Computational Biophotonics

Cancer diagnosis and tumor therapy are frequently performed using interventional procedures. In this context, replacement of traditional open surgery procedures with minimally-invasive interventions for tumor diagnosis, staging and therapy represents one of the most important challenges in modern healthcare. Minimally-invasive procedures provide numerous advantages in contrast to open surgery, including reduced surgical trauma, less pain medication, earlier convalescence, better cosmetic results, shorter hospitalization terms and lower costs. Given the recent advances in the field of miniaturization of optical and photoacoustic imaging devices, we aim to further advance the field of endoscopic interventional imaging computationally. Our primary goal is to investigate whether holistic processing of multi-modal structural and functional information can facilitate both local tissue classification and global instrument guidance within the scope of endoscopic soft tissue interventions. The research is based on the systematic integration and mutual enhancement of biophotonics and CAI techniques. (more)

- Sebastian Wirkert

- Anant Vemuri

Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases. While photoacoustic (PA) imaging is a novel imaging concept with great potential to measure optical absorption deep inside tissue, quantification of the measurements remains a major challenge to be addressed. This work introduce the first machine learning-based approach to quantitative PA tomography (qPAT), which relies on learning the fluence in a voxel to deduce the corresponding optical absorption. The bottleneck of training data generation is overcome by encoding relevant information of the measured signal and the characteristics of the imaging system in voxel-based context images, which allow the generation of thousands of training samples from a single simulated PAT image. Comprehensive in silico experiments demonstrate that the concept of local context encoding (LCE) enables highly accurate and robust quantification (1) of the local fluence and the optical absorption from single wavelength PAT images as well as (2) of local oxygenation from multi wavelength PAT images. (more)

- Thomas Kirchner

- Janek Gröhl 

The identification of relevant structures during minimally-invasive, laparoscopic interventions is a challenging task. Intraoperative registrations of preoperative data (e.g. CT data) with the laparoscopic video stream can help to localize hidden or invisible structures like tumorous tissue, vessels or nerves. However, intraoperative registrations are hardly used during clinical routine as currently published methods have disadvantages such as the lack of robustness, no real-time capability, or long preparation times just to name a few. Fluorescent markers have the potential to improve such registrations. The detection of traditional needle-shaped fiducial markers remains difficult under clinically challenging conditions such as smoke, blood or tissue in the field of view of the laparoscope. Our new fluorescent markers consist of a near-infrared (NIR) fluorescent dye (ICG), a contrast agent for CT-imaging and a binding agent (cyanoacrylate). The emitted NIR light better penetrates through blood, smoke or tissue compared to visible light. Therefore, the fluorescent markers can be more easily detected. Recent ex and in vivo studies showed the ability of fluorescent markers to be used for fast intraoperative registrations and the superior robustness of these markers compared to needle fiducials.

- Esther Stenau