In breast cancer assessment, tissue removed during biopsy or surgery is sent to a pathology lab for analysis. To achieve high sensitivity for detecting disease, the diagnostic gold standard requires submission of a substantial portion of the resected specimen, which results in a labor and time-intensive process to obtain a diagnosis. There is an unmet need to identify regions of diagnostic interest in breast tissue samples to increase the efficiency of the clinical pathology workflow.
Optical coherence tomography (OCT), a noninvasive imaging modality capable of depth-resolved, high-resolution, and in vivo imaging of tissue at large fields of view, enables effective assessment of this tissue. However, there is a two-fold problem: the large size of resected tissue to be imaged within clinical time constraints, and the high density of multi-dimensional OCT image data. An approach that enables comprehensive imaging by reducing both imaging time and data density is compressed sensing (CS), a theory that enables undersampling far below the Nyquist sampling rate and guarantees high accuracy image recovery. Therefore, the objective of this work is to demonstrate that compressed sensing techniques can be applied to OCT imaging to revise current optical hardware and improve the efficiency of image acquisition. CS-OCT has high potential for significantly altering the presently established workflow for breast cancer assessment.
In this work, we prove that current OCT systems require further reduction of data sampling rate, to enable effective integration of the systems into the clinical pathology workflow. In addition, we identify challenges associated with the matching of OCT and histologic data that may be important to consider in the context of in vivo imaging.
Further, we demonstrate the application of a novel and improved compressed sensing algorithm capable of reconstructing OCT volumes from highly undersampled imaging data. We show that these reconstructions preserve high spatial resolution and key image features, and we illustrate its improved performance over traditional reconstruction methods.
Lastly, we integrate our compressed sensing techniques to physical OCT hardware. We demonstrate a pilot OCT system that integrates efficient undersampling schemes with subsequently successful 3-D image reconstructions. We evaluate acquisition patterns that take advantage of the typical forward and backward scan cycle of OCT systems to accomplish native subsampling of target data to varying degrees of compression. Using our CS-OCT algorithm, we successfully reconstruct OCT image volumes and demonstrate qualitative and quantitative preservation of image quality down to compression levels of 5% of total data.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/pwyx-5n82 |
| Date | January 2024 |
| Creators | Song Cho, Diego Miong Su |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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