Creating Anthropomorphic Phantoms via Unsupervised Convolutional Neural Networks

Junyu Chen, Ye Li, Yong Du, Eric Frey

Keywords: Computerized Phantoms, Image Registration, Convolutional Neural Networks

Abstract: Computerized phantoms play an important role in medical imaging research. They can serve as a gold standard for evaluating and optimizing medical imaging analysis, processing, and reconstruction methods. Existing computerized phantoms model anatomical variations through organ and phantom scaling, which does not fully capture the range of anatomical variations seen in humans. Here, we present a registration-based method for creating highly realistic and detailed anthropomorphic phantoms. The proposed registration method is built on the use of an unsupervised convolutional neural network (ConvNet) that warps the four-dimensional Xtended Cardiac-Torso (XCAT) phantom to a patient CT scan. The registration ConvNet iteratively optimizes an SSIM-based loss function for a given image pair without prior training. We experimentally show substantially improved image similarity of the generated phantom using the proposed method to a patient image.

Paper Type: both

Primary Subject Area: Application: Radiology

Secondary Subject Area: Image Registration

Paper Status: based on accepted/submitted journal paper

Source Code Url: https://github.com/junyuchen245/Fully_Unsupervised_CNN_Registration_Keras

Data Set Url:

https://wiki.cancerimagingarchive.net/display/Public/NaF+Prostate
https://olv.duke.edu/technologies/4d-extended-cardiac-torso-xcat-phantom-version-2-0/

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