Caltech Young Investigators Lecture
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The purpose of this work was to develop novel optical imaging technology and algorithms as a nondestructive method for detection and diagnosis of cancer in breast specimens. There are many ways in which the diagnosis of disease can benefit from fast and intelligent optical imaging technology. Our existing ability to provide this diagnosis depends on time-consuming pathology analysis. Optical coherence tomography (OCT) is a non-invasive optical imaging modality that provides depth-resolved, high-resolution images of tissue microstructure in real-time. OCT could provide a rapid evaluation of specimens while patients are still in the office, and has strong potential to improve the efficiency in evaluation of breast pathology specimens (biopsy or surgical).
In this work, we demonstrate an imaging system to address this unmet clinical need, artificial intelligence algorithms to interpret the images, and early work towards miniaturizing the technology.
We present an OCT system that achieves a line scan rate of 250kHz, meaning we can image a pathology cassette in 41 seconds, which is more than double the fastest scan rate in the field. By utilizing a multiplexed superluminescent diode (SLD) light source, which has strong noise performance over imaging speed, we achieve high resolution imaging under 5 μm in tissue (axially and laterally). The system features a 1.1 mm 6-dB sensitivity fall-off range when imaging at 250 kHz. The scanner features large-area scanning with the implementation of a 2-axis motorized stage, enabling visualization of areas up to 10 cm x 10 cm (prior work visualizes 3 mm x 3mm). We showcase the results of demonstrating the performance of this system on a 100-patient clinical imaging study of breast biopsies, as well as imaging of clinical pathology specimens from the breast, prostate, lung, and pancreas in an IRB-approved study.
Further, we show our work towards developing artificial intelligence (AI) for cancer detection within OCT images. Using retrospective data, we developed a type of AI algorithm known as a convolutional neural network (CNN) to classify OCT images of breast tissue from 49 patients. The binary cancer classification achieved 94% accuracy, 96% sensitivity, and 92% specificity. This framework had higher accuracy than the 88% accuracy of 7 clinician readers combined in our lab's earlier multi-reader study.
Lastly, we demonstrate a supercontinuum light source based on a 1 mm2 Si3N4 photonic chip for OCT imaging that has better performance than the state-of-the-art laser. Existing broadband laser sources for OCT are large, bulky, and have high excess noise. Our Si3N4 chip fundamentally eliminates the excess noise common to lasers and achieves 105 dB sensitivity and 1.81 mm 6-dB sensitivity roll-off with only 300 μW power on the sample.
Diana Mojahed is a Postdoctoral Associate in the laboratory of Professor Juejun (JJ) Hu in the Department of Materials Science & Engineering at MIT. Her multidisciplinary research is focused on developing chip-based optical instruments for medical imaging and sensing applications. Diana received her Ph.D. in Biomedical Engineering from Columbia University in 2021 under Professor Christine Hendon. In her Ph.D., she developed an optical imaging device to rapidly evaluate breast biopsies and pathology specimens to improve the quality of care for patients, and performed a pilot clinical study at Columbia University Irving Medical Center (CUIMC). Her work has been supported by the Columbia BiomedX Technology Accelerator Program, published in journals including Science Advances, and she has won awards including the Best Presentation at the Women in Science at Columbia Symposium, the SPIE Optics and Photonics Education Scholarship, and MKS Instruments Award.
This talk is part of the Caltech Young Investigators Lecture Series, sponsored by the Division of Engineering and Applied Science.