Presentation on theme: "Point Spread Function, Spectral Calibration & Spectral Separation: Quality Assurance Testing Light Microscopy Research Group Richard W. Cole Wadsworth."— Presentation transcript:
Point Spread Function, Spectral Calibration & Spectral Separation: Quality Assurance Testing Light Microscopy Research Group Richard W. Cole Wadsworth Center / NYSDOH Albany, New York
Why ? – until the last 5-10 yrs, simply observing a specimen was sufficient; advances in light microscopes necessitates traceable standards & procedures Overall Goal – the creation of a range of imaging parameters traceable to standard references NIH – Realizes the need for and supports the “core” model – 40% of S10 grants funded were imaging in general; 13% confocal NIST goal of moving medical imaging & lab testing from an art to a science FDA –ensure manufacturers systems are reliable, guaranteeing that the drugs will be safe & efficacious Congress – provide the financial support for comparable standards of research Quality and standards: Making bioimaging ‘measure up’ Susan M. Reiss BioOptics World, Jan/Feb 2010, Vol.3 No.1, p Access sparks action Lila Guterman NCRR Reporter, Winter 2010, p.4-8
Phase One a worldwide research study to ascertain the current state of light microscope performance using simple, efficient & robust tests for LASER stability, field illumination & coregistration define & improve cross-platform standards--assist core managers & users maintaining microscopes for optimal operation with the ultimate goal of improving the validity of quantitative measurements in light microscopy the results of this study were accepted for publication in late 2010 in Microscopy and Microanalysis, one of the highest rated imaging journals throughout 2011, the LMRG tested and defined additional areas of instrument performance (Phase Two) and refined the methodology for determining a system’s Spectral calibration, Spectral separation ability and finally the Point Spread Function of an imaging system What we have done Stack, R., Bayles, C., Girard, A., Martin, K., Opansky, C., Schulz, K., and Cole, R. (2011) Quality Assurance Testing for Modern Optical Imaging Systems. Microscopy & Microanalysis 17(4): Cole, R.W., Jinadasa, T., and Brown, C.M. (2011) Resolution and Quality Control of Confocal Microscopy Optics. Nature Prot. 6 (12): 1929–1941.
Spectral calibration Purpose: Measure spectral calibration of the detection system. MIDL lamp / mirror slide protocol: Use 10x lens or no lens (system dependent) Set up the MIDL lamp as the illumination source or use laser(s) and mirror slide (remove blocking) Set the PMT gains to be ‘equivalent’ Perform a lambda scan and measure the signal-to-noise Compare acquired spectra with published spectra Analysis: 1.Determine if your PMT(s) show significant spectral variation (sliders) or signs of aging reference:
PARISS Spectral Calibration Lamp, Lightform,Inc. Asheville, NC Overlay of 5 PMT responses and MIDL lamp calibrated output / before repair
PARISS Spectral Calibration Lamp, Lightform,Inc. Asheville, NC Overlay of 5 PMT responses and MIDL lamp calibrated output / after repair
Quality of Spectral un-mixing: Purpose: Measure the spectral un-mixing capability of an imaging system. Protocol: Bead slide: 6.0 µm FocalCheck Double Orange fluorescent microspheres (excitation/emission maxima: core = 532/552 & shell = 545/565) o use same optical settings/components (i.e. laser line/excitation, dichroic filter) to acquire reference and experimental spectra o set detection to maximize S/N without any pixel saturation o select a detection bandwidth wide enough to encompass full emission range (e.g. DoubleOrange beads nm) o if available, choose detection set-up (i.e. parallel vs. lambda) o split detection into smallest discreet ‘bins’ if using lambda scanning mode o select an area of the reference spectra (via ROI) with the highest S/N and store in database Analysis: Select the most appropriate unmixing data-processing algorithm available: automatic mode (1 st pass / not generally adequate) parallel mode (simultaneous data acquisition across multiple PMTs) lambda mode (lambda scanning utilizing one PMT)
Spectral separation FocalCheck™ fluorescence microscope test slide core = 532/552 & shell = 545/565 ) Image of a bead where the core and ring have a small spectral separation Ring and core are pseudo-colored for illustration purposes
Linear Unmixing Algorithms The measured spectra of a ‘mixed pixel’ is broken down into a collection of component spectra (endmembers) and a set of subsequent fractions (abundances) that indicate the ratio of each endmember in the pixel Three distinct stages of spectral unmixing : - dimension reduction (i.e. data reduction) - endmember determination (i.e. # of distinct spectra) - inversion (i.e. abundance estimation) Employs a linear mixing model A Survey of Spectral Unmixing Algorithms Nirmal Keshava Lincoln Laboratory Journal, Vol.14 No.1, 2003, p.55-78
Brain tissue (5 different labels)
blue = cell nuclei, green = Nissl-specific for neurons, yellow = reactive astrocytes, red = microglia, purple = endothelial cells representing blood vessels.
Resolution point at which two objects are perceived as separate and distinct from one another Resolution obtained from an imaging system is affected by: the specific wavelength of light in use the diffraction of light (Rayleigh, Abbe & Sparrow limits) lens aberrations sample prep (coverslip thickness, mounting media, RI matching) Lens imperfections such as coma, astigmatism and spherical aberrations will result in a loss of resolution microscope resolution and the extent of image ‘blur’ is typically described in terms of its Point Spread Function (PSF) an ‘ideal’ PSF demonstrates symmetric balance and proportion Limit of resolution: d = (λ) / N.A.
What is a Point Spread Function & why is it so important ? a measure of the degree of blurring of an object & any potential aberrations speaks directly to the quality/resolution of an imaging system Image = convolution of an object and the point spread function an object plane light wave refocused by a lens produces a blurred focal plane point commonly referred to as an ‘airy disc / airy pattern’ sub-resolutional beads are typically used
Point Spread Function: Purpose: Measure the point spread function of an imaging system. Protocol: Bead slide: 175 nm PS-Speck beads (mixture of blue, green, orange & deep red single-color beads) o test multiple lens: i.e. 20x, 40x, 63x & 100x (all objectives routinely used for imaging in your lab) o collect a Z series or scan in XZY mode o if needed, suitably rotate image to obtain a ‘side view’ o if your system is filter based (non-AOBS), check various dichroic filters Analysis: use the MetroloJ plug-in (Fiji / ImageJ) to determine the FWHM lateral & axial resolution compare the experimental vs. theoretical resolution values check the curve fits for all three
MetroloJ PSF report
‘Idealized’ PSF images courtesy of Zeiss
Theoretical PSF images / Confocal vs. Widefield courtesy of Media Cybernetics
Widefield PSF of thick specimen coverslip increasing depth & worsening PSFs | V
‘3D’ Widefield PSF
20x / Refractive Index mismatch collar incorrectly set to water // RI(water)=1.33, RI(Leica imm.oil)=1.518
Corrective Actions Spectral Calibration – a.Service call Spectral Unmixing – a.Try a different unmixing algorithm - avoid using ‘automatic’ - try various linear algorithms - try non-linear (e.g. SWCCA) algorithms b.Try a different detector set-up - use (5) PMTs with simultaneous scanning OR - use (1) PMT with lambda scanning c. Improve the signal-to-noise Point Spread Function – a.Clean the lens and optics / remove all air bubbles b.Check for any possible refractive index mismatches c.Try a different lens d.Open pinhole aperture to mimic widefield conditions e.Check for optical misalignment ** It is important to note that the above suggestions DO NOT encompass all possible solutions to these issues **
The test specimens proposed for both phases of this study were decided upon by the members of the LMRG for their applicability, robustness, ease-of-use and relative cost. While the phase I & II tests utilize materials from specific vendors who offer excellent products for these purposes, neither the members of the LMRG nor the ABRF endorse the use of these specific vendors, and fully acknowledge the use of legitimate alternatives for the purposes of instrument performance testing.
Acknowledgements Light Microscopy Research Group Carol BaylesCornell University Claire Brown (chair)McGill University Richard ColeWadsworth Center / NYSDOH Brady Eason McGill University Anne-Marie GirardOregon State University Jay JeromeVanderbilt University Tushare Jinadasa McGill University Karen Jonscher(EB Liaison)University of Colorado Cynthia OpanskyBlood Center of Wisconsin George McNamaraUniversity of Miami Katherine SchulzBlood Center of Wisconsin Marc Thibault Ecole Polytechnique * We would also like to thank the ABRF for their financial support and commitment to this project *