An accurate tumor delineation in neurosurgery is still a very challenging problem which we are addressing with optical coherence elastography (OCE). Because of the highly viscoelastic properties of brain tissue, we developed a new Air-Jet based tissue excitation source and evaluated the tissue stiffness with a 3.2 MHz swept-source Optical Coherence Tomography (OCT) system with a line scan rate of 2.45 kHz. The phase based displacement per pixel is measured and stiffness maps are calculated for brain tumor samples. However, certain features in the stiffness maps are seemingly not correlatable to the tissue features in the histological sections. Therefore, the structural properties of the histological sections e.g. fiber orientation, cell nuclei concentration and the “onion structure” with their rotational direction for meningioma were given greater consideration. The structural information are extracted from the histological sections via color deconvolution and structural tensor analysis. First results show that the stiffness transitions correlate with some structures of the histological sections. In summary, the Air-Jet OCE seems to be capable of measuring the stiffness as well as the structural composition of the sample. The long-term aim of this project is to establish OCE to support tumor delineation in the field of neurosurgery.
Microscope integrated real time 4D MHz-OCT operating at high scanning densities are capable of capturing additional visual contrast resolving depth and tissue. Even within a plain C-scan en-face projection structures are recognizable, that are not visible in a white light camera image. With advanced post processing methods, such as absorption coefficient mapping, and morphological classifiers more information is extracted. Presentation to the user in an intuitive way poses practical challenges that go beyond the implementation of a mere overlay display. We present our microscope integrated high speed 4D OCT imaging system, its clinical study use for in-vivo brain tissue imaging, and user feedback on the presentation methods we developed. In neurosurgery the de-facto standard contrast agents used for visibly highlighting brain tumors are Fluorescin and ALA, both of which come with certain caveats. As part of a clinical study we developed a microscope integrated real time 4D MHz-OCT system, operating as high scanning densities, with the intent of creating visual tissue contrast without the use of such contrast agents. Advanced post processing methods to classify tissue can be derived from static properties such as light absorption and morphology, and from dynamic properties, such as perfusion and elastography. However we also noticed that even in a plain C-scan en-face projection structures of interest could be recognized, that were not visible in the corresponding white light camera image. As part of a clinical study so far we collected data from 20 patients, used it for machine learning based classifiers and developing data presentation modalities for eventual use in a surgical environment. We present the challenges in implementing our microscope integrated high speed 4D OCT imaging system, a selection of the imaging data we collected so far during brain tumor surgeries, and the avenues toward presenting processed data to the surgeon.
Ultrasonic aspirators are commonly used for volume reduction of neurosurgical tumours. Bleeding occurs occasionally during ultrasonic debulking since ultrasonic aspirators do not coagulate affected vessels. Usually bipolar forceps are used for haemostasis, however requiring a change of instrumentation by the surgeon. Thulium laser emitting at a wavelength of 1940 nm in a strong water absorption band are suitable for tissue and blood vessel coagulation with subsequent haemostasis. Therefore, such laser system was combined with an ultrasonic aspirator by adapting the light transmitting multimode fiber tip to the distal tip of the ultrasonic aspirator. The thulium laser showed very good haemostasis during tumour debulking. Instrumental changes to bipolar forceps were reduced, surgeon’s feedbacks were convincingly positive.
Neuro-surgery is challenged by the difficulties of determining brain tumor boundaries during excisions. Optical coherence tomography is investigated as an imaging modality for providing a viable contrast channel. Our MHz-OCT technology enables rapid volumetric imaging, suitable for surgical workflows. We present a surgical microscope integrated MHz-OCT imaging system, which is used for the collection of in-vivo images of human brains, with the purpose of being used in machine learning systems that shall be trained to identify and classify tumorous tissue.
The long-term aim of this project is to establish optical coherence elastography for tumor delineation in the field of neurosurgery. Because of the challenging highly viscoelastic properties of brain tissue, we developed a new Air-Jet based excitation source. With pulse duration of up to 700 ms and real time force measurement, this novel system allows the sample to reach a semi-steady state. In parallel with a 3.2 MHz swept-source optical coherence tomography system over 800 line scans are acquired over the whole sample excitation process. The phase data is extracted, unwrapped and the displacement per pixel is calculated. This system enables the measurement of mechanical properties like stiffness and Young’s modulus, similar to the standard indentation measurement. As well as viscoelastic properties i.e. relaxation times, in non-contact. The first processing step is to split the excitation progression into three main time ranges: the high dynamic, the steady state, and the viscoelastic range. In each range typical features of the displacement curve are extracted for every pixel in the B-scan. For those features, various mechanical parameters are calculated mainly, the stiffness and Young’s modulus and stored as feature matrices. The results are processed, visualized and overlaid with either the OCT intensity image or the histological sections. Strain stress curves are generated for some selected positions in the B-scan leading to a specific viscoelastic hysteresis. The feature matrices will be utilized as a fingerprint for each tissue, and are the first step for an AI based classification of the tissue.
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