AIMC Topic: Signal-To-Noise Ratio

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Virtual organelle self-coding for fluorescence imaging via adversarial learning.

Journal of biomedical optics
SIGNIFICANCE: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.

Impact of Encapsulation Tissue Growth on Selective Recording in Nerve Cuff Electrodes: A Simulation Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation systems. Recent efforts have aimed to improve the recording s...

Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed b...

Computational cannula microscopy of neurons using neural networks.

Optics letters
Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more ...

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Optics letters
Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, const...

Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm.

Korean journal of radiology
OBJECTIVE: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reco...

[Application of Convolutional Neural Network for Evaluating CT Dose Using Image Noise Classification: A Phantom Study].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: It is well known that there is a trade-off relationship between image noise and exposure dose in X-ray computed tomography (CT) examination. Therefore, CT dose level was evaluated by using the CT image noise property. Although noise power sp...

Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images.

Current medical imaging
BACKGROUND: The Gaussian and impulse noises corrupt the Computed Tomography (CT) images either individually or collectively, and the conventional fixed filters do not have the potential to suppress these noise.

A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images.

Current medical imaging
BACKGROUND: Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, ...