Deblurring in iterative reconstruction of half CBCT for image guided brain radiosurgery

A high spatial resolution iterative reconstruction algorithm is proposed for a half cone beam CT (HCBCT) geometry. The proposed algorithm improves spatial resolution by explicitly accounting for image blurriness caused by different factors, such as extended X-ray source and detector response. The blurring kernel is estimated using the MTF slice of the Catphan phantom. The proposed algorithm is specifically optimized for the new Leksell Gamma Knife Icon (Elekta AB, Stockholm, Sweden) which incorporates the HCBCT geometry to accommodate the existing treatment couch while covering down to the base-of-skull in the reconstructed field-of-view. Image reconstruction involves a Fourier-based scaling simultaneous algebraic reconstruction technique (SART) coupled with total variation (TV) minimization and non-local mean denoising, solved using a split Bregman separation technique that splits the reconstruction problem into a gradient based updating step and a TV-based deconvolution algorithm. This formulation preserves edges and reduces the staircase effect caused by regular TV-penalized iterative algorithms. Our experiments indicate that our proposed method outperforms the conventional filtered back projection and TV penalized SART methods in terms of line pair resolution and retains the favorable properties of the standard TV-penalized reconstruction.