title: 3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy.
abstract: OBJECTIVE
  To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy.
  
  
  METHODS
  Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset.
  
  
  RESULTS
  For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s.
  
  
  CONCLUSIONS
  Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection.
authors.affiliation: Center for Advanced Radiotherapy Technologies and Department of Radiation Oncology, University of California San Diego, La Jolla, California 92037, USA.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
authors.forename: John H
authors.initials: JH
authors.lastname: Lewis
authors.forename: Xun
authors.initials: X
authors.lastname: Jia
authors.forename: Xuejun
authors.initials: X
authors.lastname: Gu
authors.forename: Michael
authors.initials: M
authors.lastname: Folkerts
authors.forename: Chunhua
authors.initials: C
authors.lastname: Men
authors.forename: William Y
authors.initials: WY
authors.lastname: Song
authors.forename: Steve B
authors.initials: SB
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D003199:Computer Systems; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*