Overcoming nonlinear partial volume effects in known-component reconstruction of Cochlear implants

Abstract

Nonlinear partial volume (NLPV) effects can be significant for objects with large attenuation differences and fine detail structures near the spatial resolution limits of a tomographic system. This is particularly true for small metal devices like cochlear implants. While traditional model-based approaches might alleviate these artifacts through very fine sampling of the image volume and subsampling of rays to each detector element, such solutions can be extremely burdensome in terms of memory and computational requirements. The work presented in this paper leverages the model-based approach called “known-component reconstruction” (KCR) where prior knowledge of a surgical device is integrated into the estimation. In KCR, the parameterization of the object separates the volume into an unknown background anatomy and a known component with unknown registration. Thus, one can model projections of an implant at very high spatial resolution while limiting the spatial resolution of the anatomy - in effect, modeling NLPV effects where they are most significant. We present modifications of the KCR approach that can be used to largely eliminate NLPV artifacts, and demonstrate the efficacy of the modified technique (with improved image quality and accurate implant position estimates) for the cochlear implant imaging scenario.

Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra

“…The authors have previously explored additional modifications to KCR methods using a mixed forward model where the component and its projections are modeled using much smaller voxels and sub-pixel integration at the detector to help reduce nonlinear partial volume effects. (Stayman et al 2013) Such model enhancements for Poly-KCR are the subject of future work as are modified statistical weightings that account for the additional uncertainties in long path lengths through metal.…”

“…In this work, we leverage the same kind of decomposition as used in (Stayman et al 2013) which decouples physical effects in the patient anatomy with those effects due to the metal components. This permits a mixed-fidelity system model where metal components can be modeled with high-fidelity using a simple parameterized energy dependence (i.e., a spectral transfer function), while the surrounding patient anatomy can be modeled with a “standard-fidelity” monoenergetic model.…”

Augmented Likelihood Image Reconstruction

“…Stayman et al have addressed the problem of nonlinear partial volume effects when very small objects such as cochlear implants or wires are present [62], [63]. It is thinkable to modify the proposed algorithm with a similar approach based on a refined forward model for the metal object.…”

Modelling the physics in the iterative reconstruction for transmission computed tomography

“…NLPV is caused by inconsistencies in projection data arising from attenuation gradients occurring within the field of view of a single detector cell due to the logarithmic relationship between attenuation and measured intensity (Glover and Pelc 1980, Joseph and Spital 1981). NLPV is therefore an unavoidable result of using finite detector apertures, but can be alleviated if the reconstruction accounts for the process of formation of this artifact by finely discretising the object space (to better capture the image gradients) and by subsampling the detector cells (to capture the averaging of detected intensities across image gradients) (Stayman et al 2013, Van Slambrouck and Nuyts 2012). Since the NLPV artifacts are most pronounced around high-intensity image gradients, e.g., around metallic implants, strategies where the object discretisation is made finer only in the vicinity of such structures were proposed (Stayman et al 2013, Van Slambrouck and Nuyts, 2012).…”

“…NLPV is therefore an unavoidable result of using finite detector apertures, but can be alleviated if the reconstruction accounts for the process of formation of this artifact by finely discretising the object space (to better capture the image gradients) and by subsampling the detector cells (to capture the averaging of detected intensities across image gradients) (Stayman et al 2013, Van Slambrouck and Nuyts 2012). Since the NLPV artifacts are most pronounced around high-intensity image gradients, e.g., around metallic implants, strategies where the object discretisation is made finer only in the vicinity of such structures were proposed (Stayman et al 2013, Van Slambrouck and Nuyts, 2012). In (Van Slambrouck and Nuyts 2012), grouped coordinate ascent is employed to allow for sequential update (and associated faster convergence) of image regions with different discretisation.…”