AI Medical Compendium Topic

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Learning low-dose CT degradation from unpaired data with flow-based model.

Medical physics
BACKGROUND: There has been growing interest in low-dose computed tomography (LDCT) for reducing the X-ray radiation to patients. However, LDCT always suffers from complex noise in reconstructed images. Although deep learning-based methods have shown ...

MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.

Magnetic resonance imaging
PURPOSE: To compare capabilities of compressed sensing (CS) with and without deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) with and without DLR for improving examination time and image quality of shoulder MRI for...

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.

Scientific reports
Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM's high affinity with various analytical options into account. As is well...

Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain.

Sensors (Basel, Switzerland)
The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforw...

Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

Annals of nuclear medicine
OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over an hour after injection to obtain sufficient image quality can be challenging for patients with susp...

Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks.

IEEE transactions on medical imaging
Encoding-decoding (ED) CNNs have demonstrated state-of-the-art performance for noise reduction over the past years. This has triggered the pursuit of better understanding the inner workings of such architectures, which has led to the theory of deep c...

Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.

Ultrasound in medicine & biology
Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non-linear mapping from the projection data to the sound-speed image (SSI). The traditional reconstruct...

Comparative study of deep learning algorithms for atomic force microscopy image denoising.

Micron (Oxford, England : 1993)
Atomic force microscopy (AFM) enables direct visualisation of surface topography at the nanoscale. However, post-processing is generally required to obtain accurate, precise, and reliable AFM images owing to the presence of image artefacts. In this s...

Multimodal image translation via deep learning inference model trained in video domain.

BMC medical imaging
BACKGROUND: Current medical image translation is implemented in the image domain. Considering the medical image acquisition is essentially a temporally continuous process, we attempt to develop a novel image translation framework via deep learning tr...

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements.

Sensors (Basel, Switzerland)
The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type commun...