Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery

Abstract

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm which was increased to 1.2 mm by SDIR, at half maximum.

Cone‐Beam CT image contrast and attenuation‐map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

“…The proposed framework called CALI (Contrast and Attenuation–map Linearity Improvement) considers three important sources of the non‐linearity in CBCT images, namely scattering, beam hardening, and blurriness. To improve the spatial resolution of the CBCT images, a high spatial resolution iterative reconstruction algorithm called simultaneous deblurring and iterative reconstruction (SDIR) was proposed, which estimates the blurriness in the image domain . SDIR improves the spatial resolution, preserves edges, and improves the visibility of soft tissues in the brain .…”

“…To improve the spatial resolution of the CBCT images, a high spatial resolution iterative reconstruction algorithm called simultaneous deblurring and iterative reconstruction (SDIR) was proposed, which estimates the blurriness in the image domain . SDIR improves the spatial resolution, preserves edges, and improves the visibility of soft tissues in the brain . For example, some brain folds and structures, such as ventricles, become clearly visible in the images reconstructed with SDIR.…”

“…The CBCT system provides the stereotactic reference, which together with co‐registration of the planning image volume and the CBCT volume gives the transformation mapping of the planned isocenter positions to stereotactic coordinates. Since the X‐ray beams cannot penetrate through the frame fixation on the treatment bed, a half cone geometry is used in the Icon system, which maximizes the field‐of‐view by aligning the X‐ray tube parallel and close to the frame plane . The half cone geometry results in more pronounced shading and cone‐beam artifacts than the regular full cone geometries used in other commercial systems.…”

“…Unlike in CT where a calibration phantom is commonly used to map the measured CT# to the electron density of the tissues, in the native CBCT images this process is prohibited due to the resulting artifacts that are present. Improvement of the sources of artifacts in cone‐beam CT has been an area of active research within the past few years, with dedicated frameworks being researched to correct for: shading artifact, scatter, and system blur . A major area of focus of CBCT image quality improvement has been scatter reduction, with several model‐based approaches including a dedicated cone‐beam breast CT model .…”

Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties

“…Bian et al showed that the TV-based iterative algorithm was more robust to different data conditions, such as number of views and exposure levels, than the FDK algorithm for CBCT reconstruction [21]. Hashemi et al proposed a simultaneous deblurring and iterative reconstruction to explicitly account for image unsharpness caused by different factors in the CBCT reconstruction formulation when using the TV penalty [14]. Huang and Hsiao proposed an iterative reconstruction method to accelerate the ordered-subsets reconstruction with a power factor, in combination with the TV minimization method [15].…”