21 March 2016 Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features
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Abstract
In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This is a non-trivial task since MRI intensities are neither uniquely nor quantitatively related to electron density. Typical approaches involve either a classification or regression model requiring specialized MRI sequences to solve intensity ambiguities, or an atlas-based model necessitating multiple registrations between atlases and subject scans. In this work, we explore a machine learning approach for creating a pCT of the pelvic region from conventional MRI sequences without using atlases. We use a random forest provided with information about local texture, edges and spatial features derived from the MRI. This helps to solve intensity ambiguities. Furthermore, we use the concept of auto-context by sequentially training a number of classification forests to create and improve context features, which are finally used to train a regression forest for pCT prediction. We evaluate the pCT quality in terms of the voxel-wise error and the radiologic accuracy as measured by water-equivalent path lengths. We compare the performance of our method against two baseline pCT strategies, which either set all MRI voxels in the subject equal to the CT value of water, or in addition transfer the bone volume from the real CT. We show an improved performance compared to both baseline pCTs suggesting that our method may be useful for MRI-only radiotherapy.
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Daniel Andreasen, Daniel Andreasen, Jens M. Edmund, Jens M. Edmund, Vasileios Zografos, Vasileios Zografos, Bjoern H. Menze, Bjoern H. Menze, Koen Van Leemput, Koen Van Leemput, } "Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978417 (21 March 2016); doi: 10.1117/12.2216924; https://doi.org/10.1117/12.2216924
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