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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

机译:双模态血管内光学相干断层扫描(OCT)和近红外荧光(NIRF)成像:用于定量分子成像的NIRF信号强度距离校准的全自动算法

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摘要

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.
机译:血管内光学相干断层扫描(IVOCT)是一种用于体内动脉粥样硬化高分辨率研究的成熟方法。血管内近红外荧光(NIRF)成像是一种用于评估与冠状动脉疾病相关的分子过程的新技术。将NIRF和IVOCT技术集成在单个导管中可提供从动脉壁同时获取共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NIRF数据需要在IVOCT图像中准确测量血管壁。考虑到双模态,血管内OCT-NIRF系统以很高的帧速率(> 100帧/秒)获取数据,因此每次回撤都需要分析大量图像,因此手动处理OCT-NIRF数据非常耗时。为了克服这一局限性,我们开发了一种自动校正双模态OCT-NIRF图像的算法。通过比较自动和手动分割结果,对来自6个新西兰白兔动脉粥样硬化的吲哚花青素的180张体内图像进行自动分割结果,从而验证了该方法的有效性。绿色(ICG)注射。发现较高的Dice相似系数(0.97±0.03)以及22μm的平均单个A线误差(即,大约是IVOCT的轴向分辨率的两倍),每个图像的处理时间为44 ms。以类似的方式,使用来自人类冠状动脉中8种不同体内回撤的120个IVOCT临床图像对算法进行了验证。结果表明,提出的算法能够实现双模态OCT-NIRF回调的全自动可视化,并提供准确有效的NIRF数据校准,以量化动脉粥样硬化血管壁中的分子制剂。

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