AIMC Topic: Signal-To-Noise Ratio
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Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.
BMC medical imaging
Jul 1, 2025
BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, ...
Retrospective Studies
Female
Aged
Aged, 80 and over
Humans
Neoplasm Staging
Rectal Neoplasms
Image Interpretation, Computer-Assisted
Signal-To-Noise Ratio
Deep Learning
Diffusion Magnetic Resonance Imaging
Adult
Image Processing, Computer-Assisted
ROC Curve
Male
Magnetic Resonance Imaging
Middle Aged
Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.
BMC medical imaging
Jul 1, 2025
BACKGROUND: T2-weighted imaging (T2WI), renowned for its sensitivity to edema and lesions, faces clinical limitations due to prolonged scanning time, increasing patient discomfort, and motion artifacts. The individual applications of artificial intel...
A multi-domain collaborative denoising bearing fault diagnosis model based on dynamic inter-domain attention mechanism and noise-aware loss function.
PloS one
Jun 26, 2025
Rolling bearings are the core transmission components of large-scale rotating machinery such as wind power gearboxes and aviation engines, so timely and effective monitoring and diagnosis of their status are crucial to ensure the stable operation of ...
Recognition of common shortwave protocols and their subcarrier modulations based on multi-scale convolutional GRU.
PloS one
Jun 24, 2025
Shortwave communication plays a vital role in disaster relief and remote communications due to its long-range capabilities and resilience to interference. However, challenges such as multipath propagation, frequency-selective fading, and low signal-t...
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.
Physics in medicine and biology
Jun 16, 2025
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...
Adaptive network steganography using deep learning and multimedia video analysis for enhanced security and fidelity.
PloS one
Jun 5, 2025
This study presents an advanced adaptive network steganography paradigm that integrates deep learning methodologies with multimedia video analysis to enhance the universality and security of network steganography practices. The proposed approach util...
Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging.
AJNR. American journal of neuroradiology
Jun 3, 2025
BACKGROUND AND PURPOSE: Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating the use of smoother reconstruction kernels that reduce resolution below the 0.125-mm maximum spatial re...
Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.
Biomedical physics & engineering express
May 19, 2025
This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided ...
Improving lower-extremity artery depiction and diagnostic confidence using dual-energy technique and popliteal artery monitoring in dual-low dose CT angiography.
Scientific reports
May 9, 2025
To assess the utility of dual-energy CT scanning (DECTs) with popliteal artery (PA) monitoring in dual low-dose (radiation and contrast) lower-extremity CT angiography (LE-CTA). 135 patients undergoing LE-CTA were prospectively included and divided i...
A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.
Sensors (Basel, Switzerland)
May 5, 2025
Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite t...
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