A computer simulated phantom study of tomotherapy dose optimization based on probability density functions (PDF) and potential errors caused by low reproducibility of PDF.

Lung tumor motion trajectories measured by four-dimensional CT or dynamic MRI can be converted to a probability density function (PDF), which describes the probability of the tumor at a certain position, for PDF based treatment planning. Using this method in simulated sequential tomotherapy, we study the dose reduction of normal tissues and more important, the effect of PDF reproducibility on the accuracy of dosimetry. For these purposes, realistic PDFs were obtained from two dynamic MRI scans of a healthy volunteer within a 2 week interval. The first PDF was accumulated from a 300 s scan and the second PDF was calculated from variable scan times from 5 s (one breathing cycle) to 300 s. Optimized beam fluences based on the second PDF were delivered to the hypothetical gross target volume (GTV) of a lung phantom that moved following the first PDF The reproducibility between two PDFs varied from low (78%) to high (94.8%) when the second scan time increased from 5 s to 300 s. When a highly reproducible PDF was used in optimization, the dose coverage of GTV was maintained; phantom lung receiving 10%-20% prescription dose was reduced by 40%-50% and the mean phantom lung dose was reduced by 9.6%. However, optimization based on PDF with low reproducibility resulted in a 50% underdosed GTV. The dosimetric error increased nearly exponentially as the PDF error increased. Therefore, although the dose of the tumor surrounding tissue can be theoretically reduced by PDF based treatment planning, the reliability and applicability of this method highly depend on if a reproducible PDF exists and is measurable. By correlating the dosimetric error and PDF error together, a useful guideline for PDF data acquisition and patient qualification for PDF based planning can be derived.

Duke Scholars

Author Jing Cai Radiation Oncology