Research Article
mDixon-Based Synthetic CT Generation via Patch Learning
@INPROCEEDINGS{10.1007/978-3-030-51103-6_37, author={Xin Song and Jiamin Zheng and Chao Fan and Hongbin Yu}, title={mDixon-Based Synthetic CT Generation via Patch Learning}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Synthetic CT generation mDixon-based MR Abdomen Attenuation correction (AC) Patch learning (PL)}, doi={10.1007/978-3-030-51103-6_37} }- Xin Song
Jiamin Zheng
Chao Fan
Hongbin Yu
Year: 2020
mDixon-Based Synthetic CT Generation via Patch Learning
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_37
Abstract
We proposed a new method for generating synthetic CT on abdomen from modified Dixon (mDixon) MR data of abdomens to address the challenges of PET/MR attenuation correction (AC). AC is necessary in process of PET/MR but MR data lack photon attenuation, thus multiple methods are proposed to generate synthetic CT. However, these existing methods requires advantaged MR sequences which needs fine acquisition and huge cost consumption. To address this problem, we proposed a new method for generating synthetic CT using Patch Learning (SCG-PL). Global model of SCG-PL is transfer learning and patch model is semi-supervised classification. The advantages of our method can be summarized into two points. (1) Patch learning is a gradual learning process with gradually updating global model on remodeling patch model, so our SCG-PL method is gradually capable of generating synthetic CT. (2) Semi-supervised classification adopted in the process of patch learning, only small amount of labeled data is needed in SCG-PL, which greatly reduced the workload of radiologists. The experimental results indicate that proposed SCG-PL method can effectively generate synthetic CT image from challenging abdomen images using mDixon MR sequence data only.
