Optimal Reconstruction Algorithm Research Articles

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

AbstractBreast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes.

3D-OSEM versus FORE + OSEM: Optimal Reconstruction Algorithm for FDG PET with a Short Acquisition Time.

Objective In this study, we investigated the optimal reconstruction algorithm in fluorodeoxyglucose (FDG) positron emission tomography (PET) with a short acquisition time. Materials and Methods In the phantom study, six spheres filled with FDG solution (sphere size: 6.23-37 mm; radioactivity ratio of spheres to background = 8:1) and placed in a National Electrical Manufacturers Association phantom were evaluated. Image acquisition time was 15 to 180 seconds, and the obtained image data were reconstructed using each of the Fourier rebinning (FORE) + ordered subsets expectation-maximization (OSEM) and 3D-OSEM algorithms. In the clinical study, mid-abdominal images of 19 patients were evaluated using regions of interest placed on areas of low, intermediate, and high radioactivity. All obtained images were investigated visually, and quantitatively using maximum standardized uptake value (SUV) and coefficient of variation (CV). Results In the phantom study, FORE + OSEM images with a short acquisition time had large CVs (poor image quality) but comparatively constant maximum SUVs. 3D-OSEM images showed comparatively constant CVs (good image quality) but significantly low maximum SUVs. The results of visual evaluation were well correlated with those of quantitative evaluation. Small spheres were obscured on 3D-OSEM images with short acquisition time, but image quality was not greatly deteriorated. The clinical and phantom studies yielded similar results. Conclusion FDG PET images with a short acquisition time reconstructed by FORE + OSEM showed poorer image quality than by 3D-OSEM. However, images obtained with a short acquisition time and reconstructed with FORE + OSEM showed clearer FDG uptake and more useful than 3D-OSEM in the light of the detection of lesions.

Joint Sampling and Reconstruction of Time-Varying Signals Over Directed Graphs

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal properties that can be exploited for effective graph signal reconstruction from limited samples. However, existing approaches are not directly applicable when the signal's frequency occupancy changes with time. Moreover, while e.g., sensor network applications can benefit from directed graph models, the non-orthogonality of the graph eigenvectors can challenge spectral-based signal reconstruction algorithms. In this context, here we consider <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -sparse time-varying signals with unknown frequency supports. By exploiting the smoothness of the varying graph frequency supports and employing shift operations over directed graphs, we study joint sampling of multiple varying signals based on Schur decomposition to reconstruct each signal by orthogonal frequency components. Firstly, joint frequency support of the multiple signals is identified by proposing a two-stage Individual-Joint sampling scheme. Based on the estimated frequency support, the GFT coefficients of each signal can be recovered using data collected in individual sampling stage. Greedy algorithms are proposed for vertex set selection and graph shift order selection, which enable a robust signal reconstruction against additive noise. Considering the signals in applications may be approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -sparse, we further exploit the samples in both individual and joint sampling stages and investigate the optimal signal reconstruction as a convex optimization problem with adaptive frequency support selection. The proposed optimal sampling and reconstruction algorithms outperform several existing schemes in random network and sensor network data gathering.

Improving 90Y PET Scan Image Quality Through Optimized Reconstruction Algorithms.

This study aimed to improve the quality of 90Y PET imaging by optimizing the reconstruction algorithm. Methods: We recruited 10 patients with neuroendocrine tumor metastatic to the liver or primary hepatocellular carcinoma who were qualified for 90Y-labeled selective internal radiation therapy or peptide receptor radionuclide therapy. They underwent posttherapeutic PET/CT imaging using 3 different reconstruction parameters: VUE Point HD with a 6.4-mm filter cutoff, 24 subsets, and 2 iterations (algorithm A); VUE Point FX with a 6.0-mm filter cutoff, 18 subsets, and 3 iterations using time of flight (algorithm B); and VUE Point HD (LKYG) with a 5-mm filter cutoff, 32 subsets, and 1 iteration (algorithm C). The reconstructed PET/CT images were assessed by 10 nuclear medicine physicians using 4-point semiqualitative scoring criteria. A P value of less than 0.05 was considered significant. Results: The median quality assessment scores for algorithm C were consistently scored the highest, with algorithms A, B, and C, scoring 3, 2, and 4, respectively. The 90Y PET scans using algorithm C were deemed diagnostic 91% of the time. There was a statistically significant difference in quality assessment scores among the algorithms by the Kruskal-Wallis rank sum test ([Formula: see text] = 86.5, P < 0.001), with a mean rank quality score of 130.03 for algorithm A, 109.76 for algorithm B, and 211.71 for algorithm C. Subgroup analysis for quality assessment scoring of post-peptide receptor radionuclide therapy imaging alone showed a statistically significant difference between different scanning algorithms ([Formula: see text] = 35.35, P < 0.001), with mean rank quality scores of 45.85 for algorithm A, 50.05 for algorithm B, and 85.6 for algorithm C. Similar results were observed for quality assessment scoring of imaging after selective internal radiation therapy ([Formula: see text] = 79.90, P < 0.001), with mean ranks of 82.33 for algorithm A, 55.79 for algorithm B, and 133.38 for algorithm C. Conclusion: The new LKYG algorithm that was featured by decreasing the number of iterations, decreasing the cutoff of the filter thickness, and increasing the number of subsets successfully improved image quality.

Strip detector array (SDA) for beam monitoring in radiotherapy: reconstruction of MLC parameters from multiple projections of flux

Objective. In this paper we propose and investigate a new detector with multiple strip detector arrays (SDA) for monitoring MLC shaped x-ray beams for radiotherapy treatment. Approach. Each SDA measures 1D dose profiles equivalent to dose projections. The goal of such a detector is to determine individual MLC leaf positions as well as the Monitor Units (MU) per MLC segment during radiotherapy. In the present work we investigate an optimal SDA detector configuration and reconstruction algorithm. We determine the accuracy of SDA for different treatment sites (spine, pelvis, retroperitoneum, prostate, brain SRT, SRS, lung and head and neck). We perform a simulation study accounting for different type of MLC leaf positional errors: random MLC leaf, systematic for the whole leaf bank and systematic for an individual leaf. In a similar fashion, we also account for errors in Monitor Units per segment. Main results. We demonstrate that for a broad range of IMRT treatment plans a robust reconstruction of errors is achievable with only 3 projections (3 sets of SDA oriented at at 0°, 45° and 135°). The SDA is capable of capturing both systematic errors in leaf banks and individual leaves as well as random errors sufficient for practical clinical purposes. Significance. These features of the SDA detector makes it suitable for real-time Quality Control of MLC collimated linac output.

Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

This study was to analyze the application value of a reconstruction algorithm in CT images of patients with coronary heart disease and analyze the correlation between epicardial fat volume and coronary heart disease. An optimized reconstruction algorithm was constructed based on compressed sensing theory in this study. Then, the optimized algorithm was applied to the image reconstruction of multislice spiral CT image data after testing its sensitivity, accuracy, and specificity. 60 patients with suspected angina pectoris were divided into lesion group (40 cases) and normal group (20 cases) according to whether there were coronary atherosclerotic plaques in cardiac vessels. The results showed that the sensitivity, specificity, and accuracy of the optimized reconstruction algorithm were 91.78%, 84.27%, and 95.32%, and the running time was (12.18 ± 2.49) s. The CT value of the liver and the CT ratio of the liver and spleen in the lesion group were (53.81 ± 5.91) and (3.88 ± 0.67), respectively. There was no significant difference between the two groups ( P &gt; 0.05 ). The body mass index and epicardial fat volume in the lesion group were (31.93 ± 4.54) kg/m2 and (120.09 ± 22.01) cm3, respectively. The body mass index and fat volume in the lesion group were significantly higher than those in the normal group ( P &lt; 0.05 ). The epicardial fat constitution increased with the increase of the number of coronary arteries involved, and there was a positive correlation between them. Among patients with different coronary atherosclerotic plaques, the epicardial fat volume in patients with mixed plaques was the largest ( P &lt; 0.05 ). In summary, optimizing CT images under compressed a sensing reconstruction algorithm could effectively improve the diagnostic accuracy of doctors. Epicardial fat volume was positively correlated with coronary heart disease. Epicardial fat volume could be used as one of the important indexes to predict coronary heart disease.

Use of Deep Learning to Improve the Computational Complexity of Reconstruction Algorithms in High Energy Physics

The optimization of reconstruction algorithms has become a key aspect in the field of experimental particle physics. Since technology has allowed gradually increasing the complexity of the measurements, the amount of data taken that needs to be interpreted has grown as well. This is the case with the LHCb experiment at CERN, where a major upgrade currently undergoing will considerably increase the data processing rate. This has presented the need to search for specific reconstruction techniques that aim to accelerate one of the most time consuming reconstruction algorithms in LHCb, the electromagnetic calorimeter clustering. Together with the use of deep learning techniques and the understanding of the current reconstruction algorithm, we propose a method that decomposes the reconstruction process into small parts that can be formulated as a cellular automaton. This approach is shown to benefit the generalized learning of small convolutional neural network architectures and also simplify the training dataset. Final results applied to a complete LHCb simulation reconstruction are compatible in terms of efficiency, and execute in nearly constant time with independence on the complexity of the data.

Performance Prediction, Sensitivity Analysis and Parametric Optimization of Electrical Impedance Tomography Using A Bioelectrical Tissue Simulation Platform.

There is an urgent need to bring forth portable, low-cost, point-of-care diagnostic instruments to monitor patient health and wellbeing. This is elevated by the COVID-19 global pandemic in which the availability of proper lung imaging equipment has proven to be pivotal in the timely treatment of patients. Electrical impedance tomography (EIT) has long been studied and utilized as such a critical imaging device in hospitals especially for lung ventilation. Despite decades of research and development, many challenges remain with EIT in terms of 1) optimal image reconstruction algorithms, 2) simulation and measurement protocols, 3) hardware imperfections, and 4) uncompensated tissue bioelectrical physiology. Due to the inter-connectivity of these challenges, singular solutions to improve EIT performance continue to fall short of the desired sensitivity and accuracy. Motivated to gain a better understanding and optimization of the EIT system, we report the development of a bioelectric facsimile simulator demonstrating the dynamic operations, sensitivity analysis, and reconstruction outcome prediction of the EIT sensor with stepwise visualization. By building a sandbox platform to incorporate full anatomical and bioelectrical properties of the tissue under study into the simulation, we created a tissue-mimicking phantom with adjustable EIT parameters to interpret bioelectrical interactions and to optimize image reconstruction accuracy through improved hardware setup and sensing protocol selections.

Rehabilitation Effect of Exercise Therapy on Knee Osteoarthritis Evaluated by Computed Tomography Image under Optimized Reconstruction Algorithm

The aim of this study was to explore the treatment of knee osteoarthritis (KOA) with combined exercise therapy and to evaluate the curative effect through CT imaging technology based on optimized reconstruction algorithms. A total of 60 patients with osteoarthritis admitted to our hospital from September 2020 to March 2021 were randomly recruited as the research objects. Patients were rolled into the routine treatment group and exercise group (routine treatment + targeted exercise therapy) according to different treatments they received, with 30 cases in each group. Then, the CT imaging results of the two groups were compared after treatment. The positive rates of peripheral osteogenesis, joint space narrowing, cartilage lesions, and synovial lesions in the exercise group were inferior to those in the routine group, and the difference was statistically significant P &lt; 0.05 . However, the change rate of positive indicators before and after treatment was not statistically significant P &gt; 0.05 . The change range of digital pain score and Lysholm knee function score in the exercise group was significantly superior to that in the routine group, and the difference was statistically significant P &lt; 0.05 . CT images with optimized reconstruction algorithms still had certain limiting factors in the evaluation of the curative effect of KOA and still needed to be improved. It was also proved that exercise therapy had good application value in the treatment of KOA through other methods.

Optimized Reconstruction Algorithm Based Analysis of Effects of Exercise Therapy Combined with Celecoxib Capsules on the Rehabilitation of Knee Osteoarthritis Using Computed Tomography Images

The aim was to explore the application value of computed tomography (CT) images based on optimized reconstruction algorithm (ORA) in knee osteoarthritis (KOA) and to analyze the rehabilitation effect of exercise therapy combined with celecoxib capsules on KOA. On the basis of iterative reconstruction algorithm, sparse data and compressed sensing (CS) algorithm were introduced in this study for the image reconstruction and were compared with the reconstruction effects of other algorithms. 105 patients with KOA were selected as the subjects investigated, who were treated in the hospital from December 2018 to January 2020. They were divided randomly into group A (exercise therapy), group B (celecoxib capsule therapy), and group C (exercise therapy combined with celecoxib capsules), with 35 patients in each group. All the patients were scanned by CT. Visual analogue score (VAS) method was adopted to compare the pain symptoms of patients in the three groups before and after treatment. The Lysholm knee function score was employed to evaluate the knee function of the patient before and after treatment, and finally the difference in the total effective rate of treatment was compared. The results showed that the reconstruction algorithm in this study had a higher degree of coincidence with the original image, and its root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were greatly higher than those of filtered back-projection (FBP) algorithm. There was no huge difference between Lysholm score and VAS in patients of the three groups before treatment ( P &gt; 0.05 ). Besides, Lysholm score in all dimensions and its total score after treatment were higher than those of patients in the three groups before treatment ( P &lt; 0.05 ), and those of patients in group C increased extremely in contrast to group A ( P &lt; 0.05 ). VAS after treatment were all lower in patients of the three groups than those before treatment ( P &lt; 0.05 ), and VAS of patients in group C were sharply lower than those of group A ( P &lt; 0.05 ), but the total effective rate of patients in group C was dramatically higher than the rate of group A ( P &lt; 0.05 ). It indicated that an algorithm with short scanning time and good image quality was established based on iterative reconstruction algorithm. Exercise therapy combined with celecoxib capsules could alleviate the pain symptoms and improve the function of knee joint, which was more advantageous than the simple rehabilitation comprehensive therapy. Therefore, it had higher clinical value for the patients with KOA.

Reconstruction of arctic SST data and generation of multi-source satellite fusion products with high temporal and spatial resolutions

ABSTRACT The use of remote sensing data, whether for change detection, spatial and temporal analysis, or numerical simulation of water bodies, requires a complete data set. Here, high temporal and spatial resolution sea surface temperature (SST) data for the Arctic are produced based on optimal interpolation and data reconstruction algorithms with a temporal resolution of 12 h and a spatial resolution of 9 km. The accuracy of the reconstructed data sets is verified using Argo measurements to demonstrate the validity and reliability of the data interpolation empirical orthogonal functions method. The root mean square error of the fusion data (a temporal resolution of 12 h) is relatively stable, ranging from 0.49°C to 0.62°C. In comparison with remote sensing systems (RSS) MW-IR SST and Advanced Very High Resolution Radiometer (AVHRR) OISST, the accuracy of the fusion data (a temporal resolution of 24 h) is in the range of 0.48–0.65°C, while that of the MW-IR SST is in the range of 0.48–0.60°C and AVHRR OISST is in the range of 0.66–0.79°C. The fusion data is more accurate than those of AVHRR OISST, and there is no obvious difference with RSS MW-IR SST.

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is the conventional method for estimating smooth T2 distributions. Despite the improved estimation provided by this method compared to non-regularized NNLS, the solution is still sensitive to the underlying noise and the regularization weight. This is especially relevant for clinically achievable signal-to-noise ratios. In the literature of inverse problems, various well-established approaches to promote smooth solutions, including first-order and second-order Tikhonov regularization, and different criteria for estimating the regularization weight have been proposed, such as L-curve, Generalized Cross-Validation, and Chi-square residual fitting. However, quantitative comparisons between the available reconstruction methods for computing the T2 distribution, and between different approaches for selecting the optimal regularization weight, are lacking. In this study, we implemented and evaluated ten reconstruction algorithms, resulting from the individual combinations of three penalty terms with three criteria to estimate the regularization weight, plus non-regularized NNLS. Their performance was evaluated both in simulated data and real brain MRI data acquired from healthy volunteers through a scan-rescan repeatability analysis. Our findings demonstrate the need for regularization. As a result of this work, we provide a list of recommendations for selecting the optimal reconstruction algorithms based on the acquired data. Moreover, the implemented methods were packaged in a freely distributed toolbox to promote reproducible research, and to facilitate further research and the use of this promising quantitative technique in clinical practice.

Correlation analysis of epicardial adipose tissue volume quantified by computed tomography images and coronary heart disease under optimized reconstruction algorithm.

Objectives:This paper was aimed to explore the adoption value of low-dose computed tomography (CT) imaging based on optimized ordered subset expectation maximization (OSEM) reconstruction algorithm in the correlation analysis between epicardial adipose tissue (EAT) volume and coronary heart disease (CHD).Methods:A total of 110 patients with CHD were selected for CT angiography (CTA) and coronary arteriography (CAG) examinations from October 2017 to October 2019. The predictive value of EAT for CHD was analyzed via receiver operating characteristic (ROC) curve.Results:The results showed that the iteration time and error of the improved OSEM reconstruction algorithm were better than that of MLEM algorithm under the same number of iterations. Age, smoking, hypertension, diabetes, and EAT in control group were obviously lower in contrast to those in CHD group (P<0.05). EAT in control group was (124.50±26.72) mL, and EAT in the CHD group was (159.41±38.51) mL. EAT (B=0.023, P=0.003) was an independent risk factor for CHD, which was suggested by Multiple linear regression analysis. Moreover, EAT was a risk factor for CHD, and was positively correlated with the degree and NSCV.Conclusion:The optimized OSEM algorithm was used to improve the reconstruction quality of low-dose CT images and used in quantitative measurement of epicardial fat volume. Results showed EAT was an independent risk factor for CHD, and was positively correlated with the number of coronary lesions and Gensini score. It was of great value for the prediction of CHD.

Effect of Automatic Extraction Accuracy by Different Image Reconstruction Methods Using a Three-dimensional Image Analysis System for Pulmonary Segmentectomy Preoperative CT Angiography

This study aimed to determine the optimal image reconstruction method for preoperative computed tomography (CT) angiography for pulmonary segmentectomy. This study enrolled 20 patients who underwent contrast-enhanced CT examination for pulmonary segmentectomy. The optimal image reconstruction algorithm among four different reconstruction algorithms (filtered back projection, hybrid iterative reconstruction, model- based iterative reconstruction, and deep learning reconstruction [DLR]) was investigated by assessing the CT numbers, vessel extraction ratios, and misclassification ratios. The vessel extraction ratios for main and subsegment branches reconstructed using DLR were significantly higher than those using other reconstruction algorithms (96.7% and 90.8% for pulmonary artery and vein, respectively). The misclassification ratios at the right upper lobe pulmonary vessels (V1 and V2) were especially high because they were close to the superior vena cava, and their CT numbers were similar in all four reconstructions. In conclusion, the DLR allows a high extraction rate of pulmonary blood vessels and a low misclassification rate of automatic extraction.

Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton

The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of deep learning techniques and the understanding of the current algorithm, our proposal decomposes the reconstruction process into small parts that benefit the generalized learning of small neural network architectures and simplifies the training dataset. This approach takes as input the full simulation data of the calorimeter and outputs a list of reconstructed clusters in a nearly constant time without any dependency in the event complexity.

Research algorithm restoring signals in the basis of exponential functions

The paper proposes a method for constructing digital filters for solving inverse problems of recovering signals, time series, and images using an approximation approach. The considered inverse problems belong to the class of incorrectly posed ones and require the use of certain regularizing procedures to synthesize optimal reconstruction algorithms and solve computational problems associated with this.&#x0D; In this regard, a method is proposed for constructing an approximation model of the weight function of the inverse (reconstruction) filter, based on the criterion of the minimum quadratic error of the mismatch of the distorted signal model obtained after the direct (distorting) filter processes the reconstructed (unknown) signal and the existing distorted signal. The weight function of the direct filter is assumed to be known.&#x0D; The statement of the problem of reconstructing signals, time series, and images in the case of a one-dimensional point scattering function is formulated. An algorithm is presented that allows one to reduce the amount of computation when finding the values ​​of the weight function of the inverse filter.&#x0D; The algorithms were tested on model examples when processing real images obtained by remote sensing of the Earth, as well as on specially formed contrast images. To quantify the quality of reconstruction, a relative mean-square measure of the difference between the reference and reconstructed signals (images) was used. The results of testing show that using this approach allows to reduce the error of recovery, which gives an advantage in solving problems of approximation and data recovery.

Dose assessment and reconstruction algorithm optimization in simultaneous breast and lung CT imaging.

Cancer is the second leading cause of death in the world, and therefore, there is an undeniable need to ensure early screening and detection systems worldwide. The aim of this project was to study the feasibility of a Cone Beam Computed Tomography (CBCT) scanner for simultaneous breast and lung lesion imaging. Additionally, the development of reconstruction algorithms and the study of their impact to the image quality was considered.Monte Carlo (MC) simulations were performed using the PENELOPE code system. A geometry model of a CBCT scanner was implemented for energies of 30 keV and 80 keV for hypothetical scanning protocols. Microcalcifications were inserted into the breast and lung of the computational phantom (ICRP Adult Female Reference), used in the simulations for dose assessment and projection acquisition. Dosimetric and imaging performances were evaluated through Computed Tomography Dose Index and Catphan phantoms, respectively. Reconstructed images were analyzed in terms of Contrast-to-Noise Ratio (CNR) and dose calculations were performed for two protocols, using a normalization factor of 2 mGy in the breast and another with 5 mGy in the lungs.MC geometry model and reconstruction algorithm were validated by means of on-field measurements and data acquisition in a clinical center. Results indicate that for both protocols, the absorbed dose in both organs is the same, allowing the optimization of protocol strategies regarding radiosensitive organs. The best implementation of the reconstruction algorithm was achieved with 80 keV, using linear interpolation and hanning filter. More specifically for a spherical lung lesion (r = 7 mm) a 30% CNR gain was found when the number of projections varied from 12 to 36 (corresponding to a dose increase of a factor of 3).This study suggests the applicability of a CBCT modulated beam scanner for imaging simultaneously breast and lung lesions while ensuring dose reduction without compromising image quality as a possibility.

Interferometric measurement of freeform surfaces using irregular subaperture stitching

Irregular subaperture stitching interferometry (ISSI) is proposed for non-null optical freeform surface testing. Compared with circular and annular subapertures, irregular subapertures have a better performance in matching the locally resolvable interferograms due to the rotational asymmetry of freeform surfaces. A modified multi-aperture simultaneous reverse optimization reconstruction algorithm is employed for the subaperture stitching, allowing not only non-complementary subapertures but also no subaperture overlaps. Numerical simulations with error analyses display the accuracy of our method. Experiments showing the effectiveness of the ISSI are presented by testing a Φ20 mm biconic surface with cross-validation.

HDR Imaging With Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction

High dynamic range (HDR) imaging is one of the biggest achievements in modern photography. Traditional solutions to HDR imaging are designed for and applied to CMOS image sensors (CIS). However, the mainstream one-micron CIS cameras today generally have a high read noise and low frame-rate. These, in turn, limit the acquisition speed and quality, making the cameras slow in the HDR mode. In this paper, we propose a new computational photography technique for HDR imaging. Recognizing the limitations of CIS, we use the Quanta Image Sensor (QIS) to trade the spatial-temporal resolution with bit-depth. QIS is a single-photon image sensor that has comparable pixel pitch to CIS but substantially lower dark current and read noise. We provide a complete theoretical characterization of the sensor in the context of HDR imaging, by proving the fundamental limits in the dynamic range that QIS can offer and the trade-offs with noise and speed. In addition, we derive an optimal reconstruction algorithm for single-bit and multi-bit QIS. Our algorithm is theoretically optimal for \emph{all} linear reconstruction schemes based on exposure bracketing. Experimental results confirm the validity of the theory and algorithm, based on synthetic and real QIS data.

Optimization of 89Zr PET Imaging for Improved Multisite Quantification and Lesion Detection Using an Anthropomorphic Phantom.

Our objective was to harmonize multicenter 89Zr PET imaging for oncology trials and to evaluate lesion detection. Methods: Seven PET scanners were evaluated using a custom chest oncology phantom with 9 spheric lesions 7-20 mm in diameter. A 4:1 signal-to-background ratio simulated a patient dose of 92.5 MBq. Various image reconstructions were evaluated. Images were assessed for lesion detection, and recovery coefficients and background signal variance were measured. Results: Two scanners failed to provide acceptable images and data. Optimal reconstruction algorithms enabling adequate lesion detection and reliable quantification across the other 5 scanners were determined without compromising the data quality. On average, 95% of the 10-mm lesions were detected, and the 7-mm lesion was visualized by only 1 scanner. Background variance was 8.6%-16%. Conclusion: We established multicenter harmonization procedures for 89Zr PET imaging in oncology, optimizing small-lesion (≥10 mm) detectability and accurate quantification.