title: Accelerating T1ρ cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE.
abstract: OBJECTIVE
  To accelerate T1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE.
  
  
  METHODS
  To reconstruct T1ρ images from accelerated acquisition at different time of spin-lock (TSLs), we propose an approach to combine an advanced compressed sensing (CS) based reconstruction technique, LAISD (locally adaptive iterative support detection), and an advanced parallel imaging technique, JSENSE. Specifically, the reconstruction process alternates iteratively among local support detection in the domain of principal component analysis, compressed sensing reconstruction of the image sequence, and sensitivity estimation with JSENSE. T1ρ quantification results from accelerated scans using the proposed method are evaluated using in vivo knee cartilage data from bilateral scans of three healthy volunteers.
  
  
  RESULTS
  T1ρ maps obtained from accelerated scans (acceleration factors of 3 and 3.5) using the proposed method showed results comparable to conventional full scans. The T1ρ errors in all compartments are below 1%, which is well below the in vivo reproducibility of cartilage T1ρ reported from previous studies.
  
  
  CONCLUSIONS
  The proposed method can significantly accelerate the acquisition process of T1ρ quantification on human cartilage imaging without sacrificing accuracy, which will greatly facilitate the clinical translation of quantitative cartilage MRI.
authors.affiliation: Department of Biomedical Engineering, Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, USA.
authors.forename: Yihang
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
authors.forename: Prachi
authors.initials: P
authors.lastname: Pandit
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
authors.forename: Valentina
authors.initials: V
authors.lastname: Pedoia
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
authors.forename: Julien
authors.initials: J
authors.lastname: Rivoire
authors.affiliation: Department of Biomedical Engineering, Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, USA.|School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Biomedical Engineering, Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, USA.
authors.forename: Leslie
authors.initials: L
authors.lastname: Ying
mesh_terms: D000328:Adult; D000465:Algorithms; D002358:Cartilage, Articular / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007719:Knee Joint / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
keywords: T1ρ mapping; cartilage imaging; compressed sensing; iterative support detection; joint sensitivity estimation; principal component analysis