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

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...