Application of four-dimensional cone beam computed tomography in lung cancer radiotherapy

Abstract

Objective: This study explored the application of four-dimensional cone beam computed tomography (4D CBCT) in lung cancer patients, seeking to improve the accuracy of radiotherapy and to establish a uniform protocol for the application of 4D CBCT in radiotherapy for lung cancer. Methods: 4D CBCT was applied to evaluate tumor volume response (TVR), motion, and center coordinates during radiotherapy in 67 eligible individuals with lung cancer diagnoses. The differences between 4D CBCT and 3D CBCT in different registration methods were compared. Results: TVR was observed during treatment in 41% of patients (28/67), with a mean volume reduction of 41.7% and a median time to TVR of 19 days. Tumor motion was obvious in 16 patients, with a mean value of 0.52 cm (0.22 to 1.34 cm), and in 3 of 6 tumors close to the diaphragm (0.28 to 0.66 cm). Gray value registration based on mean density projection could still achieve close results to the 4D gray value registration. However, when the registration was based on bone alone, partial off-targeting occurred in the treatment in 41.8% of cases. The off-target rate was 19.0% when the tumor motion was ≤0.5 cm and 52.2% when the motion was >0.5 cm. Conclusion: Tumor volume and motion of intrapulmonary lesions in individuals diagnosed with lung cancer varied significantly in the third week of radiotherapy. 4D CBCT may be more advantageous for isolated lesions without reference to relative anatomical structures or those near the diaphragm. Grayscale registration based on mean density projection is feasible.

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

“…Due to the long acquisition time, patient anatomical motion, mostly respiration (3-5 s per breathing cycle), results in artifacts and blurriness in the reconstructed CBCTs (Rit et al 2011). To address the artifacts and resolve the underlying motion, four-dimensional (4D)-CBCT was developed as the current clinical state-of-the-art (Sonke et al 2005, Zhang et al 2013, Abulimiti et al 2023. 4D-CBCT sorts the projections into a pre-defined set of motion bins and stacks semi-static CBCTs reconstructed from each bin to represent an averaged motion pattern.…”