Journal: Scientific Reports
Article Title: Bridging the macro to micro resolution gap with angiographic optical coherence tomography and dynamic contrast enhanced MRI
doi: 10.1038/s41598-022-07000-1
Figure Lengend Snippet: Co-registered macro DCE-MRI to micro svOCT vascular correlations. ( A) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{2}$$\end{document} T 2 -weighted structural MRI scan of the window chamber with tumor delineated by the red contour. ( B) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{trans}$$\end{document} k trans parameter map of the tumor, averaged over two depth slices (total depth of 1 mm to correspond with svOCT’s imaging penetration), in units of min −1 indicated by the colour bar. ( C) svOCT segmented depth-encoded vasculature coregistered to ( B) . The grey dotted line in ( B) and ( C) shows one position of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1 \space{\mathrm{mm}}^{3}$$\end{document} 1 mm 3 sliding window VOI with numbered edges that correspond to the number locations in ( D) and ( E) . The various semi-quantitative and quantitative MR vascular metrics in the resulting DCE-MRI voxels (8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{trans}$$\end{document} k trans voxels in this example) ( D ) are directly compared to microvascular biomarkers derived from the corresponding svOCT 3D microvascular map ( E ). The VOI then slides throughout the delineated tumor contour, with such analysis repeated at all positions. ( B) –( E ) were generated using MATLAB R2020A software (MathWorks, Inc., Natick, MA, USA).
Article Snippet: OCT data were analyzed using in-house built software written in MATLAB R2020A (MathWorks, Inc., Natick, MA, USA).
Techniques: Imaging, Derivative Assay, Generated, Software
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![Co-registered macro DCE-MRI to micro svOCT vascular correlations. ( A) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{2}$$\end{document} T 2 -weighted structural MRI scan of the window chamber with tumor delineated by the red contour. ( B) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{trans}$$\end{document} k trans parameter map of the tumor, averaged over two depth slices (total depth of 1 mm to correspond with svOCT’s imaging penetration), in units of min −1 indicated by the colour bar. ( C) svOCT segmented depth-encoded vasculature coregistered to ( B) . The grey dotted line in ( B) and ( C) shows one position of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1 \space{\mathrm{mm}}^{3}$$\end{document} 1 mm 3 sliding window VOI with numbered edges that correspond to the number locations in ( D) and ( E) . The various semi-quantitative and quantitative MR vascular metrics in the resulting DCE-MRI voxels (8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{trans}$$\end{document} k trans voxels in this example) ( D ) are directly compared to microvascular biomarkers derived from the corresponding svOCT 3D microvascular map ( E ). The VOI then slides throughout the delineated tumor contour, with such analysis repeated at all positions. ( B) –( E ) were generated using MATLAB R2020A software (MathWorks, Inc., Natick, MA, USA).](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_3467/pmc08873467/pmc08873467__41598_2022_7000_Fig3_HTML.jpg)
![Healthy vs. tumor tissue quantification by svOCT and DCE-MRI. Significant differences in the microvasculature and corresponding DCE-MRI concentration–time curves were observed when comparing healthy and tumor tissue. ( A) segmented depth-encoded svOCT microvascular map of healthy (bare skin) mouse and corresponding DCE-MRI Gd time concentration curve ( B ). ( C) and ( D) present analogous results for a tumor-bearing mouse. Gd time concentration curves and svOCT vascular metrics were calculated within a 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{mm}}^{3}$$\end{document} mm 3 volume of interest (blue dotted line) shown in ( A) and ( C) . The solid blue line in ( C) shows the tumor contour and the red line in ( B) and ( D) are the Toft’s model fits to the data. ( A) and ( C) were generated using MATLAB R2020A software (MathWorks, Inc., Natick, MA, USA).](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_3467/pmc08873467/pmc08873467__41598_2022_7000_Fig4_HTML.jpg)