Medical image reconstruction using compressible latent space invertible networks

Varun A. Kelkar, Sayantan Bhadra, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Many modern medical imaging systems are computed in nature and need computational reconstruction to form an image. When the acquired measurements are insufficient to uniquely determine the sought-after object, prior knowledge about the object needs to be utilized in order to successfully recover an image. An emerging line of research involves the use of deep generative models such as generative adversarial networks (GANs) as priors in the image reconstruction procedure. However, when GANs are employed, reconstruction of images outside the range of the GAN leads to errors that result in realistic but false image estimates. To address this issue, an image reconstruction framework is proposed that is formulated in the latent space of an invertible generative model. A novel regularization strategy is introduced that takes advantage of the architecture of certain invertible neural networks (INNs). To evaluate the performance of the proposed method, numerical studies of reconstructing images from stylized MRI measurements are performed. The method outperforms classical reconstruction methods in terms of traditional image quality metrics and is comparable to a state-of- the-art adaptive GAN based framework, while benefiting from a deterministic procedure and easier tuning of the regularization parameters.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Wei Zhao, Lifeng Yu
ISBN (Electronic)9781510640191
StatePublished - 2021
EventMedical Imaging 2021: Physics of Medical Imaging - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2021: Physics of Medical Imaging
Country/TerritoryUnited States
CityVirtual, Online


  • Compressed sensing
  • Generative adversarial networks
  • Generative models
  • Invertible neural networks
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials


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