AI Medical Compendium Topic

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Multimodal Imaging

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A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utili...

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation.

Computers in biology and medicine
Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting...

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application.

Physics in medicine and biology
. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET act...

Artificial Intelligence in Oncological Hybrid Imaging.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND: Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications a...

Breast PET/MRI Hybrid Imaging and Targeted Tracers.

Journal of magnetic resonance imaging : JMRI
The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledg...

Automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks.

Analytica chimica acta
Whole blood cell analysis is widely used in medical applications since its results are indicators for diagnosing a series of diseases. In this work, we report automatic whole blood cell analysis from blood smear using label-free multi-modal imaging w...

Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging.

Ophthalmology. Retina
OBJECTIVE: To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjus...

Controlled Formation of Conduction Channels in Memristive Devices Observed by X-ray Multimodal Imaging.

Advanced materials (Deerfield Beach, Fla.)
Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based...

Multimodal Magnetic Resonance Imaging to Diagnose Knee Osteoarthritis under Artificial Intelligence.

Computational intelligence and neuroscience
This work aimed to investigate the application value of the multimodal magnetic resonance imaging (MRI) algorithm based on the low-rank decomposition denoising (LRDD) in the diagnosis of knee osteoarthritis (KOA), so as to offer a better examination ...

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

European radiology experimental
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds gr...