Signal Processing--Optics Co-Design for In Vivo Optical Biopsy

Waheed Bajwa ECE and Mark Pierce BME received a grant from the NSF Engineering Directorate (ECCS Division/CCSS Program $359,986 for 3 years) for a multidisciplinary project, entitled "Signal Processing--Optics Co-Design for In Vivo Optical Biopsy."


For cancer and many chronic conditions, detecting early stage disease is the most critical factor in successfully curing patients and improving long-term survival rates. Current clinical practice involves taking biopsy samples from suspicious sites, followed by tissue processing and microscopic examination for abnormalities. This is a low yield, expensive, painful, and slow process. This project develops a new approach for microscopic imaging of living cells and tissues within the body in real time, which will improve the ability of physicians to detect early stage disease. The developed approach will also greatly increase the number of diagnostically useful biopsies being collected, improve the accuracy of margin identification during surgical resection, and permit non-invasive monitoring of post-surgical sites for recurrence. Lowering costs associated with unnecessary biopsies, multiple clinic visits, and repeat surgeries due to undetected residual disease will also positively impact the economics of healthcare delivery in the US.

The technical focus of this project is on the design of a fiber-optic probe that will enable examination of tissue for signs of disease in real-time, non-invasively, at the level of traditional pathology. This involves breaking conventional resolution limitations in fiber-optic imaging to deliver a real-time "optical biopsy." This is accomplished through integration of mathematical concepts from the compressed sensing field with hardware design and engineering for the clinical setting. The main goals of this project in this regard are (1) to design and engineer two candidate hardware architectures for optical biopsy, (2) to design, analyze and optimize the computational algorithms required to generate high-resolution images from these specific architectures, (3) to design and train automated feature recognition algorithms to assist the physician in interpreting optical biopsy images in real-time, and (4) to complete benchmark validation of the system using calibrated test targets and biological phantoms.

The intellectual merit of this project stems from the tight integration between fiber-optic-based endomicroscopy and signal processing theory and algorithms. This project does not simply apply image analysis algorithms to previously-acquired data in post-processing. Instead, the optical hardware components and signal processing elements are co-designed to enable imaging at 2-4 times higher spatial resolution than possible from developing the hardware or software alone.

This project will thus advance the medical imaging field by developing new signal processing techniques to increase resolution and field-of-view that do not require additional breakthroughs in microfabrication methods. This project will also advance the signal processing field by introducing new algorithms to solve missing data problems, address ill-posed inverse problems, and implement compressive sensing theory, all at previously unexplored length scale. However, the most transformative aspect of this work is that it has the potential to change clinical practices from reliance on 100 year old pathology practices to real-time information on tissue status at the patient's bedside.