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Delineation of three-dimensional tumor margins based on normalized absolute difference mapping via volumetric optical coherence tomography

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Abstract
The extent of surgical resection is an important prognostic factor in the treatment of patients with glioblastoma. Optical coherence tomography (OCT) imaging is one of the adjunctive methods available to achieve the maximal surgical resection. In this study, the tumor margins were visualized with the OCT image obtained from a murine glioma model. A commercialized human glioblastoma cell line (U-87) was employed to develop the orthotopic murine glioma model. A swept-source OCT (SS-OCT) system of 1300nm was used for three-dimensional imaging. Based on the OCT intensity signal, which was obtained via accumulation of each A-scan data, an en-face optical attenuation coefficient (OAC) map was drawn. Due to the limited working distance of the focused beam, OAC values decrease with depth, and using the OAC difference in the superficial area was chosen to outline the tumor boundary, presenting a challenge in analyzing the tumor margin along the depth direction. To overcome this and enable three-dimensional tumor margin detection, we converted the en-face OAC map into an en-face difference map with x- and y-directions and computed the normalized absolute difference (NAD) at each depth to construct a volumetric NAD map, which was compared with the corresponding H&E-stained image. The proposed method successfully revealed the tumor margin along the peripheral boundaries as well as the margin depth. We believe this method can serve as a useful adjunct in glioma surgery, with further studies necessary for real-world practical applications. © The Author(s) 2024.
Author(s)
Park, Jae-SungYoon, TaeilPark, Soon A.Lee, Byeong HaJeun, Sin-SooEom, Tae Joong
Issued Date
2024-04
Type
Article
DOI
10.1038/s41598-024-56239-3
URI
https://scholar.gist.ac.kr/handle/local/9629
Publisher
Nature Research
Citation
Scientific Reports, v.14, no.1
ISSN
2045-2322
Appears in Collections:
Department of Electrical Engineering and Computer Science > 1. Journal Articles
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