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.
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 (Presented at SPIE Medical Imaging: February 14, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254571.
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