title: Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions.
abstract: BACKGROUND
  In this pilot study we evaluated the performance of a substitute CT (s-CT) image derived from MR data of the brain, as a basis for optimization of intensity modulated rotational therapy, final dose calculation and derivation of reference images for patient positioning.
  
  
  METHODS
  S-CT images were created using a Gaussian mixture regression model on five patients previously treated with radiotherapy. Optimizations were compared using D max, D min, D median and D mean measures for the target volume and relevant risk structures. Final dose calculations were compared using gamma index with 1%/1 mm and 3%/3 mm acceptance criteria. 3D geometric evaluation was conducted using the DICE similarity coefficient for bony structures. 2D geometric comparison of digitally reconstructed radiographs (DRRs) was performed by manual delineation of relevant structures on the s-CT DRR that were transferred to the CT DRR and compared by visual inspection.
  
  
  RESULTS
  Differences for the target volumes in optimization comparisons were small in general, e.g. a mean difference in both D min and D max within ±0.3%. For the final dose calculation gamma evaluations, 100% of the voxels passed the 1%/1 mm criterion within the PTV. Within the entire external volume between 99.4% and 100% of the voxels passed the 3%/3 mm criterion. In the 3D geometric comparison, the DICE index varied between approximately 0.8-0.9, depending on the position in the skull. In the 2D DRR comparisons, no appreciable visual differences were found.
  
  
  CONCLUSIONS
  Even though the present work involves a limited number of patients, the results provide a strong indication that optimization and dose calculation based on s-CT data is accurate regarding both geometry and dosimetry.
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden. joakim.jonsson@radfys.umu.se.
authors.forename: Joakim H
authors.initials: JH
authors.lastname: Jonsson
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden. mehdiakhtari@hotmail.com.
authors.forename: Mohammad M
authors.initials: MM
authors.lastname: Akhtari
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden. magnus.g.karlsson@vll.se.
authors.forename: Magnus G
authors.initials: MG
authors.lastname: Karlsson
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden. adam.johansson@radfys.umu.se.
authors.forename: Adam
authors.initials: A
authors.lastname: Johansson
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden. thomas.asklund@onkologi.umu.se.
authors.forename: Thomas
authors.initials: T
authors.lastname: Asklund
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden. tufve.nyholm@radfys.umu.se.
authors.forename: Tufve
authors.initials: T
authors.lastname: Nyholm
mesh_terms: D001932:Brain Neoplasms / Q000532:radiotherapy*; D005909:Glioblastoma / Q000532:radiotherapy*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods*