Latest AI and machine learning research in radiology for healthcare professionals.
PURPOSE: PET images often make small lesions difficult to identify because of noise and system blur. We address this by developing and evaluating MLPETÂ , a fast localized machine-learning method that approximates a computationally expensive probabilistic sampling approach while reducing noise and increasing spatial resolution. METHODS: Building on a probabilistic deconvolution framework with infor...
OBJECTIVE: Deep Learning has shown promise in accelerating MRI by reconstructing high-quality images from under-sampled data. While recent work has leveraged multi contrast information to improve reconstruction performance, these methods rely on supervised learning, which requires fully sampled k-space for training. One method, self-supervised learning via data undersampling (SSDU), enables direct...
OBJECTIVES: Great saphenous vein (GSV) incompetence is common, but numerous treatment options complicate patient-treatment matching. This narrative re...
RATIONALE AND OBJECTIVES: MRI-proton density fat fraction (MRI-PDFF) is widely applied in clinical practice for hepatic fat quantification. However, t...
Post-mortem computed tomography (PMCT) has become an increasingly important tool in cetacean stranding investigation, providing reusable, three-dimens...
Body composition research utilizing computed tomography (CT) has increased over the past several decades as researchers use clinically acquired CT for...
INTRODUCTION: The chorionic bump (CB) is a rare sonographic finding defined as an irregular protrusion from the choriodecidual surface into the gestat...
Automated identification of urinary system diseases on non-contrast computed tomography (NCCT) can facilitate early diagnosis and inform treatment dec...
BACKGROUND: PET/MR combines molecular and functional imaging but faces challenges such as prolonged scans, noise from reduced tracer activity, and sub...
PURPOSE: To compare the image quality of diffusion-weighted imaging (DWI) between deep learning reconstruction (DLR)-applied Periodically Rotated Over...
OBJECTIVES: This systematic review evaluates the available evidence on the efficacy of commercially available AI-software applications for lung nodule...
BACKGROUND: Diagnosing breast cancer with mammography continues to be an incidentally challenging process because of the numerous different ways of ac...
BACKGROUND: Clinical decision-making requires integrating history, physical examination, laboratory, and imaging data. In the emergency department (ED...
OBJECTIVE: Develop a multimodal fusion model combining MRI radiomics and deep learning (DL) to predict pathologic complete response (pCR) in breast ca...
OBJECTIVE: To develop and validate a multiparametric MRI-based radiomics model for noninvasive preoperative prediction of microsatellite instability (...
BACKGROUND: Hashimoto's thyroiditis (HT) is the most common autoimmune thyroid disorder, often diagnosed using ultrasound. However, conventional gray-...
The diagnostic image quality of positron emission tomography (PET) acquisitions strongly depends on the administered radiotracer activity and acquisit...
PURPOSE: To evaluate the feasibility of a locally deployable large language model (LLM) system for automated MRI protocol selection addressing data pr...
Computer-assisted diagnostic tools may help improve the consistency and reliability of ultrasonographic screening for developmental dysplasia of the h...
BACKGROUND: Skin neglected tropical diseases (NTDs) pose significant diagnostic and management challenges in resource-limited settings due to constrai...