Coherent Raman Scattering (CRS) Microscopy

Stimulated Raman Histology (SRH)

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Figure modified from Nature Biomedical Engineering, 1(2), pp. 1-13. Reference listed below. 

The NIO Laser Imaging System is based on Stimulated Raman Histology (SRH), first described by Orringer et al. in 2017 and based on Stimulated Raman Scattering Microscopy, which was first described by Freudiger et al. in 2008. SRH allows three-dimensional imaging of thick specimens using optical sectioning and relies on laser spectroscopy to interrogate the chemical composition of the sample. As such, it does not require physical sectioning, (e.g. with a microtome on frozen or paraffin-embedded tissue) or dye staining, and it allows optical imaging of fresh tissue specimens with minimal tissue preparation.

 

In contrast to other laser spectroscopy techniques, SRH is unique in that it performs a spectroscopic measurement at each pixel and displays the results as a pseudo-color image, instead of a point spectrum. For example, the figure shows how light absorption by CH2 molecular vibrations (e.g. from lipids) and CH3 molecular vibrations (e.g. from proteins and DNA) can be displayed in a pink/purple color scheme. While SRH is not identical to hematoxylin & eosin (H&E) staining, this color scheme has many similarities to traditional stains, such as a high contrast for nuclear features. The NIO Laser Imaging System uses a high numerical aperture objective with 25x magnification and a 0.5mm scan width. Larger areas up to 10mm x 10mm can then be acquired by stitching multiple fields of view in a fully automated process.

 

References:

Orringer, D.A., Pandian, B., Niknafs, Y.S., Hollon, T.C., Boyle, J., Lewis, S., Garrard, M., Hervey-Jumper, S.L., Garton, H.J., Maher, C.O. and Heth, J.A., 2017. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nature biomedical engineering, 1(2), pp.1-13.

https://www.nature.com/articles/s41551-016-0027

Freudiger, C.W., Min, W., Saar, B.G., Lu, S., Holtom, G.R., He, C., Tsai, J.C., Kang, J.X. and Xie, X.S., 2008. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science, 322(5909), pp.1857-1861.

https://science.sciencemag.org/content/322/5909/1857

 

Freudiger, C.W., Yang, W., Holtom, G.R., Peyghambarian, N., Xie, X.S. and Kieu, K.Q., 2014. Stimulated Raman scattering microscopy with a robust fibre laser source. Nature photonics, 8(2), pp.153-159.

https://www.nature.com/articles/nphoton.2013.360

Artificial Intelligence (Research Use Only)

A taxonomy of diagnostic classes was selected specifically to inform intraoperative decision-making.
A taxonomy of diagnostic classes was selected specifically to inform intraoperative decision-making.

Representative example SRH images from each of the 13 diagnostic class are shown.
Representative example SRH images from each of the 13 diagnostic class are shown.

Multiclass confusion matrices for both the control arm and the experimental arm.
Multiclass confusion matrices for both the control arm and the experimental arm.

A taxonomy of diagnostic classes was selected specifically to inform intraoperative decision-making.
A taxonomy of diagnostic classes was selected specifically to inform intraoperative decision-making.

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Figure was modified from Nature Medicine, 26(1), pp.52-58. Reference listed below. 

 

Artificial Intelligence (AI) based on Machine Learning (ML) is being widely explored for clinical applications and is poised to change the future of medicine. SRH images are perfect for AI/ML approaches in that they are based on detailed spectroscopic information of the native specimen and do not rely on manual staining and sectioning, which are known to cause significant site-to-site variation in traditional histologic data. Hollon et al. demonstrated in 2020 that a convolutional neuronal network (CNN) that was trained on over 2.5 million labeled image patches from over 415 patients was able to classify an SRH image into one of the 13 most common disease classes encountered in neuropathology.

 

The decision tree and examples for each class are shown in the figure. The output was validated on 278 independent samples from three (3) institutions, which were not included in the  training set. Based on these promising results and the ever-growing training dataset, Invenio is collaborating with our customers to develop AI-based image classification products that will serve as aids to surgeons and pathologists. Current  AI implementations are for research use only. Not for use in diagnostic procedures.

 

Additional references:

 

Hollon, T.C., Pandian, B., Adapa, A.R., Urias, E., Save, A.V., Khalsa, S.S.S., Eichberg, D.G., D’Amico, R.S., Farooq, Z.U., Lewis, S. and Petridis, P.D., 2020. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature medicine, 26(1), pp.52-58.

https://www.nature.com/articles/s41591-019-0715-9

Hollon, T.C., Pandian, B., Urias, E., Save, A.V., Adapa, A.R., Srinivasan, S., Jairath, N.K., Farooq, Z., Marie, T., Al-Holou, W.N. and Eddy, K., 2021. Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks. Neuro-oncology, 23(1), pp.144-155.

https://doi.org/10.1093/neuonc/noaa162

DICOM Integration

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Example of a typical integration of the NIO using its DICOM interface

The NIO Laser Imaging System supports integration with cloud-based and on-premise IT infrastructure for viewing and archiving via the vendor-agnostic DICOM interface of the imaging operating software and supporting PC hardware. The figure shows an example of an integration of the NIO with 3rd party infrastructure. Images are exported to a picture archiving system (PACS) or vendor-neutral archive (VNA) and can be accessed via a DICOM or browser-based viewer.

 

Case information can also be retrieved by the NIO using a Modality Worklist (MWL) query generated from the patient record for a scheduled procedure. Implementation of an integrated system using the NIO needs to be validated independently by the customer prior to use in accordance with regional and federal laws and requirements and existing standard operating procedures.