Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo.

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study.

BACKGROUND: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed. METHODS: Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. RESULTS: A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. CONCLUSION: Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.

Spectral reconstruction for shifted-excitation Raman difference spectroscopy (SERDS).

Fluorescence emission is one of the major obstacles to apply Raman spectroscopy in biological investigations. It is usually several orders more intense than Raman scattering and hampers further analysis. In cases where the fluorescence emission is too intense to be efficiently removed via routine mathematical baseline correction algorithms, an alternative approach is needed. One alternative approach is shifted-excitation Raman difference spectroscopy (SERDS), where two Raman spectra are recorded with two slightly different excitation wavelengths. Ideally, the fluorescence emission at the two excitations does not change while the Raman spectrum shifts according to the excitation wavelength. Hence the fluorescence is removed in the difference of the two recorded Raman spectra. For better interpretability a spectral reconstruction procedure is necessary to recover the fluorescence-free Raman spectrum. This is challenging due to the intensity variations between the two recorded Raman spectra caused by unavoidable experimental changes as well as the presence of noise. Existent approaches suffer from drawbacks like spectral resolution loss, fluorescence residual, and artefacts. In this contribution, we proposed a reconstruction method based on non-negative least squares (NNLS), where the intensity variations between the two measurements are utilized in the reconstruction model. The method achieved fluorescence-free reconstruction on three real-world SERDS datasets without significant information loss. Thereafter, we quantified the performance of the reconstruction based on artificial datasets from four aspects: reconstructed spectral resolution, precision of reconstruction, signal-to-noise-ratio (SNR), and fluorescence residual. The artificial datasets were constructed with varied Raman to fluorescence intensity ratio (RFIR), SNR, full-width at half-maximum (FWHM), excitation wavelength shift, and fluorescence variation between the two spectra. It was demonstrated that the NNLS approach provides a faithful reconstruction without significantly changing the spectral resolution. Meanwhile, the reconstruction is almost robust to fluorescence variations between the two spectra. Last but not the least the SNR was improved after reconstruction for extremely noisy SERDS datasets.

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

BACKGROUND: Due 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. METHODS: In 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. RESULTS: The 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. CONCLUSIONS: As 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.

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.

Multimodal nonlinear microscopy of head and neck carcinoma - toward surgery assisting frozen section analysis.

BACKGROUND: Treatment of early cancer stages is deeply connected to a good prognosis, a moderate reduction of the quality of life, and comparably low treatment costs. METHODS: Head and neck squamous cell carcinomas were investigated using the multimodal combination of coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation (SHG) microscopy. RESULTS: An increased median TPEF to CARS contrast was found comparing cancerous and healthy squamous epithelium with a p value of 1.8·10(-10) . A following comprehensive image analysis was able to predict the diagnosis of imaged tissue sections with an overall accuracy of 90% for a 4-class model. CONCLUSION: Nonlinear multimodal imaging is verified objectively as a valuable diagnostic tool that complements conventional staining protocols and can serve as filter in future clinical routine reducing the pathologist's workload. © 2016 Wiley Periodicals, Inc. Head Neck 38: First-1552, 2016.

Fiber probe for nonlinear imaging applications.

Over the past years it had been demonstrated that multimodal imaging combining the nonlinear modalities coherent anti-Stokes Raman scattering (CARS), two-photon excited auto-fluorescence (TPEF) and second harmonic generation (SHG) show a great potential for tissue diagnosis and tumor identification. To extend the applicability of this multimodal imaging approach for in-vivo tissue screening of difficult to access body regions the development of suitable fiber optic probes is required. Here we report about a novel CARS imaging fiber probe consisting of 10,000 coherent light guiding elements preserving the spatial relationship between the entrance and the output of the fiber. Therefore the scanning procedure can be shifted from the distal to the proximal end of the fiber probe and no moving parts or driving current are required to realize in-vivo CARS endoscopy.