Optical Neural System Imaging Survey November 15 1999

Optical Neural System Imaging Survey November 15, 1999 Andreas G. Nowatzyk

Outline • Background and Motivation • System Overview • Backscatter Imager • Fluorescent Imager • Microtome • Staining Unit • Computational Aspects • Summary

Context • Enabling technologies – Moore’s Law: Billions of cycles – Interconnect networks: Multi-Gbytes/s I/O – Optics: confocal microscopy, lasers – Biochemistry: selective staining – Computer science: Image processing – Mechanics: Chip fabrication technologies • Interdisciplinary research: Combining all these new tools to open up new research areas

Ultimate Goal: Scan a Mouse in 3 D • Eventually, reverse engineer a mouse brain, including the entire rodent nervous system

Computational Challenge • 2 x 4 cm 3 specimen volume • 0. 2 mm resolution • = 100, 000 x 200, 000 voxels • 6 x 12 bit per voxel • 16, 763, 806 Gbytes of raw data • 200: 1 compression (lossy) • => 1700 tapes (8 mm, 50 Gbytes/tape)

Computational Challenge • 20 Msamples/s per channel (12 bits each) • 6 x 64 channels • ~50% scan duty cycle • 2 x for complex phase sensing • = 11, 520 Mbytes/s average data rate

System Overview Basic approach, instrument architecture, and computing infrastructure

Functional Imaging • Selective labeling with Fluorescent Dyes • Genetically engineered Labels (GFP) VS.

Basic Approach • Use of light microscopy plus selective functional imaging • Five step process – Bulk imaging into freshly cut sample – Mechanical sectioning via integrated microtome – Automated, continuous staining – Functional, fluorescent imaging – Data fusion, compression and archival storage

Confocal Light Microscopy • Using one objective lens twice • Point-spread function squared

Instrument Overview Backscatter Imager Carrier Tape Transfer Unit Specimen Holder Microtom Staining Unit Fluorescent Imager

Backscatter Imager Optical system, mechanical components, scan path

Design Objectives • Need to maximize throughput • Maximize practical resolution • Optical sectioning into the exposed sample surface

XZ Detection Function High NA (0. 9) objective with pinhole detector PMT

XZ Detection Function Confocal illumination PMT

XZ Detection Function Michelson interferometer PMT

Acousto-Optical Modulator • Exploit Doppler shift to split laser into two components with differing frequency RF w-e w+e

XZ Detection Function Heterodyne detection X PMT f w+e w-e

Heterodyne Detection • Elimination of black-level drift through ACcoupled amplifiers • Contrast independent of reference beam intensity (within dynamic range of detector) • Optical phase transferred to electrical domain • Simultaneous capture of phase and magnitude • Enables wave-front reconstruction / holography

Solid State Detector • Better quantum efficiency (~ 85 vs 30 %) • Better dynamic range (> 120 db)

Qualitative Verification

First Tests

Z-Axis Selection Vs. Bandwidth

Backscatter Imager Summary • Optimized for single purpose – less relay optic, no eye piece, no turret, etc. • Scan system optimized for bulk operation • Heterodyne detection for improved Z-axis resolution (full sampling of the optical phase) • Multiple wavelengths • Requires computer controlled alignment

Fluorescent Imager Optical system, mechanical components, coherent detection

Design Objectives • Need to match throughput of backscatter imager • Film based input media, accessible from both sides with symmetric, optical properties • Support for optical sectioning

Problems • Dye saturation • Signal to noise ratio • Incoherent signal ØNeed to maximize light gathering ability and quantum efficiency of detector

Scan System • Sorry, the few slides were removed due to pending (but incomplete) patent applications

Microtom Functions, mechanical components

Design Objectives • Integrated with optical system • Fully automatic • Precise control of cutting plane and related parameters

Transferring fragile Objects to Film • • • Microtom with stationary knife Floating pick-up Match surface velocity to cutting speed Minimize surface tension Electrostatic transfer

Knife Assembly

Instrument Base

Microscope Environment • -20 to -30 degree C, controlled operating temperature • dust free, controlled flow, dry nitrogen atmosphere • vibration isolation • issue: sublimation (prevent by index matching fluid)

Mechanical components • Linear air bearings • Voice-coil motor direct drive • Laser interferometer position sensing with 10 nm resolution • Piezo-actuators for knife positioning

Specimen chamber

Staining Unit Outline, Challenges

Film as Sample Slice Carrier • Candidate: Du. Pont Cronar 410 polyester flim, gelatin coated, 100 mm • Need to maintain sample adhesion during stating process • Optically clear, substitute for cover-slips (need to be witting correction range) • Chemically inert

Continuous Staining Process • Tight control of process parameters (temperature, flow rate, chemical concentrations, etc. ) • Clean-room environment: dust-free, high purity, no manual steps • Uniform, predictable distortions

Computational Aspects Where does all the money go?

Computational Infrastructure • Instrument control • Front-end signal processing

Control System Overview • Linux based • 3 Functional Blocks: – System Controler – Signal Processing – File system / Database

Image processing • • Deconvolution (CAT scan, MRI) Super-Resolution [Cheeseman et al] 3 D reconstruction Image fusion • 3 D Compression

Circuit Extraction Algorithm • Linear scan of the data set • Connectivity function integrated with Bayesian super-resolution algorithm

Summary of Instrument Capabilities • 6 band (3 backscatter + 3 fluorescent labeled) confocal microscope • 0. 2 mm (or better) 3 D resolution • Integrated sectioning and staining • Image acquisition and processing speed sufficient for large volume scanning

Summary • Large scale, high resolution scanning of biological specimen will become practical • Automated tracing of neurological systems is conceivable • Provides a clear, long term research focus • Significant Research potential • Intermediate, practical spin-off potential