A Patient-Specific correspondence model to track tumor location in thorax during radiation therapy

Sharareh Fakhraei, Eric Ehler, David Sterling, L. Chinsoo Cho, Parham Alaei

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: We present a patient-specific model to estimate tumor location in the thorax during radiation therapy using chest surface displacement as the surrogate signal. Methods: Two types of data are used for model construction: Four-dimensional computed tomography (4D-CT) images of the patient and the displacement of two points on the patient's skin on the thoracic area. Principal component analysis is used to fit the correspondence model. This model incorporates the recorded surrogate signals during radiation delivery as input and delivers the 3D trajectory of the tumor as output. We evaluated the accuracy of the proposed model on a respiratory phantom and five lung cancer patients. Results: For the respiratory phantom, the location of the center of the sphere during treatment was calculated in three directions: Left-Right (LR), Anterior-Posterior (AP) and, Superior-Inferior (SI). The error of localization was less than 1 mm in the LR and AP directions and less than 2 mm in the SI direction. The location of the tumor center for two of the patients, and the location of the apex of the diaphragm for the other three, was calculated in three directions. For all patients, the localization error in the LR and AP directions was less than 1.1 mm for two fractions and the maximum localization error in the SI direction was 6.4 mm. Conclusions: This work presents a feasibility study of utilizing surface displacement data to locate the tumor in the thorax during radiation treatment. Future work will validate the model on a larger patient population.

Original languageEnglish (US)
Article number103167
JournalPhysica Medica
Volume116
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Associazione Italiana di Fisica Medica e Sanitaria

Keywords

  • Correspondence model
  • Respiratory motion
  • Surrogate signals
  • Tumor localization

PubMed: MeSH publication types

  • Journal Article

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