Semiautomatic vertebrae visualization, detection, and identification for online palliative radiotherapy of bone metastases of the spinea)

Abstract

A treatment process which integrates simulation, planning, and delivery in one single session of < or =30 min on a treatment unit capable of cone-beam CT imaging (CBCT) is under development in our institution for palliation of spinal metastases. The objective of this work is to develop and validate a semiautomatic vertebra detection and identification algorithm to streamline the target definition process and improve the consistency of online planning on cone-beam CT data sets while the patient is on the treatment couch. Key issues pertaining to this work are the limited field of view and image quality of CBCT, the inter- and intrapatient variation of vertebra morphology, and the spine curvature. An initial library of ten patient CBCT data sets was used to derive the vertebra detection and identification method and set the parameters used by the algorithm. In this method, sagittal and coronal "curved" digitally reconstructed radiographs (cDRRs) are first created by projecting a subvolume of the CBCT data orthogonally to the centerline of a cylinder model positioned manually. The detection of the vertebra centers is then performed on the cDRRs based on an edge detection algorithm. The identification of the vertebrae by name is based on the detection of one or more of four different reference anatomical landmarks on cDRRs. The validation of the vertebra detection and identification algorithm was performed on a library of 27 patient CBCT data sets with an average detection success rate of 92.8% and 89.9% for sagittal and coronal cDRRs, respectively, for three different users. The entire process including manual steps and user approval was performed on average in 3.23-3.45 min (n=37, three users), with only 0.14 min for the automatic detection and identification of the vertebrae. The semiautomatic identification and segmentation of vertebrae on CBCT images was shown to be robust and effective. The next step will be the clinical implementation of the algorithm within the online planning and delivery treatment technique for patients with spinal bone metastases.