The Compensation of Head Motion Artefacts using an Infrared Tracking System for CT (Computerized Tomography) Imaging

Zafar, Shahila; Younis, Waheed A.; Sheikh, N.M.

doi:10.1109/icee.2007.4287347

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…Early rigid motion correction methods for CT were limited to in-plane motion. Correction for in-plane translation of the object during helical CT acquisition was achieved by applying compensatory shifts to the projection data prior to interpolation into parallel projection views and reconstruction (Wang and Vannier 1995), and other similar approaches (Schäfer et al 2004, Yu et al 2006, Zafar et al 2007, Pauchard and Boyd 2008). In-plane (i.e.…”

Section: Ctmentioning

confidence: 99%

Motion estimation and correction in SPECT, PET and CT

Kyme¹,

Fulton²

2021

Phys. Med. Biol.

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Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and x-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art deep learning methods may have a unique role to play in this context.

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Section: Ctmentioning

confidence: 99%