ABSTRACT
Catheter/endoscope-based optical coherence tomography (OCT) is a powerful modality that visualizes structural information in luminal organs. Increases in OCT speed have reduced motion artifacts by enabling acquisition faster than or comparable to the time scales of physiological motion. However motion distortion remains a challenge because catheter/endoscope OCT imaging involves both circumferential and longitudinal scanning of tissue. This paper presents a novel image processing method to estimate and correct motion distortion in both the circumferential and longitudinal directions using a single en face image from a volumetric data set. The circumferential motion distortion is estimated and corrected using the en face image. Then longitudinal motion distortion is estimated and corrected using diversity of image features along the catheter pullback direction. Finally, the OCT volume is resampled and motion corrected. Results are presented on synthetic images and clinical OCT images of the human esophagus.
ABSTRACT
Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects -34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.
