MR‐based synthetic CT generation using a deep convolutional neural network method

By Xiao Han. 2017

740 citations recorded by the source corpus.

Subfields: Radiation, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiation

1. Arvid Lundervold
2. Keewon Shin
3. Maryellen Lissak Giger
4. Namkug Kim
5. Ryoungwoo Jang
6. Hyun-Jin Bae
7. Berkman Sahiner
8. Xiaosong Wang

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Lubomir M. Hadjiiski
3. Xiaosong Wang
4. Ronald M. Summers
5. Kenny H. Cha
6. Curtis P. Langlotz
7. Berkman Sahiner
8. Karen Drukker

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Tested on 105 subjects from the Human Connectome Project Brain age [273] Chronological age prediction from raw brain T1-MRI data, also testing the heritability of brain-predicted age using a sample of 62 monozygotic and dizygotic twins Alzheimer’s disease [274] Landmark-based deep multi-instance lea”
• Understanding Deep Learning Techniques for Image Segmentation (2019, neutral): “Natural Berkeley Segmentation Dataset [140] Scenes PASCAL VOC [54] Stanford Background Dataset [72] Microsoft COCO [122] MIT Scene parsing data(ADE20K) [222, 223] Semantic Boundaries Dataset [75] Microsoft Research Cambridge Object Recognition Image Database (MSRC) [188] Video Densely Annotated Vide”
• Deep Learning in Medical Imaging (2019, methodology): “Han 85 used an encoder-decoder network for MRI to CT to transfer modality.”
• Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network (2018, background): “Whereas initial works focused only on the quantitative accuracy of CNN-derived pseudoCT images (23), recent studies have demonstrated accurate PET reconstruction.”
• A Gentle Introduction to Deep Learning in Medical Image Processing (2018, neutral): “In [99], Han et al. attempt to convert MR volumes to CT volumes.”
• Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction (2018, methodology): “To avoid overfitting, we performed data augmentation by applying random displacements of 5 voxels and a random flip in the slices, similarly to Han ( 21 ).”
• Deep learning in medical imaging and radiation therapy. (2018, methodology): “Han et al. 244 used DL to reduce streak artifacts resulting from limited number of radial lines in radial k-space sampling in MRI.”
• ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis (2021, neutral): “synthesis [22], [23], [25]–[28], and CT synthesis [66]–[69].”
• Deep Learning in Neuroradiology (2018, background): “21 In clinical trials, situations arise in which patients may not be able to undergo a certain diagnostic technique, such as patients with MR imaging–incompatible implants.”