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Found 7 papers, 0 papers with code
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
The representation is expressed using a deep neural network with the patient's prior images as network input.
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network
After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.
A Noise-level-aware Framework for PET Image Denoising
Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.
LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method
Additionally, we compare the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.
STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction
Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame.
Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs).
