Radiology

Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment.

RATIONALE AND OBJECTIVES: To assess image quality, contrast volume and radiation dose reduction potential and diagnostic performance with the use of high-strength deep learning image reconstruction (DLIR-H) in transcatheter aortic valve implantation (TAVI) planning CT.

[Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering].

PURPOSE: To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susce...

Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, France) designed to automatically detect > 50% coron...

Brain age of rhesus macaques over the lifespan.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level b...

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [F]FDG PET/CT can provide prognostic informa...

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promisin...

Design and Control of the Magnetically Actuated Micro/Nanorobot Swarm toward Biomedical Applications.

Recently, magnetically actuated micro/nanorobots hold extensive promises in biomedical applications due to their advantages of noninvasiveness, fuel-f...

Automated Spontaneous Echo Contrast Detection Using a Multisequence Attention Convolutional Neural Network.

OBJECTIVE: Spontaneous echo contrast (SEC) is a vascular ultrasound finding associated with increased thromboembolism risk. However, identification re...

3D CNN-based Deep Learning Model-based Explanatory Prognostication in Patients  with Multiple Myeloma using Whole-body MRI.

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artifici...

Effectiveness and safety of robot-assisted versus fluoroscopy-assisted pedicle screw implantation in scoliosis surgery: a systematic review and meta-analysis.

This study aimed to assess the effectiveness and safety of robot-assisted versus fluoroscopy-assisted pedicle screw implantation in scoliosis surgery....

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

BACKGROUND: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images ...

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modal...

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach.

OBJECTIVE: This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic card...

Of editorial processes, AI models, and medical literature: the Magnetic Resonance Audiometry experiment.

The potential of artificial intelligence (AI) in the field of medical research is unquestionable. Nevertheless, the scientific community has raised se...

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patient...

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to con...

Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocard...

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor meas...

Natural language processing to phenotype coronary computed tomography angiography: Development, validation, and initial results of a large multi-institution cohort.