Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 3; pp. 569 - 579
• Main Authors: Wang, Yan, Ma, Guangkai, An, Le, Shi, Feng, Zhang, Pei, Lalush, David S., Wu, Xi, Pu, Yifei, Zhou, Jiliu, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Dictionaries; Emission analysis; Humans; Image edge detection; Image Enhancement - methods; Image quality; Image reconstruction; Injection; Local coordinate coding (LCC); Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Multimodal Imaging - methods; Neuroimaging; NMR; Nuclear magnetic resonance; Pattern Recognition, Automated - methods; Positron emission; Positron emission tomography; positron emission tomography (PET); Positron-Emission Tomography - methods; Radiation Dosage; Radiation Exposure - prevention & control; Radiation Protection - methods; Reproducibility of Results; semisupervised tripled dictionary learning (SSTDL); Sensitivity and Specificity; sparse representation (SR); Subtraction Technique; Supervised Machine Learning; Tomography; Training
• DOI: 10.1109/TBME.2016.2564440
• ISSN: 0018-9294; 1558-2531

Objective: To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods: It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semisupervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results: Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion: This paper proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance: The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients.