Abstract
Respiratory motion is a complicating factor for many applications in medical imaging and there is significant interest in dynamic imaging that can be used to estimate such motion. Magnetic resonance imaging (MRI) is an attractive modality for motion estimation but current techniques cannot achieve good image contrast inside the lungs. Manifold learning is a powerful tool to discover the underlying structure of high-dimensional data. Aligning the manifolds of multiple datasets can be useful to establish relationships between different types of data. However, the current state-of-the-art in manifold alignment is not robust to the wide variations in manifold structure that may occur in clinical datasets. In this work we propose a novel, fully automatic technique for the simultaneous alignment of large numbers of manifolds with varying manifold structure. We apply the technique to reconstruct high-resolution and high-contrast dynamic 3D MRI images from multiple 2D datasets for the purpose of respiratory motion estimation. The proposed method is validated on synthetic data with known ground truth and real data. We demonstrate that our approach can be applied to reconstruct significantly more accurate and consistent dynamic images of the lungs compared to the current state-of-the-art in manifold alignment.
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Baumgartner, C.F., Kolbitsch, C., McClelland, J.R., Rueckert, D., King, A.P. (2013). Groupwise Simultaneous Manifold Alignment for High-Resolution Dynamic MR Imaging of Respiratory Motion. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_20
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DOI: https://doi.org/10.1007/978-3-642-38868-2_20
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