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利用 LINAC X 射线片扫描和光谱解混技术进行单像素超光谱切伦科夫激发荧光成像。

Single pixel hyperspectral Cherenkov-excited fluorescence imaging with LINAC X-ray sheet scanning and spectral unmixing.

出版信息

Opt Lett. 2020 Nov 15;45(22):6130-6133. doi: 10.1364/OL.401286.

DOI:10.1364/OL.401286
PMID:33186932
Abstract

Cherenkov light induced from megavolt (MV) X-rays during external beam radiotherapy serves as an internal light source to excite phosphors or fluorophores within biological tissues for molecular imaging. The broad spectrum of Cherenkov light leads to significant spectral overlap with any luminescence emission and, to overcome this problem, a single pixel hyperspectral imaging methodology was demonstrated here by coupling the detection with light sheet scanning and filtered back projection reconstruction of hyperspectral images. Thin scanned sheets of MV X-rays produce Cherenkov light to illuminate the planes deep within the tissue-simulating media. A fluorescence probe was excited by Cherenkov light, and a complete hyperspectral sinogram of the data was obtained through translation and rotation of the beam. Hyperspectral 2D images finally were reconstructed. Through this approach of spectral unmixing, it was possible to resolve hyperspectral images of both the Cherenkov and resulting fluorescence intensity from molecular sensors.

摘要

兆伏 (MV) X 射线在外部束放射治疗过程中产生的切伦科夫光可作为内部光源,激发生物组织内的磷光体或荧光团进行分子成像。切伦科夫光的光谱很宽,与任何发光发射都有很大的光谱重叠,为了解决这个问题,本文通过将检测与光片扫描耦合,并对超光谱图像进行滤波反投影重建,展示了一种单像素超光谱成像方法。薄的 MV X 射线扫描片产生切伦科夫光,以照亮组织模拟介质深处的平面。切伦科夫光激发荧光探针,并通过光束的平移和旋转获得数据的完整超光谱正弦图。最后重建超光谱 2D 图像。通过这种光谱解混方法,可以从分子传感器中分辨出切伦科夫光和荧光强度的超光谱图像。

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引用本文的文献

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X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.基于三组件深度学习算法的 X 射线切伦科夫发光断层重建:Swin 变压器、卷积神经网络和局部模块。
J Biomed Opt. 2023 Feb;28(2):026004. doi: 10.1117/1.JBO.28.2.026004. Epub 2023 Feb 16.