Tomography

Purpose: The present study aimed to assess the radiation dose associated with low-dose (LD) CT pelvimetry compared with conventional radiography and to evaluate the adequacy of the resulting image quality. Methods: The absorbed dose was measured using thermoluminescent dosimeters positioned in an anthropomorphic female phantom, including uterine locations, to estimate the fetal dose. Conventional radiographic pelvimetry and LD-CT pelvimetry were performed using clinically implemented protocols. Effective dose was calculated using Monte Carlo–based modeling applying acquisition parameters and retrospective patient dose registry data. Image quality of LD-CT pelvimetry was independently evaluated in 14 consecutive clinical cases using a four-point ordinal scale. Results: LD-CT pelvimetry reduced the mean absorbed pelvic dose by approximately 50% compared with conventional pelvimetry (0.18 vs. 0.39 mGy) and decreased estimated fetal dose by 40% (0.21 vs. 0.37 mGy). These estimates were based on standardized single acquisitions and did not incorporate additional radiation from retakes commonly observed in conventional practice. CT demonstrated substantially more homogeneous dose distribution, whereas conventional pelvimetry exhibited marked heterogeneity with peak values up to 2.3 mGy. The maternal effective dose was lower for LD-CT (0.16 mSv) than for conventional pelvimetry (0.36 mSv); inclusion of retakes increased the conventional effective dose to 0.71 mSv. All CT examinations were diagnostically adequate, and no recalls were required. Conclusions: Optimized low-dose CT pelvimetry significantly reduces radiation dose compared with conventional radiographic pelvimetry while maintaining reliable diagnostic image quality. These results support the clinical adoption of CT-based pelvimetry as a dose-efficient and reproducible alternative to conventional techniques. Full article

Background: The Detection and localization of breast lesions remain challenging in mammography and digital breast tomosynthesis (DBT) due to tissue overlap and information loss during volumetric reconstruction. Sinograms preserve the full angular projection data acquired during scanning, enabling analysis of tissue structure without reconstruction. Methods: This study proposes a direct segmentation approach for mammography and DBT sinograms using a U-Net architecture. Experiments were conducted on 1082 annotated mammography mass images from the CBIS-DDSM dataset (521 benign, 561 malignant) and 272 annotated DBT images from the Breast Cancer Screening DBT dataset (136 benign, 136 malignant). Dataset splitting was performed at the patient level to prevent data leakage, and all reported quantitative results correspond to the independent test set, with the validation set used solely for model selection and early stopping. Three input configurations were evaluated: mammography sinograms, DBT sinograms, and a combined model. Results: The mammography model achieved the highest performance (Dice: 0.94 training, 0.90 test), outperforming DBT alone (0.77 training, 0.70 test). Multimodal fusion improved DBT results (Dice: 0.84 test). Centroid analysis showed 99.11% correspondence with reference annotations (average distance: 2.83 pixels), and partial back-projection reconstructions confirmed anatomical consistency. Compared with YOLOv5x, the proposed approach provided superior lesion localization, particularly for small or multiple lesions. Conclusions: Direct sinogram segmentation is an efficient, clinically viable strategy for breast lesion detection and localization. Full article

Background/Objectives: Automatic segmentation and identification of vertebrae in spinal CT are essential for assisting diagnosis of spinal disorders and for preoperative planning. The task is challenging due to the high structural similarity between adjacent vertebrae and the morphological variability of vertebrae. Most existing methods rely on fully supervised deep learning and, constrained by limited annotations, struggle to remain robust in complex scenarios. Methods: We propose a semi-supervised approach built on a dual-branch 3D U-Net. Mamba modules are inserted between the encoder and decoder to model long-range dependencies along the cranio–caudal axis. The identification branch employs a 3D convolutional block attention module (3D-CBAM) to enhance class discriminability. A unified semi-supervised objective is formulated via teacher–student consistency: for each unlabeled sample, weakly and strongly augmented views are generated, and cross-branch consistency is enforced, together with confidence-based filtering and class-frequency reweighting. In addition, a connected-component analysis is used to enforce anatomically plausible sequential continuity of vertebral indices in the outputs. Results: Experiments on VerSe 2019 and 2020 show that, on the public VerSe 2019 test set (with VerSe 2020 scans used as unlabeled training data), the supervised baseline achieved a Dice score of 89.8% and an identification accuracy of 92.3%. Incorporating unlabeled data improved performance to 91.6% Dice and 97.5% identification accuracy (relative gains of +1.8 and +5.2 percentage points). Compared with competing methods, the proposed semi-supervised model attains higher or comparable segmentation accuracy and the highest identification accuracy. Conclusions: Without additional annotation cost, the proposed method markedly improves the overall performance of vertebra segmentation and identification, offering more robust automated support for clinical workflows. Full article

Background/Objectives: Skull base osteomyelitis and necrotising otitis externa require prolonged antibiotic therapy, yet determining optimal treatment cessation timing remains challenging. Conventional imaging modalities demonstrate persistent abnormalities beyond infection resolution, confounding treatment decisions. This systematic review evaluated the diagnostic test accuracy of ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) for treatment response monitoring in skull base osteomyelitis and necrotising otitis externa. Methods: We conducted a systematic review following PRISMA-DTA guidelines, searching MEDLINE, Embase, CENTRAL, CINAHL, Scopus, and Web of Science from inception to November 2025. Studies evaluating ¹⁸F-FDG PET diagnostic accuracy for treatment response assessment in confirmed skull base osteomyelitis or necrotising otitis externa were included. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2. Bivariate random-effects meta-analysis was performed using MetaBayesDTA to obtain pooled sensitivity and specificity. Results: Eight studies comprising 154 lesions contributed to the primary analysis. Pooled sensitivity was 95.2% (95% credible interval 85.6–99.0%) and pooled specificity was 89.1% (95% credible interval 70.7–96.7%). The positive likelihood ratio was 8.7 (95% credible interval 3.2–28.4) and negative likelihood ratio was 0.05 (95% credible interval 0.01–0.17), with a diagnostic odds ratio of 172.0. Seven studies evaluating detection rate at initial presentation yielded a pooled rate of 96.1% (95% confidence interval 91.3–98.3%). SUVmax was the most frequently used metabolic parameter. Conclusions: ¹⁸F-FDG PET, specifically using SUVmax, demonstrates high sensitivity and good specificity for treatment response monitoring, with excellent capacity to rule out persistent infection. However, evidence quality is limited by retrospective designs and substantial heterogeneity. Prospective studies with standardised thresholds are needed to validate clinical utility. Full article

Background: In temporal lobe epilepsy (TLE), locally reduced glucose metabolism (i.e., hypometabolism) is indicative of the epileptogenic onset zone (EZ). Here, we investigate the potential value of resting-state fMRI (rs-fMRI) for localizing the EZ with fluorodeoxyglucose positron emission tomography (FDG-PET) as ground truth. Methods: Twelve PET-positive patients (34.1 ± 13.1 y; 5 females) with unilateral drug-resistant TLE were included. FDG-PET and rs-fMRI were acquired simultaneously at a hybrid 3T PET-MR scanner. Hypometabolic regions were identified on the FDG-PET images by a nuclear medicine expert. The FDG-PET images were compared with a clinical FDG-PET control dataset with normal glucose uptake distribution. The output z-score maps were thresholded at z < −2 to produce a binary mask of the significantly hypometabolic regions. The hypometabolism masks were mirrored onto the contralateral hemisphere for the asymmetry comparison. Regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) were calculated from the rs-fMRI in conventional (0.01–0.1 Hz) and slow-3 (0.073–0.198 Hz) frequency bands. Asymmetry indices (AIs) were calculated using the ipsilateral and contralateral hypometabolic masks in the PET-positive subjects and assessed via the one-sample Wilcoxon test and Spearman correlation coefficients. Results: The AIs of conventional fALFF were significantly lower in the hypometabolic zone (p < 0.05). A significant negative correlation was found between the AIs of FDG-PET and fALFF in the slow-3 band (r = −0.62; p < 0.05). Conclusions: Conventional and slow-3 band fALFF showed a potential to mimic the FDG-PET findings in terms of EZ localization. Further research with extended cohorts and histopathological validation is required to determine the clinical value. Full article

Background/Objectives: Stroke is a leading cause of long-term disability, and predicting functional outcome at discharge, such as the modified Rankin Scale (mRS), is important for guiding treatment and rehabilitation. Many existing approaches depend on advanced imaging or complex corticospinal tract (CST) segmentation from multi-shell diffusion MRI, limiting clinical feasibility. Automated lesion segmentation is also challenging due to lesion heterogeneity and MRI variability. This study proposes a clinically feasible multimodal MRI pipeline based on routine imaging. Methods: Lesion segmentation models were trained and evaluated on the ISLES 2022 dataset (250 training, 150 test cases). Zero-shot external evaluation was performed on 149 cases from ISLES 2024 using standard MRI sequences only. An ensemble of deep learning models (SEALS, NVAUTO, FACTORIZER) was evaluated on ISLES 2022, while SEALS alone was used for external testing. CST segmentation was performed using TractSeg on single-shell diffusion-weighted imaging. Imaging biomarkers included lesion volume, shape, ADC-based texture features, CST integrity, and lesion–CST overlap. These features were used to train machine learning models for binary mRS prediction at discharge. Results: The ensemble achieved a Dice score of 0.82 on ISLES 2022, while zero-shot evaluation on ISLES 2024 achieved 0.57. In exploratory analysis, CatBoost achieved the highest point estimates (accuracy 0.88, F1-score 0.87, ROC-AUC 0.83). Key predictors included lesion–CST overlap, lesion volume, surface area, dissimilarity, and contrast. Conclusions: This exploratory study demonstrates the feasibility of combining automated lesion segmentation with anatomically informed biomarkers using routine clinical MRI, supporting interpretable stroke outcome modelling and motivating future large-scale validation. Full article

Background: Stroke is a leading cause of mortality and long-term disability, and aphasia is among its most common and debilitating sequelae. Diffusion tensor imaging (DTI) and advanced diffusion imaging techniques enable the assessment of white matter integrity and provide clinically relevant measures in post-stroke aphasia. Methods: We conducted a comprehensive review of studies applying DTI or advanced diffusion imaging to investigate structural connectivity in adults with post-stroke aphasia (PSA). PubMed, CENTRAL, Ovid MEDLINE, and Embase were searched, and eligible studies were synthesized according to their diagnostic, prognostic, or therapeutic focus. Results: Ninety-five studies were included. Of these, 59 were classified as diagnostic, 17 as prognostic, and 19 as therapeutic. Most studies employed conventional DTI (n = 77), while a growing body of research utilized advanced diffusion models, including CSD, DSI, and DKI (n = 18). Conclusions: This comprehensive synthesis demonstrates the evolution of diffusion imaging in PSA research. While conventional DTI has provided foundational insights, advanced diffusion methods offer superior characterization of complex fiber architecture and improved clinical–anatomical correlation. Diffusion-derived markers of dorsal and ventral language pathways were consistently associated with language performance, while connectome-level analyses highlighted the importance of preserved global network architecture for recovery. Continued efforts are needed to translate diffusion imaging findings into clinical applicable biomarkers to guide personalized aphasia rehabilitation, with greater use of advanced methods. Full article

Background/Objectives: To investigate and develop a non-invasive parametric radiomics model derived from dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) kinetics for predicting breast cancer molecular subtypes (HR+/HER2−, HER2+ and triple-negative breast cancer). Methods: This multicenter retrospective study enrolled 935 female patients with histologically confirmed breast cancer who underwent pretreatment breast DCE-MRI from August 2017 to July 2022. Based on the wash-in rate (WIR) and the area under the TIC, the original multiphase DCE-MRI images were converted into two types of parametric images. Radiomics features were extracted from TIC-WIR and TIC-Area images and analyzed using low variance filtering, the elimination of highly correlated features, and the least absolute shrinkage and selection operator regression. The categorical boosting algorithm was employed to develop multiclass prediction models for breast cancer molecular subtyping. A TIC-Combined model was further established by integrating the calibrated probability outputs of the TIC-WIR and TIC-Area models using a decision-level fusion strategy. The discrimination, calibration, and interpretability of the models were evaluated in the study datasets. Results: The TIC-Combined model achieved superior predictive performance in both the internal validation set (micro-average AUC: 0.79, macro-average AUC: 0.77) and the external validation set (micro-average AUC: 0.77, macro-average AUC: 0.75). For subtype-specific classification by the TIC-Combined model, the highest one-vs-rest AUCs were 0.81 for triple-negative breast cancer in the internal validation set and 0.76 for HER2+ breast cancer in the external validation set. The TIC-Combined model also showed good calibration and high interpretability which ensured reliable predictions and provided clear insights into feature importance. Conclusions: Interpretable parametric radiomics from TIC-derived parametric maps links kinetic features to molecular phenotypes, enabling accurate and non-invasive classification of breast cancer molecular subtypes. Full article

Background/Objectives: To evaluate and characterize anatomical variations in the sphenoid sinus in the Saudi population using computed tomography (CT). Methods: This retrospective cross-sectional study included patients aged ≥18 years who underwent multi-detector CT (MDCT) of the paranasal sinuses at King Fahd University Hospital between July 2018 and 2023 for different indications. Radiological variables analyzed included sphenoid sinus pneumatization type, presence and number of inter-sphenoid septa, and deviation or attachment to adjacent structures. Results: The data of 2433 patients were analyzed (44.5% males, 55.5% females; mean age 40 ± 15 years). The mean sphenoid sinus volume was 20.4 ± 8.7 cm³, significantly larger in males (p < 0.001). The most common sinus shape was quadrilateral (33%), whereas the predominant pneumatization pattern was post-sellar (57.1%), followed by sellar (32.1%), pre-sellar (9.2%), and conchal (1.6%). Adjacent-structure pneumatization was frequent, most notably in the greater wing of the sphenoid (47.4%) and pterygoid (39%) processes. Optic-canal protrusion and dehiscence were observed in 13.9% and 4.1%, respectively, whereas carotid canal protrusion occurred in 22.2% and dehiscence in 3.2%. Intra-sinus septation was identified in 97.7% of assessable cases, most commonly as a single septum (59.6%). Several variants showed significant sex-related associations, including sinus volume, anterior clinoid process/posterior clinoid process pneumatization, and dehiscence patterns. Conclusions: CT imaging revealed considerable diversity in the sphenoid-sinus anatomy among the Saudi population. Awareness of these variations, particularly their relationship with critical neurovascular structures, is crucial for radiologists and surgeons to ensure accurate diagnosis and safe surgical planning. Full article

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology. Full article

Background: Acute abdominal emergencies represent a major diagnostic challenge in emergency medicine, requiring rapid and accurate integration of clinical, laboratory, and imaging data. Although laboratory parameters play a central role in real-world diagnostic workflows, the extent to which they are systematically represented and integrated within radiology research publications remains unclear. Objective: To evaluate how laboratory data are represented, contextualized, and functionally utilized in radiology publications focusing on computed tomography (CT)–based evaluation of acute abdominal emergencies. Methods: A methodological content analysis was conducted on 72 original radiology research articles published between 2020 and 2024. Eligible studies focused on CT-based imaging of acute abdominal emergency conditions. Publications were screened and analyzed at the title and abstract level using a predefined coding framework to assess the presence of laboratory data, types of laboratory parameters reported, their contextual role (background information, imaging trigger, diagnostic modifier, or prognostic indicator), degree of laboratory–imaging integration, and presence of decision-oriented reporting. Descriptive statistics were used to summarize reporting patterns. Results: Laboratory data were reported in 61.1% of all included studies (n = 44/72). However, their functional utilization varied substantially. Laboratory parameters were most frequently presented as background clinical information, whereas explicit use as imaging triggers (26.4%, n = 19/72), diagnostic modifiers (19.4%, n = 14/72), or components of explicit laboratory–imaging integration (15.3%, n = 11/72) was less common. Decision-oriented reporting was present in 23.6% of all studies (n = 17/72). Explicit integration was described in publications addressing diagnostically complex and time-sensitive conditions, such as acute bowel ischemia and severe acute pancreatitis. Conclusion: Laboratory data are commonly reported in CT-based radiology publications addressing acute abdominal emergencies; however, the manner in which these data are incorporated into imaging-centered diagnostic narratives varies across studies. Differences are observed in how laboratory–imaging relationships are described, with some publications presenting integrated discussion and others reporting imaging findings independently of laboratory context. These observations characterize reporting practices within the analyzed literature and do not imply statistical associations or causal effects. Full article

Objectives: Due to collimator limitations, Single-Photon Emission Computed Tomography (SPECT) suffers from relatively low spatial resolution, which hampers the detection of small lesions. This study proposes a super-resolution (SR) reconstruction algorithm for a parallel-beam, low-resolution (LR) multi-detector SPECT system and employs a neural network to estimate and correct for geometric errors in the LR detectors. Methods: A parallel-beam LR multi-detector SPECT system is presented, in which the detectors perform relative sub-pixel shifts. At each sampling angle, an SR reconstruction algorithm synthesizes high-resolution (HR) SPECT images from LR projections acquired by four offset LR detectors. To correct for geometric errors among these detectors, a randomly distributed gamma point source was designed to generate training data. A neural network was then employed to estimate the geometric errors, thereby refining the SR reconstruction. Results: Numerical simulation demonstrated that the proposed neural network could accurately identify the displacement-based geometric errors of the LR detectors. Utilizing these estimated parameters to correct the SR reconstruction process yielded results comparable to those obtained from direct reconstruction of HR projections, achieving a two-fold resolution improvement. Conclusions: Preliminary proof-of-principle for SR reconstruction in a parallel-beam LR multi-detector SPECT system was established. Further validation of the hardware performance is warranted. Full article

Background: Visceral artery aneurysms (VAAs) are rare but potentially life-threatening vascular lesions often clinically silent until rupture. The widespread use of advanced imaging has increased incidental detection, highlighting the need for accurate, noninvasive diagnostic strategies. Dual-Energy Computed Tomography Angiography (DECTA) offers potential advantages over conventional CT across diagnostic and post-treatment settings; however, its role in VAAs remains incompletely defined. This narrative review summarizes current evidence on DECTA applications in VAAs, focusing on diagnosis, emergency evaluation, and post-treatment follow-up. Methods: A non-systematic literature search of PubMed and Embase focusing on English-language articles up to June 2025 was performed. The search included peer-reviewed original research articles, systematic reviews, and meta-analyses addressing dual-energy CT and spectral CT in vascular and aneurysmal imaging. Case reports without technical data and non-English articles were excluded. Results: In the diagnostic phase, DECTA enhances tissue differentiation through virtual monoenergetic images, iodine maps, and material decomposition reconstructions. In the post-treatment setting, DECTA supports assessment after endovascular procedures, including coil embolization or stent graft placement. In VAAs, these techniques may improve aneurysm delineation, reduce metal artifacts after endovascular treatment, enable accurate detection of endoleaks or residual perfusion, and support volumetric follow-up. Virtual Non-Contrast images may reduce radiation exposure without compromising diagnostic confidence. Conclusions: DECTA represents a versatile imaging modality with potential benefits across the diagnostic, emergency, and post-treatment phases of VAA management. Although many applications are extrapolated from aortic and peripheral vascular disease, emerging evidence supports its growing clinical relevance. Further dedicated studies are needed to define its role in VAA-specific decision-making and follow-up. Full article

Objectives: To evaluate regional anatomical differences in brain volume, surface area, and cortical thickness between children with breath-holding spells (BHSs) and a control group using morphometric MRI analyses. Methods: Three-dimensional T1-weighted cranial MRI data from 48 children with BHSs and 50 control children were retrospectively analyzed, yielding volumetric, surface area, and cortical thickness measures for 135 brain regions. All measurements were assessed relative to total intracranial volume (ICV). Group comparisons were performed using analysis of covariance with age, sex, and ICV as covariates, followed by Benjamini–Hochberg false discovery rate correction (q < 0.05). Results: The BHS group exhibited reduced bilateral amygdala volumes (left: q = 0.042; right: q = 0.038). Both cortical thickness and volume were reduced in the right anterior insula (thickness: q = 0.046; volume: q = 0.049). In addition, cortical thickness was reduced in the bilateral anterior cingulate cortices (left: p = 0.019, q = 0.045; right: p = 0.017, q = 0.043) as well as in the right medial frontal cortex (p = 0.009, q = 0.036). Subregional cerebellar analysis demonstrated volume reductions in the right lobule VI (q = 0.031), left lobule VIIA (Crus I) (q = 0.043), and vermis IX–X (q = 0.039). Conclusions: Detecting measurable morphometric changes in brain regions involved in autonomic and emotional regulation in children with BHSs will contribute to understanding the neurobiological characteristics associated with BHSs. Full article

Objectives: This retrospective, multi-center study analyzed pre-existing anonymized clinical data from electronic health records and imaging archives. The analysis utilized real-world clinical data from 200 patients across four tertiary care centers, without additional patient recruitment or interventions. This study aims to investigate the impact of metabolic and physiological factors—specifically blood glucose levels, cortisol concentrations, fasting duration, and tumor histology—on the quality and diagnostic reliability of ¹⁸F-FDG PET/CT imaging in patients with primary brain tumors and inflammatory lesions. Methods: A total of 200 patients with primary brain tumors (including astrocytoma, glioblastoma, meningioma, and oligodendroglioma) were evaluated across four institutions using standardized protocols. The study examined the effects of prolonged fasting (>12 h), hyperglycemia (>150 mg/dL), and strict fasting (4–6 h) on tumor-to-background contrast and visual analog scale (DQS) scores. Results: Prolonged fasting was associated with elevated cortisol levels (correlation +0.54, p < 0.001), while hyperglycemia significantly reduced tumor SUVmax by up to 20% (r = −0.35, p = 0.012). Strict fasting and glucose control resulted in improved tumor-to-background contrast and DQS scores (r = +0.83, p < 0.001). Glioblastomas exhibited the highest SUVmax (9.1 ± 3.5), indicating aggressive metabolic activity, whereas meningiomas showed elevated cortisol levels (20.5 ± 6.8 µg/dL) linked to disruption of the hypothalamic–pituitary axis. Regression analysis confirmed that both cortisol and glucose levels independently degraded image quality (β = −0.25 and −0.18, respectively; p < 0.05). Conclusions: The findings highlight the necessity for harmonized patient preparation protocols. Recommendations are in alignment with the SNMMI Procedure Standard/EANM Practice Guideline for Brain [¹⁸F] FDG PET imaging. Full article

Background: This study aimed to investigate the association between femoroacetabular impingement (FAI) morphology and femoral head bone marrow edema of unknown etiology on hip magnetic resonance imaging (MRI), and to assess the added value of computed tomography-based three-dimensional maximum intensity projection (CT-MIP) measurements in identifying a predisposition to acetabular overcoverage. Methods: Hip MRI examinations performed between January 2007 and 2025 were retrospectively reviewed. Cases with bone marrow edema attributable to identifiable etiologies were excluded. Twenty-six patients with available hip or pelvis computed tomography (CT) examinations obtained within one year were included, along with an age- and sex-matched control group imaged for indications unrelated to hip pain. A total of 104 hip joints were evaluated. Alpha angles were measured on axial oblique CT reformations. Virtual pelvic radiographs generated from CT-based three-dimensional reconstructions were used for lateral center-edge angle (LCEA) measurements, and acetabular coverage was quantified using the acetabular coverage index derived from CT-MIP images. Appropriate statistical analyses were performed, with p < 0.05 considered statistically significant. Results: FAI was identified in 82.7% of cases with bone marrow edema of unknown etiology on MRI (p < 0.001), with pincer-type morphology being the most prevalent subtype (55.8%). Bone marrow edema was significantly more common in pincer-type FAI compared with other subtypes (p < 0.001) and predominantly involved the posterolateral femoral head. Mean alpha angle, LCEA, and acetabular coverage index values were significantly higher in the case group than in controls (p < 0.001). For the detection of pincer-type FAI, CT-MIP-based acetabular coverage index demonstrated superior diagnostic performance compared with LCEA (AUC, 0.917 vs. 0.855; p = 0.017), with an optimal cutoff value of 0.93 yielding high specificity and accuracy. All measurements showed excellent intraobserver and interobserver reliability. Conclusions: Femoral head bone marrow edema of unknown etiology may serve as a radiologic clue to underlying pincer-type FAI, while CT-MIP-based analyses may provide incremental value beyond conventional angular measurements in characterizing acetabular overcoverage. Full article

Background/Objectives: Tissue conductivity reflects ionic composition (e.g., sodium), providing critical insights into various diseases. Ultrashort echo time quantitative conductivity mapping (UTE-QCM) offers a method to obtain this information, which is particularly effective for musculoskeletal (MSK) tissues with short T2 relaxation times. The aim of this study is to develop a UTE-QCM framework using ultrashort echo time double echo steady-state (UTE-DESS) and validate its feasibility in the knee. Methods: An ultrashort echo time double echo steady-state (UTE-DESS) sequence was used to acquire S+ and S− images and estimate the transmit radiofrequency field (B1⁺) phase at 3T. The B1⁺ phase was derived by canceling the phase evolution in the free induction decay using these images. This phase data was then processed using two widely used QCM reconstruction methods for comparison: parabolic fitting and an integral-based method. The proposed UTE-QCM framework was validated using a phantom containing three different concentrations of sodium chloride (0%, 0.5%, and 1%). Additionally, three healthy volunteers were recruited to validate UTE-QCM in knee imaging. Results: In both phantom and in vivo experiments, the integral-based QCM demonstrated improved robustness to noise compared to parabolic fitting. In the sodium phantom, the estimated conductivity showed high linearity with sodium concentrations. In the in vivo knee, the generated conductivity maps successfully visualized both long and short T2 tissues. Conclusions: We demonstrated the feasibility of UTE-QCM as a novel quantitative imaging tool targeting short T2 tissues in the MSK system. This technique may facilitate the diagnosis and prognosis of joint disorders. Full article

Background/Objectives: We compared the visibility of breast cancer using the newly developed standing automated breast ultrasound system (MammouS-N) and digital breast tomosynthesis (DBT), and identified factors influencing lesion visibility. Methods: We prospectively enrolled 100 women (mean age: 51.6 years; range: 26–76 years) who were diagnosed with breast cancer and were scheduled to undergo DBT between January and July 2024. They underwent DBT and an ultrasound on the same day. Two radiologists evaluated the visibility scores (0–5) of lesions corresponding to biopsy-confirmed breast cancers identified using magnetic resonance imaging. The Wilcoxon signed-rank test was used to compare the visibility scores of cancers identified on DBT and/or MammouS-N images. Results: Among the 100 women, invasive ductal carcinoma was the most common malignancy (73%). DBT findings included negative findings (7%), masses (46%), masses with calcification (29%), calcifications only (15%), and architectural distortions (3%). On MammouS-N ultrasound, most lesions were classified as masses (93%), whereas 7% were non-mass lesions. For Reviewer 1, MammouS-N demonstrated significantly higher visibility scores (higher scores: 26 on MammouS-N, seven on DBT; equal scores: 67, z = −3.234, p = 0.001). For Reviewer 2, the two modalities showed no significant difference in visibility (higher scores: 27 on MammouS-N, 28 on DBT, equal scores: 45, z = −0.040, p = 0.968). Noncalcified lesions that were obscured on DBT were better visualized on MammouS-N (p < 0.001) by both reviewers. Conclusions: MammouS-N holds promise as an imaging modality complementary to DBT in women with dense breast tissue, particularly for non-calcified lesion detection. Full article