You have a novel optical technology and have demonstrated feasibility in solving a real clinical need. Better yet, you have further matured the technology and shown early success within an academic clinical study. Now it’s time to take the leap towards commercialization to disseminate and bring your disruptive product to the masses. Successful commercialization of a Medical Device extends beyond good product development and in fact is underpinned by establishing the right Regulatory strategy. In this presentation, we will provide an overview for the rapid development of an Optical Coherence Tomography Imaging System and discuss regulatory strategy, challenges and best practices to help future entrepreneurs navigate this critical yet delicate pathway.
Optical coherence tomography (OCT) is an imaging technique optically analogous to ultrasound that can generate depth-resolved images with micrometer-scale resolution. Advances in fiber optics and miniaturized actuation technologies allow OCT imaging of the human body and further expand OCT utilization in applications including but not limited to cardiology and gastroenterology. This review article provides an overview of current OCT development and its clinical utility in the gastrointestinal tract, including disease detection/differentiation and endoscopic therapy guidance, as well as a discussion of its future applications.
KEYWORDS: Optical coherence tomography, Tissues, Image segmentation, Refractive index, Lung, Refraction, 3D modeling, In vivo imaging, Error analysis, 3D metrology
Optical coherence tomography (OCT) has been increasingly used for imaging pulmonary alveoli. Only a few studies, however, have quantified individual alveolar areas, and the validity of alveolar volumes represented within OCT images has not been shown. To validate quantitative measurements of alveoli from OCT images, we compared the cross-sectional area, perimeter, volume, and surface area of matched subpleural alveoli from microcomputed tomography (micro-CT) and OCT images of fixed air-filled swine samples. The relative change in size between different alveoli was extremely well correlated (r>0.9, P<0.0001), but OCT images underestimated absolute sizes compared to micro-CT by 27% (area), 7% (perimeter), 46% (volume), and 25% (surface area) on average. We hypothesized that the differences resulted from refraction at the tissue-air interfaces and developed a ray-tracing model that approximates the reconstructed alveolar size within OCT images. Using this model and OCT measurements of the refractive index for lung tissue (1.41 for fresh, 1.53 for fixed), we derived equations to obtain absolute size measurements of superellipse and circular alveoli with the use of predictive correction factors. These methods and results should enable the quantification of alveolar sizes from OCT images in vivo.
Three-dimensional (3-D) visualization of the fine structures within the lung parenchyma could advance our understanding of alveolar physiology and pathophysiology. Current knowledge has been primarily based on histology, but it is a destructive two-dimensional (2-D) technique that is limited by tissue processing artifacts. Micro-CT provides high-resolution three-dimensional (3-D) imaging within a limited sample size, but is not applicable to intact lungs from larger animals or humans. Optical reflectance techniques offer the promise to visualize alveolar regions of the large animal or human lung with sub-cellular resolution in three dimensions. Here, we present the capabilities of three optical reflectance techniques, namely optical frequency domain imaging, spectrally encoded confocal microscopy, and full field optical coherence microscopy, to visualize both gross architecture as well as cellular detail in fixed, phosphate buffered saline-immersed rat lung tissue. Images from all techniques were correlated to each other and then to corresponding histology. Spatial and temporal resolution, imaging depth, and suitability for in vivo probe development were compared to highlight the merits and limitations of each technology for studying respiratory physiology at the alveolar level.
Identifying the three-dimensional content of non-small cell lung cancer tumors is a vital step in the pursuit of
understanding cancer growth, development and response to treatment. The majority of non-small cell lung cancer
tumors are histologically heterogeneous, and consist of the malignant tumor cells, necrotic tumor cells, fibroblastic
stromal tissue, and inflammation. Geometric and tissue density heterogeneity are utilized in computed tomography (CT)
representations of lung tumors for distinguishing between malignant and benign nodules. However, the correlation
between radiolographical heterogeneity and corresponding histological content has been limited. In this study, a
multimodality dataset of human lung cancer is established, enabling the direct comparison between histologically
identified tissue content and micro-CT representation. Registration of these two datasets is achieved through the
incorporation of a large scale, serial microscopy dataset. This dataset serves as the basis for the rigid and non-rigid
registrations required to align the radiological and histological data. The resulting comprehensive, three-dimensional
dataset includes radio-density, color and cellular content of a given lung tumor. Using the registered datasets, neural
network classification is applied to determine a statistical separation between cancerous and non-cancerous tumor
regions in micro-CT.
Micro-CT, a technique for imaging small objects at high resolution using micro focused x-rays, is becoming widely available for small animal imaging. With the growing number of mouse models of pulmonary pathology, there is great interest in following disease progression and evaluating the alteration in longitudinal studies. Along with the high resolution associated with micro CT comes increased scanning times, and hence minimization of motion artifacts is required. We propose a new technique for imaging mouse lungs in vivo by inducing an intermittent iso-pressure breath hold (IIBH) with a fixed level of positive airway pressure during image acquisition, to decrease motion artifacts and increase image resolution and quality.
Mechanical ventilation of the respiratory system for such a setup consists of three phases, 1) tidal breathing (hyperventilated), 2) a breath hold during a fixed level of applied positive airway pressure, 3) periodic deep sighs. Image acquisition is triggered over the stable segment of the IIBH period.
Comparison of images acquired from the same mouse lung using three imaging techniques (normal breathing / no gating, normal breathing with gating at End Inspiration (EI) and finally the IIBH technique) demonstrated substantial improvements in resolution and quality when using the IIBH gating. Using IIBH triggering the total image acquisition time increased from 15 minutes to 35 minutes, although total x-ray exposure time and hence animal dosage remains the same. This technique is an important step in providing high quality lung imaging of the mouse in vivo, and will provide a good foundation for future longitudinal studies.
Mouse models are important for pulmonary research to gain insight into structure and function in normal and diseased states, thereby extending knowledge of human disease conditions. The flexibility of human disease induction into mice, due to their similar genome, along with their short gestation cycle makes mouse models highly suitable as investigative tools. Advancements in non-invasive imaging technology, with the development of micro-computed tomography (μ-CT), have aided representation of disease states in these small pulmonary system models. The generation ofμCT 3D airway reconstructions has to date provided a means to examine structural changes associated with disease. The degree of accuracy ofμCT is uncertain. Consequently, the reliability of quantitative measurements is questionable. We have developed a method of sectioning and imaging the whole mouse lung using the Large Image Microscope Array (LIMA) as the gold standard for comparison. Fixed normal mouse lungs were embedded in agarose and 250μm sections of tissue were removed while the remaining tissue block was imaged with a stereomicroscope. A complete dataset of the mouse lung was acquired in this fashion. Following planar image registration, the airways were manually segmented using an in-house built software program PASS. Amira was then used render the 3D isosurface from the segmentations. The resulting 3D model of the normal mouse airway tree developed from pathology images was then quantitatively assessed and used as the standard to compare the accuracy of structural measurements obtained from μ-CT.
Stereomicroscopy is an important method for use in image acquisition because it provides a 3D image of an object when other microscopic techniques can only provide the image in 2D. One challenge that is being faced with this type of imaging is determining the top surface of a sample that has otherwise indistinguishable surface and planar characteristics. We have developed a system that creates oblique illumination and in conjunction with image processing, the top surface can be viewed. The BFST consists of the Leica MZ12 stereomicroscope with a unique attached lighting source. The lighting source consists of eight light emitting diodes (LED's) that are separated by 45-degree angles. Each LED in this system illuminates with a 20-degree viewing angle once per cycle with a shadow over the rest of the sample. Subsequently, eight segmented images are taken per cycle. After the images are captured they are stacked through image addition to achieve the full field of view, and the surface is then easily identified. Image processing techniques, such as skeletonization can be used for further enhancement and measurement. With the use of BFST, advances can be made in detecting surface features from metals to tissue samples, such as in the analytical assessment of pulmonary emphysema using the technique of mean linear intercept.
Randomly selected pathology sections of lung tissue are used to correlate lung pathology with Computer Tomography (CT) images. The randomly selected pathology sections provide physicians with little freedom to thoroughly investigate specific areas of interest as identified via CT images. A Large Image Microscope Array (LIMA) was designed to serially section and image entire organs for direct correlation between lung pathology and CT. The LIMA consists of a novel vibratome, capable of sectioning tissue down to a thickness of 40mm at specimen dimensions of 20cm by 30cm to a total depth of 30cm. A camera and a stereomicroscope, mounted on a XYZ gantry above the vibratome is moved through an automated raster scan to capture the entire surface area of the tissue via many high magnification images. A custom software program was developed to automate all hardware components. The alignment and stitching of the images is achieved though custom C++ code in conjunction with the Insight Segmentation and Registration Toolkit (ITK). The resulting high magnification, high-resolution pathology images are registered with corresponding CT images. Through point-to-point correlation between the two imaging techniques a pathological and CT ground truth may be established.
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