Sandro Heuke

Multimodal mapping of human skin

The combination of coherent anti‐Stokes Raman scattering (CARS), second harmonic generation (SHG) and two‐photon excited fluorescence (TPEF) imaging – referred to as multimodal imaging – provides complementary contrast based on molecular vibrations, the structure of various tissue components and endogenous fluorophores, respectively.

Detection and Discrimination of Non-Melanoma Skin Cancer by Multimodal Imaging

Non-melanoma skin cancer (NMSC) belongs to the most frequent human neoplasms. Its exposed location facilitates a fast ambulant treatment. However, in the clinical practice far more lesions are removed than necessary, due to the lack of an efficient pre-operational examination procedure: Standard imaging methods often do not provide a sufficient spatial resolution. The demand for an efficient in vivo imaging technique might be met in the near future by non-linear microscopy. As a first step towards this goal, the appearance of NMSC in various microspectroscopic modalities has to be defined and approaches have to be derived to distinguish healthy skin from NMSC using non-linear optical microscopy. Therefore, in this contribution the appearance of ex vivo NMSC in a combination of coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG) and two photon excited fluorescence (TPEF) imaging—referred as multimodal imaging—is described. Analogous to H&E staining, an overview of the distinct appearances and features of basal cell and squamous cell carcinoma in the complementary modalities is derived, and is expected to boost in vivo studies of this promising technological approach.

Multimodal Imaging Spectroscopy of Tissue

Advanced optical imaging technologies have experienced increased visibility in medical research, as they allow for a label-free and nondestructive investigation of tissue in either an excised state or living organisms. In addition to a multitude of ex vivo studies proving the applicability of these optical imaging approaches, a transfer of various modalities toward in vivo diagnosis is currently in progress as well. Furthermore, combining optical imaging techniques, referred to as multimodal imaging, allows for an improved diagnostic reliability due to the complementary nature of retrieved information. In this review, we provide a summary of ongoing multifold efforts in multimodal tissue imaging and focus in particular on in vivo applications for medical diagnosis. We also discuss the advantages and potential limitations of the imaging methods and outline opportunities for future developments.

Seamless stitching of tile scan microscope images

For diagnostic purposes, optical imaging techniques need to obtain high‐resolution images of extended biological specimens in reasonable time. The field of view of an objective lens, however, is often smaller than the sample size. To image the whole sample, laser scanning microscopes acquire tile scans that are stitched into larger mosaics. The appearance of such image mosaics is affected by visible edge artefacts that arise from various optical aberrations which manifest in grey level jumps across tile boundaries. In this contribution, a technique for stitching tiles into a seamless mosaic is presented. The stitching algorithm operates by equilibrating neighbouring edges and forcing the brightness at corners to a common value. The corrected image mosaics appear to be free from stitching artefacts and are, therefore, suited for further image analysis procedures. The contribution presents a novel method to seamlessly stitch tiles captured by a laser scanning microscope into a large mosaic. The motivation for the work is the failure of currently existing methods for stitching nonlinear, multimodal images captured by our microscopic setups. Our method eliminates the visible edge artefacts that appear between neighbouring tiles by taking into account the overall illumination differences among tiles in such mosaics. The algorithm first corrects the nonuniform brightness that exists within each of the tiles. It then compensates for grey level differences across tile boundaries by equilibrating neighbouring edges and forcing the brightness at the corners to a common value. After these artefacts have been removed further image analysis procedures can be applied on the microscopic images. Even though the solution presented here is tailored for the aforementioned specific case, it could be easily adapted to other contexts where image tiles are assembled into mosaics such as in astronomical or satellite photos.

Beyond endoscopic assessment in inflammatory bowel disease: real-time histology of disease activity by non-linear multimodal imaging

Assessing disease activity is a prerequisite for an adequate treatment of inflammatory bowel diseases (IBD) such as Crohn’s disease and ulcerative colitis. In addition to endoscopic mucosal healing, histologic remission poses a promising end-point of IBD therapy. However, evaluating histological remission harbors the risk for complications due to the acquisition of biopsies and results in a delay of diagnosis because of tissue processing procedures. In this regard, non-linear multimodal imaging techniques might serve as an unparalleled technique that allows the real-time evaluation of microscopic IBD activity in the endoscopy unit. In this study, tissue sections were investigated using the non-linear multimodal microscopy combination of coherent anti-Stokes Raman scattering (CARS), two-photon excited auto fluorescence (TPEF) and second-harmonic generation (SHG). After the measurement a gold-standard assessment of histological indexes was carried out based on a conventional H&E stain. Subsequently, various geometry and intensity related features were extracted from the multimodal images. An optimized feature set was utilized to predict histological index levels based on a linear classifier. Based on the automated prediction, the diagnosis time interval is decreased. Therefore, non-linear multimodal imaging may provide a real-time diagnosis of IBD activity suited to assist clinical decision making within the endoscopy unit.

Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool

BackgroundDue to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making.MethodsIn this contribution we will show that the combination of the three label-free non-linear imaging modalities CARS (coherent anti-Stokes Raman-scattering), TPEF (two-photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo-HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman-spectroscopy retrieving the tissue’s molecular fingerprint.ResultsThe results suggest that the combination of non-linear multimodal imaging and Raman-spectroscopy possesses the potential as a precise and fast tool in routine histopathology.ConclusionsAs the key advantage, both optical methods are non-invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold-standard.

Automated seeding-based nuclei segmentation in nonlinear optical microscopy

Nonlinear optical (NLO) microscopy based, e.g., on coherent anti-Stokes Raman scattering (CARS) or two-photon-excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy; there is a lack of robust and durable nuclei segmentation methods capable of accurate image processing in cases of variable image contrast, nuclear density, and type of investigated tissue. Nonetheless, such algorithms specifically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e.g., in pathology or intraoperatively for surgical guidance. In this paper, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian filter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods followed by the verification of the detected nuclei performs high average sensitivity and specificity when applied to various types of NLO microscopy images.

Bessel beam CARS of axially structured samples

We report about a Bessel beam CARS approach for axial profiling of multi-layer structures. This study presents an experimental implementation for the generation of CARS by Bessel beam excitation using only passive optical elements. Furthermore, an analytical expression is provided describing the generated anti-Stokes field by a homogeneous sample. Based on the concept of coherent transfer functions, the underling resolving power of axially structured geometries is investigated. It is found that through the non-linearity of the CARS process in combination with the folded illumination geometry continuous phase-matching is achieved starting from homogeneous samples up to spatial sample frequencies at twice of the pumping electric field wave. The experimental and analytical findings are modeled by the implementation of the Debye Integral and scalar Green function approach. Finally, the goal of reconstructing an axially layered sample is demonstrated on the basis of the numerically simulated modulus and phase of the anti-Stokes far-field radiation pattern.

Hepatic Vitamin A Content Investigation Using Coherent Anti‐Stokes Raman Scattering Microscopy

Standard techniques for examining the distribution of vitamin A in liver either require staining or lead to rapid photobleaching of the molecule. A potentially better alternative approach is to use coherent anti-Stokes Raman scattering (CARS) microscopy; a fast, label-free, non-disruptive imaging method that provides contrast based on molecular vibrations. This contribution evaluates the viability of CARS microscopy for imaging vitamin A within thick hepatic tissue under physiological conditions by tuning into its characteristic vibrational band in the fingerprint region. Additional information about the morphology and architecture of the tissue was acquired using second harmonic generation (SHG) and multi-photon excited fluorescence (MPEF) to help mapping the intra-lobular positions of the vitamin A droplets. We demonstrate the capability of our multimodal imaging framework to selectively image lipid-soluble vitamin A droplets deep in bulk liver tissue with a high contrast while co-registering a complementary morphological background that clearly visualizes hepatic lobules. The results obtained envisage the good prospect of the technique for in vivo studies assessing vitamin A distribution heterogeneity and how it is affected by the progression of hepatic diseases.

Discrimination of skin diseases using the multimodal imaging approach

Optical microspectroscopic tools reveal great potential for dermatologic diagnostics in the clinical day-to-day routine. To enhance the diagnostic value of individual nonlinear optical imaging modalities such as coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG) or two-photon excited fluorescence (TPF), the approach of multimodal imaging has recently been developed. Here, we present an application of nonlinear optical multimodal imaging with Raman-scattering microscopy to study sizable human-tissue cross-sections. The samples investigated contain both healthy tissue and various skin tumors. This contribution details the rich information content, which can be obtained from the multimodal approach: While CARS microscopy, which - in contrast to spontaneous Raman-scattering microscopy - is not hampered by single-photon excited fluorescence, is used to monitor the lipid and protein distribution in the samples, SHG imaging selectively highlights the distribution of collagen structures within the tissue. This is due to the fact, that SHG is only generated in structures which lack inversion geometry. Finally, TPF reveals the distribution of autofluorophores in tissue. The combination of these techniques, i.e. multimodal imaging, allows for recording chemical images of large area samples and is - as this contribution will highlight - of high clinically diagnostic value.