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.
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.
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.
In recent years, it was demonstrated that discrimination between white matter and tumor-infiltrated white matter based on optical coherence tomography (OCT) data is possible with high accuracy. However, gray matter is also present during the tumor resection and shows similar optical properties to tumor infiltration, which aggravates the tumor classification using optical coherence tomography. A semantic segmentation approach based on a convolutional neural network was applied to the problem in order to classify healthy brain tissue from tumor infiltrated brain tissue. A dataset was created, which consisted of ex vivo OCT B-scans, which were acquired by a swept-source OCT system with a central wavelength of 1300 nm. Each OCT B-scan was indirectly annotated by transforming histological labels from a corresponding H&E section onto it. The labels differentiate between white matter, gray matter and tumor infiltration. The output of the network was modeled to a Dirichlet prior distribution, which enabled the capturing of a prediction uncertainty. This approach achieved an intersection over union score of 0.72 for healthy brain tissue and 0.69 for highly tumor infiltrated brain tissue, when only confident predictions were considered.
Optical coherence elastography represents mechanical characteristics of biological tissue in so-called mechanical contrast maps. In addition to the standard intensity image, the contrast map illustrates numerous mechanical tissue features that would otherwise be undetectable. This is of great interest as abnormal physiological changes influence the mechanical behavior of the tissue. We demonstrate an advanced mechanical contrast approach based on the phase signal of our 3.2 MHz optical coherence tomography system. The robustness and performance of this contrast approach is evaluated and discussed based on preliminary results.
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.
In neurosurgical tumor operations on the central nervous system, intraoperative haptic information often assists for discrimination between healthy and diseased tissue. Thus, it can provide the neurosurgeon with additional intraoperative source of information during resection, next to the visual information by the light microscope, fluorescent dyes and neuronavigation. One approach to obtain elastic and viscoelastic tissue characteristics non-subjectively is phase-sensitive optical coherence elastography (OCE), which is based on the principle of optical coherence tomography (OCT). While phase-sensitive OCE offers significantly higher displacement sensitivity inside a sample than commonly used intensity-based correlation methods, it requires a reliable algorithm to recover the phase signal, which is mathematically restricted in the -π to π range. This problem of phase wrapping is especially critical for inter-frame phase analysis since the time intervals between two referenced voxels is long. Here, we demonstrate a one-dimensional unwrapping algorithm capable of removing up to 4π-ambiguities between two frames in the complex phase data obtained from a 3.2 MHz-OCT system. The high sampling rate allows us to resolve large sample displacements induced by a 200 ms air pulse and acquires pixel-precise detail information. The deformation behavior of the tissue can be monitored over the entire acquisition time, offering various subsequent mechanical analysis procedures. The reliability of the algorithm and imaging concept was initially evaluated using different brain tumor mimicking phantoms. Additionally, results from human ex vivo brain tumor samples are presented and correlated with histological findings supporting the robustness of the algorithm.
Optical coherence elastography (OCE) offers the possibility of obtaining the mechanical behavior of a tissue. When also using a non-contact mechanical excitation, it mimics palpation without interobserver variability. One of the most frequently used techniques is phase-sensitive OCE. Depending on the system, depth-resolved changes in the sub-µm to nm range can be detected and visualized volumetrically. Such an approach is used in this work to investigate and detect transitions between healthy and tumorous brain tissue as well as inhomogeneities in the tumor itself to assist the operating surgeon during tumor resection in the future. We present time-resolved, phase-sensitive OCE measurements on various ex vivo brain tumor samples using an ultra-fast 3.2 MHz swept-source optical coherence tomography (SS-OCT) system with a frame rate of 2.45 kHz. 4 mm line scans are acquired which, in combination with the high imaging speed, allow monitoring and investigation of the sample's behavior in response to the mechanical load. Therefore, an air-jet system applies a 200 ms short air pulse to the sample, whose non-contact property facilitates the possibility for future in vivo measurements. Since we can temporally resolve the response of the sample over the entire acquisition time, the mechanical properties are evaluated at different time points with depth resolution. This is done by unwrapping the phase data and performing subsequent assessment. Systematic ex vivo brain tumor measurements were conducted and visualized as distribution maps. The study outcomes are supported by histological analyses and examined in detail.
The ill-defined tumor borders of glioblastoma multiforme pose a major challenge for the surgeon during tumor resection, since the goal of the tumor resection is the complete removal, while saving as much healthy brain tissue as possible. In recent years, optical coherence tomography (OCT) was successfully used to classify white matter from tumor infiltrated white matter by several research groups. Motivated by these results, a dataset was created, which consisted of sets of corresponding ex vivo OCT images, which were acquired by two OCT-systems with different properties (e.g. wavelength and resolution). Each image was annotated with semantic labels. The labels differentiate between white and gray matter and three different stages of tumor infiltration. The data from both systems not only allowed a comparison of the ability of a system to identify the different tissue types present during the tumor resection, but also enable a multimodal tissue analysis evaluating corresponding OCT images of the two systems simultaneously. A convolutional neural network with dirichlet prior was trained, which allowed to capture the uncertainty of a prediction. The approach increased the sensitivity of identifying tumor infiltration from 58 % to 78 % for data with a low prediction uncertainty compared to a previous monomodal approach.
The identification of ex vivo brain tumor tissue was investigated with two different optical coherence tomography systems exploiting two optical parameters. The optical parameters were calculated from semantically labelled OCT B-scans.
A 1.6 MHz Fourier-domain mode-locked (FDML) optical coherence tomography (OCT) was adapted to an OR-Microscope for clinical application in neurosurgery. 3D-volume scans at video rate are envisaged with approximately 50μm lateral and 20μm axial resolution
For enabling haemostasis during brain tumour resection, suitable laser application parameters for the wavelengths 1940 nm and 1480 nm were investigated as an alternative for bipolar forceps in tissue coagulation.
Tumor discrimination from healthy tissue is often performed by haptically probing tissue elasticity. We demonstrate non-contact elastography using air-puff excitation and tissue indentation measurement by phase-sensitive OCT with a 3.2 MHz FDML-laser
A precision air puff excitation system for MHz Optical Coherence Elastography in neurosurgery was developed. It enables non-contact soft-tissue excitation down to μN, with direct, noncontact force determination via gas flow measurement.
The separation of tumorous brain tissue and healthy brain tissue is still a big challenge in the field of neurosurgery, especially when it comes to the detection of different infiltration grades of glioblastoma multiforme at the tumor border. On the basis of a recently created labelled OCT dataset of ex vivo glioblastoma multiforme tumor samples the detection of brain tumor tissue and the identification of zones with varying degrees of infiltration of tumor cells was investigated. The identification was based on the optical properties, which were extracted by an exponential fit function. The results showed that a separation of tumorous tissue and healthy white matter based on these optical properties is possible. A support vector machine was trained on the optical properties to separate tumor from healthy white matter tissue, which achieved a sensitivity of 91% and a specificity of 76% on an independent training dataset.
Optical coherence tomography (OCT) has the potential to become an additional imaging modality for surgical guidance in the field of neurosurgery, especially when it comes to the detection of different infiltration grades of glioblastoma multiforme at the tumor border. Interpretation of the images, however, is still a big challenge. A method to create a labeled OCT dataset based on ex vivo brain samples is introduced. The tissue samples were embedded in an agarose mold giving them a distinctive shape before images were acquired with two OCT systems (spectral domain (SD) and swept source (SS) OCT) and histological sections were created and segmented by a neuropathologist. Based on the given shape, the corresponding OCT images for each histological image can be determined. The transfer of the labels from the histological images onto the OCT images was done with a non-affine image registration approach based on the tissue shape. It was demonstrated that finding OCT images of a tissue sample corresponding to segmented histological images without any color or laser marking is possible. It was also shown that the set labels can be transferred onto OCT images. The accuracy of method is 26 ± 11 pixel, which translates to 192 ± 75 μm for the SS-OCT and 94 ± 43 μm for the SD-OCT. The dataset consists of several hundred labeled OCT images, which can be used to train a classification algorithm.
The aim of this work is the creation of segmented data set consisting of optical coherence tomography (OCT) scans, which were taken of brain tumor tissue with different tumor infiltration rates. In an ongoing clinical study more than 140 human brain samples with different infiltration grades were recorded ex vivo with two OCT systems, a spectral domain OCT system and a swept-source OCT system that uses a 1310 nm Fourier domain mode locked laser. The histological analysis of the recorded samples builds the ground truth for labeling the corresponding OCT B-Scans. The segmented data set gained from this process will be used to train a classification algorithm, taking into account structural and optical properties such as the attenuation coefficient. In the future the classification algorithm together with a microscope integrated OCT system will be used for the in vivo identification of brain tumors as a guidance tool for the surgeon to increase tumor resection efficiency.
Optical coherence tomography (OCT) is a non-invasive imaging technique which is currently investigated for intraoperative detection of residual tumor during resection of human gliomas. Three different OCT systems were used for imaging of human glioblastoma in vivo (830nm spectral domain (SD) OCT integrated into a surgical microscope) and ex vivo (940nm SD-OCT and 1310nm swept-source MHz-OCT using a Fourier domain mode locked (FDML) laser). Before clinical data acquisition, the systems were characterized using a three-dimensional point-spread function phantom. To distinguish tumor from healthy brain tissue later on, attenuation coefficients of each pixel in OCT depth profiles are calculated. First examples from a clinical study show that the pixel-resolved calculation of the attenuation coefficient provides a good image contrast and confirm that white matter shows a higher signal and more homogeneous signal structure than tumorous tissue.
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