title: Markerless motion tracking of lung tumors using dual-energy fluoroscopy.
abstract: OBJECTIVE
  To evaluate the efficacy of dual-energy (DE) vs single-energy (SE) fluoroscopic imaging of lung tumors using a markerless template-based tracking algorithm.
  
  
  METHODS
  Ten representative patient breathing patterns were programmed into a Quasar™ motion phantom. The phantom was modified by affixing pork ribs to the surface, and a cedar insert with a small spherical volume was used to simulate lung and tumor, respectively. Sequential 60 kVp (6 mA) and 120 kVp (1.5 mA) fluoroscopic sequences were acquired. Frame-by-frame weighted logarithmic subtraction was performed resulting in a DE fluoroscopic sequence. A template-based algorithm was then used to track tumor motion throughout the DE and SE fluoroscopy sequences. Tracking coordinates were evaluated against ground-truth tumor locations. Fluoroscopic images were also acquired for two lung cancer patients, neither of which had implanted fiducials.
  
  
  RESULTS
  For phantom imaging, a total of 1925 frames were analyzed. The algorithm successfully tracked the target on 99.9% (1923/1925) of DE frames vs 90.7% (1745/1925) SE images (p < 0.01). The displacement between tracking coordinates and ground truth for the phantom was 1.4 mm ± 1.1 mm for DE vs 2.0 mm ± 1.3 mm for SE (p < 0.01). Images from two patients, one with a larger tumor and one with a smaller tumor, were also analyzed. For the patient with the larger tumor, the average displacement from physician defined ground truth was 1.2 mm ± 0.6 mm for DE vs 1.4 mm ± 0.7 mm for SE (p = 0.016). For the patient that presented with a smaller tumor, the average displacement from physician defined ground truth was 2.2 mm ± 1.0 mm for DE vs 3.2 mm ± 1.4 mm for SE (p < 0.01). Importantly, for this single patient with the smaller tumor, 15.6% of the SE frames had >5 mm displacements from the ground truth vs 0% for DE fluoroscopy.
  
  
  CONCLUSIONS
  This work indicates the potential for markerless tumor tracking utilizing DE fluoroscopy. With DE imaging, the algorithm showed improved detectability vs SE fluoroscopy and was able to accurately track the tumor in nearly all cases.
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: Rakesh
authors.initials: R
authors.lastname: Patel
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: Joshua
authors.initials: J
authors.lastname: Panfil
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: Maria
authors.initials: M
authors.lastname: Campana
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: Alec M
authors.initials: AM
authors.lastname: Block
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Harkenrider
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: Murat
authors.initials: M
authors.lastname: Surucu
authors.affiliation: Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois 60153.
authors.forename: John C
authors.initials: JC
authors.lastname: Roeske
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000818:Animals; D005240:Feasibility Studies; D005260:Female; D005471:Fluoroscopy / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D009038:Motion; D019047:Phantoms, Imaging; D012272:Ribs; D013552:Swine