Limits...
Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

Glaser JI, Zamft BM, Church GM, Kording KP - PLoS ONE (2015)

Bottom Line: This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample.We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer.The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

No MeSH data available.


Puzzle Imaging.There are many properties, such as genetic information, that are easier to determine when the original spatial information about the sample is lost. However, it may be possible to still image these properties using relative spatial information. (A) As an example, let us say that each piece of genetic information is attached to a puzzle piece. While the puzzle pieces don’t provide absolute spatial information, they provide relative spatial information: we know that nearby pieces should have similar colors, so we can use color similarity to determine how close puzzle pieces should be to one another. (B) We can make a similarity matrix of the puzzle pieces, which states how similar the puzzle pieces’ colors are to each other, and thus how close the pieces should be to one another. (C) Through dimensionality reduction techniques, this similarity matrix can be used to map each puzzle piece to its correct relative location.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4507868&req=5

pone.0131593.g001: Puzzle Imaging.There are many properties, such as genetic information, that are easier to determine when the original spatial information about the sample is lost. However, it may be possible to still image these properties using relative spatial information. (A) As an example, let us say that each piece of genetic information is attached to a puzzle piece. While the puzzle pieces don’t provide absolute spatial information, they provide relative spatial information: we know that nearby pieces should have similar colors, so we can use color similarity to determine how close puzzle pieces should be to one another. (B) We can make a similarity matrix of the puzzle pieces, which states how similar the puzzle pieces’ colors are to each other, and thus how close the pieces should be to one another. (C) Through dimensionality reduction techniques, this similarity matrix can be used to map each puzzle piece to its correct relative location.

Mentions: In order to recover a sample’s spatial information, information about its relative spatial location could be embedded and utilized. For example, imagine that each piece of genetic information was attached to a puzzle piece (the embedded relative spatial information; Fig 1A). While the puzzle pieces by themselves don’t provide spatial information, fitting the pieces together would lead to a spatially correct image of the genetic information. Thus, the use of relative spatial information (how the puzzle pieces’ locations relate to one another) could allow for higher-resolution imaging.


Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

Glaser JI, Zamft BM, Church GM, Kording KP - PLoS ONE (2015)

Puzzle Imaging.There are many properties, such as genetic information, that are easier to determine when the original spatial information about the sample is lost. However, it may be possible to still image these properties using relative spatial information. (A) As an example, let us say that each piece of genetic information is attached to a puzzle piece. While the puzzle pieces don’t provide absolute spatial information, they provide relative spatial information: we know that nearby pieces should have similar colors, so we can use color similarity to determine how close puzzle pieces should be to one another. (B) We can make a similarity matrix of the puzzle pieces, which states how similar the puzzle pieces’ colors are to each other, and thus how close the pieces should be to one another. (C) Through dimensionality reduction techniques, this similarity matrix can be used to map each puzzle piece to its correct relative location.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507868&req=5

pone.0131593.g001: Puzzle Imaging.There are many properties, such as genetic information, that are easier to determine when the original spatial information about the sample is lost. However, it may be possible to still image these properties using relative spatial information. (A) As an example, let us say that each piece of genetic information is attached to a puzzle piece. While the puzzle pieces don’t provide absolute spatial information, they provide relative spatial information: we know that nearby pieces should have similar colors, so we can use color similarity to determine how close puzzle pieces should be to one another. (B) We can make a similarity matrix of the puzzle pieces, which states how similar the puzzle pieces’ colors are to each other, and thus how close the pieces should be to one another. (C) Through dimensionality reduction techniques, this similarity matrix can be used to map each puzzle piece to its correct relative location.
Mentions: In order to recover a sample’s spatial information, information about its relative spatial location could be embedded and utilized. For example, imagine that each piece of genetic information was attached to a puzzle piece (the embedded relative spatial information; Fig 1A). While the puzzle pieces by themselves don’t provide spatial information, fitting the pieces together would lead to a spatially correct image of the genetic information. Thus, the use of relative spatial information (how the puzzle pieces’ locations relate to one another) could allow for higher-resolution imaging.

Bottom Line: This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample.We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer.The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

No MeSH data available.