Automatic generation of digital anthropomorphic phantoms from simulated MRI acquisitions

C. Lindsay; M. A. Gennert; A. Kӧnik; P. K. Dasari; M. A. King

doi:10.1117/12.2007082

15 March 2013 Automatic generation of digital anthropomorphic phantoms from simulated MRI acquisitions

C. Lindsay, M. A. Gennert, A. Kӧnik, P. K. Dasari, M. A. King

Proceedings Volume 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling; 867122 (2013) https://doi.org/10.1117/12.2007082
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In SPECT imaging, motion from patient respiration and body motion can introduce image artifacts that may reduce the diagnostic quality of the images. Simulation studies using numerical phantoms with precisely known motion can help to develop and evaluate motion correction algorithms. Previous methods for evaluating motion correction algorithms used either manual or semi-automated segmentation of MRI studies to produce patient models in the form of XCAT Phantoms, from which one calculates the transformation and deformation between MRI study and patient model. Both manual and semi-automated methods of XCAT Phantom generation require expertise in human anatomy, with the semiautomated method requiring up to 30 minutes and the manual method requiring up to eight hours. Although faster than manual segmentation, the semi-automated method still requires a significant amount of time, is not replicable, and is subject to errors due to the difficulty of aligning and deforming anatomical shapes in 3D. We propose a new method for matching patient models to MRI that extends the previous semi-automated method by eliminating the manual non-rigid transformation. Our method requires no user supervision and therefore does not require expert knowledge of human anatomy to align the NURBs to anatomical structures in the MR image. Our contribution is employing the SIMRI MRI simulator to convert the XCAT NURBs to a voxel-based representation that is amenable to automatic non-rigid registration. Then registration is used to transform and deform the NURBs to match the anatomy in the MR image. We show that our automated method generates XCAT Phantoms more robustly and significantly faster than the previous semi-automated method.

Citation Download Citation

C. Lindsay, M. A. Gennert, A. Kӧnik, P. K. Dasari, and M. A. King "Automatic generation of digital anthropomorphic phantoms from simulated MRI acquisitions", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867122 (15 March 2013); https://doi.org/10.1117/12.2007082

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KEYWORDS

Magnetic resonance imaging

Image registration

Tissues

Image segmentation

Heart

Natural surfaces

Algorithm development

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