title: Quantifying the accuracy of the tumor motion and area as a function of acceleration factor for the simulation of the dynamic keyhole magnetic resonance imaging method.
abstract: OBJECTIVE
  The dynamic keyhole is a new MR image reconstruction method for thoracic and abdominal MR imaging. To date, this method has not been investigated with cancer patient magnetic resonance imaging (MRI) data. The goal of this study was to assess the dynamic keyhole method for the task of lung tumor localization using cine-MR images reconstructed in the presence of respiratory motion.
  
  
  METHODS
  The dynamic keyhole method utilizes a previously acquired a library of peripheral k-space datasets at similar displacement and phase (where phase is simply used to determine whether the breathing is inhale to exhale or exhale to inhale) respiratory bins in conjunction with central k-space datasets (keyhole) acquired. External respiratory signals drive the process of sorting, matching, and combining the two k-space streams for each respiratory bin, thereby achieving faster image acquisition without substantial motion artifacts. This study was the first that investigates the impact of k-space undersampling on lung tumor motion and area assessment across clinically available techniques (zero-filling and conventional keyhole). In this study, the dynamic keyhole, conventional keyhole and zero-filling methods were compared to full k-space dataset acquisition by quantifying (1) the keyhole size required for central k-space datasets for constant image quality across sixty four cine-MRI datasets from nine lung cancer patients, (2) the intensity difference between the original and reconstructed images in a constant keyhole size, and (3) the accuracy of tumor motion and area directly measured by tumor autocontouring.
  
  
  RESULTS
  For constant image quality, the dynamic keyhole method, conventional keyhole, and zero-filling methods required 22%, 34%, and 49% of the keyhole size (P < 0.0001), respectively, compared to the full k-space image acquisition method. Compared to the conventional keyhole and zero-filling reconstructed images with the keyhole size utilized in the dynamic keyhole method, an average intensity difference of the dynamic keyhole reconstructed images (P < 0.0001) was minimal, and resulted in the accuracy of tumor motion within 99.6% (P < 0.0001) and the accuracy of tumor area within 98.0% (P < 0.0001) for lung tumor monitoring applications.
  
  
  CONCLUSIONS
  This study demonstrates that the dynamic keyhole method is a promising technique for clinical applications such as image-guided radiation therapy requiring the MR monitoring of thoracic tumors. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by up to a factor of five compared with full k-space methods for real-time lung tumor MRI.
authors.affiliation: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Danny
authors.initials: D
authors.lastname: Lee
authors.affiliation: School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW 2308, Australia and Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, NSW 2298, Australia.
authors.forename: Peter B
authors.initials: PB
authors.lastname: Greer
authors.affiliation: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Sean
authors.initials: S
authors.lastname: Pollock
authors.affiliation: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia and Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23219.
authors.forename: Taeho
authors.initials: T
authors.lastname: Kim
authors.affiliation: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Keall
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging* / Q000503:physiopathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D012119:Respiration