Abstract

In this paper, we introduce an algorithm framework for the automation of interferometric synthetic aperture microscopy (ISAM). Under this framework, common processing steps such as dispersion correction, Fourier domain resampling, and computational adaptive optics aberration correction are carried out as metrics-assisted parameter search problems. We further present the results of this algorithm applied to phantom and biological tissue samples and compare with manually adjusted results. With the automated algorithm, near-optimal ISAM reconstruction can be achieved without manual adjustment. At the same time, the technical barrier for the nonexpert using ISAM imaging is also significantly lowered.

Original languageEnglish (US)
Pages (from-to)2034-2041
Number of pages8
JournalApplied Optics
Volume55
Issue number8
DOIs
StatePublished - Mar 10 2016

Fingerprint

Synthetic apertures
synthetic apertures
Adaptive optics
Optical tomography
adaptive optics
Microscopic examination
tomography
microscopy
automation
Aberrations
aberration
Automation
adjusting
Tissue
Imaging techniques
Processing

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

Cite this

Automated interferometric synthetic aperture microscopy and computational adaptive optics for improved optical coherence tomography. / Xu, Yang; Liu, Yuan Zhi; Boppart, Stephen A.; Carney, P. Scott.

In: Applied Optics, Vol. 55, No. 8, 10.03.2016, p. 2034-2041.

Research output: Contribution to journalArticle

@article{47a54c76d943472791604cc6dff0f50c,
title = "Automated interferometric synthetic aperture microscopy and computational adaptive optics for improved optical coherence tomography",
abstract = "In this paper, we introduce an algorithm framework for the automation of interferometric synthetic aperture microscopy (ISAM). Under this framework, common processing steps such as dispersion correction, Fourier domain resampling, and computational adaptive optics aberration correction are carried out as metrics-assisted parameter search problems. We further present the results of this algorithm applied to phantom and biological tissue samples and compare with manually adjusted results. With the automated algorithm, near-optimal ISAM reconstruction can be achieved without manual adjustment. At the same time, the technical barrier for the nonexpert using ISAM imaging is also significantly lowered.",
author = "Yang Xu and Liu, {Yuan Zhi} and Boppart, {Stephen A.} and Carney, {P. Scott}",
year = "2016",
month = "3",
day = "10",
doi = "10.1364/AO.55.002034",
language = "English (US)",
volume = "55",
pages = "2034--2041",
journal = "Applied Optics",
issn = "0003-6935",
publisher = "The Optical Society",
number = "8",

}

TY - JOUR

T1 - Automated interferometric synthetic aperture microscopy and computational adaptive optics for improved optical coherence tomography

AU - Xu, Yang

AU - Liu, Yuan Zhi

AU - Boppart, Stephen A.

AU - Carney, P. Scott

PY - 2016/3/10

Y1 - 2016/3/10

N2 - In this paper, we introduce an algorithm framework for the automation of interferometric synthetic aperture microscopy (ISAM). Under this framework, common processing steps such as dispersion correction, Fourier domain resampling, and computational adaptive optics aberration correction are carried out as metrics-assisted parameter search problems. We further present the results of this algorithm applied to phantom and biological tissue samples and compare with manually adjusted results. With the automated algorithm, near-optimal ISAM reconstruction can be achieved without manual adjustment. At the same time, the technical barrier for the nonexpert using ISAM imaging is also significantly lowered.

AB - In this paper, we introduce an algorithm framework for the automation of interferometric synthetic aperture microscopy (ISAM). Under this framework, common processing steps such as dispersion correction, Fourier domain resampling, and computational adaptive optics aberration correction are carried out as metrics-assisted parameter search problems. We further present the results of this algorithm applied to phantom and biological tissue samples and compare with manually adjusted results. With the automated algorithm, near-optimal ISAM reconstruction can be achieved without manual adjustment. At the same time, the technical barrier for the nonexpert using ISAM imaging is also significantly lowered.

UR - http://www.scopus.com/inward/record.url?scp=84962110903&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962110903&partnerID=8YFLogxK

U2 - 10.1364/AO.55.002034

DO - 10.1364/AO.55.002034

M3 - Article

C2 - 26974799

AN - SCOPUS:84962110903

VL - 55

SP - 2034

EP - 2041

JO - Applied Optics

JF - Applied Optics

SN - 0003-6935

IS - 8

ER -