AIMC Topic: Signal-To-Noise Ratio

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[A motor imagery decoding study integrating differential attention with a multi-scale adaptive temporal convolutional network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Motor imagery electroencephalogram (MI-EEG) decoding algorithms face multiple challenges. These include incomplete feature extraction, susceptibility of attention mechanisms to distraction under low signal-to-noise ratios, and limited capture of long...

Reduction of photobleaching effects in photoacoustic imaging using noise agnostic, platform-flexible deep-learning methods.

Journal of biomedical optics
SIGNIFICANCE: Molecular photoacoustic (PA) imaging with exogenous dyes faces a significant challenge due to the photobleaching of the dye that can compromise tissue visualization, particularly in 3D imaging. Addressing this limitation can revolutioni...

Utility of Thin-slice Single-shot T2-weighted MR Imaging with Deep Learning Reconstruction as a Protocol for Evaluating Pancreatic Cystic Lesions.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: To assess the effects of industry-developed deep learning reconstruction with super resolution (DLR-SR) on single-shot turbo spin-echo (SshTSE) images with thickness of 2 mm with DLR (SshTSE) relative to those of images with a thickness of 5...

Deep Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving image quality in conventional MRI field strengths; however, there has been limited application to ul...

MC-RED: A deep learning network for motion correction in 3D CEST imaging.

Magnetic resonance in medicine
PURPOSE: Chemical exchange saturation transfer (CEST) imaging is highly sensitive to patient motion, which can compromise the reliability of quantitative molecular analysis. This study aims to develop and validate a deep learning-based motion correct...

Deep learning reconstruction combined with contrast-enhancement boost in dual-low dose CT pulmonary angiography: a two-center prospective trial.

European radiology
PURPOSE: To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the diagnostic quality of CT pulmonary angiography (CTPA) at low radiation and contrast doses, compared w...

Deep supervised transformer-based noise-aware network for low-dose PET denoising across varying count levels.

Computers in biology and medicine
BACKGROUND: Reducing radiation dose from PET imaging is essential to minimize cancer risks; however, it often leads to increased noise and degraded image quality, compromising diagnostic reliability. Recent advances in deep learning have shown promis...

AMeta-FD: Adversarial Meta-learning for Few-shot retinal OCT image Despeckling.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Speckle noise in Optical coherence tomography (OCT) images compromises the performance of image analysis tasks such as retinal layer boundary detection. Deep learning algorithms have demonstrated the advantage of being more cost-effective and robust ...

CT-Mamba: A hybrid convolutional State Space Model for low-dose CT denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-r...

Deep Learning CAIPIRINHA-VIBE Improves and Accelerates Head and Neck MRI.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study was to evaluate image quality for contrast-enhanced (CE) neck MRI with a deep learning-reconstructed VIBE sequence with acceleration factors (AF) 4 (DL4-VIBE) and 6 (DL6-VIBE).