Affiliations: Physics Department, University of Texas at El Paso,
TX, USA | Radiation Oncology Division, Radiology Department,
University of Texas Health Science Center at San Antonio, TX, USA
Abstract: Megavoltage X-ray sources are commonly used for therapy planning,
and knowledge of their spectral distribution is important for accurate dose
calculations. There are many methods that could provide reasonable estimations
of Megavoltage X-ray spectra, when very accurate attenuation data or at least
very good set of initial guesses of the spectra are available. We present here
a novel method, which can be used for accurate Megavoltage spectral
reconstruction without any prior knowledge of spectral distribution; the method
performs well even when the available transmission data are affected by noise.
The method is based on a search for a smooth function that minimizes the
differences between measured and calculated attenuation data. The algorithm is
compared with well-known existing algorithms, using computer simulated data,
both error-free and containing added random Gaussian noise. The reconstructed
spectra are subsequently used to calculate the transmission through 50 cm of
bone, muscle or fat tissue. It is shown that the relative errors in dose
calculations, using the spectra reconstructed via this method, are
significantly smaller than those obtained via well-established reconstruction
algorithms – Truncated Singular Value Decomposition (TSVD) and Expectation
Maximization (EM). These results suggest that the novel algorithm might be practical
for routine Megavoltage therapy X-ray source calibration.
