Iterative Reconstruction Algorithm Research Articles

Impact of ultra-high-resolution on cardiac CT assessment of coronary artery stenoses, plaque burden/composition, and pericoronary adipose tissue attenuation

Abstract Background Conventional coronary computed tomography angiography (CCTA) overestimates stenoses within stents and calcified arteries due to "blooming" artifacts. Ultra-high-resolution coronary CT (UHR-CT) may overcome this limitation because of higher spatial resolution. Purpose To investigate differences in coronary arterial lumen volume, stenosis severity, and plaque composition, between CCTA and UHRCT in patients with stents and/or high coronary calcium scores. Methods As part of a prospective pilot study, 14 patients referred for invasive coronary angiography for evaluation of obstructive CAD underwent UHRCT (Canon Precision, 160 x 0.25mm slices) prior to catheterization. Images of identical cardiac phases were reconstructed in two badges for comparison: 1) Ultra-high resolution using a 1,024 x 1,024 matrix, 0.25 mm, and 2) conventional resolution with a 524 x 524 matrix, 0.5 mm. All images were reconstructed using iterative reconstruction algorithm (AIDR 3D) and a sharp kernel (FC05). For the left main and three coronary arteries, an automated contour analysis using a dedicated software (QAngioCT RE 3.2, Medis, Netherlands) was performed to compare conventional CCTA vs. UHR-CT resolution images across common vascular and plaque metrics, including lumen and vessel volume, plaque burden/volume, plaque composition based upon predefined fixed intensity cut-off values of CT attenuation, minimal and maximal plaque thickness, pericoronary adipose tissue attenuation (PCAT), minimal lumen area and diameter, and diameter stenosis. All lumen and vessel contours were generated by the software, without manual editing. The segment model proposed by the SCCT was used as reference and the lesions classified as greater than 25% stenosis by visual assessment were recorded. The quality of the images was subjectively assessed using a 5-point Likert scale. Vessel segments with scores 1 (severe artifacts) and 2 (pronounced artifacts) were not included in the analysis. Results The mean patient age was 68.5 years (±6.8), 2 were female (14.3%), 6 patients had stents (11 stented segments) and the mean Agatston score was 947.6 (±449) among the 8 non-stented patients. A total of 56 vessels, 133 segments, and 57 lesions were analyzed. Eight segments (6%) were excluded from the analysis due to poor quality. Results on a per segment and per lesion basis were summarized in the table. The mean PCAT value was significantly lower for the UHRCT images compared to NR reconstruction in both segment (p<0.001) and lesion based (p=0.002) analyses. Plaque differentiation varied by UHRCT resulted in a lower fibrous plaque volumes but higher necrotic (low attenuation) plaque volumes. Conclusions Ultra-high-resolution CT image analysis results in significantly different coronary artery plaque volume and PCAT assessment vs. conventional resolution CCTA. These findings have implications for evaluating high-risk plaque features and perivascular inflammation in patients.Table 1

Large vessel vasculitis evaluation by CTA: impact of deep-learning reconstruction and “dark blood” technique

ObjectivesTo assess the performance of the “dark blood” (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients.Materials and methodsFifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single center. Arterial and delayed-phase images of the aorta were reconstructed using the hybrid iterative reconstruction (HIR) and DLR algorithms. HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a “contrast-enhancement-boost” technique. Quantitative parameters of aortic wall image quality were evaluated.ResultsCompared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNRouter (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise (p > 0.99) and increased SNR (p < 0.001), CNRouter (p = 0.006), and CNRinner (p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets.ConclusionDB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences.Critical relevance statementDeep-learning-based “dark blood” images improve vessel wall image wall quality and boundary visualization.Key PointsDark blood CTA improves image quality and provides better aortic wall visualization.Deep-learning CTA presented higher quality and subjective scores compared to HIR.Combination of dark blood and deep-learning reconstruction obtained the best overall performance.Graphical

Comparison of different iterative reconstruction algorithms with contrast-enhancement boost technique on the image quality of CT pulmonary angiography for obese patients

ObjectiveTo evaluate the effect of the contrast-enhancement-boost (CE-boost) postprocessing technique on improving the image quality of obese patients in computed tomography pulmonary angiography (CTPA) compared to hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR) algorithms.MethodsThis prospective study was conducted on 100 patients who underwent CTPA for suspected pulmonary embolism. Non-obese patients with a body mass index (BMI) under 25 were designated as group 1, while obese patients (group 2) had a BMI exceeding 25. The CE-boost images were generated by subtracting non-contrast HIR images from contrast-enhanced HIR images to improve the visibility of pulmonary arteries further. The CT value, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantitatively assessed. Two chest radiologists independently reviewed the CT images (5, best; 1, worst) across three subjective characteristics including diagnostic confidence, subjective image noise, and vascular contrast. The Friedman test and Dunn-Bonferroni correction were used for statistical analysis.ResultsThe CE-boost had significantly higher CT values than HIR and MBIR in both groups (all p < 0.001). The MBIR yielded the lowest image noise compared with HIR and CE-boost (all p < 0.001). The SNR and CNR of main pulmonary artery (MPA) were significantly higher in CE-boost than in MBIR (all p < 0.05), with HIR showing the lowest values (all p < 0.001). Group 2 MBIR received significantly better subjective image noise scores, while the diagnostic confidence and vascular contrast scored highest with the group 2 CE-boost (all p < 0.05).ConclusionCompared to the HIR algorithm, both the CE-boost technique and the MBIR algorithm can improve the image quality of CTPA in obese patients. CE-boost had the greatest potential in increasing the visualization of pulmonary artery and its branches.

A three-dimensional curve interface reconstruction algorithm for two-phase fluid flow

The curve interface reconstruction algorithm has received significant attention in the context of two-dimensional two-phase flow. However, it remains absent in the three-dimensional scenario. This paper proposes a novel three-dimensional curve interface reconstruction (CIR) algorithm to address this challenge within structured meshes for the first time. Specifically, a portion of the spherical surface is employed to reconstruct the three-dimensional curve interface segment, with the radius and center coordinates determined by curvature and mass conservation constraints, respectively. To enhance curvature accuracy, a sphere-based iterative reconstruction (SIR) algorithm is proposed to calculate the reconstructed distance function (RDF) for the three-dimensional curve interface. Various tests involving the interface reconstruction of spherical, ellipsoidal, and cubic objects demonstrate that the coupled SIR and CIR (SIR-CIR, simplified by SCIR) method achieves higher accuracy than many popular methods, particularly with coarse mesh resolutions. Additionally, the SCIR method offers the advantages of straightforward implementation and coding for interface reconstruction in two-phase flow research. This advantage results in reduced computational costs compared to the coupled volume-of-fluid and level set (VOSET) method, which also utilizes an iterative method to solve RDF.

Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness

IntroductionLow contrast resolution in abdominal computed tomography (CT) may be negatively affected by attempts to lower patient doses. Iterative reconstruction (IR) algorithms play a key role in mitigating this problem. The reconstructed slice thickness also influences image quality. The aim was to assess the interaction and influence of patient dose, slice thickness, and IR strength on image quality in abdominal CT. MethodWith a simultaneous acquisition, images at 42 and 98 mAs were obtained in 25 patients. Multiplanar images with slice thicknesses of 1, 2, and 3 mm and advanced modeled iterative reconstruction (ADMIRE) strengths of 3 (AD3) and 5 (AD5) were reconstructed. Four radiologists evaluated the images in a pairwise manner based on five image criteria. Ordinal logistic regression with mixed effects was used to evaluate the effect of tube load, ADMIRE strength, and slice thickness using the visual grading regression technique. ResultsFor all assessed image criteria, the regression analysis showed significantly (p < 0.001) higher image quality for AD5, but lower for tube load 42 mAs, and slice thicknesses of 1 mm and 2 mm, compared to the reference categories of AD3, 98 mAs, and 3 mm, respectively. AD5 at 2 mm was superior to AD3 at 3 mm for all image criteria studied. AD5 1 mm produced inferior image quality for liver parenchyma and overall image quality compared to AD3 3 mm. Interobserver agreement (ICC) ranged from 0.874 to 0.920. ConclusionADMIRE 5 at 2 mm slice thickness may allow for further dose reductions due to its superiority when compared to ADMIRE 3 at 3 mm slice thickness. Implications for practiceCombination of thinner slices and higher ADMIRE strength facilitates imaging at low dose.

거동 불편한 환자의 전신 CT 검사 시 DLIR 기법의 유용성 평가

When whole-body computed tomography (WBCT) is performed after a single injection of contrast media, the arm's position is altered during each examination to minimize beam hardening artifacts caused by the arms. In this study, the image quality of neck CT images using the deep learning iterative reconstruction (DLIR) algorithm for patients whose arms were elevated during WBCT scans was compared with images reconstructed by FBP and IR algorithms in patients who were examined with their arms lowered. The Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) of the reconstructed images were quantitatively compared, while the image quality and artifacts were qualitatively evaluated. As a result, TF-H was evaluated higher in both SNR and CNR in all organs compared to FBP. Compared to the ASIR-V at 40%, the CNR was high, and the SNR was similar. In the qualitative evaluation, it was confirmed that the quality of the image was higher when compared to the image examined with the arm lowered, surpassing that of the existing reconstruction method. Therefore, it is believed that enhancing the quality of the neck CT image through reconstruction using deep learning after the examination with the arm raised, without altering the posture, during the WBCT examination of patients with mobility impairments.

Comprehensive risk management and safety strategies in radiation use in medical imaging

Radiation use in medical imaging, while essential for accurate diagnosis and treatment planning, poses inherent risks that require comprehensive risk management and safety strategies. This review discusses the critical aspects of managing these risks in clinical settings, focusing on patient safety, staff protection, and environmental considerations. A major challenge in medical imaging is balancing the diagnostic benefits of radiation against potential harms, particularly in sensitive populations such as children and pregnant women. Comprehensive risk management involves implementing dose optimization techniques, such as using low-dose imaging protocols and advanced technologies like iterative reconstruction and artificial intelligence (AI) algorithms, which enhance image quality while minimizing exposure. Additionally, ensuring the safety of healthcare staff is paramount. This requires adherence to strict safety protocols, including the use of personal protective equipment (PPE), regular monitoring of radiation levels, and continuous education on safe radiation practices. Institutional policies must also address the proper maintenance and calibration of imaging equipment to prevent unnecessary exposure due to equipment malfunction or improper use. Environmental safety is another critical component, necessitating the safe disposal of radioactive materials and the adoption of energy-efficient technologies to reduce the environmental footprint of medical imaging. Regulatory compliance with international safety standards, such as those set by the International Commission on Radiological Protection (ICRP) and national bodies, is essential for ensuring consistent safety practices across healthcare institutions. Future directions in radiation safety include the integration of digital health technologies to monitor and manage radiation exposure more effectively, and ongoing research into novel imaging techniques that could further reduce radiation risks. Collaboration between healthcare providers, researchers, and policymakers is crucial to advancing safety standards and improving risk management strategies in medical imaging. This review highlights the importance of a comprehensive approach to managing radiation risks in medical imaging, emphasizing the need for continuous innovation, education, and policy support to protect patients, healthcare workers, and the environment.

Analysis of an iterative reconstruction method in comparison of the standard reconstruction method

ABSTRACT We present a detailed analysis of a new iterative density reconstruction algorithm. This algorithm uses a decreasing smoothing scale to better reconstruct the density field in Lagrangian space. We implement this algorithm to run on the quijote simulations, and extend it to (a) include a smoothing kernel that smoothly goes from anisotropic to isotropic, and (b) a variant that does not correct for redshift space distortions. We compare the performance of this algorithm with the standard reconstruction method. Our examinations of the methods include cross-correlation of the reconstructed density field with the linear density field, reconstructed two-point functions, and BAO parameter fitting. We also examine the impact of various parameters, such as smoothing scale, anisotropic smoothing, tracer type/bias, and the inclusion of second order perturbation theory. We find that the two reconstruction algorithms are comparable in most of the areas we examine. In particular, both algorithms give consistent fittings of BAO parameters. The fits are robust over a range of smoothing scales. We find the iterative algorithm is significantly better at removing redshift space distortions. The new algorithm will be a promising method to be employed in the ongoing and future large-scale structure surveys.

Radiation dose reduction and image quality in pediatric paranasal sinus CT: with automatic tube current modulation and iterative reconstruction technique.

Our objective is to evaluate radiation dose and image quality in pediatric paranasal sinus computed tomography (CT) with automatic tube current modulation (ATCM) and sinogram-affirmed iterative reconstruction algorithm (SAFIRE). CT scans from 80 patients were divided into two groups: Group A [80 kVp, pitch 1.5, 40 mAs, the filtered back projection (FBP) algorithm] and Group B (70 kVp, pitch 3, ATCM with reference at 40 mAs, SAFIRE strengths 1-5). We have evaluated image quality and radiation dose. Group B demonstrated significantly lower volume computed tomography dose index, dose-length product, and effective dose than Group A (0.13±0.03 vs. 1.57±0.01mGy, 2.27±0.82 vs. 19.88±2.01mGy·cm, and 0.0081±0.0017 vs. 0.079±0.013mSv, respectively; P<.001). Increasing SAFIRE strengths correlated with noise reduction and SNR enhancement. Group B's noise and SNRsoft at SAFIRE strength 5 were comparable with Group A. Images reconstructed with SAFIRE strength 5 in Group B exhibit comparable image quality with FBP in Group A.

Adaptive Sampling for Signals Associated with the Special Affine Fourier Transform

Adaptive sampling is inspired by the working principle of biological neurons, which converts the amplitude information of the received signal into a time sequence through a time encoding machine (TEM). The article mainly studies uniform sampling and adaptive sampling of non-bandlimited signals in two kinds of function spaces associated with the special affine Fourier transform (SAFT). Firstly, based on the stability characterization of the basis functions, we prove the existence of orthogonal projection operators in function spaces associated with the canonical convolution and the SAFT-convolution, respectively. Secondly, uniform sampling theorems are established for two kinds of signals living in the proposed function spaces. Finally, adaptive sampling schemes based on the crossing TEM and the integrate-and-fire TEM are studied. Moreover, the corresponding iterative reconstruction algorithms with exponential convergence are provided.

Reconstruction of bandlimited graph signals from random local sampling

Sampling and reconstruction on the spatially distributed networks is an innovative topic in graph signal processing. Recently, it has been shown that k-bandlimited graph signals can be reconstructed from a random collection of physically constrained sampled data. In this paper, we first study the random sampling scheme of k-bandlimited signals from a general local measurement, and then an iterative reconstruction algorithm based on frame theory is proposed with exponential convergence. It can yield a distributed implementation at a vertex level, which enables the devices that are limited by storage and computing power to recover signals more effectively. Numerical experiments on synthetic and real-world data are performed to validate the effectiveness of the proposed approach.

Noise characterization analysis of dynamic dual-energy CT and its advantage in suppressing statistical noise

Objective.Multi-energy CT conducted by photon-counting detector has a wide range of applications, especially in multiple contrast agents imaging. However, static multi-energy (SME) CT imaging suffers from higher statistical noise because of increased energy bins with static energy thresholds. Our team has proposed a dynamic dual-energy (DDE) CT detector model and the corresponding iterative reconstruction algorithm to solve this problem. However, rigorous and detailed analysis of the statistical noise characterization in this DDE CT was lacked.Approach.Starting from the properties of the Poisson random variable, this paper analyzes the noise characterization of the DDE CT and compares it with the SME CT. It is proved that the multi-energy CT projections and reconstruction images calculated from the proposed DDE CT algorithm have less statistical noise than that of the SME CT.Main results.Simulations and experiments verify that the expectations of the multi-energy CT projections calculated from DDE CT are the same as those of the SME projections. Still, the variance of the former is smaller. We further analyze the convergence of the iterative DDE CT algorithm through simulations and prove that the derived noise characterization can be realized under different CT imaging configurations.Significance.The low statistical noise characteristics demonstrate the value of DDE CT imaging technology.

Strategies for minimizing repeated exposures in diagnostic radiography

Advancements in diagnostic radiography have significantly improved medical imaging, yet the associated radiation exposure poses health risks, particularly with repeated examinations. Technological innovations, including the transition from analog to digital radiography, have led to substantial reductions in radiation doses while enhancing image quality. Digital systems, coupled with automatic exposure control (AEC), dynamically adjust radiation levels based on patient size and diagnostic requirements, minimizing unnecessary exposure. Iterative reconstruction algorithms in CT imaging further reduce radiation doses by efficiently processing images and reducing noise. Hardware improvements, such as high-efficiency detectors, capture high-quality images with lower radiation levels, contributing to dose reduction. Standardizing radiographic protocols across healthcare settings ensures consistent practices, reducing variability in radiation doses. Techniques such as beam collimation and appropriate shielding protect surrounding tissues and enhance image quality, while continuous education for technologists maintains proficiency in dose-reduction strategies. Patient education is crucial for minimizing repeated exposures. Informing patients about the risks of ionizing radiation and the importance of adhering to follow-up schedules can reduce unnecessary examinations. Electronic health records (EHRs) facilitate the tracking of cumulative radiation doses, enabling informed decision-making regarding additional imaging studies. Adherence to the ALARA principle (As low as reasonably achievable) in clinical practice ensures that radiation exposure is minimized while achieving diagnostic goals. Special considerations for vulnerable populations, such as children and pregnant women, further enhance safety. Implementing radiation safety measures and maintaining a commitment to ongoing professional development for healthcare providers fosters a culture of safety within radiology departments. By integrating these strategies, healthcare providers can balance the diagnostic benefits of radiography with the imperative to protect patients from unnecessary radiation exposure, ultimately enhancing patient safety and improving diagnostic outcomes. This comprehensive approach ensures that radiographic procedures remain both effective and safe, optimizing patient care in medical imaging.

Tomographic task solution using a dichotomous discretization scheme in polar coordinates and partial system matrices invariant to rotations

Objectives. The purpose of this work was to create an effective iterative algorithm for the tomographic reconstruction of objects with large volumes of initial data. Unlike the convolutional projection algorithm, widely used in commercial industrial and medical tomographic devices, algebraic iterative reconstruction methods use significant amounts of memory and typically involve long reconstruction times. At the same time, iterative methods enable a wider range of diagnostic tasks to be resolved where greater accuracy of reconstruction is required, as well as in cases where a limited amount of data is used for sparse-view angle shooting or shooting with a limited angular range.Methods. A feature of the algorithm thus created is the use of a polar coordinate system in which the projection system matrices are invariant with respect to the rotation of the object. This enables a signification reduction of the amount of memory required for system matrices storage and the use of graphics processors for reconstruction. Unlike the simple polar coordinate system used earlier, we used a coordinate system with a dichotomous division of the reconstruction field enabling us to ensure invariance to rotations and at the same time a fairly uniform distribution of spatial resolution over the reconstruction field.Results. A reconstruction algorithm was developed on the basis of the use of partial system matrices corresponding to the dichotomous division of the image field into partial annular reconstruction regions. A 2D and 3D digital phantom was used to show the features of the proposed reconstruction algorithm and its applicability to solving tomographic problems.Conclusions. The proposed algorithm allows algebraic image reconstruction to be implemented using standard libraries for working with sparse matrices based on desktop computers with graphics processors.

Artificial intelligence iterative reconstruction in abdominal CT of patients with irregular arm positioning: a case-by-case evaluation.

Streak artifacts induced by irregular arm positioning have been an issue in diagnosing the abdomen. To illustrate the risk of misdiagnosis in abdominal computed tomography (CT) of patients with irregular arm positioning through a case-by-case evaluation and to test if it can be solved by the artificial intelligence iterative reconstruction (AIIR) algorithm. By reviewing 5220 cases of chest and thoracoabdominal CT, 64 patients with irregular arm positioning were enrolled, whose image data were reconstructed using AIIR in addition to routine hybrid iterative reconstruction (HIR). Lesion detection for livers, spleens, kidneys, gallbladders, and pancreas on AIIR images, performed by two radiologists, was compared with those on HIR images. Discrepancies arising from AIIR images included both cases with additional abnormalities and those with corrections made on previous detections. For cases with discrepancies, artifact scores for organs where discrepancies were found, and contrast-to-noise ratios (CNRs) of cysts with discrepancies were compared between two image sets. Additional abnormalities were detected for 15 cases: additional liver cirrhosis (n=2); additional gallbladder stone (n=1); additional cholecystitis (n=1), additional spleen nodule (n=1); additional kidney cysts (n=8); additional liver cysts (3); and additional spleen cyst (n=1). A spleen contusion was corrected for one case. All involved artifact scores were improved on AIIR images. CNRs of involved liver, kidney, and spleen cysts were improved by up to 539.7%, 538.5%, and 245.5%, respectively. Irregular arm positioning may induce a variety of misdiagnoses in abdominal CT, which is almost totally avoidable by the AIIR algorithm.

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction.

To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT). This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test. A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05). EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning.

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques.

Iterative Stress Reconstruction Algorithm to Estimate Three-Dimensional Residual Stress Fields in Manufactured Components

Abstract Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ∼2.5 MPa in the Al plate, and ∼10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ∼2.3 MPa in the Al plate and ∼45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution.

Coupling Coherent Point Drift And Affine Least-Squares Transformation To Build Trajectories In TR-PTV

The robustness and accuracy of state-of-the-art time-resolved Particle Tracking Velocimetry algorithms is strongly supported by the use of the temporal coherence of particle trajectories. However, to exploit this coherence, the particle needs to be tracked for a sufficiently long period. This condition cannot be achieved if the time of flight of the particles across the measurement volume is not long enough, for example because the limited illumination energy requires the thickness of the volume to be thin. The authors propose to address this difficulty by providing an algorithm based on the spatial coherence of the trajectories rather than the temporal one. This algorithm results from the coupling of two methods; the Coherent Point Drift (CPD), which has proven to be accurate in pairing particles from two instants, but with an insufficient recall for time-resolved application, and the Affine Least Square Transformation (ALST), which allows transporting particles that were missed by the CPD. This algorithm is integrated into a global process that includes an Iterative Particle Reconstruction (IPR) algorithm to extract a list of particles from the images. First tests performed on a synthetic test case consisting of images of particles advected by a turbulent flow highlighted the potential of the method for tracking short tracks. With six frames, the recall is found to be 99.6% with the present method against 98.3% with the DaVis 10.2 implementation of ShakeThe-Box (STB). However, the error is much larger with a false positive rate of 5.1% with our method but only 0.2% with (STB). The cause of this high error rate is analysed and it is speculated that it is partly explained by the tendency of the IPR to detect the same real particle multiple times, which is a concern that has to be addressed in future work.

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities

PurposeTo examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities.Materials and methodsIn this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded.ResultsThe CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31–1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05).ConclusionIn indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.