We developed a compact, optical fiber scanning piezoelectric transducer (PZT) probe for endoscopic and minimally invasive optical coherence tomography (OCT). Compared with previous forward-mount fiber designs, we present a reverse-mount design that achieves a shorter rigid length. The fiber was mounted at the proximal end of a quadruple PZT tube and scanned inside the hollow PZT tube to reduce the probe length. The fiber resonant frequency was 338 Hz using a 17-mm-long fiber. A 0.9 mm fiber deflection was achieved with a driving amplitude of 35 V. Using a GRIN lens-based optical design with a 1.3× magnification, a ~6 µm spot was scanned over a 1.2 mm diameter field. The probe was encased in a metal hypodermic tube with a ~25 mm rigid length and covered with a 3.2 mm outer diameter (OD) plastic sheath. Imaging was performed with a swept source OCT system based on a Fourier domain modelocked laser (FDML) light source at a 240 kHz axial scan rate and 8 µm axial resolution (in air). En face OCT imaging of skin in vivo and human colon ex vivo was demonstrated.
Endoscopic optical coherence tomography (OCT) instruments are mostly side viewing and rely on at least one proximal scan, thus limiting accuracy of volumetric imaging and en face visualization. Previous forward-viewing OCT devices had limited axial scan speeds. We report a forward-viewing fiber scanning 3D-OCT probe with 900 µm field of view and 5 µm transverse resolution, imaging at 1 MHz axial scan rate in the human gastrointestinal tract. The probe is 3.3 mm diameter and 20 mm rigid length, thus enabling passage through the endoscopic channel. The scanner has 1.8 kHz resonant frequency, and each volumetric acquisition takes 0.17 s with 2 volumes/s display. 3D-OCT and angiography imaging of the colon was performed during surveillance colonoscopy.
Devices that perform wide field-of-view (FOV) precision optical scanning are important for endoscopic assessment and diagnosis of luminal organ disease such as in gastroenterology. Optical scanning for in vivo endoscopic imaging has traditionally relied on one or more proximal mechanical actuators, limiting scan accuracy and imaging speed. There is a need for rapid and precise two-dimensional (2D) microscanning technologies to enable the translation of benchtop scanning microscopies to in vivo endoscopic imaging. We demonstrate a new cycloid scanner in a tethered capsule for ultrahigh speed, side-viewing optical coherence tomography (OCT) endomicroscopy in vivo. The cycloid capsule incorporates two scanners: a piezoelectrically actuated resonant fiber scanner to perform a precision, small FOV, fast scan and a micromotor scanner to perform a wide FOV, slow scan. Together these scanners distally scan the beam circumferentially in a 2D cycloid pattern, generating an unwrapped 1 mm × 38 mm strip FOV. Sequential strip volumes can be acquired with proximal pullback to image centimeter-long regions. Using ultrahigh speed 1.3 μm wavelength swept-source OCT at a 1.17 MHz axial scan rate, we imaged the human rectum at 3 volumes/s. Each OCT strip volume had 166 × 2322 axial scans with 8.5 μm axial and 30 μm transverse resolution. We further demonstrate OCT angiography at 0.5 volumes/s, producing volumetric images of vasculature. In addition to OCT applications, cycloid scanning promises to enable precision 2D optical scanning for other imaging modalities, including fluorescence confocal and nonlinear microscopy.
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM. In order to improve classification accuracy, we propose novel variants of LBP features, namely average LBP (ALBP) and block based LBP (BLBP). Compared with the classic LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture structure in a local neighborhood by looking at intensity differences among neighboring pixels and among certain blocks of pixels in the neighborhood. Fourty-six freshly excised human breast tissue samples, including 27 benign (e.g. fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19 breast carcinoma (e.g. invasive ductal carcinoma, ductal carcinoma in situ and lobular carcinoma in situ) were imaged with large field OCM with an imaging area of 10×10mm2 (10, 000 × 10, 000 pixels) for each sample. Corresponding H&E histology was obtained for each sample and used to provide ground truth diagnosis. 4310 small OCM image blocks (500 × 500 pixels) each paired with corresponding H&E histology was extracted from large-field OCM images and labeled with one of the five different classes: adipose tissue (n = 347), fibrous stroma (n = 2,065), breast lobules (n = 199), carcinomas (pooled from all sub-types, n = 1,127), and background (regions outside of the specimens, n = 572). Our experiments show that by integrating a selected set of LBP and the two new variant (ALBP and BLBP) features at multiple scales, the classification accuracy increased from 81.7% (using LBP features alone) to 93.8% using a neural network classifier. The integrated feature was also used to classify large-field OCM images for tumor detection. A receiver operating characteristic (ROC) curve was obtained with an area under the curve value of 0.959. A sensitivity level of 100% and specificity level of 85.2% was achieved to differentiate benign from malignant samples. Several other experiments also demonstrate the complementary nature of LBP and the two variants (ALBP and BLBP features) and the significance of integrating these texture features for classification. Using features from multiple scales and performing feature selection are also effective mechanisms to improve accuracy while maintaining computational efficiency.
We have simulated photon migration with various sourcedetector separations based on a three-dimensional Monte Carlo code. Whole brain MRI structure images are introduced in the simulation, and the brain model is more accurate than in previous studies. The brain model consists of the scalp, skull, CSF layer, gray matter, and white matter. We demonstrate dynamic propagating movies under different source-detector separations. The multiple backscattered intensity from every layer of the brain model is obtained by marking the deepest layer that every photon can reach. Also, the influences of an absorption target on the brain cortex are revealed.
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