Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning

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

doi:10.1117/12.2254571

24 February 2017 Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning

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

Author Affiliations +

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101331I (2017) https://doi.org/10.1117/12.2254571
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Accurate CT synthesis, sometimes called electron density estimation, from MRI is crucial for successful MRI-based radiotherapy planning and dose computation. Existing CT synthesis methods are able to synthesize normal tissues but are unable to accurately synthesize abnormal tissues (i.e., tumor), thus providing a suboptimal solution. We propose a multiatlas- based hybrid synthesis approach that combines multi-atlas registration and patch-based synthesis to accurately synthesize both normal and abnormal tissues. Multi-parametric atlas MR images are registered to the target MR images by multi-channel deformable registration, from which the atlas CT images are deformed and fused by locally-weighted averaging using a structural similarity measure (SSIM). Synthetic MR images are also computed from the registered atlas MRIs by using the same weights used for the CT synthesis; these are compared to the target patient MRIs allowing for the assessment of the CT synthesis fidelity. Poor synthesis regions are automatically detected based on the fidelity measure and refined by a patch-based synthesis. The proposed approach was tested on brain cancer patient data, and showed a noticeable improvement for the tumor region.

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Junghoon Lee, Aaron Carass, Amod Jog, Can Zhao, and Jerry L. Prince "Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331I (24 February 2017); https://doi.org/10.1117/12.2254571

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