Fiducial visibility on planar images during motion-synchronized tomotherapy treatments.
William S FerrisLarry A DeWerdWesley S CulbersonPublished in: Biomedical physics & engineering express (2022)
Objective . Synchrony ® is a motion management system on the Radixact ® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach . A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to the imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results . The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance . This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.
Keyphrases
- image quality
- deep learning
- dual energy
- high resolution
- randomized controlled trial
- end stage renal disease
- machine learning
- optical coherence tomography
- computed tomography
- chronic kidney disease
- newly diagnosed
- air pollution
- electronic health record
- big data
- high speed
- patient reported outcomes
- fluorescence imaging