Search Results (96)

Measuring the three-dimensional (3D) concentration of the ubiquitous intermediate OH* across combustion systems, spanning carbon-based fuels to zero-carbon alternatives such as H₂ and NH₃, provides vital insights into flame topology, reaction pathways, and emission formation mechanisms. Optical imaging methods have attracted vital interests due to non-intrusiveness in the combustion process. However, achieving accurate 3D concentration of OH* via imaging in non-axisymmetric flames remains challenging. This work presents a near-ultraviolet (NUV) volumetric emission tomography-based OH* measuring method that integrates a three-layer OH* imaging model, a calibration procedure utilizing narrow-band NUV radiometry, and a threshold-constrained Local Filtered Back-Projection Simultaneous Algebraic Reconstruction Technique (LFBP-SART) algorithm. When applied to a non-axisymmetric Bunsen flame, the method reveals multiple small flame structures matching the fairing pattern in the reconstructed 3D OH* field, with a maximum OH* molar concentration of approximately 0.04 mol/m³ and an overall relative uncertainty of about 8.7%. Given its straightforward requirements, this technique is considered adaptable to other free radicals. Full article

Objective: The objective of this study was to compare the performance and robustness of a deep learning reconstruction method against established alternatives for soft tissue CT image reconstruction. Materials and Methods: Images were generated from portal venous phase chest–abdomen–pelvis CT scans (n = 99) acquired on a dual-layer spectral detector CT using filtered back projection, iterative model reconstruction (IMR), and deep learning reconstruction (DLR) with three parameter settings, namely ‘standard’, ‘sharper’, and ‘smoother’. Experienced raters performed a quantitative assessment by considering attenuation stability and image noise levels in ten representative structures across all reconstruction methods, as well as a qualitative assessment using a four-point Likert scale (1 = poor, 2 = fair, 3 = good, 4 = excellent) for their overall perception of ‘smoother’ DLR and IMR images. One scan was excluded due to cachexia, which limited the quantitative measurements. Results: The inter-rater reliability for quantitative measurements ranged from moderate to excellent (r = 0.63–0.96). Attenuation values did not differ significantly between reconstruction methods except for DLR against IMR in the psoas muscle (mean + 3.0 HU, p < 0.001). Image noise levels differed significantly between reconstruction methods for all structures (all p < 0.001) and were lower than FBP with any DLR parameter setting. Image noise levels with ‘smoother’ DLR were predominantly lower than or equal to IMR, while they were higher with ‘standard’ DLR and ‘sharper’ DLR. The ‘smoother’ DLR images received a higher mean rating for overall image quality than the IMR images (3.7 vs. 2.3, p < 0.001). Conclusions: ‘Smoother’ DLR images were perceived by experienced readers as having improved quality compared to FBP and IMR while also exhibiting objectively lower or equivalent noise levels. Full article

Background/Objectives: Iterative reconstruction (IR) techniques were developed to address the shortcomings of filtered back projection (FBP), yet research comparing different types of IR is still missing. This work investigates how reducing radiation dose influences both image quality and noise profiles when using two iterative reconstruction techniques—Sinogram-Affirmed Iterative Reconstruction (SAFIRE) and Advanced Modeled Iterative Reconstruction (ADMIRE)—in comparison to filtered back projection (FBP) in non-enhanced head CT (NECT). Methods: In this retrospective single-center study, 21 consecutive patients underwent standard NECT on a 128-slice CT scanner. Raw data simulated dose reductions to 90% and 70% of the original dose via ReconCT software. For each dose level, images were reconstructed with FBP, SAFIRE 3, and ADMIRE 3. Image noise power spectra quantified objective image noise. Two blinded neuroradiologists scored overall image quality, image noise, image contrast, detail, and artifacts on a 10-point Likert scale in a consensus reading. Quantitative Hounsfield unit (HU) measurements were obtained in white and gray matter regions. Statistical analyses included the Wilcoxon signed-rank test, mixed-effects modeling, ANOVA, and post hoc pairwise comparisons with Bonferroni correction. Results: Both iterative reconstructions significantly reduced image noise compared to FBP across all dose levels (p < 0.001). ADMIRE exhibited superior image noise suppression at low (<0.51 1/mm) and high (>1.31 1/mm) spatial frequencies, whereas SAFIRE performed better in the mid-frequency range (0.51–1.31 1/mm). Subjective scores for overall quality, image noise, image contrast, and detail were higher for ADMIRE and SAFIRE versus FBP at the original dose and simulated doses of 90% and 70% (all p < 0.001). ADMIRE outperformed SAFIRE in artifact reduction (p < 0.001), while SAFIRE achieved slightly higher image contrast scores (p < 0.001). Objective HU values remained stable across reconstruction methods, although SAFIRE yielded marginally higher gray and white matter (WM) attenuations (p < 0.01). Conclusions: Both IR techniques—ADMIRE and SAFIRE—achieved substantial noise reduction and improved image quality relative to FBP in non-enhanced head CT at standard and reduced dose levels on the specific CT system and reconstruction strength tested. ADMIRE showed enhanced suppression of low- and high-frequency image noise and fewer artifacts, while SAFIRE preserved image contrast and reduced mid-frequency noise. These findings support the potential of iterative reconstruction to optimize radiation dose in NECT protocols in line with the ALARA principle, although broader validation in multi-vendor, multi-center settings is warranted. Full article

The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for patient survival. Radiomics features from CE-CT images hold promise for the outcome and tumor grade prediction. However, variations in reconstruction parameters can impact the predictive value of radiomics. 127 patients with histopathologically confirmed PanNENs underwent CT scans with filtered back projection (B20f) and iterative (I26f) reconstruction kernels. 3190 radiomic features were extracted from tumors and pancreatic volumes. Wilcoxon paired tests assessed the impact of reconstruction kernels and ComBat harmonization efficiency. SVM models were employed to predict tumor grade using the entire set of radiomics features or only those identified as harmonizable. The models’ performance was assessed on an independent dataset of 36 patients. Significant differences, after correction for multiple testing, were observed in 69% of features in the pancreatic volume and 51% in the tumor volume with B20f and I26f kernels. SVM models demonstrated accuracy ranging from 0.67 (95%CI: 0.50–0.81) to 0.83 (95%CI: 0.69–0.94) in distinguishing grade 1 cases from higher grades. Reconstruction kernels alter radiomics features and iterative kernel models trended towards higher performance. ComBat harmonization mitigates kernel impacts but addressing this effect is crucial in studies involving data from different kernels. Full article

Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction—volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20–24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30–100%), and FBP. In two blinded surveys, radiologists ranked eight reconstructions and assessed four using a 5-point Likert scale in arterial and portal venous phases. DLIR consistently outperformed other methods in SNR, CNR, image quality, image interpretation, structural differentiability and diagnostic certainty. Pixelshine performed comparably only to ASIR-V 50%. No significant differences were observed between junior and senior radiologists. In conclusion, DLIR-based techniques have the potential to establish a new benchmark in clinical CT imaging, offering superior image quality for tumor staging, enhanced diagnostic capabilities, and seamless integration into existing workflows without requiring an extensive learning curve. Full article

Hyperspectral imaging (HSI) technology was combined with chemometrics to achieve rapid determination of tanshinone contents in Salvia miltiorrhiza, as well as the rapid identification of its origins. Derivative (D1), second derivative (D2), Savitzky–Golay filtering (SG), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV) were utilized to preprocess original spectrum (ORI). Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were employed to discriminate 420 Salvia miltiorrhiza samples collected from Shandong, Hebei, Shanxi, Sichuan, and Anhui Provinces. The contents of tanshinone IIA, tanshinone I, cryptotanshinone, and total tanshinones in Salvia miltiorrhiza were predicted by the back-propagation neural network (BPNN), partial least square regression (PLSR), and random forest (RF). Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and variable iterative space shrinkage approach (VISSA). The results indicated that the D1-PLS-DA model performed the best with a classification accuracy of 98.97%. SG-BPNN achieved the best prediction effect for cryptotanshinone (RMSEP = 0.527, RPD = 3.25), ORI-BPNN achieved the best prediction effect for tanshinone IIA (RMSEP = 0.332, RPD = 3.34), MSC-PLSR achieved the best prediction effect for tanshinone I (RMSEP = 0.110, RPD = 4.03), and SNV-BPNN achieved the best prediction effect for total tanshinones (RMSEP = 0.759, RPD = 4.01). When using the SPA and VISSA, the number of wavelengths was reduced below 60 and 150, respectively, and the performance of the models was all very good (RPD > 3). Therefore, the combination of HSI with chemometrics provides a promising method for predicting the active ingredients of Salvia miltiorrhiza and identifying its geographical origins. Full article

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m², significantly higher than the 2514.06 ± 691.59 µGym² obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts. Full article

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan^® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose ($MRGlu$) in seven male C57BL/6 mice ($n=7$). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of $MRGlu$ obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies. Full article

Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED. Full article

Cone-beam computed tomography (CBCT) has emerged in recent years as an adequate alternative to mammography and tomosynthesis due to the several advantages over traditional mammography, including its ability to provide 3D images, its reduced radiation dose, and its ability to image dense breasts more effectively and conduct more effective breast compressions, etc. Furthermore, CBCT is capable of providing images with high sensitivity and specificity, allowing a more accurate evaluation, even of dense breasts, where mammography and tomosynthesis may lead to a false diagnosis. Clinical and experimental CBCT systems rely on cesium iodine (CsI:Tl) scintillators for X-ray energy conversion. This study comprises an investigation among different novel CBCT detector technologies, consisting either of scintillators (BGO, LSO:Ce, LYSO:Ce, LuAG:Ce, CaF₂:Eu, LaBr₃:Ce) or semiconductors (Silicon, CZT) in order to define the optimum detector design for a future experimental setup, dedicated to breast imaging. For this purpose, a micro-CBCT system was adapted, using GATE v9.2.1, consisting of the aforementioned various detection schemes. Two phantom configurations were selected: (a) an aluminum capillary positioned at the center of the field of view in order to calculate the system’s spatial resolution and (b) a breast phantom consisting of spheres of different materials, such that their characteristics are close to the breast composition. Breast phantom contrast-to-noise ratios (CNRs) were extracted from the phantom’s tomographic images. The images were reconstructed with filtered back projection (FBP) and ordered subsets expectation-maximization (OSEM) algorithms. The semiconductors acted satisfactorily in low-density matter, while LYSO:Ce, LaBr₃:Ce, and LuAG:Ce presented adequate CNRs for all the different spheres’ densities. The energy converters that are presented in this study were evaluated for their performance against the standard CsI:Tl crystal. Full article

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the $R^2$ value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set $R^2$ value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits. Full article

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction. Full article

Purpose: To compare the accuracy of ultra-low-dose (uLDCT) to standard-of-care low-dose chest CT (LDCT) in the detection of fungal infection in immunocompromised (IC) patients. Method and Materials: One hundred IC patients had paired chest CT scans performed with LDCT followed by uLDCT. The images were independently reviewed by three chest radiologists who assessed the image quality (IQ), diagnostic confidence, and detection of major (macro nodules, halo sign, cavitation, consolidation) and minor (4–10 mm nodules, ground-glass opacity) criteria for fungal disease using a five-point Likert score. Discrepant findings were adjudicated by a fourth chest radiologist. Box–whisker plots were used to analyze IQ and diagnostic confidence. Inter-rater reliability was assessed using interclass correlation coefficients (ICCs). The statistical difference between LDCT and uLDCT results was assessed using Wilcoxon paired test. Results: Lung reconstructions had IQ and diagnostic confidence scores (mean ± std) of 4.52 ± 0.47 and 4.63 ± 0.51 for LDCT and 3.85 ± 0.77 and 4.01 ± 0.88 for uLDCT. The images were clinically acceptable except for uLDCT in obese patients (BMI ≥ 30 kg/m²), which had an IQ ranking from poor to excellent (scores 1 to 5). The accuracy in detecting major and minor radiological findings with uLDCT was 96% and 84% for all the patients. The inter-rater agreements were either moderate, good, or excellent, with ICC values of 0.51–0.96. There was no significant statistical difference between the uLDCT and LDCT ICC values (p = 0.25). The effective dose for uLDCT was one quarter that of LDCT (CTDI_vol = 0.9 mGy vs. 3.7 mGy). Conclusions: Thoracic uLDCT, at a 75% dose reduction, can replace LDCT for the detection of fungal disease in IC patients with BMI < 30.0 kg/m². Full article

Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). Post-processing of FBP-reconstructed abdominal images was even superior to IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective assessments verified these results, partly suggesting benefits, especially in soft-tissue imaging (p < 0.001). All in all, the deep learning-based denoising—which was non-inferior to IR—offers an opportunity for image quality improvement especially in institutions using older scanners without IR availability. Further studies are necessary to evaluate potential effects on dose reduction benefits. Full article

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data—including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams. Full article