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Evaluation and validation of tungsten fiducial marker-based image-guided radiotherapy.

Wajeehah ShahidRaheel MukhtarSyed Faheem Askari RizviSamiah ShahidMuhammad Aamir Iqbal
Published in: Biomedical physics & engineering express (2021)
In this research work, a simple homemade cubic phantom was designed to validate the Image-Guided Radiotherapy (IGRT) set up and verified with the help of tungsten fiducial markers (size 2-3 mm) inserted into the cubic phantom. Phantom made up of Styrofoam, was scanned with the help of 16 slice Toshiba CT scanner where each slice was of 1 mm thickness and HU level set to -1000. A radio-opaque contrast medium was rubbed on the phantom to visualize the scanner images. Once the iso-center had been marked on a phantom with the help of in-room positioning laser and the fields (RT-LAT and AP) were applied on the contoured body of the phantom in Varian's ARIA-11 Eclipse dosimeter software, the same position of the phantom was reproduced on Varian's Linear Accelerator DHX. Known shifts of 3.0 to 30.0 mm from the marked iso-center were applied on the phantom by moving the couch in all six directions one by one. On each applied couch shift, an x-ray image of the phantom was acquired with the help of an MV portal imager of Linac in AP and RT-LAT direction. This image was superimposed with a reference image of phantom and shift accuracy calculated by ARIA-11 software was noted down. It turned out that irrespective of the position of the phantom on the couch, the calculated corrected shift and deviation from reference position was always between ± 1-2 mm which is the required accuracy for IGRT according to International Atomic Energy Agency (IAEA). This process was repeated 40 times and each time, the corrected shift came out to be ± 1-2 mm. We can conclude that our system is safe and accurate enough to perfectly position the actual patient for IGRT.
Keyphrases
  • image quality
  • dual energy
  • computed tomography
  • monte carlo
  • deep learning
  • radiation therapy
  • magnetic resonance imaging
  • transcription factor
  • optical coherence tomography
  • machine learning
  • locally advanced