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Reconstructing Images from In Vivo Laser Scanning Microscope Data

Obreja, Mihaela (2011) Reconstructing Images from In Vivo Laser Scanning Microscope Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Two-photon laser-scanning microscopy can be used for in vivo neuro-imaging of small animals. Due to the very high resolution of the images, any brain motion can cause significant artifacts; often the tissue may get displaced by 10 or more pixels from its rest position. To scan an image of 512 lines it takes about 1s. During this time, at least 3 heart beats and 1 respiration happen moving the brain. Therefore some tissue locations are scanned several times while others are missed. Consequently, although the images may appear reasonable, they can lead to incorrect conclusions with respect to brain structure or function. As lines are scanned almost instantaneously (~1ms), our problem is reduced to relocating each line in a three-dimensional stack of images to its "correct" location. In order to model the movement process and quantify the effect of the physiological signal, we collected hybrid image data: fixing y and z, the microscope was set to scan in the x direction for several thousands of times. Classifying these lines using Normalized Cross-Correlation kernel function, we were able to track the trajectory that the line follows due to brain motion. Based on it, we can predict the number of replicates that we may need to reconstruct a reliable image. Also, we can study how it relates with the physiological values. To address the motion effects, we describe a Semi-Hidden Markov Model to estimate the sequence of hidden states most likely to have generated the observations. The model considers that at the scanning time the brain is either in "near-to-rest"(S1) state, or in "far-from-rest"(S2) state. Our algorithm assigns probabilities for each state based on concomitant physiological measurements. Using Viterbi's approach we estimate the most likely path of states and we select the lines observed in S1. Because there is no gold standard, we suggest comparing our result with a stack of images collected after the animal is sacrificed. Conditioned on inherent experimental and technological limitations, the results of this work offer a description of the brain movement caused by physiology and a solution for reconstructing reliable images from in vivo microscopy.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Obreja, Mihaelamio8@pitt.eduMIO8
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairIyengar, Satishssi@pitt.eduSSI
Committee CoChairEddy,
Committee MemberBlock, Henryhwb@pitt.eduHWB
Committee MemberCrowley,
Committee MemberGleser, Leongleser@pitt.eduGLESER
Date: 30 January 2011
Date Type: Completion
Defense Date: 19 November 2010
Approval Date: 30 January 2011
Submission Date: 9 September 2010
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: image filters; kernel functions; semi-hidden Markov models
Other ID:, etd-09092010-103702
Date Deposited: 10 Nov 2011 20:01
Last Modified: 15 Nov 2016 13:50


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