Significance: Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.
Aim: We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).
Approach: First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography.
Results: The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships.
Conclusions: The heavily validated capability of DL’s use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient’s bedside.
Over the past three decades, non-invasive molecular imaging via optical tomography has garnered attention in the field of preclinical imaging thanks to its high sensitivity and ability to image multiple biomarkers simultaneously. However, it is still very challenging to image intact tissues with high resolution while retaining the two aforementioned characteristics.
Over the last few years, our group has pioneered Mesoscopic Fluorescence Molecular Tomography (MFMT), a novel imaging modality that recapitulates the 3D distribution of fluorescent markers within thick and diffuse samples (< 3 mm) with spatial resolution ~100 µm. Still, as a diffuse optical inverse problem, the image formation can be challenging due to its ill-conditioned nature. Herein, we report on the fusion of MFMT with Optical Coherence Tomography (OCT) to provide both structural and molecular imaging capabilities. Moreover, we leverage the OCT information to impart structural priors that facilitate the optical inverse problem in MFMT. We demonstrate the capability and utility of this novel platform on bioprinted tissues, fluorescent polymer letters in agar phantoms, and on microfabricated beads at different imaging depths.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.