Abdominal aortic calcification (AAC) is an independent predictor of cardiovascular diseases (CVDs). AAC is typically detected as an incidental finding in spine scans. Early detection of AAC through opportunistic screening using any available imaging modalities could help identify individuals with a higher risk of developing clinical CVDs. However, AAC is not routinely assessed in clinics, and manual scoring from projection images is time-consuming and prone to inter-rater variability. Also, automated AAC scoring methods exist, but earlier methods have not accounted for the inherent variability in AAC scoring and were developed for a single imaging modality at a time. We propose an automated method for quantifying AAC from lumbar spine X-ray and Dual-energy X-ray Absorptiometry (DXA) images using an ensemble of convolutional neural network models that predicts a distribution of probable AAC scores. We treat AAC score as a normally distributed random variable to account for the variability of manual scoring. The mean and variance of the assumed normal AAC distributions are estimated based on manual annotations, and the models in the ensemble are trained by simulating AAC scores from these distributions. Our proposed ensemble approach successfully extracted AAC scores from both X-ray and DXA images with predicted score distributions demonstrating strong agreement with manual annotations, as evidenced by concordance correlation coefficients of 0.930 for X-ray and 0.912 for DXA. The prediction error between the average estimates of our approach and the average manual annotations was lower than the errors reported previously, highlighting the benefit of incorporating uncertainty in AAC scoring.

Medical imaging is crucial for clinical practice, providing insight into organ structure and function. Advancements in imaging technologies enable automated image segmentation, which is essential for accurate diagnosis and treatment planning. However, challenges like class imbalance, tissue boundary delineation, and tissue interaction complexity persist. The study introduces ConvTNet, a hybrid model that combines Transformer and CNN features to improve renal CT image segmentation. It uses attention mechanisms and feature fusion techniques to enhance precision. ConvTNet uses the KC module to focus on critical image regions, enabling precise tissue boundary delineation in noisy and ambiguous boundaries. The Mix-KFCA module enhances feature fusion by combining multi-scale features and distinguishing between healthy kidney tissue and surrounding structures. The study proposes innovative preprocessing strategies, including noise reduction, data augmentation, and image normalization, that significantly optimize image quality and ensure reliable inputs for accurate segmentation. ConvTNet employs transfer learning, fine-tuning five pre-trained models to bolster model performance further and leverage knowledge from a vast array of feature extraction techniques. Empirical evaluations demonstrate that ConvTNet performs exceptionally in multi-label classification and lesion segmentation, with an AUC of 0.9970, sensitivity of 0.9942, DSC of 0.9533, and accuracy of 0.9921, proving its efficacy for precise renal cancer diagnosis.

The ProVision scanner is a dedicated prostate PET system with limited angular coverage; it employs a new detector technology that provides high spatial resolution as well as information about depth-of-interaction (DOI) and time-of-flight (TOF). The goal of this work is to develop a flexible image reconstruction framework and study the image performance of the current ProVision scanners.
Approach: Experimental datasets, including point-like sources, an image quality phantom, and a pelvic phantom, were acquired using the ProVision scanner to investigate the impact of oblique lines of response introduced via a multi-offset scanning protocol. This approach aims to mitigate data truncation artifacts and further characterise the current imaging performance of the system. For image reconstruction, we applied the list-mode Maximum Likelihood Expectation Maximisation algorithm incorporating TOF information. The system matrix and sensitivity models account for both detector attenuation and position uncertainty.
Main Results: The scanner provides good spatial resolution on the coronal plane; however, elongations caused by the limited angular coverage distort the reconstructed images. The availability of TOF and DOI information, as well as the addition of a multi-offset scanning protocol, could not fully compensate for these distortions.
Significance: The ProVision scanner concept, with innovative detector technology, shows promising outcomes for fast and inexpensive PET without CT. Despite current limitations due to limited angular coverage, which leads to image distortions, ongoing advancements, such as improved timing resolution, regularisation techniques, and artificial intelligence, are expected to significantly reduce these artifacts and enhance image quality.

The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussusception in children younger than 8 months of age. A retrospective study with a prospective validation cohort of intussusception. The retrospective data were collected from two hospitals in south east China between January 2017 and December 2022. The prospective data were collected between January 2023 and July 2024. A total of 415 intussusception cases in patients younger than 8 months were included in the study. 280 cases collected from Centre 1 were randomly divided into two groups at a 7:3 ratio: the training cohort (n=196) and the internal validation cohort (n=84). 85 cases collected from Centre 2 were designed as external validation cohort. Pretrained DL networks were used to extract deep transfer learning features, with least absolute shrinkage and selection operator regression selecting the non-zero coefficient features. The clinical features were screened by univariate and multivariate logistic regression analyses. We constructed a combined model that integrated the selected two types of features, along with individual clinical and DL models for comparison. Additionally, the combined model was validated in a prospective cohort (n=50) collected from Centre 1. In the internal and external validation cohorts, the combined model (area under curve (AUC): 0.911 and 0.871, respectively) demonstrated better performance for predicting surgical intervention in intussusception in children younger than 8 months of age than the clinical model (AUC: 0.776 and 0.740, respectively) and the DL model (AUC: 0.828 and 0.793, respectively). In the prospective validation cohort, the combined model also demonstrated impressive performance with an AUC of 0.890. The combined model, integrating DL and clinical features, demonstrated stable predictive accuracy, suggesting its potential for improving clinical therapeutic strategies for intussusception.

Fetal MRI offers detailed three-dimensional visualisation of both fetal and maternal pelvic anatomy, allowing for assessment of the risk of cephalopelvic disproportion and obstructed labour. However, conventional measurements of fetal and pelvic proportions and their relative positioning are typically performed manually in 2D, making them time-consuming, subject to inter-observer variability, and rarely integrated into routine clinical workflows. In this work, we present the first fully automated pipeline for pelvic and fetal head biometry in T2-weighted fetal MRI at late gestation. The method employs deep learning-based localisation of anatomical landmarks in 3D reconstructed MRI images, followed by computation of 12 standard linear and circumference measurements commonly used in the assessment of cephalopelvic disproportion. Landmark detection is based on 3D UNet models within MONAI framework, trained on 57 semi-manually annotated datasets. The full pipeline is quantitatively validated on 10 test cases. Furthermore, we demonstrate its clinical feasibility and relevance by applying it to 206 fetal MRI scans (36-40 weeks gestation) from the MiBirth study, which investigates prediction of mode of delivery using low field MRI.

Fusion imaging requires initial registration of ultrasound (US) images using computed tomography (CT) or magnetic resonance (MR) imaging. The sweep position of US depends on the procedure. For instance, the liver may be observed in intercostal, subcostal, or epigastric positions. However, no well-established method for automatic initial registration accommodates all positions. A global rigid 3D-3D registration technique aimed at developing an automatic registration method independent of the US sweep position is proposed. The proposed technique utilizes the liver surface and vessels, such as the portal and hepatic veins, as landmarks. The algorithm segments the liver region and vessels from both US and CT/MR images using deep learning models. Based on these outputs, the point clouds of the liver surface and vessel centerlines were extracted. The rigid transformation parameters were estimated through point cloud registration using a RANSAC-based algorithm. To enhance speed and robustness, the RANSAC procedure incorporated constraints regarding the possible ranges for each registration parameter based on the relative position and orientation of the probe and body surface. Registration accuracy was quantitatively evaluated using clinical data from 80 patients, including US images taken from the intercostal, subcostal, and epigastric regions. The registration errors were 7.3 ± 3.2, 9.3 ± 3.7, and 8.4 ± 3.9 mm for the intercostal, subcostal, and epigastric regions, respectively. The proposed global rigid registration technique fully automated the complex manual registration required for liver fusion imaging and enhanced the workflow efficiency of physicians and sonographers.

<b><i>Background:</i></b> Colon cancer is a heterogeneous disease, and rare subtypes like triphasic colon cancer are difficult to detect with standard methods. Artificial intelligence (AI)-driven ultrasound combined with genomic analysis offers a promising approach to improve subtype identification and uncover molecular mechanisms. <b><i>Methods:</i></b> The authors used an AI-driven ultrasound model to identify rare triphasic colon cancer, characterized by a mix of epithelial, mesenchymal, and proliferative components. The molecular features were validated using immunohistochemistry, targeting classical epithelial markers, mesenchymal markers, and proliferation indices. Subsequently, ultrasound genomic techniques were applied to map transcriptomic alterations in conventional colon cancer onto ultrasound images. Differentially expressed genes were identified using the <i>edgeR</i> package. Pearson correlation analysis was performed to assess the relationship between imaging features and molecular markers. <b><i>Results:</i></b> The AI-driven ultrasound model successfully identified rare triphasic features in colon cancer. These imaging features showed significant correlation with immunohistochemical expression of epithelial markers, mesenchymal markers, and proliferation index. Moreover, ultrasound genomic techniques revealed that multiple oncogenic transcripts could be spatially mapped to distinct patterns within the ultrasound images of conventional colon cancer and were involved in classical cancer-related pathway. <b><i>Conclusions:</i></b> AI-enhanced ultrasound imaging enables noninvasive identification of rare triphasic colon cancer and reveals functional molecular signatures in general colon cancer. This integrative approach may support future precision diagnostics and image-guided therapies.

Thyroid ultrasound has emerged as a critical diagnostic modality, attracting substantial research attention. This bibliometric analysis systematically maps the 30-year evolution of thyroid ultrasound research to identify developmental trends, research hotspots, and emerging frontiers. English-language articles and reviews (1994-2023) from Web of Science Core Collection were extracted. Bibliometric analysis was performed using VOSviewer and CiteSpace to examine collaborative networks among countries/institutions/authors, reference timeline visualization, and keyword burst detection. A total of 8,489 documents were included for further analysis. An overall upward trend in research publications was found. China, the United States, and Italy were the productive countries, while the United States, Italy, and South Korea had the greatest influence. The journal Thyroid obtained the highest IF. The keywords with the greatest strength were "disorders", "thyroid volume", and "association guidelines". The timeline view of reference demonstrated that deep learning, ultrasound-based risk stratification systems, and radiofrequency ablation were the latest reference clusters. Three dominant themes emerged: the ultrasound characteristics of thyroid disorders, the application of new techniques, and the assessment of the risk of malignancy of thyroid nodules. Applications of deep learning and the development and improvement of correlation guides such as TIRADS are the present focus of research. The specific application efficacy and improvement of TI-RADS and the optimization of deep learning algorithms and their clinical applicability will be the focus of subsequent research.

Inherently lower signal-to-noise ratios hamper the broad clinical use of low-field abdominal MRI. This study aimed to investigate the technical feasibility and image quality of deep learning (DL)-enhanced T2 HASTE and T1 VIBE-Dixon abdominal MRI at 0.55 T. From July 2024 to September 2024, healthy volunteers underwent conventional and DL-enhanced 0.55 T abdominal MRI, including conventional T2 HASTE, fat-suppressed T2 HASTE (HASTE FS), and T1 VIBE-Dixon acquisitions, and DL-enhanced single- (HASTE DL_SBH) and multi-breath-hold HASTE (HASTE DL_MBH), fat-suppressed single- (HASTE FS DL_SBH) and multi-breath-hold HASTE (HASTE FS DL_MBH), and T1 VIBE-Dixon (VIBE-Dixon_DL) acquisitions. Three abdominal radiologists evaluated the scans for quality parameters and artifacts (Likert scale 1-5), and incidental findings. Interreader agreement and comparative analyses were conducted. 33 healthy volunteers (mean age: 30±4years) were evaluated. Image quality was better for single breath-hold DL-enhanced MRI (all P<0.001) with good or better interreader agreement (κ≥0.61), including T2 HASTE (HASTE DL_SBH: 4 [IQR: 4-4] vs. HASTE: 3 [3-3]), T2 HASTE FS (4 [4-4] vs. 3 [3-3]), and T1 VIBE-Dixon (4 [4-5] vs. 4 [3-4]). Similarly, image noise and spatial resolution were better for DL-MRI scans (P<0.001). No quality differences were found between single- and multi-breath-hold HASTE DL or HASTE FS DL (both: 4 [4-4]; P>0.572). The number and size of incidental lesions were identical between techniques (16 lesions; mean diameter 8±5 mm; P=1.000). DL-based image reconstruction enables single breath-hold T2 HASTE and T1 VIBE-Dixon abdominal imaging at 0.55 T with better image quality than conventional MRI.

This study aims to evaluate the potential of Dual-Energy Computed Tomography (DECT)-based radiomics in preoperative risk stratification for the prediction of initial recurrence in Papillary Thyroid Carcinoma (PTC). The retrospective analysis included 236 PTC cases (165 in the training cohort, 71 in the validation cohort) collected between July 2020 and June 2021. Tumor segmentation was carried out in both intratumoral and peritumoral areas (1 mm inner and outer to the tumor boundary). Three regionspecific rad-scores were developed (rad-score [VOI^whole], rad-score [VOI^{outer layer}], and rad-score [VOI^{inner layer}]), respectively. Three radiomics models incorporating these rad-scores and additional risk factors were compared to a clinical model alone. The optimal radiomics model was presented as a nomogram. Rad-scores from peritumoral regions (VOI^{outer layer} and VOI^{inner layer}) outperformed the intratumoral rad-score (VOI^whole). All radiomics models surpassed the clinical model, with peritumoral-based models (radiomics models 2 and 3) outperforming the intratumoral-based model (radiomics model 1). The top-performing nomogram, which included tumor size, tumor site, and rad-score (VOI^{inner layer}), achieved an Area Under the Curve (AUC) of 0.877 in the training cohort and 0.876 in the validation cohort. The nomogram demonstrated good calibration, clinical utility, and stability. DECT-based intratumoral and peritumoral radiomics advance PTC initial recurrence risk prediction, providing clinical radiology with precise predictive tools. Further work is needed to refine the model and enhance its clinical application. Radiomics analysis of DECT, particularly in peritumoral regions, offers valuable predictive information for assessing the risk of initial recurrence in PTC.