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One of the challenges during surgery is to make a proper distinction between vital tissues, structures and organs, both at the surface of the exposed area and beneath it. Currently, identification of different tissue types relies either on the skills and anatomical knowledge of the surgeon (e.g., nerves, blood vessels, different tissue types), or on the results of off-line histopathological analysis on biopsies for assessment of cancer-free margins. These approaches are observer-dependent, or, as in the case of off-line histopathological analysis, also time consuming. A quick, non-invasive, and more observer-independent intraoperative tissue recognition method, applicable both ex-vivo and in-vivo, would be of great assistance during surgical procedures. Hyper-Spectral Imaging (HSI) has a lot of potential for such applications. It combines high spatial resolution with spectral information at numerous wavelengths that cannot be separated by human eye in the visible, and also beyond the visible spectral range. This technique enables enhanced discrimination between different tissue types based on their specific spectral signatures. We will describe the approach to HSI which is being developed within van't Hoff Medical Shared Research Program at TNO in collaboration with multiple clinical, academic and industrial partners. We study and analyze wide-band reflectance spectra of different tissues types collected through a fiber-based probe, both in-vivo during open surgery procedures and ex-vivo on freshly excised tissues. These data are used to identify spectral signatures of different tissue types. Based on this information, we develop data processing techniques to identify and distinguish the target tissues. We also further develop this approach towards a HSI setup for tissue imaging. The first step with such a setup is testing on inorganic phantoms and animal tissues. At a later stage the setup will be employed for ex-vivo human tissue analysis, both at the surface and up to several millimeters beneath the surface, with the final goal of in vivo intraoperative tissue recognition and tissue-specific contrast-enhanced imaging

Distinguishing critical tissues can be challenging in (para-)thyroid surgery. As a first step towards intraoperative image contrast enhancement, we collected and analyzed spectral signatures (350-1830nm) of thyroid, parathyroid and recurrent laryngeal nerve in 10 patients.

BACKGROUND: During colorectal surgical dissections, recognizing essential anatomy is crucial to prevent iatrogenic injury. This study builds forth upon previously identified new inherent anatomical spectral contrasts in fresh human colonic samples. AIM: Assessment of optical spectrometry for discriminating critical tissues (colon, adipose tissue, artery, vein and ureter) in colorectal surgery. PROJECT DESCRIPTION: Acquisition of in vivo AND ex vivo wide-band diffuse reflectance spectra (350-1830nm) during colorectal surgery, analysis of all separate tissue spectra using principal component analysis (PCA) and comparing tissue categories. PRELIMINARY RESULTS: In 6 consecutive patients 156 in vivo spectra (32 tissue spots) and 118 ex vivo spectra (24 tissue spots) were collected. PCA derived parameters were used to differentiate between tissue types. Distinctive spectral contrast features (partly within wavelengths invisible to the naked human eye) could indeed be identified for all tissue types. Visualizing these invisible contrasts may enhance surgical imaging (either during open or endoscopic surgery).

INTRODUCTION: In thyroid and parathyroid surgery iatrogenic injury of the parathyroid glands or the recurrent laryngeal nerve (RLN) is a possible complication that needs to be prevented. The visible contrast between thyroid and parathyroid tissue is delicate to observe. The aim of this pilot study was to collect in vivo spectral reflectance-signatures of critical tissue types encountered during thyroid and parathyroid surgery, and to assess the presence of useful spectral distinctive features that might be applied for future devices enabling intraoperative tissue-specific image contrast enhancement. METHODS: Wide-band spectra (350-1830nm, 1nm resolution) were collected in vivo during thyroid and parathyroid surgery. Subjected to tissue type accessibility, on average 2 tissue types per patient were measured. For each tissue type, 5 spectra were recorded per site, covering 1-2 sites per tissue type. Mean tissue spectra were calculated for all measured tissue types. After visually comparing these mean spectra, two spectral signature features for all individual measured sites were extracted: 1. Slope within the 650-700nm range; 2. Amplitude gradient between dominant local reflectance minimum and maximum within 1350-1830nm. RESULTS: In 10 consecutive patients 158 in vivo spectra were recorded on 32 tissue sites. Based on the mean diffuse reflectance spectra for thyroid, parathyroid and RLN, the estimated features for the three tissue types were plotted. Significance was tested using a paired Student's t-test and by applying a Holm-Bonferoni correction (criterion set to p

Background: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content. Methodology/Principal Findings: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2*-w MRI signal intensity. Conclusions: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.