Search Results (70)

The mechanical characteristics of soil–rock mixtures (S-RMs) are essential for ensuring geotechnical engineering stability and are significantly influenced by the microstructure’s contact network configuration. Due to the irregularity of particle shapes and the variability in particle grading with S-RMs, their macro-mechanical characteristics and mesoscopic contact skeleton distribution exhibit increased complexity. To further elucidate the macro-mesoscopic mechanical behavior of S-RMs, this study employed the DEM to develop a model incorporating irregular specimens representing various states, based on CT scan outlines, and applied flexible boundary conditions. A main skeleton system of contact force chains is an effective methodology for characterizing the dominant structural features that govern the mechanical behavior of soil–rock mixture specimens. The results demonstrate that the strength of S-RMs was significantly influenced by gravel content and consolidation state; however, the relationship is not merely linear but rather intricately associated with the strength and distinctiveness of the contact force chain skeleton. In the critical state, the mechanical behavior of S-RMs was predominantly governed by the characteristics of the principal contact force skeleton: the contact force skeleton formed by gravel–gravel, despite having fewer contact forces, exhibits strong contact characteristics and an exceptionally high-density distribution of weak contacts, conferring the highest shear strength to the specimens. Conversely, the principal skeleton formed through gravel–sand exhibits contact characteristics that are less distinct compared to those associated with strong contacts. Simultaneously, the probability density distribution of weak contacts diminishes, resulting in reduced shear strength. The contact skeleton dominated by sand–sand contact forces displays similar micro-mechanical characteristics yet possesses the weakest macroscopic behavior strength. Consequently, the concept of the main skeleton of contact force chains utilized in this study presents a novel research approach for elucidating the macro- and micro-mechanical characteristics of multiphase media. Full article

Background: Acute ischemic stroke (AIS) remains a leading cause of mortality and disability worldwide. Accurate evaluation of collateral circulation is essential for predicting outcomes following endovascular thrombectomy (EVT). However, conventional visual collateral scoring (vCS) based on multiphase CT angiography (mCTA) is limited by subjectivity and inter-observer variability. This preliminary study introduces the multiphase quantitative collateral score (mqCS), a novel imaging biomarker designed to provide an objective and reproducible assessment of both the morphological extent and temporal dynamics of collateral flow. Methods: In this exploratory study, 54 AIS patients treated with EVT were retrospectively analyzed. Collateral status was evaluated using both vCS (graded by two blinded neuroradiologists) and mqCS, derived from mCTA-based fractal dimension (FD) and delay indicator (DI) metrics. Logistic regression and receiver operating characteristic (ROC) analyses were performed to assess the predictive value of each scoring system for favorable 90-day functional outcomes (modified Rankin scale, mRS ≤ 2). Results: The mqCS was significantly associated with favorable outcomes. Patients with mqCS ≥ 0.8674 had significantly higher odds of achieving favorable outcomes (adjusted OR = 5.98, 95% CI: 1.38–25.93, p = 0.017; AUC = 0.80). In comparison, the visual collateral score (vCS) showed a lower adjusted predictive value (adjusted OR = 2.84, 95% CI: 1.17–6.89, p = 0.02; AUC = 0.79). Patients in the highest mqCS quartiles (Q3–Q4) exhibited significantly better recovery rates (69%, p < 0.01). Conclusions: This proof-of-concept study suggests that mqCS provides a potentially more objective and robust alternative to visual scoring for collateral assessment in AIS. By integrating structural and temporal characteristics, mqCS enhances outcome prediction and may inform EVT decision-making, particularly in borderline cases. These preliminary findings warrant validation in larger, prospective cohorts and support its potential integration into automated imaging platforms. Full article

Background: This study aimed to evaluate whether low-concentration iodine contrast-enhanced multiphase low-monoenergetic computed tomography (LCLM CT; 270 mg I/mL, 40 keV) is non-inferior to standard-dose computed tomography (SDCT; 350 mg I/mL) in image quality and lesion detectability for chronic liver disease patients. Methods: Sixty-seven patients underwent both protocols. Image quality was assessed using a 5-point scale with a non-inferiority margin of −0.5. Quantitative metrics included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Lesion detectability was evaluated using jackknife free-response receiver operating characteristic (JAFROC) analysis with a −0.1 margin. Results: LCLM CT reduced iodine dose per kilogram by 21.9%. Despite higher image noise, it achieved higher CNR for the aorta and hepatic lesions, as well as superior hepatic artery clarity. Image quality was non-inferior (difference: −0.119; 95% CI: −0.192 to −0.047), and lesion detectability (FOM: 0.744 vs. 0.721; difference: 0.023; 95% CI: −0.170 to 0.218) also showed non-inferiority. Conclusions: LCLM CT maintains diagnostic performance and improves vascular contrast while reducing iodine burden, supporting its clinical utility in longitudinal HCC surveillance. Full article

Background: Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic characteristics. We aimed to develop a machine learning-based diagnostic model using CT-derived radiomic features to classify the five most common renal tumor subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), angiomyolipoma (AML), and oncocytoma. Methods: A total of 499 patients with pathologically confirmed renal tumors who underwent preoperative contrast-enhanced CT and nephrectomy were retrospectively analyzed. Results: We extracted and analyzed radiomic features from 1548 multi-phase CT scans from 499 patients, focusing on fat-poor tumors. Five machine learning classifiers including Linear SVM, Rbf SVM, Random Forest, and XGBoost were involved. Among the models, XGBoost showed the best classification performance, with an average AU-PRC: mean = 0.757, standard error = 0.033 and a renal angiomyolipoma-specific AU-ROC: mean = 0.824, standard error = 0.023. These results outperformed other single-phase CT radiomic feature-based machine learning models trained with 20% of principal components. Conclusions: This study demonstrates the effectiveness of radiomics-based machine learning in classifying renal tumor subtypes and highlights the potential of AI in medical imaging. The findings, particularly the utility of single-phase CT and feature optimization, offer valuable insights for future precision medicine approaches. Such methods may support more personalized diagnosis and treatment planning in renal oncology. Full article

Investigating the correlation between patient dimensions (Body Mass Index (BMI), weight, height) and radiation dose, the study also assesses the impact of the isocenter on dosage and X-ray tube. This retrospective study analyzed 258 consecutive three-phase liver CT exams (135 women, 123 men) performed on a Siemens SOMATOM Definition Edge scanner between January 3 and December 15, 2023. BMI, weight, height, maximum abdominal width (from topograms), and vertical isocenter status were extracted using the Dose&Care system and Horos software. BMI strongly correlated with total DLP in abdomen-plus-pelvis scans (r = 0.70) and moderately in abdomen-only scans (r = 0.54). Liver-phase DLP correlations were weaker (r = 0.44 and r = 0.28, respectively). Abdominal width showed similar associations with total DLP (r = 0.67 and r = 0.64) and liver-phase DLP (r = 0.41 and 0.37). Vertical mis-centering did not significantly affect total DLP (p = 0.174 and p = 0.705) or tube load. Patient size—not minor deviations from the isocenter—is the principal driver of radiation dose in multiphase liver CT. Automated, size-adapted protocols and restriction of scan range to clinically essential regions can preserve image quality while minimizing unnecessary radiation in routine practice. Full article

Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights. However, its effectiveness depends on the selection of dimensionality reduction techniques, clustering methods, and hyperparameter optimization, an area with limited exploration in the literature. We present a novel bootstrapping-based hyperparameter search approach to optimize clustering efficacy, treating dimensionality reduction and clustering as a connected process chain. The hyperparameter search was guided by the Adjusted Rand Index (ARI) and Davies–Bouldin Index (DBI) within a bootstrapping framework of 100 iterations. The cluster assignments were generated through 10-fold cross-validation, and a grid search strategy was used to explore hyperparameter combinations. We evaluated ten unsupervised learning pipelines using both simulation studies and real-world radiomics data derived from multiphase CT images of renal cell carcinoma. In simulations, we found that Non-negative Matrix Factorization (NMF) and Spectral Clustering outperformed the traditional Principal Component Analysis (PCA)-based approach. The best-performing pipeline (NMF followed by K-means clustering) successfully identified all three simulated clusters, achieving a Cramér’s V of 0.9. The simulation also established a reference framework for understanding the concordance patterns among different pipelines under varying strengths of clustering effects. High concordance reflects strong clustering. In the real-world data application, we observed a moderate clustering effect, which aligned with the weak associations to clinical outcomes, as indicated by the highest AUROC of 0.63. Full article

Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. We implemented three convolutional neural network architectures (U-Net, SegNet, and NablaNet) and assessed their performance on a dataset comprising 868 adrenal glands from contrast-enhanced abdominal CT scans. Performance was evaluated using the Dice similarity coefficient (DSC), alongside practical implementation metrics including training and deployment time. U-Net demonstrated superior segmentation performance (DSC: 0.630 ± 0.05 for right, 0.660 ± 0.06 for left adrenal glands) compared to NablaNet (DSC: 0.552 ± 0.08 for right, 0.550 ± 0.07 for left) and SegNet (DSC: 0.320 ± 0.10 for right, 0.335 ± 0.09 for left). While all models achieved high specificity, boundary delineation accuracy remained challenging. Our findings demonstrate the feasibility of deep learning-based adrenal gland segmentation while highlighting the persistent challenges in achieving the segmentation quality observed with larger abdominal organs. U-Net provides the optimal balance between accuracy and computational requirements, establishing a foundation for further refinement of AI-assisted adrenal imaging tools. Full article

Background/Objectives: Parathyroidectomy is a curative procedure for primary hyperparathyroidism, and multi-phase CT is an integral part of surgical planning. While patients may be referred to centers specializing in endocrine surgery, their imaging may be performed at other facilities without the same high-volume expertise. Methods: A retrospective review was performed of radiologist second reads of outside neck CT imaging in patients with hyperparathyroidism referred for surgical management. Results: The initial outside report was 59% sensitive for localization of parathyroid adenoma in the 74 patients with surgical pathologic confirmation. Second reads of the same CT scans correctly identified the parathyroid adenoma in an additional 24% of patients, for a total sensitivity of 83%. For the 23% of patients with pathologically confirmed multi-gland involvement, the initial outside report was 21% sensitive for lesion detection, and the second read of the same scans was 68% sensitive. Conclusions: Endocrine surgeons should be aware that community-based radiology interpretation of neck CT may be less sensitive than reported series from academic and high-volume practices. In the present study, interpretation via second read of outside CT scans by a neuroradiologist engaged with the endocrine surgery service line increased the sensitivity of detecting candidate lesions, both for single-gland and multi-gland involvement. While it is preferred to have preoperative imaging and interpretation within the same high-volume center as the surgeon for consistency of imaging quality, experience and communication, radiologist second reads deserve financial and service line support when that is not possible given the impact on surgical planning and patient care. Full article

The increasing use of computed tomography (CT) has led to a rise in cumulative radiation dose due to medical imaging, raising concerns about potential long-term adverse effects. Large-scale epidemiological studies indicate a higher tumor incidence associated with CT examinations, but the underlying biological mechanisms remain largely unexplained. To gain further insights into the cellular response triggered by routine CT diagnostics, we investigated CT-induced changes of gene expression in peripheral blood cells using whole transcriptome sequencing. RNA was isolated from peripheral blood cells of 40 male patients with asymptomatic microhematuria, sampled before and after multi-phase abdominal CT (CTDIvol: 3.75–26.95 mGy, median: 6.55 mGy). On average, 22.11 million sequence reads (SD 5.71) per sample were generated to identify differentially expressed genes 6 h post-exposure by means of DESeq2. To assess the dose dependency of CT-induced effects, we additionally divided samples into three categories: low exposure (≤6.55 mGy, n = 20), medium exposure (>6.55 mGy and <12 mGy, n = 16), and high exposure (≥12 mGy, n = 4), and repeated gene expression analysis for each subset and their corresponding prae-exposure sample. CT exposure caused consistent and dose-dependent upregulation of six genes (EDA2R, AEN, FDXR, DDB2, PHLDA3, and MIR34AHG; padj < 0.1). These genes share several functional commonalities, including regulation by TP53 and involvement in the DNA damage response. The biological pathways highlighted by Gene Set Enrichment Analysis (GSEA) suggest a dose-dependent increase of cellular damage and metabolic particularities in the low-exposure subset, which may be related to a potential adaptive cellular response to low-dose irradiation. Irrespective of applied dose, AEN emerged as the most robust biomarker for CT exposure among all genes. Routine abdominal CT scans cause dose-dependent gene deregulation in association with DNA damage in peripheral blood cells after in vivo exposure. Regarding risk assessment of CT, our results support the commonly applied “As Low–As –Reasonably Achievable (ALARA)” principle. Evidence of additional gene expression changes associated with metabolic processes indicates a rather complex molecular response beyond DNA damage after CT exposure, and emphasizes the need for further targeted investigations. Full article

Background/Objective: Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancer. Computed tomography (CT) is the imaging modality used to evaluate liver nodules and differentiate HCC from ICC. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been used in multiple studies in the field of radiology. The purpose of this study was to determine potential CT features for the differentiation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Methods: Patients with radiological and pathologically confirmed diagnosis of HCC and ICC between January 2013 and December 2015 were included in this retrospective study. Two board-certified diagnostic radiologists independently reviewed multiphase CT images on a picture archiving and communication system (PACS). Arterial hyperenhancement, portal vein thrombosis, lymph node enlargement, and cirrhosis appearance were evaluated. We then calculated sensitivity, specificity, the likelihood ratio for diagnosis of HCC and ICC. Inter-observed agreement of categorical data was evaluated using Cohen’s kappa statistic (k). Results: A total of 74 patients with a pathologically confirmed diagnosis, including 48 HCCs and 26 ICC, were included in this study. Most of HCC patients showed arterial hyperenhancement at 95.8%, and interobserver agreement was moderate (k = 0.47). Arterial enhancement in ICC was less frequent, ranging from 15.4% to 26.9%, and agreement between readers was substantial (k = 0.66). The two readers showed a moderate agreement of cirrhosis appearance in both the HCC and ICC groups, k = 0.43 and k = 0.48, respectively. Cirrhosis appeared in the HCC group more frequently than the ICC group. Lymph node enlargement was more commonly seen in ICC than HCC, and agreement between the readers was almost perfect (k = 0.84). Portal vein invasion in HCC was seen in 14.6% by both readers with a substantial agreement (k = 0.66). Portal vein invasion in ICC was seen in 11.5% to 19.2% of the patients. The diagnostic performance of the two radiologists was satisfactory, with a corrected diagnosis of 87.8% and 94.6%. The two radiologists had high sensitivity in diagnosing HCCs (95.8% to 97.9%) and specificity in diagnosing ICCs (95.8% to 97.9%). Conclusions: Cirrhosis and lymph node metastasis could be ancillary and adopted in future AI training algorithms. Full article

In the process of reservoir water flooding development, the characteristics of underground seepage field have changed, resulting in increasingly complex oil–water distribution. The original understanding of reservoir physical property parameters based on the initial stage of development is insufficient to guide reservoir development efforts in the extra-high water cut stage. To deeply investigate the spatio-temporal evolution of heterogeneity in the internal seepage field of layered reservoirs during water flooding development, water–oil displacement experimental simulations were conducted based on layered, normally graded models. By combining CT scanning technology and two-phase seepage theory, the variation patterns of heterogeneity in the seepage field of medium-to-high permeability, normally graded reservoirs were analyzed. The results indicate that the effectiveness of water flooding development is doubly constrained by differences in oil–water seepage capacities and the heterogeneity of the seepage field. During the development process, both the reservoir’s flow capacity and the heterogeneity of the seepage field are in a state of continuous change. Influenced by the extra resistance brought about by multiphase flow, the reservoir’s flow capacity drops to 41.6% of the absolute permeability in the extra-high water cut stage. Based on differences in the variation amplitudes of oil–water-phase permeabilities, changes in the heterogeneity of the internal seepage field of the reservoir can be broadly divided into periods of drastic change and relative stability. During the drastic change stage, the fluctuation amplitude of the water-phase permeability variation coefficient is 114.5 times that of the relative stable phase, while the fluctuation amplitude of the oil-phase permeability variation coefficient is 5.2 times that of the stable stage. This study reveals the dynamic changes in reservoir seepage characteristics during the water injection process, providing guidance for water injection development in layered reservoirs. Full article

Patients diagnosed with multiple endocrine neoplasia type-1 (MEN1) often initially present with primary hyperparathyroidism (pHPT), and typically undergo surgical intervention. While laboratory tests are fundamental for diagnosis, imaging is crucial for localizing pathological parathyroids to aid in precise surgical planning. In this pictorial review, we will begin by comprehensively examining key imaging techniques and their established protocols, evaluating their effectiveness in detecting abnormal parathyroid glands. This analysis will emphasize both the advantages and potential limitations within the clinical context of MEN1 patients. Additionally, we will explore integrated imaging approaches that combine multiple modalities to enhance localization accuracy and optimize surgical planning—an essential component of holistic management in MEN1 cases. Various imaging techniques are employed for presurgical localization, including ultrasound (US), multiphase parathyroid computed tomography (CT) scanning (4D CT), magnetic resonance imaging (MRI), and nuclear medicine techniques like single photon emission computed tomography/CT (SPECT/CT) and positron emission tomography/CT (PET/CT). US is non-invasive, readily available, and provides high spatial resolution. However, it is operator-dependent and may have limitations in certain cases, such as intrathyroidal locations, the presence of bulky goiters, thyroid nodules, and previous thyroidectomy. Four-dimensional CT offers dynamic imaging, aiding in the identification of enlarged parathyroid glands, particularly in cases of ectopic or supernumerary glands. Despite concerns about radiation exposure, efforts are underway to optimize protocols and reduce doses, including the use of dual-energy CT. MR imaging offers excellent soft tissue contrast without radiation exposure, potentially providing superior differentiation between parathyroid glands and the surrounding structures. Radionuclide imaging, especially PET/CT using radiopharmaceuticals like [¹⁸F]FCH, shows promising results in localizing parathyroid tumors, particularly in patients with MEN1. [¹⁸F]FCH PET/CT demonstrates high sensitivity and may provide additional information compared to other imaging modalities, especially in cases of recurrent HPT. Full article

Objective: In suspected acute ischemic stroke, it is now reasonable to expand the conventional “stroke protocol” (non-contrast computed tomography (NCCT), arterial CT angiography (CTA), and optionally CT perfusion (CTP)) to early and late venous head scans yielding a multiphase CTA (MP-CTA) to increase diagnostic confidence. Diagnostic reference levels (DRLs) have been defined for neither MP-CTA nor CTP. We therefore present dosimetry data, while also considering image quality, for a large, unselected patient cohort. Methods: A retrospective single-center study of 1790 patients undergoing the extended stroke protocol with three scanners (2× dual-source, DSCT; 1× single-source, SSCT) between 07/21 and 12/23 was conducted. For each sequence, we analyzed the radiation dose (volumetric CT dose index (CTDIvol); dose length product; effective dose); objective image quality using manually placed regions of interest (contrast-to-noise ratio (CNR)); and subjective image quality (4-point scale: 1 = non-diagnostic, 4 = excellent). The DRL was defined as the 75% percentile of the CTDIvol distribution. The Kruskal-Wallis test was used initially to test for overall equality of median values in each data group. Single post-test comparisons were performed with Dunn’s test, with an overall statistical significance level of 0.05. Results: Dosimetry values were significantly higher for SSCT (p < 0.001, each). Local DRLs ranged between 37.3 and 49.1 mGy for NCCT, 3.6–5.5 mGy for arterial CTA, 1.2–2.5 mGy each for early/late venous CTA, and 141.1–220.5 mGy for CTP. Protocol adjustment (DSCT-1: CTP) yielded a 28.2% dose reduction. The highest/lowest CNRs (arterial/early venous CTA, respectively) were recorded for SSCT/DSCT-2 (p < 0.001). Subjective image quality was rated excellent except for slightly increased MP-CTA noise at DSCT-2 (median = 3). Conclusions: Our data imply that additive MP-CTA scans only yield a minor increase in radiation exposure, particularly when using DSCT. CTP should be limited to selected patients. Full article

The multiphase components of natural ice contain gas, ice, unfrozen water, sediment and brine. X-ray computed tomography (CT) analysis of ice multiphase components has the advantage of high precision, non-destructiveness and visualization; however, it is limited by the segmentation thresholds. Due to the proximity of the CT value ranges of gas, ice, unfrozen water, sediment and brine within the samples, there is uncertainty in the artificial determination of the CT image segmentation thresholds, as well as unsuitability of the global threshold segmentation methods. In order to improve the accuracy of multi-threshold segmentation in CT images, a CT system was used to scan the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice. The threshold ranges of multiphase components within the ice were determined by watershed algorithm to construct a high-precision three-dimensional ice model. The results indicated that CT combined with watershed algorithm was an efficient and non-destructive method for obtaining microscopic information within ice, which accurately segmented the ice into multiphase components such as gas, ice, unfrozen water, sediment, and brine. The gas CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −1024 Hu~−107 Hu, −1024 Hu~−103 Hu, and −1024 Hu~−160 Hu, respectively. The ice CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −103 Hu~−50 Hu, −100 Hu~−38 Hu, −153 Hu~−51 Hu. The unfrozen water CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from −8 Hu~18 Hu, −8 Hu~13 Hu. The sediment CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from 20 Hu~3071 Hu, 20 Hu~3071 Hu, and the brine CT values of the Arctic sea ice ranged from −6 Hu~3071 Hu. The errors between the three-dimensional ice model divided by threshold ranges and measured sediment content were less than 0.003 g/cm³, which verified the high accuracy of the established microscopic model. It provided a scientific basis for ice engineering, ice remote sensing, and ice disaster prevention. Full article

To accurately predict the relative permittivity of cement-stabilized base materials, a study on the dielectric mixing model for cracked base materials was conducted. Based on the electromagnetic mixing theory of multiphase composites, a comprehensive dielectric mixing model of cement-stabilized base materials was derived. The volume ratios and relative permittivity values of the specimen constituents in different cracking states of the cement-stabilized base were determined using industrial CT and a Percometer relative permittivity meter, with comprehensive consideration given to the effects of different initial porosities and crack widths on the dielectric properties. Based on the volumetric and dielectric properties of the base material specimens in both intact and cracked states, as well as the error analysis between the predicted and measured values of the relative permittivity constant, the u-optimal solution of the dielectric mixing model for cement-stabilized base material was determined to be 1. Consequently, an optimization dielectric mixing model for semi-rigid base course materials in a cracked state was developed. The optimization model proposed is suitable for predicting the dielectric properties of cement-stabilized base material with crack widths generally greater than 3 mm during the service life of semi-rigid base course in engineering practice. Full article