Radiology

A Hybrid Model Combining U-Net and Transformers for Joint Segmentation and Beamforming of Plane-wave Ultrasound Images.

OBJECTIVE: Recent advancements in deep learning have shown significant potential in ultrasound imaging. However, most approaches focus solely on image enhancement or segmentation, without integrating these tasks into a unified framework. To address this gap, we developed a novel architecture that combines the U-Net and Transformer models to simultaneously segment and beamform plane-wave images acq...

Ultrasound super-resolved hemodynamic estimation in microvessel using physics-informed neural networks and data assimilation.

BACKGROUND AND OBJECTIVE: Ultrasound super-resolution imaging (SRI) enables the visualization of microvascular structure and velocity, but enhancing the spatial resolution of instantaneous velocity field and simultaneously capturing pressure field remains challenging. METHODS: This study proposes a method combining physics-informed neural networks (PINN) with data assimilation to assist microvascu...

Early clinical experiences with AI-based EVAR planning using the Endoleak Risk Index support its value for individualized decision-making and education.

OBJECTIVE: The aim of this study was to evaluate our first experience with the use of the artificial intelligence-based Endoleak Risk Index (ERI) in t...

Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.

OBJECTIVE: This study aims to develop and validate an integrated multi-task framework for hepatocellular carcinoma analysis by combining deep learning...

Artificial Intelligence and Machine Learning for Osteoarthritis and Cartilage Assessment.

Osteoarthritis (OA) affects over 500 million people globally and poses diagnostic and management challenges due to its complex pathophysiology. Artifi...

Artificial Intelligence for CT and MRI Protocoling: A Meta-Analysis of Traditional Machine Learning, BERT, and Large Language Models.

Background: Examination protocoling is a resource-intensive task. Various artificial intelligence (AI) approaches have been investigated to automate t...

Golay-Net: Deep learning-based Golay coded excitation for ultrasound imaging.

Ultrasound imaging modality, which operates by transmitting and receiving short ultrasound pulses, offers a promising approach for real-time, high-res...

Preventing exotic pet beetle invasion with an improved lightweight and efficient pest detection model deployed on mobile devices.

BACKGROUND: The illegal smuggling of exotic pet beetles presents a growing threat to global ecosystems. Customs authorities play a critical role in pr...

CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.

BACKGROUND: Colony-stimulating factor-1 receptor (CSF1R) signaling is crucial for the ability of tumor-associated macrophages (TAMs) to establish an i...

Magnetic resonance imaging-based artificial intelligence model predicts neoadjuvant therapy response in triple-negative breast cancer.

PURPOSE: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options and poorer overall survival tha...

Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.

PURPOSE: To develop and evaluate a deep learning model that integrates ultra-widefield fundus photography and B-scan ultrasonography for automated cla...

NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine learning: a two-center retrospective study.

PURPOSE: NHOC and NHOP, defined as the normalized distances from peak uptake to tumour centroid and perimeter, are novel PET/CT metrics of tumour aggr...

Free-breathing dynamic MRI reconstruction via joint time-dependent coil sensitivity estimation using implicit neural representation.

Dynamic magnetic resonance imaging (MRI) often encounters a trade-off between spatial and temporal resolutions due to slow data acquisition, particula...

Automatic Follicle Counting From Ultrasound Images of Ovaries Using MARDSE-UNET Model.

Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as ...

Histopathology-based Artificial Intelligence Algorithms for the Prediction of Prostate Cancer Metastasis After Radical Prostatectomy.

BACKGROUND AND OBJECTIVE: Multimodal artificial intelligence (AI) algorithms have been validated to predict prostate cancer (PCa) metastasis using com...

A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.

OBJECTIVES: Artificial intelligence (AI) promises to accelerate and democratize medical imaging, yet low- and middle-income countries (LMICs) face dis...

Apparent Diffusion Coefficient Magnetic Resonance Imaging as a Predictive Biomarker for Hypoxia, Treatment De-escalation, and Recurrence in Human Papillomavirus-Associated Oropharyngeal Cancer.

PURPOSE: To evaluate whether quantitative diffusion-weighted magnetic resonance imaging derived apparent diffusion coefficient (ADC) parametric values...

Deep Learning-Based Motion-Compensated Reconstruction for Accelerating 4-Dimensional Magnetic Resonance Fingerprinting.

PURPOSE: To develop and validate DeepMocor, a deep learning-based method for motion-compensated 4-dimensional magnetic resonance fingerprinting (4D-MR...

AI-driven diagnosis of vulnerable intracranial atherosclerotic plaques using large language models and vision transformers: a multi-center study.

OBJECTIVES: High-resolution vessel wall imaging (HR-VWI) is essential for diagnosing vulnerable intracranial atherosclerotic plaques, but its interpre...

coPERCIST: AI-assisted PET-CT response assessment.

PURPOSE: The PET Response Criteria in Solid Tumours (PERCIST) 1.0 provides a standardized framework for evaluating treatment response using [18F]fluor...