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Application of discrete cosine transform to assess the effect of tumor motion variations on the definition of ITV in lung and liver SBRT

Published:April 21, 2021DOI:https://doi.org/10.1016/j.ejmp.2021.03.036

      Highlights

      • DCT allows for precise characterization of tumor breathing motion.
      • Interfraction and intrafraction effects can be included in ITV definition.
      • ITV is less affected by amplitude variations than by baseline shift variations.
      • 80% increase in motion range compared to that in 4DCT is caused by baseline shift.

      Abstract

      Purpose

      To use Discrete Cosine Transform to include tumor motion variations on ITV definition of SBRT patients.

      Methods

      Data from 66 patients was collected. 2D planar fluoroscopy images (FI) were available for 54 patients. Daily CBCT projections (CBCTp) from 29 patients were employed to measure interfraction amplitude variability. Systematic amplitude variations were obtained from 17 patients with data from both FI and CBCTp.
      Tumor motion curves obtained from FI were characterized with a Cosine model (CM), based on cosine functions to the power of 2, 4 or 6, and DCT. Performance of both models was evaluated by means of R2 coefficient and by comparing their results on Internal Target Volume (ITV) margins against those calculated from original tumor motion curves.
      Amplitude variations from CBCTp, as well as estimations of baseline shift variations were added to the DCT model to account for their effect on ITV margins.

      Results

      DCT replicated tumor motion curves with a mean R2 values for all patients of 0.86, 0.91 and 0.96 for the lateral (LAT), anterior-posterior (AP) and cranio-caudal (CC) directions respectively. CM yielded worst results, with R2 values of 0.64, 0.61 and 0.74 in the three directions.
      Interfraction amplitude variation increased ITV margins by a 9%, while baseline shift variability implied a 40% and 80–100% increase for normalized values of baseline shift of 0.2 and 0.4 respectively.

      Conclusions

      Probability distribution functions of tumor positions can be successfully characterized with DCT. This permits to include tumor motion variablilities obtained from patient population into patient specific ITVs.

      Keywords

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