Feasibility of patient dose reduction based on various noise suppression filters for cone-beam computed tomography in an image-guided patient positioning system

Abstract

We investigated the feasibility of patient dose reduction based on six noise suppression filters for cone-beam computed tomography (CBCT) in an image-guided patient positioning (IGPP) system. A midpoint dose was employed as a patient dose index. First, a reference dose (RD) and low-dose (LD)-CBCT images were acquired with a reference dose and various low doses. Second, an automated rigid registration was performed for three axis translations to estimate patient setup errors between a planning CT image and the LD-CBCT images processed by six noise suppression filters (averaging filter, median filter, Gaussian filter, edge-preserving smoothing filter, bilateral filter, and adaptive partial median filter (AMF)). Third, residual errors representing the patient positioning accuracy were calculated as Euclidean distances between the setup error vectors estimated using the LD-CBCT and RD-CBCT images. Finally, the residual errors as a function of the patient dose index were estimated for LD-CBCT images processed by six noise suppression filters, and then the patient dose indices for the filtered LD-CBCT images were obtained at the same residual error as the RD-CBCT image. This approach was applied to an anthropomorphic phantom and four cancer patients. The patient dose for the LD-CBCT images was reduced to 19% of that for the RD-CBCT image for the phantom by using AMF, while keeping a same residual error of 0.47 mm as the RD-CBCT image by applying the noise suppression filters to the LD-CBCT images. The average patient dose was reduced to 31.1% for prostate cancer patients, and it was reduced to 82.5% for a lung cancer patient by applying the AMF. These preliminary results suggested that the proposed approach based on noise suppression filters could decrease the patient dose in IGPP systems.