Improvement of Whole-body Bone Planar Images on a Bone-dedicated Single-photon Emission Computed Tomography Scanner by Blind Deconvolution Algorithm.

Abstract

Purpose: We have developed a bone-dedicated collimator with higher sensitivity but slightly degraded resolution on single-photon emission computed tomography (SPECT) for planar bone scintigraphy, compared with conventional low-energy high-resolution collimator. In this work, we investigated the feasibility of using the blind deconvolution algorithm to improve the resolution of planar images on bone scintigraphy.

Materials and methods: Monte Carlo simulation was performed with the NCAT phantom for modeling bone scintigraphy on the clinical dual-head SPECT scanner (Imagine NET 632, Beijing Novel Medical Equipment Ltd.) equipped with the bone-dedicated collimator. Maximum likelihood estimation method was used for the blind deconvolution algorithm. The initial estimation of point spread function (PSF) and iteration number for the method were determined by comparing the deblurred images obtained from different input parameters. We simulated different tumors in five different locations and with five different diameters to evaluate the robustness of the initial inputs. Furthermore, we performed chest phantom studies on the clinical SPECT scanner. The quantified increased contrast ratio (CR) between the tumor and the background was evaluated.

Results: The 2 mm PSF kernel and 10 iterations provided a practical and robust deblurred image on our system. Those two inputs can generate robust deblurred images in terms of the tumor location and size with an average increased CR of 21.6%. The phantom studies also demonstrated the ability of blind deconvolution, using those two inputs, with increased CRs of 17%, 17%, 22%, 20%, and 13% for lesions with diameters of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively.

Conclusions: It is feasible to use the blind deconvolution algorithm to deblur the planar images for SPECT bone scintigraphy. The appropriate values of the PSF kernel and the iteration number for the blind deconvolution can be determined using simulation studies.