Library

eCommons

 

IMAGE RECONSTRUCTION DEVELOPMENT AND SIGNAL-TO-NOISE ANALYSIS FOR NANOMETER-SCALE MAGNETIC RESONANCE IMAGING

Simple item page
dc.contributor.authorNguyen, Hoang Long
dc.contributor.chairMarohn, John A.
dc.contributor.committeeMemberChen, Peng
dc.contributor.committeeMemberDoerschuk, Peter
dc.date.accessioned2018-10-23T13:22:42Z
dc.date.available2020-06-04T06:01:35Z
dc.date.issued2018-05-30
dc.description.abstractThe imaging capability of magnetic resonance force microscopy has been demonstrated with a 25 nm resolution spin-noise image of a single electron spin and a 4-to-10 nm resolution spin-noise image of few hundred nuclear spins. A recent demonstration of hyperpolarization in a mechanically detected magnetic resonance experiment opens up new possibilities for utilizing polarized spin signal for nanoscale imaging. In this thesis, we present our development of new image reconstruction methods for both nuclear spin ensembles and individual electron spins, and our signal-to-noise analysis to determine the quickest path to a highest-resolution image. First, we speed up the image-reconstruction process in spin noise imaging experiments by revising the iterative Landweber algorithm, and employing the Fourier convolution theorem to reduce the reconstruction time from three days to a few hours. We invent a fast reconstruction scheme to further speed up the process, combining Fourier deconvolution and Tikhonov regularization. This reconstruction scheme yields a three-dimensional image within a few minutes of calculation, but compromises the image signal-to-noise ratio by creating false peaks in the resulting image. This problem of false peaks is resolved by a third reconstruction scheme based on Bayesian Monte Carlo calculation, capable of producing error bars for each pixel in the image. Second, we develop a new detection and image-reconstruction protocol for individual electron spins attached to a single biomolecule. The image-reconstruction algorithm combines a fast Fourier deconvolution with a rigorous Markov-chain reverse Monte Carlo calculation. We demonstrate this new protocol via numerical simulations for a protein molecule with two nitroxide spin labels attached. The sparsely located electron spins allow three-dimensional coordinates of the spin labels to be reconstructed from a two-dimensional scanned signal map, reducing the required signal-acquisition time by 64-to-128 times compared to the three-dimensional scanning in previous imaging experiments. Finally, we derive the signal-to-noise ratios for Fourier-encoded imaging experiments in two polarization limits: detecting the spin fluctuations in the spin-noise limit, and detecting the Curie-law signal from hyperpolarized nuclear spins. Our results indicate that hyperpolarization, when done right, can help reduce the signal-acquisition time in nanoscale imaging experiments and bring the three-dimensional image resolution to the sub-nanometer scale.
dc.identifier.doihttps://doi.org/10.7298/X4765CKK
dc.identifier.otherNguyen_cornellgrad_0058F_10729
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:10729
dc.identifier.otherbibid: 10489501
dc.identifier.urihttps://hdl.handle.net/1813/59416
dc.language.isoen_US
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectPhysical chemistry
dc.subjectmagnetic resonance
dc.subjectMarkov chain Monte Carlo
dc.subjectMetropolis
dc.subjectspins
dc.subjectImage reconstruction
dc.subjectImaging
dc.titleIMAGE RECONSTRUCTION DEVELOPMENT AND SIGNAL-TO-NOISE ANALYSIS FOR NANOMETER-SCALE MAGNETIC RESONANCE IMAGING
dc.typedissertation or thesis
dcterms.licensehttps://hdl.handle.net/1813/59810
thesis.degree.disciplineChemistry and Chemical Biology
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Chemistry and Chemical Biology

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Nguyen_cornellgrad_0058F_10729.pdf
Size:
45.09 MB
Format:
Adobe Portable Document Format
Download

Collections

Cornell Theses and Dissertations