title: Contrast-enhanced proton radiography for patient set-up by using x-ray CT prior knowledge.
abstract: OBJECTIVE
  To obtain a contrasted image of the tumor region during the setup for proton therapy in lung patients, by using proton radiography and x-ray computed tomography (CT) prior knowledge.
  
  
  METHODS
  Six lung cancer patients' CT scans were preprocessed by masking out the gross tumor volume (GTV), and digitally reconstructed radiographs along the planned beam's eye view (BEV) were generated, for a total of 27 projections. Proton radiographies (PR) were also computed for the same BEV through Monte Carlo simulations. The digitally reconstructed radiograph was subtracted from the corresponding proton image, resulting in a contrast-enhanced proton radiography (CEPR). Michelson contrast analysis was performed both on PR and CEPR. The tumor region was then automatically segmented on CEPR and compared to the ground truth (GT) provided by physicians in terms of Dice coefficient, accuracy, precision, sensitivity, and specificity.
  
  
  RESULTS
  Contrast on CEPR was, on average, 4 times better than on PR. For 10 lateral projections (±45° off of 90° or 270°), although it was not possible to distinguish the tumor region in the PR, CEPR offers excellent GTV visibility. The median ± quartile values of Dice, precision, and accuracy indexes were 0.86 ± 0.03, 0.86 ± 0.06, and 0.88 ± 0.02, respectively, thus confirming the reliability of the method in highlighting tumor boundaries. Sensitivity and specificity analysis demonstrated that there is no systematic over- or underestimation of the tumor region. Identification of the tumor boundaries using CEPR resulted in a more accurate and precise definition of GTV compared to that obtained from pretreatment CT.
  
  
  CONCLUSIONS
  In most proton centers, the current clinical protocol is to align the patient using kV imaging with bony anatomy as a reference. We demonstrated that CEPR can significantly improve tumor visualization, allowing better patient set-up and permitting image guided proton therapy (IGPT).
authors.affiliation: Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy. Electronic address: mfspadea@unicz.it.
authors.forename: Maria Francesca
authors.initials: MF
authors.lastname: Spadea
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
authors.forename: Aurora
authors.initials: A
authors.lastname: Fassi
authors.affiliation: Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Zaffino
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; Bioengineering Unit-CNAO Foundation, Pavia, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Riboldi
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; Bioengineering Unit-CNAO Foundation, Pavia, Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Baroni
authors.affiliation: Department of Radiation Oncology, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Depauw
authors.affiliation: Department of Radiation Oncology, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: Joao
authors.initials: J
authors.lastname: Seco
mesh_terms: D000465:Algorithms; D003287:Contrast Media; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D011522:Protons; D012189:Retrospective Studies; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D047368:Tumor Burden
chemical_list: D003287:Contrast Media; D011522:Protons