Tomography, Volume 12, Issue 6 (June 2026) – 15 articles

Background/Objectives: Left ventricular (LV) geometry reflects structural adaptation to aging and biological sex. While cardiac magnetic resonance (CMR) provides precise morphologic assessment, most prior studies have focused on volumetric and mass-based parameters rather than routinely reported linear indices. This study aimed to evaluate the influence of age and sex on LV geometry using wall thickness, LV end-diastolic diameter (LVEDD), and proportional indices derived from standard CMR reports. Methods: In this retrospective cross-sectional study, 95 adult patients who underwent clinically indicated CMR were included. LV wall thickness, LVEDD, relative wall thickness (RWT), and wall thickness-to- LVEDD ratio (WT/LVEDD) were recorded. Participants were stratified by sex and age groups (18–40, 41–60, >60 years). Group comparisons, correlation analysis, multivariable linear regression, logistic regression, and Age × Sex interaction testing were performed to evaluate independent associated parameters of LV morphology and concentric remodeling. Results: The mean age was 34.94 ± 16.00 years; 60.0% were male. Males had significantly larger LVED (43.12 ± 6.83 mm vs. 39.76 ± 6.11 mm, p = 0.014) and greater wall thickness measurements (p < 0.05 for septal and posterior wall thickness). Age showed a significant positive correlation with mean LV wall thickness (r = 0.275, p = 0.007) and WT/LVEDD ratio (r = 0.241, p = 0.019), but not with LVEDD (p = 0.414). In multivariable analysis, male sex was independently associated with larger LVED (B = 3.345, p = 0.017), whereas age was independently associated with WT/LVEDD ratio (B = 0.0018, p = 0.019). Logistic regression demonstrated that age independently increased the odds of concentric remodeling (OR = 1.041 per year, 95% CI: 1.011–1.072, p = 0.006). No significant Age × Sex interaction was observed. Conclusions: Advancing age was independently associated with proportional LV geometric remodeling, whereas male sex primarily influenced absolute ventricular dimensions. Routine CMR report-derived linear measurements were sufficient to detect these distinct structural patterns. These findings highlighted the feasibility of using standardized morphologic indices in daily clinical practice to identify early age-related concentric remodeling. Full article

Objectives: To evaluate the diagnostic performance of automated vertebral trabecular Hounsfield unit (HU) measurements derived from routine fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) for identifying low bone density. Methods: This retrospective study included 131 consecutive women (mean age, 53.5 ± 9.6 years) undergoing health screening with FDG PET/CT and dual-energy X-ray absorptiometry (DXA) between January 2020 and December 2024. A deep learning-based model (TotalSegmentator) automatically segmented the lumbar vertebrae (L1–L4). HU-based metrics in trabecular regions were calculated, and their correlations with DXA-derived bone mineral density (BMD) were assessed. Diagnostic performance was evaluated using receiver operating characteristic analysis. A multivariable logistic regression model incorporating mean HU, age, and body mass index was developed and internally validated using bootstrap resampling. Results: According to WHO criteria, 47 of 131 participants (35.9%) had low bone density. Mean HU demonstrated strong diagnostic performance (area under the curve [95% confidence interval]: L1, 0.861 [0.800–0.923]; L2, 0.852 [0.788–0.915]; L3, 0.861 [0.800–0.921]; L4, 0.845 [0.781–0.909]). L1 mean HU provided the most balanced performance (sensitivity, 0.851; specificity, 0.750); L3 mean HU was slightly inferior. L1 mean HU was strongly correlated with BMD (r = 0.821, p < 0.001). In multivariable analysis, mean HU independently predicted low bone density (odds ratio: 0.949, p < 0.001). The model achieved an accuracy of 0.786 and demonstrated favorable calibration performance. Conclusions: The automated assessment of vertebral trabecular HU from routine FDG PET/CT provides a reliable and highly efficient method for screening low bone density without additional radiation exposure or cost. Full article

Background/Objectives: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate characterization of suspicious mammographic microcalcifications is essential for improving diagnostic decision-making and reducing unnecessary invasive procedures. Microcalcifications classified as BI-RADS 4 and BI-RADS 5 are clinically important radiological findings; however, differentiating benign from malignant lesions remains challenging because of overlapping morphological and distribution patterns. This study aimed to develop a structured feature-based machine learning model for predicting the pathological diagnosis of breast microcalcifications by integrating mammographic descriptors, patient age, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement findings. Methods: The dataset included 53 biopsy-confirmed cases and consisted of clinical and radiological variables, including patient age, calcification morphology, calcification size, distribution pattern, DCE-MRI contrast enhancement status, and histopathological outcome. Several conventional machine learning algorithms were evaluated, including Logistic Regression, Support Vector Machine with radial basis function kernel, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and CatBoost. Hyperparameter optimization was performed using grid search with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Results: Logistic Regression achieved the highest overall performance, with an accuracy of 0.909 and an F1-score of 0.889, while AdaBoost achieved a recall of 1.000 in the internal evaluation. However, given the limited sample size and lack of external validation, these findings should be interpreted as preliminary. Conclusions: The results suggest that structured radiological descriptors combined with DCE-MRI enhancement information may support malignancy risk stratification of BI-RADS 4–5 microcalcifications, although larger multicenter studies are required before clinical implementation. Full article

Background/Objectives: Apparent diffusion coefficient (ADC) calculated from diffusion-weighted MRI (DWI) can predict tumor response to neoadjuvant chemotherapy for breast cancer. However, obtaining consistently adequate image quality in breast DWI can be challenging, and the effect of image quality on ADC’s predictive performance is unclear. The objective of this study was to evaluate inter-reader variability in image quality assessment and the effect of DWI image quality on the predictive performance of ADC. Methods: This multi-institutional study included 428 patients. Two readers assessed three DWI image quality factors—fat suppression, artifacts, and signal-to-noise ratio (SNR). Inter-reader agreement was estimated using Fleiss’ Kappa. The percent change in tumor ADC from pretreatment (T0) to early treatment (T1) was used to predict pathologic complete response (pCR), assessed at surgery. Results: Out of 428 patients, 134 were excluded (missing pCR [n = 17]; missing/incorrect DWI [n = 23]; inability to define region-of-interest [ROI, n = 94]) and 294 were included in the analysis. Kappa coefficients were estimated as: 0.47 (95% confidence interval [CI]: 0.42, 0.52) for fat suppression, 0.54 (0.50, 0.59) for artifact, and 0.38 (0.32, 0.44) for SNR. The AUC of ADC calculated from DWI with adequate (high or medium at both time points) image quality was 0.61 (95% CI: 0.52, 0.702), while it was 0.68 (95% CI: 0.53, 0.83) from DWI with inadequate image quality at either T0 or T1. The p-value for the difference in AUCs was 0.45. Conclusions: The inter-reader agreement was moderate to fair across all three quality categories. When a manually delineated tumor ROI was possible, no statistically significant difference in ADC predictive performance was observed between the quality-adequate and quality-inadequate cohorts; still, both were predictive of pCR. Furthermore, no statistically significant differences were observed in inter-reader agreement or ADC predictive performance between 1.5T and 3T scanners. These findings are clinically relevant to the use of ADC as an imaging biomarker in real-world conditions. Full article

Background/Objectives: The relationship between cervical central canal and neural foraminal dimensions remains undefined. This study examined correlations between these structures in a young adult CT cohort screened to exclude apparent cervical spinal pathology. Methods: We retrospectively reviewed computed tomography images of 1000 patients aged 18 to 35 years screened to exclude apparent cervical spinal pathology. Central canal dimensions included anteroposterior diameter, interpedicular distance, and cross-sectional area. Neural foraminal dimensions included axial width, craniocaudal height, and area. Pearson correlation tests were used to assess associations between the central canal and neural foraminal dimensions. Results: Neural foraminal area showed the most consistent associations with interpedicular distance bilaterally, though these relationships were modest-to-moderate in magnitude. Axial width and craniocaudal height exhibited no consistent correlations with central canal dimensions. No strong correlations were observed between any combination of central canal and neural foraminal dimensions at any disc level. Conclusions: In this young adult CT cohort without apparent cervical spinal pathology, cervical central canal and neural foraminal dimensions demonstrated no strong correlations across levels. These findings suggest that central canal dimensions should not be used as a surrogate for neural foraminal dimensions in quantitative morphometric assessment. Full article

Objectives: This study evaluated the utility of adaptive complex signal average (ACSA) diffusion-weighted imaging (DWI) specifically in breath-hold (BH) liver imaging, with a focus on signal intensity (SI) improvement, intrahepatic signal homogeneity, and apparent diffusion coefficient (ADC) behavior, and compared these findings with conventional non-ACSA DWI and free-breathing (FB) ACSA DWI. Methods: This retrospective study included 62 patients (mean age, 67.8 ± 13.6 years; 27 women) who underwent liver MRI with both FB and BH DWI on a 3-T system. Non-ACSA images were generated using conventional magnitude reconstruction, and ACSA images were reconstructed from identical raw data. SI, signal-to-noise ratio (SNR) and ADC were measured in the left lateral segment and right hepatic lobe. The signal intensity difference ratio (SIDR) between ACSA and non-ACSA, signal intensity ratio (SIR) and ADC ratio between right lobe and lateral segment were calculated. Results: In both FB and BH imaging, SI and SNR in both liver regions were significantly higher on ACSA DWI than on non-ACSA DWI (p < 0.01). ADC values were significantly lower with ACSA. SIDR was significantly higher in the left lateral segment (p < 0.01), indicating greater SI improvement in motion-prone regions. SIR and ADC ratios between lobes were significantly smaller with ACSA in both respiratory conditions (p < 0.01). FB-ACSA showed smaller SIR than BH-ACSA, while ADC ratios did not differ. Conclusions: ACSA DWI significantly improves SI, intrahepatic uniformity, and ADC reliability even under BH liver imaging. BH ACSA DWI may represent a potentially useful application complementary to FB ACSA DWI, supporting its consideration as a post-processing strategy for improving qualitative and quantitative liver DWI in future investigations. Full article

Background/Objectives: Normal pressure hydrocephalus (NPH) is a treatable cause of gait impairment and fall risk in older adults, yet it remains frequently underdiagnosed. This study aimed to validate an automated measurement of the callosal angle, a recognized imaging marker of NPH, adapted for use on routine head computed tomography (CT). Methods: We performed a retrospective analysis of 198 patients with probable NPH, confirmed by gait improvement following lumbar tap test, and 198 age- and sex-matched controls presenting with headache and negative head CT findings (mean age 74 ± 7 years; 60% male in both groups). Manual callosal angle measurements were independently obtained by trained residents and reviewed by neuroradiologists. Automated and manual measurements were compared using intraclass correlation, and diagnostic performance was assessed across threshold values. Results: Automated callosal angle measurements demonstrated strong agreement with manual measurements (ICC = 0.90). Using an automated callosal angle threshold of <90°, diagnostic accuracy was 84.1%, with sensitivity of 90.4% and specificity of 77.8%. Optimization to a 95° threshold yielded an accuracy of 85.9%, with both sensitivity and specificity of 85.9%. The area under the receiver operating characteristic curve was 0.915 (95% CI, 0.897–0.933). Conclusions: Automated callosal angle assessment on routine head CT provides reliable and scalable detection of NPH, supporting its use as a screening tool to facilitate earlier diagnosis and treatment of a potentially reversible cause of dementia. Full article

Purpose: To evaluate how often history taking and physical examination are omitted before MRI referral and whether their omission is associated with clinical reasoning quality and MRI diagnostic yield. Materials and Methods: In this prospective study, adults undergoing MRI at a tertiary academic hospital were surveyed before imaging to determine whether the referring clinician had taken their history and performed a physical examination. Multivariable regression was used to assess determinants of omission and associations with clinical reasoning quality (defined as agreement between the suspected diagnosis and MRI findings) and MRI positivity (defined as findings relevant to the indication). Results: Among 275 patients (median age 61 years; 50.0% male), history taking was omitted in 18.2% of cases and physical examination was omitted in 70.9%. History taking was less likely during surveillance than during new/first visits (odds ratio (OR) 0.140, p < 0.001) and more likely when MRI was requested by residents rather than medical specialists (OR 4.645, p = 0.018). Physical examination was more likely when MRI was requested by residents (OR 3.174, p = 0.007) or nurse specialists/physician assistants (OR 3.145, p = 0.033), and less likely during follow-up visits (OR 0.183, p < 0.001) and surveillance visits (OR 0.061, p < 0.001). Omission of physical examination was not associated with clinical reasoning quality (p = 0.370). Neither omission of history taking nor omission of physical examination was associated with MRI positivity (p = 0.430 and p = 0.286, respectively). Conclusions: History taking and physical examination were often omitted before MRI referral. Although no statistically significant association was observed between omission of bedside assessment and clinical reasoning quality or MRI positivity, reduced bedside assessment may limit the clinical context informing referral and interpretation. Full article

Background/Objectives: Photon-counting computed tomography (PCCT) combined with radiomics enables advanced myocardial tissue characterization beyond conventional imaging. This study investigated whether myocardial radiomic features derived from PCCT are associated with nicotine status in patients without coronary artery disease. Methods: In this retrospective, single-center study, 104 patients (38 men, 66 women; median age 54 years) without coronary calcification (Agatston score = 0) underwent cardiac PCCT. Myocardial septal thickness was measured at three points during the 65–70% cardiac phase. Myocardial tissue was manually segmented, and 105 radiomic features were extracted. After correlation-based feature reduction, 45 independent features were used for analysis. Patients were categorized based on nicotine status. Machine learning models, including logistic regression, random forest, and gradient boosting, were trained and evaluated using stratified five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC) and additional classification metrics. Results: No significant differences in myocardial septal thickness were observed between smokers and non-smokers (p > 0.05). However, radiomic features enabled moderate discrimination between smokers and non-smokers. Logistic regression with L2 regularization achieved the best performance (ROC-AUC 0.66, balanced accuracy 0.67), outperforming random forest and gradient boosting models. The most relevant radiomic features primarily comprised higher-order texture and shape-based parameters associated with spatial gray-level heterogeneity and subtle variations in myocardial tissue architecture. Conclusions: PCCT-based radiomics may capture subtle myocardial imaging signatures associated with smoking status, even in the absence of structural changes detectable by conventional metrics. These findings highlight the potential of cardiac radiomics as a non-invasive imaging biomarker for early cardiovascular risk assessment and support its integration into advanced cardiac imaging workflows. Future multicenter studies with larger cohorts, external validation, and multimodal correlation are warranted to improve robustness and facilitate clinical translation. Full article

Background: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) interpolation and segmentation are critical for clinical diagnosis, anatomical quantification and personalized treatment. Most existing methods perform these two tasks separately, leading to computational redundancy and insufficient mining of shared spatial features. This study aims to construct an integrated multi-task learning framework for the synchronous processing of medical image interpolation and segmentation. Methods: We propose a unified multi-task framework named TASC-SwinMT for joint interpolation and multi-frame segmentation of CT and MRI images. It employs a shared SwinUNet encoder to extract general spatial features, matched with two task-specific decoders for frame prediction and mask generation. Three functional modules are designed for cross-task synergistic learning, and a dynamic multi-task loss function is used to balance objective optimization. Experiments are performed on Medical Segmentation Decathlon Task02_Heart and Task06_Lung datasets. Results: Our method outperforms baseline models and ablation variants in both tasks with outstanding accuracy and significantly reduced computational overhead. It exhibits superior performance in lesion boundary depiction, small object segmentation and inter-slice consistency, and anatomical prior constraints with frequency-domain modeling further enhance prediction quality. Conclusions: The cross-task feature sharing and joint optimization strategy are validated effective. The proposed TASC-SwinMT framework has favorable stability and generalization ability, providing a reliable solution for clinical medical image analysis. Full article

Objectives: To investigate whether hepatic capsular thickness (HCT) measured on ultrasonography (US) is associated with HCT measured on arterial-phase computed tomography (CT), and to evaluate the potential discriminative performance of US-measured HCT in women with Fitz–Hugh–Curtis syndrome (FHCS). Methods: In this retrospective dual-center case–control study, 17 women with clinically diagnosed FHCS who underwent both arterial-phase CT and abdominal US within a 3-day interval were included. Thirty-five healthy women served as controls. HCT was measured on CT and US by two abdominal radiologists blinded to clinical information. HCT values were compared between groups, the association between CT and US measurements was assessed, interobserver agreement was evaluated using the intraclass correlation coefficient (ICC), and receiver operating characteristic analysis was performed to explore candidate cutoff values for discriminating FHCS from controls. Results: Median HCT on CT was significantly greater in the FHCS group than in the control group [1.80 mm (IQR, 1.60–2.00) vs. 0.60 mm (IQR, 0.40–0.70); U = 595.0, p < 0.001]. Median HCT on US was also significantly greater in the FHCS group than in the control group [1.50 mm (IQR, 1.30–2.00) vs. 0.70 mm (IQR, 0.60–0.80); U = 589.0, p < 0.001]. CT- and US-based HCT measurements showed a significant positive correlation (rho = 0.66, p < 0.001). Interobserver agreement for HCT measurement was good in the overall cohort (ICC, 0.804; 95% confidence interval [CI], 0.66–0.89). In exploratory receiver operating characteristic (ROC) analysis, the candidate cutoff values were 1.1 mm for CT and 0.85 mm for US. These ROC-derived metrics should be interpreted as exploratory estimates from an idealized case–control setting rather than as real-world diagnostic performance. Conclusions: US-measured HCT was significantly increased in women with clinically diagnosed FHCS and showed a significant positive correlation of moderate strength with CT-measured HCT. These findings suggest that US-based HCT assessment may provide supportive imaging information in patients with suspected FHCS. Further validation in larger cohorts, particularly in clinically relevant control populations, is warranted. Full article

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T₁-weighted, T₂-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T₁/T₂-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P₂ (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P₂ values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P₂ was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema. Full article

Purpose: This study aimed to assess MRI-related claustrophobia severity and patient-reported experiences among Saudi patients to examine their associations with selected demographic variables. Methodology: A cross-sectional study was conducted using a structured questionnaire administered to 200 Saudi patients who had previously undergone MRI examinations. The questionnaire comprised five sections covering demographic data, phobia severity and patient-reported experiences before, during and after MRI examinations. Statistical analysis was performed using SPSS statistical package (IBM SPSS Statistics version 26, IBM Corp., Armonk, NY, USA), applying chi-square tests to examine associations between demographic variables and questionnaire responses. Results: A significant majority of participants, 76.5%, reported a positive MRI experience, whereas only 6.5% reported a negative experience. Shortness of breath during the MRI examination was the most frequently reported source of discomfort (75%). Significant associations were identified between demographic characteristics and phobia severity. Age and gender were significantly correlated with sudden fear responses, while educational level was strongly associated with receiving adequate pre-scan information and overall examination experience. Conclusions: Despite the high percentage of positive experiences, a notable proportion of participants reported anxiety-related distress during MRI examinations. The observed associations between demographic variables and claustrophobia-related responses suggest the potential value of patient-centred approaches, particularly improved pre-scan education, to improve the MRI-related patient experience and reduce anxiety-related distress. Full article

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment. Full article