The indirect use of CT numbers to establish material properties needed for Monte Carlo calculation of dose distributions in patients.
A number of Monte Carlo codes are available, which can be used to calculate dose distributions n patients with high accuracy. Patient geometry can readily be derived with adequate spatial resolution from CT scans. To perform the Monte Carlo calculation with the same spatial resolution, it is necessary to enter the atomic composition and density of the tissue in each voxel of the CT image. This means entering 65,536 discrete values for a CT slice with a 256 x 256 matrix size. The need for automated methods of setting up the material data files is obvious. Because there is no direct unique relationship between CT numbers and material composition, the aim of our work was to devise a method whereby the atomic composition and density in each voxel could be assigned automatically by indirect derivation from the CT numbers. The set of all tissues types in the human body was divided into subsets that are dosimetrically equivalent, based on Monte Carlo calculated depth dose curves in homogeneous phantoms of each tissue. CT number ranges corresponding to each tissue subset were determined from the calibration curve linking electron density with CT number for the specific CT scanner. Further subdivision was found to be necessary for the lung and bone type tissues. This was done by keeping the atomic composition constant and varying the physical density. It was found that 57 distinct tissue subsets were needed to represent the 16 main tissue types in the body at a 1% dose level. Corresponding CT number intervals of 30 HU were needed in the lung and soft tissue region, whereas in the bone region the intervals could be increased to 100 HU. A computer algorithm was set up to convert automatically from CT number to corresponding equivalent material number for the Monte Carlo preprocessor code.
du Plessis
F C
FC
Biophysics Department, Faculty of Medicine, University of the Free State, Bloemfontein, South Africa. gnbifct@med.uovs.ac.za
Willemse
C A
CA
Lötter
M G
MG
Goedhals
L
L
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D009010:Monte Carlo Method; D009369:Neoplasms / Q000532:radiotherapy*; D019047:Phantoms, Imaging; D011879:Radiotherapy Dosage / Q000592:standards; D014018:Tissue Distribution / Q000528:radiation effects; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data