Algebraic and analytic reconstruction methods for dynamic tomography

Desbat, Laurent; Rit, Simon; Clackdoyle, Rolf; Mennessier, Catherine; Promayon, Emmanuel; Ntalampeki, S.

doi:10.1109/iembs.2007.4352393

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Another way to mitigate motion-induced artifacts is to apply retrospective motion correction. To date several methods have been proposed for correcting for rigid motion (Wang and Vannier 1995, Schäfer et al 2004, Bodensteiner et al 2007, Yu and Wang 2007, Jacobson and Stayman 2008, Ens et al 2010, Zafar 2011, Bhowmik et al 2012 and nonrigid motion (Crawford et al 1996, Marchant et al 2001, Lu and Mackie 2002, Roux et al 2004, Schäfer et al 2004, Desbat et al 2007, Isola et al 2008, Taguchi and Kudo 2008, Rit et al 2009, Schretter et al 2009 in various types of CT imaging. In this paper we address the correction of rigid motion, characterized by six degrees-of-freedom (d.o.f.)…”

Section: Introductionmentioning

confidence: 99%

A rigid motion correction method for helical computed tomography (CT)

et al. 2015

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We propose a method to compensate for six degree-of-freedom rigid motion in helical CT of the head. The method is demonstrated in simulations and in helical scans performed on a 16-slice CT scanner. Scans of a Hoffman brain phantom were acquired while an optical motion tracking system recorded the motion of the bed and the phantom. Motion correction was performed by restoring projection consistency using data from the motion tracking system, and reconstructing with an iterative fully 3D algorithm. Motion correction accuracy was evaluated by comparing reconstructed images with a stationary reference scan. We also investigated the effects on accuracy of tracker sampling rate, measurement jitter, interpolation of tracker measurements, and the synchronization of motion data and CT projections. After optimization of these aspects, motion corrected images corresponded remarkably closely to images of the stationary phantom with correlation and similarity coefficients both above 0.9. We performed a simulation study using volunteer head motion and found similarly that our method is capable of compensating effectively for realistic human head movements. To the best of our knowledge, this is the first practical demonstration of generalized rigid motion correction in helical CT. Its clinical value, which we have yet to explore, may be significant. For example it could reduce the necessity for repeat scans and resource-intensive anesthetic and sedation procedures in patient groups prone to motion, such as young children. It is not only applicable to dedicated CT imaging, but also to hybrid PET/CT and SPECT/CT, where it could also ensure an accurate CT image for lesion localization and attenuation correction of the functional image data.

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