arXiv Open Access 2020

Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks

Sayantan Bhadra Weimin Zhou Mark A. Anastasio

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Abstrak

Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is usually incorporated by means of some sparsity-promoting regularization. Recently, prior distributions for images estimated using generative adversarial networks (GANs) have shown great promise in regularizing some of these image reconstruction problems. In this work, we apply an image-adaptive GAN-based reconstruction method (IAGAN) to reconstruct high fidelity images from incomplete medical imaging data. It is observed that the IAGAN method can potentially recover fine structures in the object that are relevant for medical diagnosis but may be oversmoothed in reconstructions with traditional sparsity-promoting regularization.

Topik & Kata Kunci

eess.IV cs.LG stat.ML

Penulis (3)

Sayantan Bhadra

Weimin Zhou

Mark A. Anastasio

Format Sitasi

APA MLA BibTeX

Bhadra, S., Zhou, W., Anastasio, M.A. (2020). Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. https://arxiv.org/abs/2001.10830

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓