Auto-correlation for multi-view deconvolved reconstruction in light sheet microscopy

Daniele Ancora; Gianluca Valentini; Antonio Giovanni Pifferi; Andrea Bassi

doi:10.1117/12.2583004

5 March 2021 Auto-correlation for multi-view deconvolved reconstruction in light sheet microscopy

Daniele Ancora, Gianluca Valentini, Antonio Giovanni Pifferi, Andrea Bassi

Proceedings Volume 11649, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII; 116490X (2021) https://doi.org/10.1117/12.2583004
Event: SPIE BiOS, 2021, Online Only

Abstract

Tomographic inspection of fluorescent labels distributed within a specimen is an important aspect in biology. Light sheet fluorescent microscopy (LSFM) offers a powerful and simple tool to selectively slice the sample and let us directly obtain a tomographic view of the specimen. However, due to non-isotropic resolution of the technique along the axial scanning, one may want to combine different views of the object and add deconvolution to the process in order to achieve higher resolution. Typically, multi-view Bayesian methods based on Richardson-Lucy deconvolution are used for this task once the datasets are exactly registered against each other. In this work, instead, we begin to investigate how to avoid the alignment procedure and use a direct algorithm to form a multi-view tomographic reconstruction. To do this, we developed a new framework based on auto-correlation analysis that let us achieve deconvolved reconstructions starting from blurred auto-correlations. Since the latter are insensitive to shifts, we can combine the auto-correlations coming from multi-view acquisitions without taking care of the registration procedure.

Conference Presentation

Citation Download Citation

Daniele Ancora, Gianluca Valentini, Antonio Giovanni Pifferi, and Andrea Bassi "Auto-correlation for multi-view deconvolved reconstruction in light sheet microscopy", Proc. SPIE 11649, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII, 116490X (5 March 2021); https://doi.org/10.1117/12.2583004

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available