This Conference Presentation, 2022 winner of Britton Chance Biomedical Optics Award, was recorded at SPIE Photonics West held in San Francisco, California, United States.

The primary standard of care for Head and Neck (H&N) cancer patients is the complete surgical removal of cancer. Tissue classifiers based of autofluorescence lifetime imaging (FLIm) parameters have shown potential to differentiate healthy from cancer tissue in H&N patients and thus enhance the accuracy of this procedure. Here we report how collective autofluorescence trends (100-patient cohort, oral/oropharyngeal cancer) driving healthy vs. tumor contrast depend on anatomical location, patient medical history (e.g. tobacco use) and surgical context (in vivo vs. ex vivo). Accounting for such biological variables may further improve the accuracy of FLIm-guided H&N cancer surgery.

The standard treatment for infiltrative gliomas is surgery to remove as much tumor tissue as possible without compromising neurological functions. Thus, real-time identification of infiltrative tumor tissue is necessary. Here a fluorescence lifetime imaging (FLIm) was used to distinguish between healthy brain and areas with different degrees of tumor cellularity as defined by histopathology. We conducted FLIm measurements and collected microbiopsies from tumor resection margins to identify the FLIm characteristics of tumor edges in cortex and white matter of low- and high-grade gliomas. Results from a 13-patient cohort indicate that FLIm identifies infiltrations of up to moderate tumor cellularity.

Fluorescence guided surgery has high potential for improved patient outcomes but often lacks quantification of fluorophore depth which is needed to determine surgical margins of solid tumors. To address this need, a dual wavelength excitation approach was applied that capitalizes on the wavelength-dependent attenuation of light in tissue to provide depth information independent of fluorophore concentration. A portable near infrared dual wavelength excitation fluorescence imaging system was built and tested using tissue mimicking phantoms and is currently being tested to determine breast tumor margin status in a first-in-human clinical trial investigating LS301, a novel near infrared tumor-targeted contrast agent.

Surgeons find it challenging to preserve healthy parathyroid glands (PGs) and confirm removal of diseased PGs during parathyroidectomies, often leading to postoperative complications and costly frozen section analysis (FSA). PTeye™ - a fiber-optic probe-based system that detects near infrared autofluorescence (NIRAF) was FDA-cleared to serve as an intraoperative adjunct device to aid in intraoperative PG identification. We sought to assess the impact of NIRAF detection during parathyroidectomies for the first time through a single-center randomized clinical trial. Our current findings indicate that PTeye™ could improve a surgeon’s confidence in PG identification, while potentially reducing FSAs needed during parathyroidectomies.

5-ALA-based fluorescence-guided surgery (FGS) is a state-of-the-art treatment for brain tumors, but may miss areas due to its low sensitivity. To improve the sensitivity we developed dual PpIX and Moxifloxacin Fluorescence Confocal Imaging method that allows simultaneous imaging of 5-ALA and moxifloxacin by excitation with a single light source. This method verified by comparing confocal images in glioblastoma specimen. The distribution of cells expressing 5-ALA and moxifloxacin fluorescence signals showed the same correlation. In large-area tumor sample images, PplX showed differences in signal intensity, but moxifloxacin was constant. These results show the potential to improve sensitivity than with 5-ALA alone.

Inadvertent nerve injury is a common complication during many surgical procedures. Providing surgeons with a means to visualize nerves within a surgical field could reduce the risk of nerve injury and improve patient outcomes. Several wavelengths leveraging the intrinsic optical properties of nerves have been identified to enhance nerve contrast in an animal model. Here, we demonstrate the clinical feasibility of this technique in patients undergoing thyroidectomies. Spectral imaging successfully discriminated nerves from surrounding tissues within the surgical field. The results of this study demonstrate the clinical viability of spectral imaging for nerve visualization.

Fringe projection profilometry (FPP) is being developed as a 3D vision system to assist robotic surgery and autonomous suturing. Conventionally, fluorescence markers are placed on a target tissue to indicate suturing landmarks, which not only increase the system complexity, but also impose safety concerns. To address these problems, we propose a numerical landmark detection algorithm based on deep learning. A landmark heatmap is regressed using an adopted U-Net from the four channel data generated by the FPP. A Markov random field leveraging the structure prior is developed to search the correct set of landmarks from the heatmap. The accuracy of the proposed method is verified through ex-vivo porcine intestine landmark detection experiments.

Cancers of the upper gastrointestinal (GI) tract remain a major contributor to the global cancer risk. This study uses a diffuse reflectance spectroscopic probe for tissue characterisation. Optical tracking of the DRS probe is used to aid histology correlation. Supervised classification algorithms are used for discrimination between tumour and non-tumour tissue and evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve. A live augmented view with all the tracked and classified biopsy sites is presented, providing visual feedback to the surgeons. Thus, the system provides real-time tissue discrimination, whilst clinical outcomes for patients are optimised.

Diffuse reflectance spectroscopy (DRS) has already been successfully used for tissue discrimination during colorectal cancer surgery. In clinical practice, however, tissue often consists of several layers. Therefore, a novel multi-output convolutional neural network (CNN) was designed to classify multiple layers of colorectal cancer tissue simultaneously. DRS data was acquired with an array of six fibers with different fiber distances to sample at multiple depths. After training a 2D CNN with the DRS data as input, the first, second, and third tissue layers could be classified with mean accuracies of 0.90, 0.71, and 0.62, respectively.

Iatrogenic nerve injuries often occur during surgical procedures yielding high morbidity. Numerous optical techniques are being investigated to provide nerve visualization and monitoring to both avoid and detect nerve injury. However, there is insufficient VIS-IR optical properties data for nerve and surrounding tissues, to facilitate needed advancements in the biophotonic approaches being developed for deployment. To fill this gap, we determined the absorption and reduced scattering coefficients of ex vivo nerve, muscle, tendon, and adipose tissues from four rats and human cadavers between 350-2500nm. The results provide valuable insights for optimizing the optical contrast of nerves for improved surgical guidance.

Since the recognition that entry factors of SARS-CoV-2 are highly expressed in the nasal epithelium, a custom personal protection booth was developed to enable intranasal µOCT studies to be safely performed on Covid-positive subjects. The booth was designed to preserve familiarity of the prior established intranasal imaging techniques used by clinicians. A group of Covid-positive subjects (n=13) were imaged, shortly after onset of symptoms (7.8 ± 3.7 days). Historical data from healthy controls (n=10) were included in the study. While the blinded analysis is underway, an interim analysis of the µOCT data revealed several notable abnormalities in the nasal epithelium including delayed mucociliary transport, epithelial injury, and high inflammatory cell count in a pilot cohort. We anticipate that the pathophysiologies captured by intranasal µOCT will provide invaluable insights to the mechanisms of Covid-19 related mucociliary dysfunction.

Optical coherence tomography (OCT) shows potential as an intraoperative guidance tool. However, OCT images are difficult to interpret and real-time analysis methods are needed to promote its clinical use. This study investigates deep learning-based OCT image classification with application on thyroid diseases. To evaluate the impact of data pre-processing and model architecture on classification performance, 2D and 3D deep learning models were implemented and trained on OCT data from ex-vivo thyroid samples. For 2D classification, deeper models and the ones using information from different spatial resolution achieved highest performance. However, 3D models outperform the 2D counterparts in most classification tasks.

Optical coherence tomography (OCT) is being studied to provide rapid biopsy evaluation. Here we developed a deep learning algorithm to rapidly identify disease in OCT images in an 87-patient IRB-approved clinical study. Pathologists labelled each biopsy into two categories: non-interest (no disease) and interest (for further pathological analysis). Our dataset was split by patients into training (n = 70) and validation (n = 17). The Resnet18 architecture used the Adam optimizer, had a learning rate of 0.01, batch size of 8, and ran for 30 epochs. The network achieved 97% training accuracy and 70% validation accuracy.

When the epidural needle is punctured into human body during epidural anesthesia surgery, the location of the needle tip is of great importance. In our study, we developed an OCT endoscopic system to help locate the needle tip in real time. Backbones from pigs were utilized to test our system. According to the tissue types that epidural needle punctures through, we imaged five different tissues (fat, ligament, flavum, epidural space and spinal cord). Furthermore, deep-learning methods were used to automatically distinguish the tissue types and predict the distance between the needle tip and the spinal cord. We achieved an average prediction accuracy of 96.65% in tissue classification, and an absolute percentage error at 3.05%±0.55% in distance measurement.

We demonstrate 3-photon fluorescence micro-endoscopy using a negative curvature hollow core fiber and a 2.2 mm miniature scanning head. The fiber design allows distortion-less, delivery of <100 fs pulses without dispersion pre-compensation requirements. The fiber also features a double cladding allowing the back-collection of nonlinear signals through the same fiber. Sub-micron spatial resolution together with large field of view is made possible by the combination of a miniature distal objective lens with a functionnalization of the fiber output with a GRIN fiber spliced to the output facet. 3-photon fluoresence imaging is demonstrated on various biological samples.

Deep venous thrombosis is a global health problem with significant complications and high recurrence. Supporting the search for more effective therapies, polarization-sensitive optical coherence tomography (PS-OCT) may offer diagnostic imaging to determine the age of thrombus and personalize treatment. To assess sensitivity to thrombus aging, the IVC of two rat cohorts were imaged in-vivo at 24 hours (acute) or 28 days (chronic) after thrombus creation. The PS-OCT metrics were capable of differentiating acute and chronic thrombi with 98.2% total accuracy. These results demonstrate that PS-OCT is sensitive to structural changes in thrombi and could help guide advanced thrombolytic therapies.

Current cervical screening techniques are fairly effective at assessing the ectocervical surface, but they are limited in their capacity to assess the endocervical canal. There is a need for tools that examine the endocervical canal for cancerous or pre-cancerous lesions. This pilot study explores whether an endoscopic imaging approach combining structural and functional imaging techniques (optical coherence tomography (OCT) and autofluorescence imaging (AFI) respectively) can visualize cancerous or pre-cancerous changes in the endocervical canal. We present findings from an on-going in vivo imaging study including sample cases demonstrating precancers and cancers and preliminary features of interest.