Spatial frequency domain correlation mapping optical coherence tomography for nanoscale structural characterization
McNamara, Paul M.
MetadataShow full item record
This item's downloads: 142 (view details)
Cited 5 times in Scopus (view citations)
Alexandrov, Sergey, McNamara, Paul M., Das, Nandan, Zhou, Yi, Lynch, Gillian, Hogan, Josh, & Leahy, Martin. (2019). Spatial frequency domain correlation mapping optical coherence tomography for nanoscale structural characterization. Applied Physics Letters, 115(12), 121105. doi: 10.1063/1.5110459
Most of the fundamental pathological processes in living tissues exhibit changes at the nanoscale. Noninvasive, label-free detection of structural changes in biological samples pose a significant challenge to both researchers and healthcare professionals. It is highly desirable to be able to resolve these structural changes, during physiological processes, both spatially and temporally. Modern nanoscopy largely requires labeling, is limited to superficial 2D imaging, and is generally not suitable for in vivo applications. Furthermore, it is becoming increasingly evident that 2D biology often does not translate into the real 3D situation. Here, we present a method, spatial frequency domain correlation mapping optical coherence tomography (sf-cmOCT), for detection of depth resolved nanoscale structural changes noninvasively. Our approach is based on detection and correlation of the depth resolved spectra of axial spatial frequencies of the object which are extremely sensitive to structural alterations. The presented work describes the principles of this approach and demonstrates its feasibility by monitoring internal structural changes within objects, including human skin in vivo. Structural changes can be visualized at each point in the sample in space from a single image or over time using two or more images. These experimental results demonstrate possibilities for the study of nanoscale structural changes, without the need for biomarkers or labels. Thus, sf-cmOCT offers exciting and far-reaching opportunities for early disease diagnosis and treatment response monitoring, as well as a myriad of applications for researchers.
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.
The following license files are associated with this item: