Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery
- PMID: 28248652
- DOI: 10.1088/1361-6560/aa5ed2
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-1 which was increased to 1.2 mm-1 by SDIR, at half maximum.
Similar articles
-
Effects of ray profile modeling on resolution recovery in clinical CT.Med Phys. 2014 Feb;41(2):021907. doi: 10.1118/1.4862510. Med Phys. 2014. PMID: 24506628
-
Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT.Technol Cancer Res Treat. 2019 Jan 1;18:1533033818823054. doi: 10.1177/1533033818823054. Technol Cancer Res Treat. 2019. PMID: 30803367 Free PMC article.
-
Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization.Med Phys. 2014 May;41(5):051909. doi: 10.1118/1.4870375. Med Phys. 2014. PMID: 24784388
-
[Incident Photon Number and Reconstructed Linear Attenuation Coefficients in Iterative CT Image Reconstruction].Igaku Butsuri. 2019;38(4):143-158. doi: 10.11323/jjmp.38.4_143. Igaku Butsuri. 2019. PMID: 30828046 Review. Japanese.
-
Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography.J Med Invest. 2020;67(1.2):30-39. doi: 10.2152/jmi.67.30. J Med Invest. 2020. PMID: 32378615 Review.
Cited by
-
Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties.IEEE Trans Med Imaging. 2017 Dec;36(12):2588-2599. doi: 10.1109/TMI.2017.2766185. IEEE Trans Med Imaging. 2017. PMID: 29192888 Free PMC article.
-
Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure.Phys Med Biol. 2019 Oct 31;64(21):215010. doi: 10.1088/1361-6560/ab489e. Phys Med Biol. 2019. PMID: 31561247 Free PMC article.
-
Cone-Beam CT image contrast and attenuation-map linearity improvement (CALI) for brain stereotactic radiosurgery procedures.J Appl Clin Med Phys. 2018 Nov;19(6):200-208. doi: 10.1002/acm2.12477. Epub 2018 Oct 19. J Appl Clin Med Phys. 2018. PMID: 30338919 Free PMC article.
-
Statistical Iterative CBCT Reconstruction Based on Neural Network.IEEE Trans Med Imaging. 2018 Jun;37(6):1511-1521. doi: 10.1109/TMI.2018.2829896. IEEE Trans Med Imaging. 2018. PMID: 29870378 Free PMC article.
-
Accelerated Stimulated Raman Projection Tomography by Sparse Reconstruction From Sparse-View Data.IEEE Trans Biomed Eng. 2020 May;67(5):1293-1302. doi: 10.1109/TBME.2019.2935301. Epub 2019 Aug 14. IEEE Trans Biomed Eng. 2020. PMID: 31425010 Free PMC article.
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Miscellaneous