In the context of growing safety concerns regarding the use of gadolinium-based contrast agents in contrast-enhanced MRI, there is a need for dose reduction without compromising diagnostic accuracy. A deep learning (DL) method is proposed and evaluated in this study for predicting full-dose contrast-enhanced T1w images from multiparametric MRI acquired with 20% of the standard dose of gadolinium-based contrast agents.

Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detection and localization in CXRs.

To explore the predictive value of quantitative parameters from intravoxel incoherent movement (IVIM) imging and dynamic contrast enhancement MRI (DCE-MRI) for HER2 expression in breast cancer.

This study aims to evaluate white matter microstructure integrity in patients diagnosed with presbycusis (age-related hearing loss) using diffusion tensor imaging (DTI) and to investigate the relationship between DTI parameters and hearing loss severity.

Hyperpolarized Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of Xe signal-void to the anatomic H MRI thoracic-cavity-volume. VDP is associated with airway inflammation and disease control and serves as a treatable trait in therapy studies. Semi-automated VDP pipelines require time-intensive observer interactions. Current convolutional neural network (CNN) approaches for quantifying VDP lack external validation, which limits multicenter utilization. Our objective was to develop an automated and externally validated deep-learning pipeline to quantify pulmonary Xe MRI VDP.

To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC).

To develop a radiomics model for non-invasive prediction of colony-stimulating factor 3 (CSF3) expression in ovarian cancer (OC) and evaluate its prognostic value.

The Radiology Research Alliance (RRA) of the Association of Academic Radiology (AAR) convenes task forces to study trends that will shape the future of radiology. This article presents the findings of the AAR-RRA task force on balancing high clinical volumes and non-RVU-generating activities, which set out to analyze and underscore the full value of academic radiologists' contributions beyond RVU-generating clinical work. The Task Force's efforts are detailed in a two-part report. This first part describes the current landscape, while the second part focuses on future directions for academic radiology departments aiming to achieve a more optimal balance between high clinical volumes and non-RVU-generating activities.

This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).

Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the Association of Academic Radiology-Radiology Research Alliance convened a task force to explore the promise and pitfalls of using LLMs such as ChatGPT in radiology. This white paper explores the impact of LLMs on radiology education, highlighting their potential to enrich curriculum development, teaching and learning, and learner assessment. Despite these advantages, the implementation of LLMs presents challenges, including limits on accuracy and transparency, the risk of misinformation, data privacy issues, and potential biases, which must be carefully considered. We provide recommendations for the successful integration of LLMs and LLM-based educational tools into radiology education programs, emphasizing assessment of the technological readiness of LLMs for specific use cases, structured planning, regular evaluation, faculty development, increased training opportunities, academic-industry collaboration, and research on best practices for employing LLMs in education.

A suitable diagnostic method can be beneficial owing to the high prevalence of myocardial infarction (MI) and structural changes that affect systolic and diastolic performance. In this systematic review, we focused on the possibility of using DTI instead of current methods such as cardiac biopsy, an invasive procedure.

Although dysfunction of the glymphatic system in adult epilepsy has been extensively studied, there is a lack of research on the changes in this system during childhood development, particularly in children with Rolandic epilepsy (RE). This study aimed to investigate the changes in diffusion MRI measures related to the glymphatic function in children with RE.

Intratumoral heterogeneity means single site tumor biopsy might not be representative. Here we develop and optimize an MRI-informed robotic multiregional 'smart' biopsy technique in retroperitoneal and pelvic sarcomas (RPS). We also explore the relationship between imaging and histological biomarkers.

Radiology resident readout practices were adapted during the COVID pandemic, with several institutions transitioning to virtual and asynchronous readouts. Some pandemic-era practices persist today, with unclear effects on resident education. We developed institutional Readout Best Practices and assessed implementation.

The escalating incidence of placental accreta spectrum (PAS), a pregnancy complication, underscores the need for accurate prenatal diagnosis to guide optimal management strategies. This study aims to develop, validate, and compare various prenatal PAS prediction models integrating clinical data, MRI signs, and radiomics signatures.

To propose an automatic virtual contrast-enhanced chest computed tomography (CT) synthesis using dual-energy CT and a Residual U-Net with an attention module to detect clinically significant hilar lymphadenopathy.

To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.

Metastatic bone tumors significantly reduce patients' quality of life and expedite cancer spread. Traditional diagnostic methods rely on time-consuming manual annotations by radiologists, which are prone to subjectivity. Employing deep learning for rapid, precise segmentation of bone metastases can greatly improve patient outcomes and survival. However, accurate segmentation remains challenging due to the diverse and complex nature of osteoblastic, osteolytic, or mixed lesions.