BACKGROUND Fluorescent-guided techniques in vascular neurosurgery can be demonstrated via black and white indocyanine green videoangiography (ICG-VA). Multispectral imaging (MFL) is a new method, which overlaps fluorescence with the white light and provides a fluorescent white light augmented reality image to the surgeon. OBJECTIVE To investigate (a) whether MFL can enhance the visualization of the blood-flow with simultaneous visualization of the anatomic structures and (b) if MFL can ergonomically improve the microvascular surgical treatment compared to ICG-VA. METHODS A digital imaging of the blood flow after intravenous injection of ICG on 7 pigs was performed in real time under white light, standard fluorescence, and MFL. The blood flow was interrupted with a surgical clip, demonstrating the blockage of the blood flow. We prospectively included 30 patients with vascular deformities. The vasculature was visualized on the microscope's monitor and through the microscope's eyepiece. RESULTS In the animal experiment, the visualization of the anatomy and the blood flow under MFL produced high resolution images. The occlusion of blood vessels demonstrated sufficiently the blockage of tissue perfusion and its reperfusion after clip removal. During all 30 surgical cases, the MFL technique and the direct delivery of the pseudo-colored image through the eyepiece allowed for enhanced anatomic and dynamic data. CONCLUSION MFL was shown to be superior to the classic ICG-VA, delivering enhanced data and notably improving the workflow due to the simultaneous and precise white light visualization of the blood flow and the surrounding anatomic structures.

When we talk about visualization methods in surgery, it is important to mention that the diagnosis of tumors and how we define tumor borders intraoperatively in a correct way are two main things that would not be possible to achieve without this grand variety of visualization methods we have at our disposal nowadays. In addition, histopathology also plays a very important role, and its importance cannot be neglected either. Some biopsy specimens, e.g., frozen sections, are examined by a histopathologist and lead to tumor diagnosis and the definition of its borders. Furthermore, surgical resection is a very important point when it comes to prognosis and life survival. Confocal laser endomicroscopy (CLE) is an imaging technique that provides microscopic information on the tissue in real time. CLE of disorders, such as head, neck and brain tumors, has only recently been suggested to contribute to both immediate tumor characterization and detection. It can be used as an additional tool for surgical biopsies during biopsy or surgical procedures and for inspection of resection margins during surgery. In this review, we analyze the development, implementation, advantages and disadvantages as well as the future directions of this technique in neurosurgical and otorhinolaryngological disciplines.

The therapy of choice in the treatment of abnormalities in the human body, is to attempt a personalized diagnosis and with minimal invasiveness, ideally resulting in total resection (surgery) or turning off (intervention) of the pathology with preservation of normal functional tissue, followed by additional treatments, e [...]

Confocal Laser Endomicroscopy (CLE) is a new technique that is able to show cell structures during surgery. The interpretation of CLE data for tissue characterisation during brain tumour resection is challenging even among experts and it can lead to considerable inter-observer variability. Different kinds of deep machine learning programs and models were developed for better interpretation of the cell findings. A few-shot learning framework is proposed to assess the diagnostic value of CLE data and to classify them into healthy tissue and different brain tumour types, namely glioblastoma, meningioma, or astrocytoma. Performance evaluation on ex vivo and in vivo data shows that the rejection of data with low diagnostic value improves the classification accuracy by 37.5% while the proposed tissue characterisation framework achieves 96.20% classification accuracy.