Simulation and Synthesis in Medical Imaging

A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis

Simulation and Synthesis in Medical Imaging

Simulation and synthesis in medical imaging : second International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings. SASHIMI (Workshop) (2nd : 2017 : Quebec City, Can..., September 2017

30221260

978-3-31-968126-9, 978-3-31-968127-6

Can Zhao, Aaron Carass, Junghoon Lee, Amod Jog, Jerry L. Prince

Synthesizing magnetic resonance (MR) and computed tomography (CT) images (from each other) has important implications for clinical neuroimaging. The MR to CT direction is critical for MRI-based radiotherapy planning and dose computation, whereas the CT to MR direction can provide an economic alternative to real MRI for image processing tasks. Additionally, synthesis in both directions can enhance MR/CT multi-modal image registration. Existing approaches have focused on synthesizing CT from MR. In this paper, we propose a multi-atlas based hybrid method to synthesize T1-weighted MR images from CT and CT images from T1-weighted MR images using a common framework. The task is carried out by: (a) computing a label field based on supervoxels for the subject image using joint label fusion; (b) correcting this result using a random forest classifier (RF-C); (c) spatial smoothing using a Markov random field; (d) synthesizing intensities using a set of RF regressors, one trained for each label. The algorithm is evaluated using a set of six registered CT and MR image pairs of the whole head.