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Abstract #3988

A Study of Simulated Training Data for Image Reconstruction from Subsampled MR Data using Artificial Neural Network

kinam kwon1, Jaejin Cho1, Seohee So1, Byungjai Kim1, Yoonmee Lee1, kyungtak Min1, and HyunWook Park1

1KAIST, Daejeon, Korea, Republic of

Recently, several works have applied the deep learning technique to medical imaging problems such as lesion classification and image reconstruction. The deep learning techniques have advantages of learning from big data, however, in medical imaging, collecting an amount of training data is not easy because of expense, privacy, and so on. Strategies to supplement insufficient training data are important topics for applying deep learning to medical imaging field. In this study, training data are generated from the simulated images and the acquired MR images, which are utilized to learn the architecture of multilayer perceptron to reduce imaging time.

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