری تجربی، دو نمونه مورد آزمون در دو هندسه با چرخش نیم‌صفحه‌ای با نموِ زاویه‌ای ‌5/0 و 1 درجه توسط باریکه نوترون پرتودهی شده‌اند. تصاویر نمونه با استفاده از الگوریتم‎های بازسازی تصاویر مقطع‌نگاری شامل روش‌های تحلیلی FBP و روش‌های تکرارشونده ART، SARTو SIRT به دست آمده‌اند. تصویر حجمی نمونه با روی هم قرار دادن پشته‌ای از مجموعه 1670 تایی از تصاویر مقطعی حاصل شده است. از زبان برنامه‌نویسی پایتون برای پردازش و بازسازی تصویر از افکنش استفاده شده است. برای حصول افکنش‌های با کیفیت مناسب برای بازسازی تصویر، ابتدا مشخصات سیستم داده‌برداری تجربی بهینه‌سازی شده‌اند. سپس، با استفاده از پیش‌پردازش، کیفیت تصاویر افکنش‎ها افزایش یافته است. براساس نتایج، اجزای نمونه مورد آزمون و عیوب موجود در آن در تمام تصاویر، از یک‌دیگر قابل تمایز می‌باشند. مقدار وضوح و آهنگ وضوح به نویز در هندسه با نمو زاویه‌ای کم‌تر و با استفاده از روش SIRT بهبود یافته است.

Advanced Image Post-Processing Methods for Photoacoustic Tomography: A Review

Photoacoustic tomography (PAT) is a promising imaging technique that utilizes the detection of light-induced acoustic waves for both morphological and functional biomedical imaging. However, producing high-quality images using PAT is still challenging and requires further research. Besides improving image reconstruction, which turns the raw photoacoustic signal into a PAT image, an alternative way to address this issue is through image post-processing, which can enhance and optimize the reconstructed PAT image. Image post-processing methods have rapidly emerged in PAT and are proven to be essential in improving image quality in recent research. In this review, we investigate the need for image post-processing in PAT imaging. We conduct a thorough literature review on the latest PAT image post-processing articles, including both general and PAT-specific post-processing techniques. In contrast to previous reviews, our analysis focuses specifically on advanced image post-processing rather than image reconstruction methods. By highlighting their potential applications, we hope to encourage further research and development in PAT image post-processing technology.