System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Epublish 2 citations

PubMed: 38495703 DOI: 10.1364/BOE.504954

Towards ultrafast quantitative phase imaging via differentiable microscopy [Invited].

Biomed Opt Express 15(3):1798-1812

Authors: Udith Haputhanthri, Kithmini Herath, Ramith Hettiarachchi, Hasindu Kariyawasam, Azeem Ahmad, Balpreet S Ahluwalia, Ganesh Acharya, Chamira U S Edussooriya, Dushan N Wadduwage

Affiliations

  • Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA. Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka.
  • Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
  • Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka.
  • Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, 9037, Norway.
  • Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, Sweden.

Abstract

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the pixel-rate of the image sensors. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form so that more information can be transferred beyond the existing hardware bottleneck of the image sensor. To this end, we present a numerical simulation of a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy (∂-QPM) first uses learnable optical processors as image compressors. The intensity representations produced by these optical processors are then captured by the imaging sensor. Finally, a reconstruction network running on a computer decompresses the QPM images post aquisition. In numerical experiments, the proposed system achieves compression of × 64 while maintaining the SSIM of ∼0.90 and PSNR of ∼30 dB on cells. The results demonstrated by our experiments open up a new pathway to QPM systems that may provide unprecedented throughput improvements.

MeSH

Microscopy, Quantitative Phase Imaging

Cited by