The Hong Kong Polytechnic University

Jing Cai

Tian Li

Haonan Xiao

Wen Li

The present disclosure provides a system and method for precision tumor delineation in cancer treatment or diagnosis for subjects with carcinoma, in particular, nasopharyngeal carcinoma, based on contrast agent-free, virtual contrast-enhanced MRI data (VCE-MRI) synthesized by a modified multimodality-guided synergistic neural network trained with a more diversified training dataset and having a higher generalizability by minimizing data distribution variation between an external dataset and the training dataset through a data distribution matching mechanism before VCE-MRI synthesis.

System and methods for precision tumor delineation in cancer treatment or diagnosis for subjects with carcinoma based on contrast agent-free, virtual contrast-enhanced MRI data

A computer-implemented method for training a convolutional neural network (CNN) using training data comprising a pair of original and downsampled 4D magnetic resonance imaging (MRI) data is provided. The CNN is used to generate multi-parametric 4D magnetic resonance (MR) images based on multi-parametric 3D MR images in real-time. The method includes receiving a 4D MR image formed by a plurality of fixed images of different frames; converting the plurality of fixed images into a plurality of k-space data by non-uniform fast Fourier transform (NUFFT); applying radial scan to the k-space data to simulate real-time MR image acquisition, and generating a plurality of downsampled fixed images by inverse NUFFT; training a CNN with training data comprising the 4D MR image and the corresponding downsampled 4D MR image; and estimating the multi-parametric 4D MR image in real-time by applying apply the predicted DVF to the multi-parametric 3D MR images.

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Hong Kong, China

Jing Cai