Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire 03755, USA.
J Biomed Opt. 2013 Aug;18(8):86007. doi: 10.1117/1.JBO.18.8.086007.
Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.
多模态方法结合近红外(NIR)和常规成像方式已被证明可显著改善光参数估计,因此代表了 NIR 成像的主流趋势。这些方法通常涉及应用磁共振成像/计算机断层扫描/超声图像的解剖模板来指导光参数的恢复。然而,使用当前技术合并这些数据集需要多个软件包、大量专业知识、大量时间投入,并且通常导致光图像重建的网格质量不可接受,这对于多模态近红外成像的转化研究来说是一个重大障碍。这项工作通过引入自动数字成像和通信在医学图像堆栈分割和一个新的一键式三维网格生成器,专门针对多模态 NIR 成像进行了优化,并将这些功能组合到一个具有简化工作流程的单个软件包中(可免费下载),直接解决了这些挑战。针对四种常见的多模态 NIR 应用(乳房、大脑、胰腺和小动物)检查了图像处理时间和网格质量基准,并与商业图像处理包进行了比较。在没有额外的网格后处理的情况下,这些工具的应用将图像处理时间减少了五倍,最小网格质量提高了 62%。这些功能代表了在开源平台上为专家和非专家用户实现转化多模态 NIR 研究的重要一步。