Restoration of Light Sheet Multi-View Data

Figure 3 : Skewing and de-skewing of LSFM images. Some LSFM setups have their excitation and emission objectives positioned at an oblique angle relative to the sample stage. The sample mounted on the stage is scanned horizontally to move the focus through the sample (see arrow), which leads to a skew in the recorded 3D image stack and also in the PSF. The Huygens Object Stabilizer option can de-skew all z-slices to their correct position.

T is article describes these two main restoration methods in some detail, specifi cally in relation to the Huygens Fusion and Deconvolution Wizard. Finally, two examples are described in which LSFM images are shown to benefi t from this deconvo- lution and multi-view fusion approach.

Materials and Methods

Figure 2 : LSFM artifacts and imaging parameters. (A) Photon scattering and absorption by the sample can be a major issue in LSFM imaging because of the typically large sample sizes that are imaged. The most prominent distortions present in LSFM images are: the blurring process (convolution) where each point in the sample is imaged as a 3D light distribution known as the point spread function (PSF) and Poisson noise, which is dependent on the number of photons recorded. (B) Huygens reads the generic and LSFM-specifi c image parameters from the acquired data, indicates the reliability of each parameter, and allows editing of them. The parameters are used to automatically calculate a PSF during a deconvolution run.

was based on the well-established technique of deconvolution, where knowledge of the optical properties of the imaging system is used to revert the blurring induced by the diff raction-limited optics of the microscope. Deconvolution is critically dependent on the inherent resolution of the data and on the signal-to-noise ratio. Because light-sheet data provide a good compromise in this regard, it is well suited for deconvolution. T us, deconvo- lution combined with multi-view reconstruction, allows a signifi cant improvement/restoration of LSFM data [ 5 ].

2018 September •

Deconvolution of microscopy images . Image restoration is recognized as an important technique in microscopy because of its ability to compensate for the distortions of the true object that are introduced by the optical imaging process. One of the most important distortion factors is the resolution- limiting eff ect of diff raction that aff ects both the excitation and detection beams of the LSFM. Diff raction imposes a hard limit on the range of spatial frequencies that can be transmitted to the image and thereby determines the fi nest object details that can be resolved. As a consequence, fl uorescent light sources within a sample appear as blurred objects in the image. T is blurring process, also known as convolution, is a process whereby each point in the sample is imaged as a 3D light distribution known as the point spread function (PSF) ( Figure 2A ), eff ectively attenuating the higher spatial frequencies of the object [ 6 ].

The blurring of the image can to some extent be reversed using inverse filtering techniques, but this will amplify the noise. Noise is another main distortion factor added to the recorded image ( Figure 2A ), and it generally follows a Poisson distribution as a consequence of recording only a limited number of photons per pixel. Unlike simple inverse filters, iterative restoration methods make use of a maximum


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