We present a combination of optical coherence tomography (OCT) and Raman spectroscopy (RS) for improved diagnosis and discrimination of different stages and grades of bladder cancer ex vivo by linking the complementary information provided by these two techniques. Bladder samples were obtained from biopsies dissected via transurethral resection of the bladder tumor (TURBT). As OCT provides structural information rapidly, it was used as a red-flag technology to scan the bladder wall for suspicious lesions with the ability to discriminate malignant tissue from healthy urothelium. Upon identification of degenerated tissue via OCT, RS was implemented to determine the molecular characteristics via point measurements at suspicious sites. Combining the complementary information of both modalities allows not only for staging, but also for differentiation of low-grade and high-grade cancer based on a multivariate statistical analysis. OCT was able to clearly differentiate between healthy and malignant tissue by tomogram inspection and achieved an accuracy of 71% in the staging of the tumor, from pTa to pT2, through texture analysis followed by k-nearest neighbor classification. RS yielded an accuracy of 93% in discriminating low-grade from high-grade lesions via principal component analysis followed by k-nearest neighbor classification. In this study, we show the potential of a multi-modal approach with OCT for fast pre-screening and staging of cancerous lesions followed by RS for enhanced discrimination of low-grade and high-grade bladder cancer in a non-destructive, label-free and non-invasive way.

Two-photon excitation laser induced fluorescence (2p-LIF) is used here for imaging an optically dense atomizing spray. The main advantage of the approach is that very little fluorescence interference originating from multiple light scattering is generated. This leads to high image contrast and a faithful description of the imaged fluid structures. While point measurement 2p-LIF imaging is a well-known approach used in life science microscopy, it has, to the best of the authors' knowledge, never been tested for analyzing liquid structures in spray systems. We take advantage of this process, here, at a macroscopic scale (∼ 5 × 5 mm field of view) by imaging the central part of a light sheet of 10 mm height. To generate enough 2p-LIF signal at such a scale and with single-shot detection, ultra-short laser pulses of 25 fs, centered at 800 nm wavelength and having 2.5 mJ pulse energy, have been used. The technique is demonstrated by imaging a single spray plume from a 6 hole commercial Gasoline Direct Injection (GDI) system running at 200 bar injection pressure. The proposed approach is very promising for detailed analysis of liquid breakups in optically dense sprays and can be used for other fluid mechanics related applications.

Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We therefore developed a line scan Raman microspectroscopy (LSRM) system to detect, discriminate and hyperspectrally visualize pituitary gland from pituitary adenomas based on molecular differences. By applying principal component analysis followed by a k-nearest neighbor algorithm, specific hormone states were identified and a clear discrimination between pituitary gland and various adenoma subtypes was achieved. The classifier yielded an accuracy of 95% for gland tissue and 84–99% for adenoma subtypes. With an overall accuracy of 92%, our LSRM system has proven its potential to differentiate pituitary gland from pituitary adenomas. LSRM images based on the presence of specific Raman bands were created, and such images provided additional insight into the spatial distribution of particular molecular compounds. Pathological states could be molecularly differentiated and characterized with texture analysis evaluating Grey Level Cooccurrence Matrices for each LSRM image, as well as correlation coefficients between LSRM images.

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