Purpose: This study aimed to assess the utility of native T1 mapping for the evaluation of myocardial iron overload in patients with Thalassemia Major. T1 was compared to T2*, which represents the gold standard for iron quantification in the heart and liver. Methods: Consecutive patients with Thalassemia Major who performed cardiac MRI at the University Hospital of Sassari between 2022 and 2024 were prospectively included. All patients underwent a 1.5 T MRI with the same scanner (Philips Ingenia). T2* and native T1 mapping (MOLLI) sequences were performed in all patients on a mid-ventricular single 8 mm short-axis slice of the left ventricle. A region of interest was manually drawn in the septal wall. A T2* value < 20 ms was considered indicative of significant myocardial iron overload. A normal lower limit value of 990 ms was adopted for native T1 mapping. Results: In total, 100 patients with Thalassemia Major were included (median age, 45 [range, 7–80] years; 55% were male). The median myocardial T2* value was 31.4 (range, 5.1–47) and median T1 was 941 ms (range, 557–1131). A total of 12 patients (12%) exhibited T2* values < 20 ms; the T1 values in these patients (median, 733.8 ms [range, 557–975]) were significantly lower compared to those with a T2* of 20 ms or greater (median, 961 ms [range, 820–1131]), p < 0.001. No patient with T2* < 20 ms had a T1 value greater than or equal to 990 ms. Among the 88 patients with T2* ≥ 20 ms, 56 (64%) had T1 < 990 ms (median, 939.2 ms [range, 820–986]). Using a T1 threshold of 990 ms, the sensitivity was 100%, but the specificity was only 36%. ROC analysis identified an optimal T1 value of 895.5 ms, corresponding to 92% sensitivity and 100% specificity. Conclusions: Native T1 mapping is highly sensitive for detecting myocardial iron overload in Thalassemia Major, but the standard 990 ms threshold generates many false-positive results. In our cohort, adopting an ROC-optimized threshold of 895.5 ms markedly improved specificity while preserving excellent sensitivity.

Background/Objectives: We aim to evaluate whether digital imaging and communications in medicine (DICOM)-level deep learning-based iodine-boosting applied to dual-source dual-energy computed tomography (DECT) source DICOM improves image quality in low-iodine-dose abdominal DECT in adults undergoing post-procedure follow-up computed tomography (CT). Methods: This retrospective study included 43 adults (April–September 2025) who underwent dynamic dual-source DECT using a low-iodine protocol. Three CT reconstructions were compared: mixed images, conventional 50-keV virtual monoenergetic images (VMIs), and 50-keV VMIs generated after applying DICOM-based deep learning iodine-boosting/denoising to the tube-specific dual-energy source DICOM series prior to VMI/iodine-map reconstruction (deep learning-based reconstruction [DLR]-VMI). Iodine material density (IMD) images were compared between the conventional and DLR-processed datasets. Quantitative attenuation and signal-to-noise ratio (SNR) were assessed using paired and repeated-measures tests. Image quality was scored by two readers using a five-point Likert scale. Results: Attenuation varied across CT reconstructions for all regions of interest in both phases (all overall p < 0.001). Liver attenuation increased from 94.9 ± 22.0 Hounsfield units (HU) (VMI) to 114.5 ± 34.6 HU (DLR-VMI) during the arterial phase and from 127.6 ± 25.6 HU to 166.6 ± 39.9 HU during the portal venous phase (both p < 0.001). Liver SNR improved with DLR-VMI compared to VMI (arterial: 9.11 ± 3.62 vs. 6.06 ± 1.90; portal: 12.74 ± 3.56 vs. 7.90 ± 1.82; both p < 0.001). On IMD images, DLR increased HU-equivalent values and liver SNR (arterial: 5.20 ± 2.89 vs. 2.61 ± 1.39; portal: 9.22 ± 2.81 vs. 4.48 ± 1.28; both p < 0.001). Qualitatively, DLR-VMI yielded the highest overall image-quality scores for both reviewers in both phases (Reviewer 1, arterial/portal: 4 (4–5)/5 (4–5); Reviewer 2, arterial/portal: 4 (3–4)/4 (4–4)). DLR also improved the overall image quality of IMD images for both reviewers (all p < 0.001). Conclusions: Raw DICOM-level iodine-boosting DLR applied to dual-source DECT-source DICOM enabled enhanced image quality and improved quantitative and qualitative metrics in low-iodine-dose abdominal DECT.

Background: The fat attenuation index (FAI) derived from coronary computed tomography angiography (CTA) is an emerging imaging biomarker of perivascular inflammation. Coronary flow reserve (CFR), assessed by phase-contrast (PC) cine cardiac magnetic resonance (CMR) of the coronary sinus, reflects coronary microvascular function. Although FAI has been linked to adverse outcomes in coronary artery disease (CAD), its relationship with CFR across different CAD stages is not well defined. Methods: We retrospectively evaluated 241 patients (mean age 73.4 ± 10.8 years; 149 men [61.8%]) who underwent both coronary CTA and CMR (122 with known CAD and 119 with suspected CAD). FAI was measured in the proximal left anterior descending (LAD), left circumflex (LCX), and right coronary (RCA) arteries. Impaired CFR was defined as <2.0. Univariable and multivariable logistic regression analyses were performed to identify factors associated with impaired CFR. Results: Impaired CFR was observed in 38 of 122 patients (31.1%) with known CAD and 26 of 119 (21.8%) with suspected CAD. Higher LAD-FAI was associated with impaired CFR in both groups: OR 1.06 (95% CI 1.01–1.11; p = 0.018) in known CAD and OR 1.08 (95% CI 1.02–1.15; p = 0.017) in suspected CAD. Correlation analysis also demonstrated an inverse relationship between LAD-FAI and CFR (p < 0.001), and the strength of association was comparable between the two groups. Conclusions: LAD-FAI was associated with impaired CFR in both suspected and known CAD, with comparable strength of association across the two groups. These findings indicate that perivascular inflammation, reflected by FAI, may relate to coronary microvascular dysfunction in different stages of CAD.

Acute traumatic aortic injury (ATAI) is a rare but life-threatening consequence of blunt trauma that requires prompt diagnosis and accurate imaging assessment. This review presents an imaging-based approach to ATAI, with emphasis on computed tomography angiography (CTA) as the first-line modality for diagnosis, grading, treatment planning, and follow-up. CTA enables the detection of both direct and indirect signs while also allowing for the assessment of lesion severity, extent, and associated findings that may influence management. Familiarity with common mimics and anatomic variants improves diagnostic confidence and helps avoid false positive interpretations. Careful protocol optimization, including multiphasic acquisition, bolus timing, and postprocessing reconstructions, can further enhance image quality and diagnostic performance. Recognition of patient-related and technical CTA artifacts, along with strategies to reduce them, including the selective use of ECG-gated CTA, may further decrease diagnostic uncertainty. We also discuss the complementary roles of emerging CT technologies and magnetic resonance angiography in selected patients. Finally, we review current classification systems, imaging-guided management, post-treatment surveillance, and potential complications. Awareness of ATAI imaging findings, protocol optimization, and diagnostic pitfalls is essential for accurate interpretation and effective multidisciplinary care.

Background: The Magnetic Resonance Imaging (MRI)-based tumor segmentation remains a challenging problem in medical imaging due to tumor heterogeneity, unpredictable morphological features, and the high complexity of calculations needed to implement it in clinical practice, putting it out of the scope of real-time applications. Although neural networks have significantly improved segmentation performance, they still struggle to capture morphological tumor features while maintaining computational efficiency. This work introduces Hybrid Adaptive Attention U-Net (HAAU-Net) framework, combining context-aware morphologically stable features and spatial channel attention to achieve high-quality tumor segmentation with less computational cost. Methods: The proposed HAAU-Net framework integrates multi-scale Adaptive Attention Blocks (AAB), Context-Aware Morphological Feature Module (CAMFM) and Spatial-Channel Hybrid Attention Mechanism (SCHAM). CAMFM is used to maintain the stability of morphological features by hierarchical aggregation and dynamic normalization of features. SCHAM enhances feature representation by modelling channels and spatial regions where the strongest feature are determined to use in segmentation. On the BRaTS 2022/2023 data, the proposed HAAU-Net is evaluated using four modalities including T1, T1GD, T2 and T2-FLAIR sequences. Results: The proposed model able to obtain 96.8% segmentation accuracy with a Dice coefficient of 0.89 on the entire tumor region, outperforming the alternative U-Net (0.83) and conventional CNN methods of segmentation (0.81). The proposed HAAU-Net architecture cuts the computational complexity of the standard deep learning models by 43% and still achieve real-time inference (28 FPS on a regular GPU). The hybrid model used to predict survival has a C-Index of 0.91 which is higher than the traditional SVM-based methods (0.72). Conclusions: Spatial-channel attention, combined with morphologically stable features, can be combined to allow clinically significant interpretability in attention maps. The proposed framework significantly improves segmentation performance while maintaining computational effeciency. This broad system has a serious potential of AI-enabled clinical decision support system and early prognostic diagnosis in neuro-oncology with practical deployment capability.

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.

Background/Objectives: Diffusion-weighted imaging (DWI) is a magnetic resonance technique used to map the apparent diffusion coefficient (ADC) of water in human tissue. ADC assessment plays a central role in clinical diagnostics, as malignant tissues typically exhibit reduced water mobility and, thus, lower ADC values. Accurately measuring the ADC requires effective fat suppression to prevent contamination from the residual fat signal, which is commonly believed to cause ADC underestimation. This study aimed to demonstrate that ADC overestimation may occur as well. Methods: Our theoretical analysis shows that out-of-phase conditions between fat and water signals lead to ADC overestimations. We performed demonstration experiments on fat-water phantoms and the breasts of 10 healthy female volunteers. In particular, we considered three out-of-phase conditions: First and second, short-time inversion recovery (STIR) fat suppression with incorrect inversion time and incorrect flip angle, respectively. Third, phase differences due to spectral fat saturation. The ADC values were assessed in regions of interest (ROIs) that included both water and residual fat signals. Results: In the phantoms and the volunteer data, ROIs containing both fat and water signals consistently exhibited lower ADC values under in-phase conditions and higher ADC values under out-of-phase conditions. Conclusions: We demonstrated that out-of-phase conditions can result in ADC overestimation in the presence of residual fat signals, potentially resulting in false-negative classifications where malignant lesions are misinterpreted as benign due to an elevated ADC. Out-of-phase fat and water signals might also reduce lesion conspicuity in high b-value images, potentially masking clinically relevant findings.

Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI) serves as a visual biofeedback tool, providing real-time imaging to enhance PFM training, motor learning, and treatment adherence. Aim: This narrative review evaluates the role and efficacy of RUSI in pelvic floor rehabilitation. Method: A comprehensive search of PubMed, Cochrane, and MEDLINE was conducted using keywords related to pelvic floor rehabilitation, ultrasound, and biofeedback, limited to English-language publications up to July 2025. Systematic reviews, meta-analyses, and clinical trials were prioritized. Results: Transperineal and transabdominal ultrasound improve PFM function across diverse populations. In post-prostatectomy men, transperineal ultrasound-guided training enhanced PFM contraction and reduced urinary leakage. In postpartum women with pelvic girdle pain, transabdominal ultrasound-guided biofeedback combined with exercises decreased pain and improved function. Ultrasound-guided pelvic floor muscle contraction demonstrated superior performance compared to verbal instruction. Notably, 57% of participants who were unable to contract the pelvic floor muscles with verbal cues achieved a correct contraction with ultrasound biofeedback, and this approach also resulted in more sustained improvements in PFM strength. Compared to other biofeedback modalities, RUSI demonstrated outcomes that are comparable to or superior to those of alternative methods. However, evidence is limited by a lack of standardized protocols and randomized controlled trials comparing RUSI with other modalities. Conclusions: RUSI is an effective visual biofeedback tool that enhances outcomes of PFM training in pelvic floor rehabilitation. It supports clinical decision-making and patient engagement, particularly in cases where traditional assessments are challenging. Further research, including the development of standardized protocols and comparative trials, is necessary to optimize the clinical integration of this method and confirm its superiority over other biofeedback methods.

The pterygomaxillary region is a complex anatomical area formed by the junction of the maxillary, palatine, and sphenoid bones and contains critical neurovascular structures. Accurate assessment of this region during Le Fort I osteotomy is essential, particularly to prevent hemorrhage and nerve injury that may occur during the pterygomaxillary separation phase. This study aims to investigate the morphometric characteristics of the pterygomaxillary region using cone-beam computed tomography (CBCT) and to evaluate the effects of age, sex, and laterality on these anatomical parameters. In this retrospective study, CBCT scans of 200 individuals (100 males and 100 females) aged 20-80 years were analyzed. Axial measurements included distances between the piriform rim, the descending palatine artery, the pterygomaxillary osteotomy line, and the pterygomaxillary fissure. Additionally, the thickness and width of the pterygomaxillary region and pterygoid process, lengths of the medial and lateral pterygoid laminae, and the distance between the greater palatine canal and the medial pterygoid lamina apex were recorded. Measurements were statistically evaluated by sex, age group, and laterality. The following parameters demonstrated statistically significant differences based on the conducted measurements: The distance between the piriform rim and the descending palatine artery was significantly greater on the left side (p < 0.001). The length of the lateral pterygoid lamina increased with advancing age (p = 0.048). The thickness of the pterygomaxillary region was significantly greater in females (p = 0.014). Additionally, the distance between the greater palatine canal and the terminal point of the medial pterygoid lamina was significantly higher in males (p < 0.001). The pterygomaxillary region exhibits anatomical variations that may lead to serious complications during Le Fort I osteotomy. Detailed preoperative evaluation of this area using CBCT can guide surgical planning and help prevent potential vascular and neural complications.

Background/Objectives: Pancreatic cancer is among the most aggressive malignancies, with poor survival rates. Emerging evidence suggests that body composition, including skeletal muscle mass and adiposity distribution, plays a crucial role in predicting patient outcomes. However, its impact on survival in pancreatic cancer remains incompletely understood. The aim of this systematic review was to assess the correlation between body composition parameters and survival outcomes in pancreatic cancer patients, focusing on overall survival. Methods: A comprehensive literature search was conducted, including three main components: pancreatic cancer, body composition, and survival outcomes. Results: 23 studies were included in this review. The findings indicate that body composition can serve as a predictor of survival in pancreatic cancer patients, with 21 studies reporting a significant correlation. The most frequently observed predictor, with 11 studies reporting, was not a baseline parameter but rather changes in parameters over time during treatment. However, discrepancies remain regarding the extent of predictive power and the relative importance of individual components. Conclusions: Specific body composition parameters hold potential as prognostic indicators of survival in pancreatic cancer patients. However, further research is necessary to establish consistent patterns and to clarify which parameters are most predictive and under what conditions.