title: A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion.
abstract: OBJECTIVE
  Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors' proposed approach.
  
  
  METHODS
  A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset.
  
  
  RESULTS
  The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques.
  
  
  CONCLUSIONS
  The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.
authors.affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459.
authors.forename: Y X
authors.initials: YX
authors.lastname: Yang
authors.affiliation: Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632.
authors.forename: S-K
authors.initials: SK
authors.lastname: Teo
authors.affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459.
authors.forename: E
authors.initials: E
authors.lastname: Van Reeth
authors.affiliation: Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433.
authors.forename: C H
authors.initials: CH
authors.lastname: Tan
authors.affiliation: Department of Radiation Oncology, National University Cancer Institute, Singapore 119082.
authors.forename: I W K
authors.initials: IW
authors.lastname: Tham
authors.affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459.
authors.forename: C L
authors.initials: CL
authors.lastname: Poh
mesh_terms: D003198:Computer Simulation; D066264:Datasets as Topic; D005240:Feasibility Studies; D020342:Finite Element Analysis; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging / Q000473:pathology*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D064847:Multimodal Imaging / Q000379:methods*; D012119:Respiration; D014057:Tomography, X-Ray Computed / Q000379:methods*