Radiology

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

OBJECTIVES: To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC).

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model...

Estimating the Severity of Oral Lesions Via Analysis of Cone Beam Computed Tomography Reports: A Proposed Deep Learning Model.

OBJECTIVES: Several factors such as unavailability of specialists, dental phobia, and financial difficulties may lead to a delay between receiving an ...

Multi-modality deep learning-based [Ga]Ga-DOTA-FAPI-04 PET polar map generation: potential value in detecting reactive fibrosis after myocardial infarction.

PURPOSE: Generating polar map (PM) from [Ga]Ga-DOTA-FAPI-04 PET images is challenging and inaccurate using existing automatic methods that rely on the...

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model.

The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to...

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images.

Automated detection and segmentation of breast masses in ultrasound images are critical for breast cancer diagnosis, but remain challenging due to lim...

Deep learning for intracranial aneurysm segmentation using CT angiography.

This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography ...

Holistic evaluation of a machine learning-based timing calibration for PET detectors under varying data sparsity.

Modern PET scanners offer precise TOF information, improving the SNR of the reconstructed images. Timing calibrations are performed to reduce the wors...

Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning.

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early ...

Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.

BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasoun...

Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function ...

Deep learning-based material decomposition of iodine and calcium in mobile photon counting detector CT.

Photon-counting detector (PCD)-based computed tomography (CT) offers several advantages over conventional energy-integrating detector-based CT. Among ...

Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging.

BACKGROUND: There is increasing interest in utilizing AI-generated content for gadolinium-free contrast-enhanced breast MRI.

Automatic robotic doppler sonography of leg arteries.

PURPOSE: Robot-assisted systems offer an opportunity to support the diagnostic and therapeutic treatment of vascular diseases to reduce radiation expo...

Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques.

Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increa...

Parkinson's image detection and classification based on deep learning.

OBJECTIVE: There are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images bet...

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared D...

Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.

OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve i...

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.

BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is ...

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

PURPOSE: Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various me...