AIMC Topic: Signal-To-Noise Ratio
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Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.
IEEE transactions on medical imaging
May 2, 2025
Near-infrared spectral tomography (NIRST) is a non-invasive imaging technique that provides functional information about biological tissues. Due to diffuse light propagation in tissue and limited boundary measurements, NIRST image reconstruction pres...
Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.
BMC medical imaging
May 1, 2025
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...
Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data.
BMC medical imaging
Apr 29, 2025
BACKGROUND: To assess the effect of the combination of deep learning reconstruction (DLR) and time-resolved maximum intensity projection (tMIP) or time-resolved average (tAve) post-processing method on image quality of CTA derived from low-dose cereb...
SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 29, 2025
Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an ...
An end-to-end neural network for 4D cardiac CT reconstruction using single-beat scans.
Physics in medicine and biology
Apr 22, 2025
Motion artifacts remain a significant challenge in cardiac CT imaging, often impairing the accurate detection and diagnosis of cardiac diseases. These artifacts result from involuntary cardiac motion, and traditional mitigation methods typically rely...
Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Apr 22, 2025
Resolving arterial flows is essential for understanding cardiovascular pathologies, improving diagnosis, and monitoring patient condition. Ultrasound contrast imaging uses microbubbles to enhance the scattering of the blood pool, allowing for real-ti...
Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps.
The Journal of international medical research
Apr 21, 2025
ObjectiveCompared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques a...
Assessment of AI-accelerated T2-weighted brain MRI, based on clinical ratings and image quality evaluation.
European journal of radiology
Apr 16, 2025
OBJECTIVE: To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2) against conventional T2- turbo spin echo brain MRI (T2).
Utilizing Pix2Pix conditional generative adversarial networks to recover missing data in preclinical PET scanner sinogram gaps.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Apr 14, 2025
BACKGROUND: The presence of a gap between adjacent detector blocks in Positron Emission Tomography (PET) scanners introduces a partial loss of projection data, which can degrade the image quality and quantitative accuracy of reconstructed PET images....
Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.
BMC medical imaging
Apr 10, 2025
OBJECTIVE: Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
Computed Tomography Angiography
Radiography, Dual-Energy Scanned Projection
Quality Improvement
Artifacts
Head
Signal-To-Noise Ratio
Head and Neck Neoplasms
Neck
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Humans
Female
Algorithms
Aged
Adult
Prospective Studies
Aged, 80 and over
Male
Middle Aged
Deep Learning
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