title: Accelerating dynamic magnetic resonance imaging (MRI) for lung tumor tracking based on low-rank decomposition in the spatial-temporal domain: a feasibility study based on simulation and preliminary prospective undersampled MRI.
abstract: OBJECTIVE
  To evaluate a low-rank decomposition method to reconstruct down-sampled k-space data for the purpose of tumor tracking.
  
  
  METHODS
  Seven retrospective lung cancer patients were included in the simulation study. The fully-sampled k-space data were first generated from existing 2-dimensional dynamic MR images and then down-sampled by 5 × -20 × before reconstruction using a Cartesian undersampling mask. Two methods, a low-rank decomposition method using combined dynamic MR images (k-t SLR based on sparsity and low-rank penalties) and a total variation (TV) method using individual dynamic MR frames, were used to reconstruct images. The tumor trajectories were derived on the basis of autosegmentation of the resultant images. To further test its feasibility, k-t SLR was used to reconstruct prospective data of a healthy subject. An undersampled balanced steady-state free precession sequence with the same undersampling mask was used to acquire the imaging data.
  
  
  RESULTS
  In the simulation study, higher imaging fidelity and low noise levels were achieved with the k-t SLR compared with TV. At 10 × undersampling, the k-t SLR method resulted in an average normalized mean square error <0.05, as opposed to 0.23 by using the TV reconstruction on individual frames. Less than 6% showed tracking errors >1 mm with 10 × down-sampling using k-t SLR, as opposed to 17% using TV. In the prospective study, k-t SLR substantially reduced reconstruction artifacts and retained anatomic details.
  
  
  CONCLUSIONS
  Magnetic resonance reconstruction using k-t SLR on highly undersampled dynamic MR imaging data results in high image quality useful for tumor tracking. The k-t SLR was superior to TV by better exploiting the intrinsic anatomic coherence of the same patient. The feasibility of k-t SLR was demonstrated by prospective imaging acquisition and reconstruction.
authors.affiliation: Department of Radiological Science, University of California, Los Angeles, California; Department of Radiation Oncology, University of California, Los Angeles, California.
authors.forename: Manoj
authors.initials: M
authors.lastname: Sarma
authors.affiliation: Department of Radiological Science, University of California, Los Angeles, California.
authors.forename: Peng
authors.initials: P
authors.lastname: Hu
authors.affiliation: Department of Radiological Science, University of California, Los Angeles, California.
authors.forename: Stanislas
authors.initials: S
authors.lastname: Rapacchi
authors.affiliation: Department of Radiological Science, University of California, Los Angeles, California.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ennis
authors.affiliation: Department of Radiological Science, University of California, Los Angeles, California.
authors.forename: Albert
authors.initials: A
authors.lastname: Thomas
authors.affiliation: Department of Radiation Oncology, University of California, Los Angeles, California.
authors.forename: Percy
authors.initials: P
authors.lastname: Lee
authors.affiliation: Department of Radiation Oncology, University of California, Los Angeles, California.
authors.forename: Patrick
authors.initials: P
authors.lastname: Kupelian
authors.affiliation: Department of Radiation Oncology, University of California, Los Angeles, California. Electronic address: ksheng@mednet.ucla.edu.
authors.forename: Ke
authors.initials: K
authors.lastname: Sheng
mesh_terms: D005240:Feasibility Studies; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009068:Movement; D012119:Respiration; D012189:Retrospective Studies