PURPOSE: Prospective motion correction of magnetic resonance (MR) scans commonly uses an external device, such as a camera, to track the pose of the organ of interest. However, in order for external tracking data to be translated into the MR scanner reference frame, the pose of the camera relative to the MR scanner must be known accurately. Here, we describe a fast, accurate, non-iterative technique to determine the position of an external tracking device de novo relative to the MR reference frame. THEORY AND METHODS: The method relies on imaging a sparse object that allows simultaneous tracking of arbitrary rigid body transformations in the reference frame of the magnetic resonance imaging (MRI) machine and that of the external tracking device. RESULTS: Large motions in the MRI reference frame can be measured using a sparse phantom with an accuracy of 0.2 mm, or approximately 1/10 of the voxel size. By using a dual quaternion algorithm to solve the calibration problem, a good camera calibration can be achieved with fewer than six measurements. Further refinements can be achieved by applying the method iteratively and using motion correction feedback. CONCLUSION: Independent tracking of a series of movements in two reference frames allows for an analytical solution to the hand-eye-calibration problem for various motion tracking setups in MRI.
PURPOSE: Prospective motion correction of magnetic resonance (MR) scans commonly uses an external device, such as a camera, to track the pose of the organ of interest. However, in order for external tracking data to be translated into the MR scanner reference frame, the pose of the camera relative to the MR scanner must be known accurately. Here, we describe a fast, accurate, non-iterative technique to determine the position of an external tracking device de novo relative to the MR reference frame. THEORY AND METHODS: The method relies on imaging a sparse object that allows simultaneous tracking of arbitrary rigid body transformations in the reference frame of the magnetic resonance imaging (MRI) machine and that of the external tracking device. RESULTS: Large motions in the MRI reference frame can be measured using a sparse phantom with an accuracy of 0.2 mm, or approximately 1/10 of the voxel size. By using a dual quaternion algorithm to solve the calibration problem, a good camera calibration can be achieved with fewer than six measurements. Further refinements can be achieved by applying the method iteratively and using motion correction feedback. CONCLUSION: Independent tracking of a series of movements in two reference frames allows for an analytical solution to the hand-eye-calibration problem for various motion tracking setups in MRI.
Authors: Murat Aksoy; Christoph Forman; Matus Straka; Tolga Çukur; Joachim Hornegger; Roland Bammer Journal: Magn Reson Med Date: 2011-08-08 Impact factor: 4.668
Authors: Murat Aksoy; Christoph Forman; Matus Straka; Stefan Skare; Samantha Holdsworth; Joachim Hornegger; Roland Bammer Journal: Magn Reson Med Date: 2011-03-22 Impact factor: 4.668
Authors: Melvyn B Ooi; Sascha Krueger; William J Thomas; Srirama V Swaminathan; Truman R Brown Journal: Magn Reson Med Date: 2009-10 Impact factor: 4.668
Authors: Lei Qin; Peter van Gelderen; John Andrew Derbyshire; Fenghua Jin; Jongho Lee; Jacco A de Zwart; Yang Tao; Jeff H Duyn Journal: Magn Reson Med Date: 2009-10 Impact factor: 4.668
Authors: Julian Maclaren; Brian S R Armstrong; Robert T Barrows; K A Danishad; Thomas Ernst; Colin L Foster; Kazim Gumus; Michael Herbst; Ilja Y Kadashevich; Todd P Kusik; Qiaotian Li; Cris Lovell-Smith; Thomas Prieto; Peter Schulze; Oliver Speck; Daniel Stucht; Maxim Zaitsev Journal: PLoS One Date: 2012-11-07 Impact factor: 3.240
Authors: F Godenschweger; U Kägebein; D Stucht; U Yarach; A Sciarra; R Yakupov; F Lüsebrink; P Schulze; O Speck Journal: Phys Med Biol Date: 2016-02-11 Impact factor: 3.609