Deconvolution, Visualization and Analysis Software for Widefield and Confocal Microscopy

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Deconvolution: A crash course

Why Deconvolution ? Each single plane contains blur from planes above and below PSF: 3D representation you get when imaging a sub-resolution latex bead Diffraction and aberration in the optical path lead to Blur / Haze 3-D diffraction pattern (= Point Spread Function PSF) Blurred, faint Sharp, bright Blurred, faint z

What is “Deconvolution” ? “Deconvolution” is a computational procedure that restores the quality of images. Usage: Microscopy: biomedical research, ophthalmology, medical diagnostics, nuclear imaging (CT), food processing, forensics, skin care/beauty product development, etc. Astronomy: telescope imaging, satellite imaging, aerial imaging (cartography), etc. Industrial: Quality control for printed circuit boards and semiconductors, etc.

(Spirogyra) Benefits of Deconvolution 1. Haze removal  More contrast  Better segmentation 2. Improved resolution  More detail revealed 3. Access to depth information 4. Produces quantitative results (non-destructive)

Algorithms: Deblurring or Deconvolution ?

Deconvolution vs. Deblurring Deconvolution –More closely approximates a reversal of the blurring process –Produces Quantitative results (reverses blurring accurately) –Better representation of actual morphology Deblurring –Can make images look better fast. –Results are strictly qualitative –Produces artifacts –Morphologically unreliable.

Qualitative vs. Quantitative No-Neighbors Single slice haze removal. Nearest-Neighbors Haze removal from a few optical sections. Inverse filteringInversion of the haze using a theoretical PSF. Constrained iterativeIterative deconvolution with a fixed PSF. Maximum likelihoodMax likelihood estimate-based iterative deconvolution with a fixed PSF. Adaptive blind Max likelihood estimate-based iterative deconvolution deconvolution deriving the PSF from information found in the ‘haze’ itself.

No/Nearest Neighbors Uses center slice and two adjacent slices Estimate amount of blur that adjacent slices contribute to center slice Subtract away the blur (remove photons) Deblur one slice at a time Fast, 2D, only qualitative (unsharp mask) 0 +z -z

Inverse/Wiener Filter Restoration algorithm – looks at whole structure Works well on large objects Fast, 3D, qualitative Regularization/Stability ImageObjectPSF DeblurredImage PSF 1 Imaging: Inverse Filter:

Constrained Iterative Deconvolution Requires PSF (theoretical or measured) Iteratively improve object estimate Impose constraints (non-negativity) Requires highly optimized imaging system Reblurred Object Estimate PSF Original Blurred Improved Estimate - UpdateError

Iterative Blind Deconvolution Estimate both PSF and Object! Need microscope parameters (NA, RI, xyz, λ) Theoretical PSF with spatial/spectral constraints Object with non-negativity and noise constraints Object Estimate Update Object PSF Estimate Update PSF Original Blurred

Why AQI and “Blind” Deconvolution ? Ease of Use –No need to image beads and measure PSF Much less expertise required by the user Less time required on the microscope Allows for processing of existing data Runs on PC Allows for deconvolution of large images The reconstructed PSF can vary over the volume Disadvantages –More processing time

When to use what? When Speed & Haze removal are important No Neighbor Nearest Neighbor Inverse Filter 3D Deconvolution When Quality & Quantitation are critical

XY Max Projections XZ Max Projections RawInverseFilterNearest Neighbors Blind Deconvolution

3D Blind Deconvolution Drosophila larvae leg

3D Blind Deconvolution

Blind deconvolution – 3D Confocal

2D Blind deconvolution – TIRF

Blind deconvolution (with Spherical aberration correction)

How do I know it’s real? raw deblurred Structure from nothing?!

slice 29 slice 37 rawdeblurred Optical Slices

2D Blind

2D Blind Deconvolution – true iterative blind deconvolution No image parameters required Ideal for optically thin specimens Assumes everything is in one plane of focus

2D Blind Deconvolution 2D deconvolution on a 3D volume

Why Deconvolve? Improved resolution of small features Better definition of structure in multiple dimensions More accurate localization of intensity for quantification and ratiometry Signal to noise (SNR) improvement

Why Media Cybernetics… Established technological focus in deconvolution – over 25 years of dedicated research Breadth of algorithms that are well known and accepted world- wide – optimized for maximum robustness Media Cybernetics stands behind all of its products and provides world class education and support. Committed focus to deconvolution, visualization and analysis R&D ensures customer access to the latest technologies for today and tomorrow.

Media Cybernetics: Offering complete post-processing solutions Image improvement, restoration, visualization and analysis that facilitates more accurate and compelling results. Media Cybernetics creates the tools to help you do the science. AQI Product Line –AutoDeblur (2D, Silver, Gold) –AutoVisualize (with improved 3D-Viewer) –AutoAnalyze (FRET, Ratio, Object Counting/Tracking, Colocalization) –Tools (automatic, manual and channel-channel alignment) Image-Pro Product Line –Image-Pro Plus and Image-Pro Plug-In module bundles –Express –Analyzer