Femoral osteoporosis prediction model using autosegmentation and machine learning analysis with PyRadiomics on abdomen-pelvic computed tomography (CT).

Background: With the advancement of artificial intelligence technology and radiomics analysis, opportunistic prediction of osteoporosis with computed tomography (CT) is a new paradigm in osteoporosis screening. This study aimed to assess the diagnostic performance of osteoporosis prediction by the combination of autosegmentation of the proximal femur and machine learning analysis with a reference standard of dual-energy X-ray absorptiometry (DXA). Methods: Abdomen-pelvic CT scans were retrospectively analyzed from 1,122 patients who received both DXA and abdomen-pelvic computed tomography (APCT) scan from January 2018 to December 2020. The study cohort consisted of a training cohort and a temporal validation cohort. The left proximal femur was automatically segmented, and a prediction model was built by machine-learning analysis using a random forest (RF) analysis and 854 PyRadiomics features. The technical success rate of autosegmentation, diagnostic test, area under the receiver operator characteristics curve (AUC), and precision recall curve (AUC-PR) analysis were used to analyze the training and validation cohorts. Results: The osteoporosis prevalence of the training and validation cohorts was 24.5%, and 10.3%, respectively. The technical success rate of autosegmentation of the proximal femur was 99.7%. In the diagnostic test, the training and validation cohorts showed 78.4% vs. 63.3% sensitivity, 89.4% vs. 98.1% specificity. The prediction performance to identify osteoporosis within the groups used for training and validation cohort was high and the AUC and AUC-PR to forecast the occurrence of osteoporosis within the training and validation cohorts were 90.8% [95% confidence interval (CI), 88.4-93.2%] vs. 78.0% (95% CI, 76.0-79.9%) and 94.6% (95% CI, 89.3-99.8%) vs. 88.8% (95% CI, 86.2-91.5%), respectively. Conclusions: The osteoporosis prediction model using autosegmentation of proximal femur and machine-learning analysis with PyRadiomics features on APCT showed excellent diagnostic feasibility and technical success.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Imaging porosity evolution of tight sandstone during spontaneous water imbibition by X-ray Micro-CT.

Water imbibition is an important process in reservoir rocks during hydraulic fracturing and water-based enhanced oil recovery operations. However, the water imbibition behavior in tight sandstones has not been fully understood due to their complex pore structure, including the presence of nano and micron-sized pores and throats, surface properties, and wide variation in mineralogy. The present study focuses on the effect of spontaneous water imbibition on the porosity evolution of a tight sandstone. Within this context, a core of Torrey Buff sandstone was characterized by using a combination of multiscale imaging methods (X-ray Computed Tomography, Scanning Electron Microscopy), laboratory experiments (porosity-permeability measurements), and analytical techniques (X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy-Energy Dispersive Spectroscopy, and Thermogravimetry). The studied tight sandstone core has a porosity of 12.3 % and permeability of 2.05mD with minerals of quartz (58 %), clays (kaolinite and illite, 23 %), K-feldspar (7 %), dolomite (7 %) and calcite (5 %). Primary and secondary pores, ranging in size from 60 to 140 µm and 30-50 µm, respectively, are mostly filled with highly-soluble carbonate minerals and hydrophilic illite, which influence the spontaneous water imbibition capacity of the tight sandstone. The multiscale imaging technique indicates that after a 10-h long water imbibition experiment, the average pore size of the tight sandstone increased by 1.28 %, reaching 2.35 % at the rock-water contact and 0.13 % at the top of the core. In other words, throughout the core, the porosity changes upon water imbibition are not uniform but show an almost linear trend. This observation could be explained by the significant contribution of highly-soluble carbonates and hydrophilic illite on the microstructure of the tight sandstone. This study implies that multiscale imaging techniques, crucial in examining spontaneous water imbibition, hold promise for further research in enhanced oil recovery or hydraulic fracking in tight sandstones.

Discordance Rate and Risk Factor of Other Diagnostic Modalities for Small Bowel Tumors Detected by Device-Assisted Enteroscopy: A Korean Association for the Study of Intestinal Disease (KASID) Multicenter Study.

Background/Aims: Despite advances in imaging and endoscopic technology, diagnostic modalities for small bowel tumors are simultaneously performed. We investigated the discrepancy rate between each modality and predictive factors of discrepancy in patients with definite small bowel tumors. Methods: Data of patients with definite small bowel tumors who underwent both device-assisted enteroscopy (DAE) and computed tomography (CT) were retrieved from web-based enteroscopy registry database in Korea. Predictive risk factors associated with discrepancy were analyzed using logistic regression analysis. Results: Among 998 patients, 210 (21.0%) were diagnosed with small bowel tumor using DAE, in 193 patients with definite small bowel tumor, DAE and CT were performed. Of these patients, 12 (6.2%) showed discrepancy between examinations. Among 49 patients who underwent DAE and video capsule endoscopy (VCE) examination, 13 (26.5%) showed discrepancy between examinations. No significant independent risk factors were associated with concordance between DAE and CT in multivariate logistic regression analysis among the patients. In a multivariate logistic regression analysis, red blood cell transfusion was negatively associated with concordance between DAE and VCE in patients with small bowel tumor (odds ratio, 0.163; 95% confidence interval, 0.026 to 1.004; p=0.050). Conclusions: For small bowel tumors, the discrepancy rate between DAE and CT was 6.2%, and 26.5% between DAE and VCE. Despite developments in cross-sectional imaging (VCE and DAE modalities), discrepancies still exist. For small bowel bleeding that require significant transfusion while showing insignificant VCE findings, DAE should be considered as the next diagnostic approach, considering the possibility of missed small bowel tumor.

Research on the Development of Methods for Detection of Substandard and Falsified Medicines by Clarifying Their Pharmaceutical Characteristics Using Modern Technology.

The existence of substandard and falsified medicines threatens people's health and causes economic losses as well as a loss of trust in medicines. As the distribution of pharmaceuticals becomes more globalized and the spread of substandard and falsified medicines continues worldwide, pharmaceutical security measures must be strengthened. To eradicate substandard and falsified medicines, our group is conducting fact-finding investigations of medicines distributed in lower middle-income countries (LMICs) and on the Internet. From the perspective of pharmaceutics, such as physical assessment of medicines, we are working to clarify the actual situation and develop methods to detect substandard and falsified medicines. We have collected substandard and falsified medicines distributed in LMICs and on the Internet and performed pharmacopoeial tests, mainly using HPLC, which is a basic analytic method. In addition to quality evaluation, we have evaluated the applicability of various analytic methods, including observation of pharmaceuticals using an electron microscope, Raman scattering analysis, near-IR spectroscopic analysis, chemical imaging, and X-ray computed tomography (CT) to detect substandard and falsified medicines, and we have clarified their limitations. We also developed a small-scale quality screening method using statistical techniques. We are engaged in the development of methods to monitor the distribution of illegal medicines and evolve research in forensic and policy science. These efforts will contribute to the eradication of substandard and falsified medicines. Herein, I describe our experience in the development of detection methods and elucidation of the pharmaceutical status of substandard and falsified medicines using novel technologies.

Improving image quality of sparse-view lung tumor CT images with U-Net.

BACKGROUND: We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence. METHODS: CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used. RESULTS: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images. CONCLUSIONS: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level. RELEVANCE STATEMENT: Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose. KEY POINTS: • Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

Reproducibility of a semiautomatic lobar lung tissue assignment technique on noncontrast CT scans: a study on swine animal model.

BACKGROUND: To evaluate the reproducibility of a vessel-specific minimum cost path (MCP) technique used for lobar segmentation on noncontrast computed tomography (CT). METHODS: Sixteen Yorkshire swine (49.9 ± 4.7 kg, mean ± standard deviation) underwent a total of 46 noncontrast helical CT scans from November 2020 to May 2022 using a 320-slice scanner. A semiautomatic algorithm was employed by three readers to segment the lung tissue and pulmonary arterial tree. The centerline of the arterial tree was extracted and partitioned into six subtrees for lobar assignment. The MCP technique was implemented to assign lobar territories by assigning lung tissue voxels to the nearest arterial tree segment. MCP-derived lobar mass and volume were then compared between two acquisitions, using linear regression, root mean square error (RMSE), and paired sample t-tests. An interobserver and intraobserver analysis of the lobar measurements was also performed. RESULTS: The average whole lung mass and volume was 663.7 ± 103.7 g and 1,444.22 ± 309.1 mL, respectively. The lobar mass measurements from the initial (MLobe1) and subsequent (MLobe2) acquisitions were correlated by MLobe1 = 0.99 MLobe2 + 1.76 (r = 0.99, p = 0.120, RMSE = 7.99 g). The lobar volume measurements from the initial (VLobe1) and subsequent (VLobe2) acquisitions were correlated by VLobe1 = 0.98VLobe2 + 2.66 (r = 0.99, p = 0.160, RSME = 15.26 mL). CONCLUSIONS: The lobar mass and volume measurements showed excellent reproducibility through a vessel-specific assignment technique. This technique may serve for automated lung lobar segmentation, facilitating clinical regional pulmonary analysis. RELEVANCE STATEMENT: Assessment of lobar mass or volume in the lung lobes using noncontrast CT may allow for efficient region-specific treatment strategies for diseases such as pulmonary embolism and chronic thromboembolic pulmonary hypertension. KEY POINTS: • Lobar segmentation is essential for precise disease assessment and treatment planning. • Current methods for segmentation using fissure lines are problematic. • The minimum-cost-path technique here is proposed and a swine model showed excellent reproducibility for lobar mass measurements. • Interobserver agreement was excellent, with intraclass correlation coefficients greater than 0.90.

Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques - a short review including new research.

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or "label-free" imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques with an emphasis on FPM and advanced X-ray microscopies. We next demonstrate with our own results computational imaging through Fourier ptychographic microscopy (FPM) and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are reported. X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are also presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days.

Objectives: This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods: From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

BACKGROUND: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). METHODS: Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. RESULTS: Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). CONCLUSIONS: AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. RELEVANCE STATEMENT: In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. KEY POINTS: • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

First X-ray spectral ptychography and resonant ptychographic computed tomography experiments at the SWING beamline from Synchrotron SOLEIL.

X-ray ptychography and ptychographic computed tomography have seen a rapid rise since the advent of fourth-generation synchrotrons with a high degree of coherent radiation. In addition to quantitative multiscale structural analysis, ptychography with spectral capabilities has been developed, allowing for spatial-localized multiscale structural and spectral information of samples. The SWING beamline of Synchrotron SOLEIL has recently developed a nanoprobe setup where the endstation's first spectral and resonant ptychographic measurements have been successfully conducted. A metallic nickel wire sample was measured using 2D spectral ptychography in XANES mode and resonant ptychographic tomography. From the 2D spectral ptychography measurements, the spectra of the components of the sample's complex-valued refractive index, δ and ß, were extracted, integrated along the sample thickness. By performing resonance ptychographic tomography at two photon energies, 3D maps of the refractive index decrement, δ, were obtained at the Ni K-edge energy and another energy above the edge. These maps allowed the detection of impurities in the Ni wire. The significance of accounting for the atomic scattering factor is demonstrated in the calculation of electron density near a resonance through the use of the δ values. These results indicate that at the SWING beamline it is possible to conduct state-of-the-art spectral and resonant ptychography experiments using the nanoprobe setup.

Low subcutaneous adipose tissue and myosteatosis are prognostic factors after allogeneic hematopoietic stem cell transplantation.

OBJECTIVE: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) represents the only curative treatment option for several hematological neoplasms. This study aimed to assess the parameters of body composition as predictors of post-transplant overall survival (OS) and adverse events in patients with leukemia, myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPN). METHODS: This was a retrospective study of 122 adult patients who underwent their first allo-HSCT. The CT-based semi-automated measurement of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), visceral-to-subcutaneous fat ratio (VSR), sarcopenia in terms of skeletal muscle index (SMI), and myosteatosis based on the skeletal muscle radiation attenuation (SM-RA) was performed. Cox regression analysis was used to assess the association of body composition parameters with OS. RESULTS: In the univariate analysis, low SAT and myosteatosis were associated with lower OS (hazard ratio [HR] 2.02, 95% confidence interval [CI] 1.16-3.51, p = 0.01) and (HR 2.50, 95% CI 1.48-4.25, p =< 0.001), respectively. This association remained significant after adjusting for relevant covariates, with HR 2.32, 95% CI 1.23-4.38, p = 0.01 and HR 2.86, 95% CI 1.51-5.43, p =< 0.001, respectively. On the contrary, VAT, VSR, sarcopenia, and sarcopenic obesity were not statistically significant in OS. Severe post-transplant adverse events were more common in the low SAT group (odds ratio [OR] 3.12, 95% CI 1.32-7.40, p = 0.01) and OR 3.17, 95% CI 1.31-7.70, p =< 0.01 in the age- and sex-adjusted analysis. CONCLUSION: Low SAT and myosteatosis may contribute to an increased risk of post-transplant mortality, while low SAT appears to increase the risk of severe post-transplant adverse events.

A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules.

RATIONALE AND OBJECTIVES: Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images. MATERIALS AND METHODS: This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. RESULTS: Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860. CONCLUSION: This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.

Feasibility of virtual non-iodine coronary calcium scoring on dual source photon-counting coronary CT angiography: a dynamic phantom study.

BACKGROUND: The aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans. METHODS: In this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60-75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm3, respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CACCCTA and CACCSCT scoring was measured using Cohen weighted linear κ with 95% CI. RESULTS: For CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CACCCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CACCCTA was not significantly different from CACCSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671-0.853). CONCLUSION: In this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial. CLINICAL RELEVANCE STATEMENT: Photon counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice. KEY POINTS: Photon-counting CTA allows for excellent detectability of low-density calcifications at all heart rates. Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed. Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT.

Capturing anatomy in computed tomography scans for genital pathology.

PURPOSE: In this cross-sectional study, we aimed to characterize how frequently the anatomy of interest (AOI) was excluded when evaluating genital pathology using the current CT pelvis protocol recommended by the American College of Radiology and evaluate how AOI exclusion affects patient management. METHODS: We retrospectively reviewed medical records, using diagnosis and CPT codes, of patients admitted with genital pathology who obtained a CT scan at our institution from July 1, 2020-April 30, 2023. Baseline patient demographics were included. Data about each index CT scan (scan obtained at our institution) were recorded and assessed for exclusion of the AOI. Statistical analysis was performed to determine the rate of AOI exclusion and to compare patient management between patients with AOI excluded versus those without AOI exclusion. RESULTS: 113 presentations for genital pathology included an index CT scan and were included for analysis. Patients were primarily men (98%) with a mean age of 53.1 years (SD 13.9). The most common diagnoses were Fournier's gangrene (35%), scrotal abscess (22%) and unspecified infection (19%). 26/113 scans (23%) did not capture the entire AOI. When the AOI was missed during the index scan, there was a higher rate of obtaining additional scans (38% vs. 21%), but a similar rate of intervention (77% vs. 63%) when compared to index scans that captured the entire AOI. 35 scans (31%) had protocol-extending instructions; index scans that captured the entire AOI were more likely to have specific protocol-extending instructions (38% vs. 8% p < 0.01). CONCLUSIONS: Creating a specific CT protocol for genital pathology could decrease the amount of inappropriate irradiation and improve AOI capture rates without relying on specific request for protocol deviation.

Trade-off between Pore-Throat Structure and Mineral Composition in Modulating the Stability of Soil Organic Carbon.

The preservation of soil organic carbon (OC) is an effective way to decelerate the emission of CO2 emission. However, the coregulation of pore structure and mineral composition in OC stabilization remains elusive. We employed the in situ nondestructive oxidation of OC by low-temperature ashing (LTA) combined with near edge X-ray absorption fine structure (NEXAFS), high-resolution microtomography (µ-CT), field emission electron probe microanalysis (FE-EPMA) with C-free embedding, and novel Cosine similarity measurement to investigate the C retention in different aggregate fractions of contrasting soils. Pore structure and minerals contributed equally (ca. 50%) to OC accumulation in macroaggregates, while chemical protection played a leading role in C retention with 53.4%-59.2% of residual C associated with minerals in microaggregates. Phyllosilicates were discovered to be more prominent than Fe (hydr)oxides in C stabilization. The proportion of phyllosilicates-associated C (52.0%-61.9%) was higher than that bound with Fe (hydr)oxides (45.6%-55.3%) in all aggregate fractions tested. This study disentangled quantitatively for the first time a trade-off between physical and chemical protection of OC varying with aggregate size and the different contributions of minerals to OC preservation. Incorporating pore structure and mineral composition into C modeling would optimize the C models and improve the soil C content prediction.

Intracranial arterial calcification in patients with unruptured and ruptured intracranial aneurysms.

OBJECTIVES: Arterial calcification is thought to protect against rupture of intracranial aneurysms, but studies in a representative population of intracranial aneurysm patients have not yet been performed. The aim was to compare the prevalence of aneurysm wall calcification and intracranial carotid artery calcification (ICAC) between patients with an unruptured intracranial aneurysm (UIA) and a ruptured intracranial aneurysm (RIA). MATERIALS AND METHODS: We matched 150 consecutive UIA patients to 150 RIA patients on age and sex. Aneurysm wall calcification and ICAC were quantified on non-contrast enhanced computed tomography images with the modified Agatston score. We compared the prevalence of aneurysm wall calcification, ICAC, and severe ICAC (defined as a modified Agatston score in the fourth quartile) between UIA and RIA patients using univariate and multivariate conditional logistic regression models adjusted for aneurysm characteristics and cardiovascular risk factors. RESULTS: Aneurysm wall calcification was more prevalent in UIA compared to RIA patients (OR 5.2, 95% CI: 2.0-13.8), which persisted after adjustment (OR 5.9, 95% CI: 1.7-20.2). ICAC prevalence did not differ between the two groups (crude OR 0.9, 95% CI: 0.5-1.8). Severe ICAC was more prevalent in UIA patients (OR 2.0, 95% CI: 1.1-3.6), but not after adjustment (OR 1.0, 95% CI: 0.5-2.3). CONCLUSIONS: Aneurysm wall calcification but not ICAC was more prevalent in UIAs than in RIAs, which corresponds to the hypothesis that calcification may protect against aneurysmal rupture. Aneurysm wall calcification should be further assessed as a predictor of aneurysm stability in prospective cohort studies. CLINICAL RELEVANCE STATEMENT: Calcification of the intracranial aneurysm wall was more prevalent in unruptured than ruptured intracranial aneurysms after adjustment for cardiovascular risk factors. Calcification may therefore protect the aneurysm against rupture, and aneurysm wall calcification is a candidate predictor of aneurysm stability. KEY POINTS: Aneurysm wall calcification was more prevalent in patients with unruptured than ruptured aneurysms, while internal carotid artery calcification was similar. Aneurysm wall calcification but not internal carotid artery calcification is a candidate predictor of aneurysm stability. Cohort studies are needed to assess the predictive value of aneurysm wall calcification for aneurysm stability.

Primary hepatic neuroendocrine neoplasms: imaging characteristics and misdiagnosis analysis.

Objective: To analyze the CT and MR features of Primary hepatic neuroendocrine neoplasms (PHNENs) in order to enhance the diagnostic accuracy of this disease. Methods: A retrospective analysis was conducted on patients diagnosed with hepatic neuroendocrine neoplasms, excluding other sites of origin through general examination and postoperative follow-up. The CT and MR signs were analyzed according to the 2018 version of Liver Imaging Reporting and Data System (LI-RADS), along with causes of misdiagnosis. Results: Twelve patients, including 6 males and 6 females, were enrolled in this study. There was no significant increase in liver tumor markers among all cases. Most masses were multiple (9/12), exhibiting low attenuation on pre-contrast CT scans, T1-hypointense signal, T2-hyperintense signal, and restricted diffusion. The majority of these masses (7/10) demonstrated similar rim arterial phase hyper-enhancement as well as peripheral "washout" during venous portal phase and delayed phase imaging. Three cases had incomplete capsules while one case had a complete capsule. Cyst/necrosis was observed in 7 out of all cases following administration of contrast agent, with 5 mainly distributed in the periphery. All masses lacked fat, calcification, vascular or bile duct tumor thrombus formation. Conclusion: The imaging findings associated with PHNENs possess certain specificity, often presenting as multiple masses within the liver accompanied by peripheral cyst/necrosis, similar rim arterial phase hyper-enhancement during venous portal phase and delayed phase imaging.

Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.

BACKGROUND: We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. METHODS: Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. RESULTS: Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). CONCLUSIONS: Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. KEY POINTS: • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.