title: PANDA-T1ρ: Integrating principal component analysis and dictionary learning for fast T1ρ mapping.
abstract: OBJECTIVE
  Long scanning time greatly hinders the widespread application of spin-lattice relaxation in rotating frame (T1ρ) in clinics. In this study, a novel method is proposed to reconstruct the T1ρ-weighted images from undersampled k-space data and hence accelerate the acquisition of T1ρ imaging.
  
  
  METHODS
  The proposed approach (PANDA-T1ρ) combined the benefit of PCA and dictionary learning when reconstructing image from undersampled data. Specifically, the PCA transform was first used to sparsify the image series along the parameter direction and then the sparsified images were reconstructed by means of dictionary learning and finally solved the images. A variation of PANDA-T1ρ was also developed for the heavy noise case. Numerical simulation and in vivo experiments were carried out with the accelerating factor from 2 to 4 to verify the performance of PANDA-T1ρ.
  
  
  RESULTS
  The reconstructed T1ρ maps using the PANDA-T1ρ method were found to be comparable to the reference at all verified acceleration factors. Moreover, the variation exhibited better performance than the original version when the k-space data were contaminated by heavy noise.
  
  
  CONCLUSIONS
  PANDA-T1ρ can significantly reduce the scanning time of T1ρ by integrating PCA and dictionary learning and provides better parameter estimation than the state-of-art methods for a fixed acceleration factor.
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China.|Shenzhen Key Laboratory for MRI, Shenzhen, Guangdong, China.
authors.forename: Yanjie
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Qinwei
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China.|Shenzhen Key Laboratory for MRI, Shenzhen, Guangdong, China.|Department of Electronic Information Engineering, Nanchang University, Nanchang, Jiangxi, China.
authors.forename: Qiegen
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Yi-Xiang J
authors.initials: YX
authors.lastname: Wang
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China.|Shenzhen Key Laboratory for MRI, Shenzhen, Guangdong, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China.
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China.|Shenzhen Key Laboratory for MRI, Shenzhen, Guangdong, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|CUHK Shenzhen Research Institute, Shenzhen, Guangdong, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013116:Spinal Cord / Q000033:anatomy & histology*; D013382:Subtraction Technique; D018511:Systems Integration
keywords: PCA; T1ρ imaging; compressed sensing; dictionary learning