Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.

Background: Accurate interpretation of meniscal anomalies on knee MRI is critical for diagnosis and treatment planning, with artificial intelligence emerging as a promising tool to support and enhance this process through automated anomaly detection.

Purpose: To evaluate the impact of an artificial intelligence (AI) anomaly detection assistant on radiologists' interpretation of meniscal anomalies in undersampled, deep learning (DL)-reconstructed knee MRI and assess the relationship between reconstruction quality metrics and anomaly detection performance.

Materials And Methods: This retrospective study included 947 knee MRI examinations; 51 were excluded for poor image quality, leaving 896 participants (mean age, 44.

A deep learning model for classification of chondroid tumors on CT images.

Background: Differentiating chondroid tumors is crucial for proper patient management. This study aimed to develop a deep learning model (DLM) for classifying enchondromas, atypical cartilaginous tumors (ACT), and high-grade chondrosarcomas using CT images.

Methods: This retrospective study analyzed chondroid tumors from two independent cohorts.

Performance of an Intravascular Tantalum Oxide-Based Nanoparticle Computed Tomography Contrast Agent in Preclinical Hepatic Tumor Detection.

Background: Noniodinated intravenous contrast agents have shown significant potential to improve computed tomography (CT) imaging; however, in vivo evidence for impact on lesion detection remains scarce.

Purpose: The aim of the study was to compare a novel intravenous carboxybetaine zwitterionic-coated tantalum oxide (TaCZ) nanoparticle contrast agent to clinical iodinated contrast agent for the detection of liver tumors in a rabbit tumor model at CT.

Methods: Following hepatic implantation of VX2 tumors, n = 10 rabbits were repeatedly scanned on a clinical CT system before and at 40, 105, and 180 seconds after intravenous contrast injection of 540 mg element (Ta or I) per kilogram of body weight using TaCZ or iopamidol.

Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement.

This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes.

Association of cartilage and relaxation time measurement with hip osteoarthritis progression: A 5-year longitudinal study using voxel-based relaxometry and Z-score normalization.

Objective: To study the longitudinal changes of cartilage and relaxation time measurements in hip-OA patients.

Methods: A calibration study compared two scanner data, Scanner-1 (GE Discovery MR750 3.0T) with unilateral acquisition protocol and Scanner-2 (GE Signa Premier 3.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI.

Objective: While risk factors for osteoarthritis (OA) are well known, it is not well understood why certain individuals maintain high mobility and joint health throughout their life while others demonstrate OA at older ages. The purpose of this study was to assess which demographic, clinical and MRI quantitative and semi-quantitative factors are associated with preserving healthy knees in older individuals.

Methods: This study analyzed data from the OA Initiative (OAI) cohort of individuals at the age of 65 years or above.

Defining hip osteoarthritis feature prevalence, severity, and change using the Scoring of Hip Osteoarthritis with MRI (SHOMRI).

Objective: To define the reporting of Scoring Hip Osteoarthritis with MRI (SHOMRI) feature prevalence and severity, and to develop criteria to monitor feature change in longitudinal investigations.

Methods: Twenty-five participants (50 hips) of the femoroacetabular impingement and hip osteoarthritis cohort study underwent baseline and 2-year follow-up 3 T hip MRIs. Eight hip OA features were assessed using the SHOMRI.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment.

Objective: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.

Materials And Methods: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature.

Is hip capsule morphology associated with hip pain in patients without another structural correlate?

Objective: The goals of this study were (i) to assess the association between hip capsule morphology and pain in patients without any other MRI abnormalities that would correlate with pain and (ii) to investigate whether hip capsule morphology in hip pain patients is different from that of controls.

Methods: In this study, 76 adults with hip pain who did not show any structural abnormalities on MRI and 46 asymptomatic volunteers were included. Manual segmentation of the anterior and posterior hip capsules was performed.

Effects of T Characteristics of Load-Bearing Hip Cartilage on Bilateral Knee Patellar Cartilage Subregions: Subjects With None to Moderate Radiographic Hip Osteoarthritis.

Background: The polyarticular nature of Osteoarthritis (OA) tends to manifest in multi-joints. Associations between cartilage health in connected joints can help identify early degeneration and offer the potential for biomechanical intervention. Such associations between hip and knee cartilages remain understudied.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.

Purpose: To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).

Methods: A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference.

Synthetic Inflammation Imaging with PatchGAN Deep Learning Networks.

: Gadolinium (Gd)-enhanced Magnetic Resonance Imaging (MRI) is crucial in several applications, including oncology, cardiac imaging, and musculoskeletal inflammatory imaging. One use case is rheumatoid arthritis (RA), a widespread autoimmune condition for which Gd MRI is crucial in imaging synovial joint inflammation, but Gd administration has well-documented safety concerns. As such, algorithms that could synthetically generate post-contrast peripheral joint MR images from non-contrast MR sequences would have immense clinical utility.

K2S Challenge: From Undersampled K-Space to Automatic Segmentation.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space.

Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease.

Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD.

Additional MRI for initial M-staging in pancreatic cancer: a cost-effectiveness analysis.

Objective: Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection.

Carpal Instability: II. Imaging.

Beyond clinical examination, the various forms of carpal instability are assessed with radiologic methods and arthroscopy. For this purpose, the imaging demand for spatial and contrast resolution is particularly high because of the small ligamentous structures involved. The entities of carpal instability are classified into degrees of severity.

F FDG PET/MRI with hepatocyte-specific contrast agent for M staging of rectal cancer: a primary economic evaluation.

Purpose: Rectal cancer is one of the most frequent causes of cancer-related morbidity and mortality in the world. Correct identification of the TNM state in primary staging of rectal cancer has critical implications on patient management. Initial evaluations revealed a high sensitivity and specificity for whole-body PET/MRI in the detection of metastases allowing for metastasis-directed therapy regimens.