Radiology

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions.

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality...

Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps.

Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. Despite their outstanding performances, some aspects of CNN functioning are still not fully understood by human operators. We postulated that the interpretability of CNNs applied to neuroimaging data could be improved by investigating their behavior when they are...

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer ...

MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convol...

Generative artificial intelligence in ophthalmology.

Generative artificial intelligence (AI) has revolutionized medicine over the past several years. A generative adversarial network (GAN) is a deep lear...

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

PURPOSE: To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high...

Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study.

AIMS: The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imaging (MRI) radiomics...

Automated Analysis of Split Kidney Function from CT Scans Using Deep Learning and Delta Radiomics.

Differential kidney function assessment is an important part of preoperative evaluation of various urological interventions. It is obtained through d...

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the autom...

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classi...

New Diagnostic Tools for Pulmonary Embolism Detection.

The presentation of pulmonary embolism (PE) varies from asymptomatic to life-threatening, and management involves multiple specialists. Timely diagnos...

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study.

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomog...

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection.

RATIONALE AND OBJECTIVES: The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resectio...

Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI.

PURPOSE: We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Net...

Shape completion in the dark: completing vertebrae morphology from 3D ultrasound.

PURPOSE: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs a...

MFMSNet: A Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network for breast ultrasound image segmentation.

Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of bre...

Artificial Intelligence in the Angio-suite: Will Algorithms be the Copilots of the Interventional Radiologist?

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mam...

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longit...

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor ...