Radiology

Synthesizing [F]PSMA-1007 PET bone images from CT images with GAN for early detection of prostate cancer bone metastases: a pilot validation study.

BACKGROUND: [F]FDG PET/CT scan combined with [F]PSMA-1007 PET/CT scan is commonly conducted for detecting bone metastases in prostate cancer (PCa). However, it is expensive and may expose patients to more radiation hazards. This study explores deep learning (DL) techniques to synthesize [F]PSMA-1007 PET bone images from CT bone images for the early detection of bone metastases in PCa, which may re...

Adversarial artificial intelligence in radiology: Attacks, defenses, and future considerations.

Artificial intelligence (AI) is rapidly transforming radiology, with applications spanning disease detection, lesion segmentation, workflow optimization, and report generation. As these tools become more integrated into clinical practice, new concerns have emerged regarding their vulnerability to adversarial attacks. This review provides an in-depth overview of adversarial AI in radiology, a topic...

BrainView: A Cloud-based Deep Learning System for Brain Image Segmentation, Tumor Detection and Visualization.

A brain tumor is an abnormal growth in the brain that disrupts its functionality and poses a significant threat to human life by damaging neurons. Ear...

The Desmoid Dilemma: Challenges and Opportunities in Assessing Tumor Burden and Therapeutic Response.

Desmoid tumors are rare, locally invasive soft-tissue tumors with unpredictable clinical behavior. Imaging plays a crucial role in their diagnosis, me...

Three-Blind Validation Strategy of Deep Learning Models for Image Segmentation.

Image segmentation plays a central role in computer vision applications such as medical imaging, industrial inspection, and environmental monitoring. ...

Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture.

Brain tumours (BTs) are among the most dangerous and life-threatening cancers in humans of all ages, and the early detection of BTs can make a huge di...

Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach.

: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methy...

Non-invasive prediction of DCE-MRI radiomics model on CCR5 in breast cancer based on a machine learning algorithm.

BackgroundNon-invasive methods with universal prognostic guidance for detecting breast cancer (BC) survival biomarkers need to be further explored.Obj...

Res-ECA-UNet++: an automatic segmentation model for ovarian tumor ultrasound images based on residual networks and channel attention mechanism.

OBJECTIVE: Ultrasound imaging has emerged as the preferred imaging modality for ovarian tumor screening due to its non-invasive nature and real-time d...

Machine learning-based fusion model for predicting HER2 expression in breast cancer by Sonazoid-enhanced ultrasound: a multicenter study.

PURPOSE: To predict human epidermal growth factor receptor 2 (HER2) expression in breast cancer (BC) using Sonazoid-enhanced ultrasound in a machine l...

Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.

Lung cancer is the second most commonly diagnosed cancer worldwide. In the present era of targeted therapy for various lung cancer mutations, it is es...

Cerebral ischemia detection using deep learning techniques.

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often lea...

Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after maximal safe surgical resection: a multicenter study.

BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study aimed to construct and validate a radiomics-based ma...

Challenges in Using Deep Neural Networks Across Multiple Readers in Delineating Prostate Gland Anatomy.

Deep learning methods provide enormous promise in automating manually intense tasks such as medical image segmentation and provide workflow assistance...

Neuroimaging Characterization of Acute Traumatic Brain Injury with Focus on Frontline Clinicians: Recommendations from the 2024 National Institute of Neurological Disorders and Stroke Traumatic Brain Injury Classification and Nomenclature Initiative Imaging Working Group.

Neuroimaging screening and surveillance is one of the first frontline diagnostic tools leveraged in the acute assessment (first 24 h postinjury) of pa...

Deep-Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors.

BACKGROUND AND PURPOSE: Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving ...

Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.

OBJECTIVES: To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thrombo...

Application of ChatGPT 4.0 in radiological dose management: Perceptions of radiographers with varying expertise.

INTRODUCTION: Conversational Artificial Intelligence (CAI) is transforming healthcare by introducing innovative tools for decision support and trainin...

FDG-PET as a pre-operative biomarker for predicting and optimizing response to subcallosal cingulate area deep brain stimulation.

BACKGROUND: Deep brain stimulation targeting the subcallosal cingulate area (SCC-DBS) has emerged as a promising therapy for treatment-resistant depre...

Trading off Iodine and Radiation Dose in Coronary Computed Tomography.

Coronary CT angiography (CCTA) has seen steady progress since its inception, becoming a key player in the non-invasive assessment of coronary artery d...