Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review
arXiv:2004.10322
Abstract
This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were summarized in the discussion part.
43 pages, 3 figures, 10 tables
References in corpus (15)
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
- Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
- Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
- Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
- GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization
- Sharpness-aware Low dose CT denoising using conditional generative adversarial network
- DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem
- Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
- MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach
- Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
- A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data
- Visual enhancement of Cone-beam CT by use of CycleGAN
- Male pelvic synthetic CT generation from T1-weighted MRI using 2D and 3D convolutional neural networks
- An indirect transmission measurement-based spectrum estimation method for computed tomography