MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.
Woojin JungHyun-Soo LeeMinkook SeoYoonho NamYangsean ChoiNa-Young ShinKook-Jin AhnBum-Soo KimJinhee JangPublished in: European radiology (2022)
• Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.
Keyphrases
- contrast enhanced
- deep learning
- neural network
- image quality
- magnetic resonance imaging
- computed tomography
- high resolution
- diffusion weighted imaging
- machine learning
- air pollution
- magnetic resonance
- quality improvement
- convolutional neural network
- dual energy
- mass spectrometry
- climate change
- electronic health record
- high speed
- virtual reality