Radiology

AI in SPECT Imaging: Opportunities and Challenges.

SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncological diseases. However, SPECT imaging suffers from limited quantitative accuracy due to low spatial resolution and high noise levels, posing significant challenges for precise diagnosis, disease monitoring, and treatment planning. Recent advances in...

Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound.

PURPOSE: Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted modalities a reliable dataset is of utmost importance. The registration of imaging to pathology is an essential step in creating such a dataset. This study presents a comprehensive framework for the registration of 3D transrectal ultrasound (TRUS) to r...

Bootstrapping BI-RADS classification using large language models and transformers in breast magnetic resonance imaging reports.

Breast cancer is one of the most common malignancies among women globally. Magnetic resonance imaging (MRI), as the final non-invasive diagnostic tool...

POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.

Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans...

Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.

AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1...

Automated segmentation of the dorsal root ganglia in MRI.

The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in somatosensory and pain processing remains unclear. Rec...

Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures.

BACKGROUND AND PURPOSE: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain ...

Deep Learning-Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases.

BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed t...

Machine learning fusion for glioma tumor detection.

The early detection of brain tumors is very important for treating them and improving the quality of life for patients. Through advanced imaging techn...

Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic reson...

Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.

BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor re...

Right ventricular dysfunction following tetralogy of Fallot correction: anatomical determinants and therapeutic strategies.

Right ventricular dysfunction following surgical correction of tetralogy of Fallot (TOF) remains a major determinant of long-term morbidity and mortal...

Global disparities in artificial intelligence-based mammogram interpretation for breast cancer: A scientometric analysis of representation, trends, and equity.

BACKGROUND: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer death among women worldwide. Artificial intelli...

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging.

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical prop...

Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.

Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in ...

Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive corona...

Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer.

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosi...

AI-based detection and classification of anomalous aortic origin of coronary arteries using coronary CT angiography images.

Anomalous aortic origin of the coronary artery (AAOCA) is a rare cardiac condition that can lead to ischemia or sudden cardiac death, yet it is often ...

Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.

BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on ga...

Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing.

BACKGROUND: Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent....