Considering marker visibility during leaf sequencing for segmental intensity-modulated radiation therapy.
OBJECTIVE
Segmental intensity-modulated radiation therapy (IMRT) delivers a sequence of segments to obtain a desired intensity distribution. Many leaf sequencing algorithms for segmental IMRT have been developed with the aim of reducing delivered monitor units (MUs) and (or) number of segments and, consequently, to reduce the total treatment delivery time. With the development of real-time detection technology, it is useful to develop leaf sequencing algorithms that consider the detecting probability of markers implanted into or near the target volume.


METHODS
In this study, the authors defined the concept of marker visibility to denote the marker's detecting probability and proposed a new leaf sequencing algorithm based on the Kamath algorithm. The new algorithm first uses the Kamath algorithm to generate an initial leaf sequence and then performs a series of column transformations to obtain a new leaf sequence that is optimal in terms of MU efficiency and marker visibility. The authors evaluated the performance of the new algorithm with six artificial fields that had randomly generated intensity matrices and 15 clinical fields that had intensity matrices from the IMRT plans for three prostate cancer patients.


RESULTS
Compared to the Kamath algorithm, the new algorithm does not increase the total delivered intensity but increases the marker visibility. For the artificial fields, the marker visibility increased from 66.67% to 91.67% for small (5 x 5) radiation fields, from 39.29% to 42.86% for medium size (10 x 10) fields, and from 31.48% to 37.04% for large (20 x 20) fields. For the clinical fields, the marker visibility increased 9%-20% for four fields, 20%-30% for three fields, 30%-40% for two fields, and more than 40% for one field. However, the marker visibility did not change for 4 out of 15 fields.


CONCLUSIONS
The authors developed a new leaf sequencing algorithm for optimal MU efficiency and marker visibility and also rigorously proved its optimality.
Zhao
Bo
B
Department of Radiation Oncology, Cancer Institute (Hospital), Chinese Academy of Medical Sciences, Beijing 100021, China.
Dai
Jianrong
J
Ling
C Clifton
CC
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008297:Male; D011336:Probability; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012984:Software