Tethered capsule endomicroscopy (TCE) is a new method for performing comprehensive microstructural OCT imaging of gastrointestinal (GI) tract in unsedated patients in a well-tolerated and cost-effective manner. These features of TCE bestow it with significant potential to improve the screening, surveillance and management of various upper gastrointestinal diseases. To achieve clinical adoption of this imaging technique, it is important to validate it with co-registered histology, the current diagnostic gold standard. One such method for co-registering OCT images with histology is laser cautery marking, previously demonstrated using a balloon-centering OCT catheter that operates in conjunction with sedated endoscopy. With laser marking, an OCT area of interest is identified on the screen and this target is marked in the patient by exposing adjacent tissue to laser light that is absorbed by water, creating superficial, visible marks on the mucosal surface. Endoscopy can then be performed after the device is removed and biopsies taken from the marks. In this talk, we will present the design of a tethered capsule laser marking device that uses a distal stepper motor to perform high precision (< 0.5 mm accuracy) laser targeting and high quality OCT imaging. Ex vivo animal tissue tests and pilot human clinical studies using this technology will be presented.
While endoscopy is the most commonly used modality for diagnosing upper GI tract disease, this procedure usually requires patient sedation that increases cost and mandates its operation in specialized settings. In addition, endoscopy only visualizes tissue superfically at the macroscopic scale, which is problematic for many diseases that manifest below the surface at a microscopic scale. Our lab has previously developed technology termed tethered capsule OCT endomicroscopy (TCE) to overcome these diagnostic limitations of endoscopy. The TCE device is a swallowable capsule that contains optomechanical components that circumferentially scan the OCT beam inside the body as the pill traverses the organ via peristalsis. While we have successfully imaged ~100 patients with the TCE device, the optics of our current device have many elements and are complex, comprising a glass ferrule, optical fiber, glass spacer, GRIN lens and prism. As we scale up manufacturing of this device for clinical translation, we must decrease the cost and improve the manufacturability of the capsule’s optical configuration.
In this abstract, we report on the design and development of simplificed TCE optics that replace the GRIN lens-based configuration with an angle-polished ball lens design. The new optics include a single mode optical fiber, a glass spacer and an angle polished ball lens, that are all fusion spliced together. The ball lens capsule has resolutions that are comparable with those of our previous GRIN lens configuration (30µm (lateral) × 7 µm (axial)). Results in human subjects show that OCT-based TCE using the ball lens not only provides rapid, high quality microstructural images of upper GI tract, but also makes it possible to implement this technology inexpensively and on a larger scale.
Fourier transform infrared (FT-IR) spectroscopic imaging is a powerful tool to obtain chemical information from
images of heterogeneous, chemically diverse samples. Significant advances in instrumentation and data processing
in the recent past have led to improved instrument design and relatively widespread use of FT-IR imaging, in a
variety of systems ranging from biomedical tissue to polymer composites. Various techniques for improving signal
to noise ratio (SNR), data collection time and spatial resolution have been proposed previously. In this paper
we present an integrated framework that addresses all these factors comprehensively. We utilize the low-rank
nature of the data and model the instrument point spread function to denoise data, and then simultaneously
deblurr and estimate unknown information from images, using a Bayesian variational approach. We show that
more spatial detail and improved image quality can be obtained using the proposed framework. The proposed
technique is validated through experiments on a standard USAF target and on prostate tissue specimens.
Histologic diagnosis is the gold standard for evaluating the presence and severity of most cancers. Unfortunately, the
manual nature of histologic recognition leads to low throughput and errors. Here, we report on the evaluation of an
automated means to accurate histologic recognition using mid-infrared spectroscopic imaging. The method does not
need dyes or probes and dispenses with human input but relies on computational approaches to provide decisions.
Hence, the results must be rigorously validated. We present here a validation of two-class models for pixel-level
histologic segmentation and pathologic classification by spatial polling for breast carcinoma. We also discuss
optimization of spectral resolution and instrumentation for clinical translation.
Fourier transform infrared (FT-IR) spectroscopic imaging is an emerging technique that combines the molecular
selectivity of spectroscopy with the spatial specificity of optical microscopy. We demonstrate a new concept in obtaining
high fidelity data using commercial array detectors coupled to a microscope and Michelson interferometer. Next, we
apply the developed technique to rapidly provide automated histopathologic information for breast cancer. Traditionally,
disease diagnoses are based on optical examinations of stained tissue and involve a skilled recognition of morphological
patterns of specific cell types (histopathology). Consequently, histopathologic determinations are a time consuming,
subjective process with innate intra- and inter-operator variability. Utilizing endogenous molecular contrast inherent in
vibrational spectra, specially designed tissue microarrays and pattern recognition of specific biochemical features, we
report an integrated algorithm for automated classifications. The developed protocol is objective, statistically significant
and, being compatible with current tissue processing procedures, holds potential for routine clinical diagnoses. We first
demonstrate that the classification of tissue type (histology) can be accomplished in a manner that is robust and rigorous.
Since data quality and classifier performance are linked, we quantify the relationship through our analysis model. Last,
we demonstrate the application of the minimum noise fraction (MNF) transform to improve tissue segmentation.