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Biomedical tissue phantoms with controlled geometric and optical properties for Raman spectroscopy and tomography.

ABSTRACT: To support the translation of Raman spectroscopy into clinical applications, synthetic models are needed to accurately test, optimize and validate prototype fiber optic instrumentation. Synthetic models (also called tissue phantoms) are widely used for developing and testing optical instrumentation for diffuse reflectance, fluorescence, and Raman spectroscopies. While existing tissue phantoms accurately model tissue optical scattering and absorption, they do not typically model the anatomic shapes and chemical composition of tissue. Because Raman spectroscopy is sensitive to molecular composition, Raman tissue phantoms should also approximate the bulk tissue composition. We describe the fabrication and characterization of tissue phantoms for Raman tomography and spectroscopy. These phantoms have controlled chemical and optical properties, and also multilayer morphologies which approximate the appropriate anatomic shapes. Tissue phantoms were fabricated to support on-going Raman studies by simulating the human wrist and rat leg. Surface meshes (triangle patch models) were generated from computed tomography (CT) images of a human arm and rat leg. Rapid prototyping was used to print mold templates with complex geometric patterns. Plastic casting techniques used for movie special effects were adapted to fabricate molds from the rapid prototypes, and finally to cast multilayer gelatin tissue phantoms. The gelatin base was enriched with additives to model the approximate chemistry and optical properties of individual tissue layers. Additional studies were performed to determine optimal casting conditions, phantom stability, layer delamination and chemical diffusion between layers. Recovery of diffuse reflectance and Raman spectra in tissue phantoms varied with probe placement. These phantoms enable optimization of probe placement for human or rat studies. These multilayer tissue phantoms with complex geometries are shown to be stable, with minimal layer delamination and chemical diffusion.

SUBMITTER: Esmonde-White FW

PROVIDER: S-EPMC3289090 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Biomedical tissue phantoms with controlled geometric and optical properties for Raman spectroscopy and tomography.

Esmonde-White Francis W L FW Esmonde-White Karen A KA Kole Matthew R MR Goldstein Steven A SA Roessler Blake J BJ Morris Michael D MD

The Analyst 20111101 21

To support the translation of Raman spectroscopy into clinical applications, synthetic models are needed to accurately test, optimize and validate prototype fiber optic instrumentation. Synthetic models (also called tissue phantoms) are widely used for developing and testing optical instrumentation for diffuse reflectance, fluorescence, and Raman spectroscopies. While existing tissue phantoms accurately model tissue optical scattering and absorption, they do not typically model the anatomic shap ...[more]

PMID: 21912794

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Project description:Simple Summary Skin and subcutaneous tumors are among the most frequent neoplasms in dogs and cats. We studied 51 samples of canine and feline skin, lipomas, soft tissue sarcomas, and mast cell tumors using a multimodal approach based on optical coherence tomography and Raman spectroscopy. A supervised machine learning algorithm detected malignant tumors with the sensitivity and specificity of 94% and 98%, respectively. The proposed multimodal algorithm is a novel approach in veterinary oncology that can outperform the existing clinical methods such as the fine-needle aspiration method. Abstract As in humans, cancer is one of the leading causes of companion animal mortality. Up to 30% of all canine and feline neoplasms appear on the skin or directly under it. There are only a few available studies that have investigated pet tumors by biophotonics techniques. In this study, we acquired 1115 optical coherence tomography (OCT) images of canine and feline skin, lipomas, soft tissue sarcomas, and mast cell tumors ex vivo, which were subsequently used for automated machine vision analysis. The OCT images were analyzed using a scanning window with a size of 53 × 53 μm. The distributions of the standard deviation, mean, range, and coefficient of variation values were acquired for each image. These distributions were characterized by their mean, standard deviation, and median values, resulting in 12 parameters in total. Additionally, 1002 Raman spectral measurements were made on the same samples, and features were generated by integrating the intensity of the most prominent peaks. Linear discriminant analysis (LDA) was used for sample classification, and sensitivities/specificities were acquired by leave-one-out cross-validation. Three datasets were analyzed—OCT, Raman, and combined. The combined OCT and Raman data enabled the best sample differentiation with the sensitivities of 0.968, 1, and 0.939 and specificities of 0.956, 1, and 0.977 for skin, lipomas, and malignant tumors, respectively. Based on these results, we concluded that the proposed multimodal approach, combining Raman and OCT data, can accurately distinguish between malignant and benign tissues.

Dataset Information

Biomedical tissue phantoms with controlled geometric and optical properties for Raman spectroscopy and tomography.

Publications

Biomedical tissue phantoms with controlled geometric and optical properties for Raman spectroscopy and tomography.

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