Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging.
Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging.
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DOI：
10.1007/s10554-014-0556-z
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发表时间：
2015-02
影响因子：
2.1
通讯作者：
Tearney, Guillermo J.
中科院分区：
文献类型：
--
作者：
Ughi, Giovanni J.;Verjans, Johan;Fard, Ali M.;Wang, Hao;Osborn, Eric;Hara, Tetsuya;Mauskapf, Adam;Jaffer, Farouc A.;Tearney, Guillermo J.
血管内光学相干断层扫描(IVOCT)是一种成熟的高分辨率体内动脉粥样硬化研究方法。血管内近红外荧光(NIRF)成像是一种评估与冠状动脉疾病相关的分子过程的新技术。NIRF和IVOCT技术在单个导管中的集成提供了同时从动脉壁获得共同定位的解剖和分子信息的能力。由于NIRF信号强度随成像导管到血管壁的距离而衰减，因此生成定量的NIRF数据需要在IVOCT图像中准确测量血管壁。考虑到双模式，血管内OCT-NIRF系统以非常高的帧速率(>；100帧/秒)采集数据，每次回调需要分析大量的图像，这使得手动处理OCT-NIRF数据非常耗时。为了克服这一局限性，我们开发了一种用于双模式OCT-NIRF图像的自动距离校正算法。我们通过比较6只新西兰大白兔注射吲哚青绿(ICG)后的180张体内图像的自动和手动分割结果，验证了该方法的有效性。Dice相似系数高(0.97±0.03)，平均个体A线误差为22μm(约为IVOCT轴向分辨率的两倍)，处理时间为44ms/幅。以类似的方式，使用来自8个不同的活体冠状动脉回缩的120张IVOCT临床图像来验证算法。结果表明，所提出的算法能够实现双模式OCT-NIRF回调的全自动可视化，并为定量动脉粥样硬化血管壁中的分子制剂提供了准确而有效的NIRF数据校准。
Intravascular optical coherence tomography (IVOCT) is a well-established method for the high-resolution investigation of atherosclerosis in vivo. Intravascular near-infrared fluorescence (NIRF) imaging is a novel technique for the assessment of molecular processes associated with coronary artery disease. Integration of NIRF and IVOCT technology in a single catheter provides the capability to simultaneously obtain co-localized anatomical and molecular information from the artery wall. Since NIRF signal intensity attenuates as a function of imaging catheter distance to the vessel wall, the generation of quantitative NIRF data requires an accurate measurement of the vessel wall in IVOCT images. Given that dual modality, intravascular OCT-NIRF systems acquire data at a very high frame-rate (>100 frames/second), a high number of images per pullback need to be analyzed, making manual processing of OCT-NIRF data extremely time consuming. To overcome this limitation, we developed an algorithm for the automatic distance-correction of dual-modality OCT-NIRF images. We validated this method by comparing automatic to manual segmentation results in 180 in vivo images from 6 New Zealand White rabbit atherosclerotic after indocyanine-green (ICG) injection. A high Dice similarity coefficient was found (0.97 ± 0.03) together with an average individual A-line error of 22 μm (i.e., approximately twice the axial resolution of IVOCT) and a processing time of 44 ms per image. In a similar manner, the algorithm was validated using 120 IVOCT clinical images from 8 different in vivo pullbacks in human coronary arteries. The results suggest that the proposed algorithm enables fully automatic visualization of dual modality OCT-NIRF pullbacks, and provides an accurate and efficient calibration of NIRF data for quantification of the molecular agent in the atherosclerotic vessel wall.
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影响因子：
24
作者：
Jaffer, Farouc A.;Calfon, Marcella A.;Rosenthal, Amir;Mallas, Georgios;Razansky, R. Nika;Mauskapf, Adam;Weissleder, Ralph;Libby, Peter;Ntziachristos, Vasilis
通讯作者：
Ntziachristos, Vasilis
影响因子：
2.1
作者：
Ha, Jinyong;Yoo, Hongki;Bouma, Brett E.
通讯作者：
Bouma, Brett E.
影响因子：
2.1
作者：
Ughi, G. J.;Adriaenssens, T.;D'hooge, J.
通讯作者：
D'hooge, J.
DOI：
10.1109/titb.2007.908000
发表时间：
2008-05-01
影响因子：
--
作者：
van Soest, Gijs;Bosch, Johan G.;van der Steen, Antonius F. W.
通讯作者：
van der Steen, Antonius F. W.
影响因子：
3.8
作者：
Yun, SH;Tearney, GJ;Bouma, BE
通讯作者：
Bouma, BE