A5025828579- Medical Imaging Techniques and Applications
- Image and Signal Denoising Methods
- Advanced X-ray and CT Imaging
- Radiation Dose and Imaging
- Photoacoustic and Ultrasonic Imaging
- Advanced Image Processing Techniques
Sixth Affiliated Hospital of Sun Yat-sen University
2024
Sun Yat-sen University
2024
Southern Medical University
2020-2021
Deep learning has recently been extensively investigated to remove artifacts in low-dose computed tomography (LDCT). However, the power of transfer for medical image denoising tasks not fully explored. In this work, we proposed a residual convolutional neural network (TLR-CNN) restore LDCT images at single and blind noise levels. A was implemented effectively estimate difference between denoised its original map, noise-free obtained by subtracting map from image. The results were compared...
Abstract Background Low‐dose computed tomography (LDCT) can mitigate potential health risks to the public. However, severe noise and artifacts in LDCT images impede subsequent clinical diagnosis analysis. Convolutional neural networks (CNNs) Transformers stand out as two most popular backbones denoising. Nonetheless, CNNs suffer from a lack of long‐range modeling capabilities, while are hindered by high computational complexity. Purpose In this study, our main goal is develop simple...
Low-dose computed tomography (LDCT) reduces radiation exposure, but the introduced noise and artifacts impair its diagnostic accuracy. Convolutional neural networks (CNNs) are widely used for LDCT denoising, they suffer from a limited receptive field. The use of larger kernel size can enlarge field boost model performance; however, computational cost greatly increases. We aimed to develop denoising CNN with large lower complexity. developed multi-scale perceptual modulation network (MSPMnet)...
Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice.We propose novel sparse segmental MECT (SSMECT) scheme corresponding reconstruction method, which cost-efficient way to realize on conventional single-source CT. For the data acquisition, X-ray source controlled maintain an energy within arc, then switch alternately another...