Deep image prior for undersampling high-speed photoacoustic microscopy.

Abstract

Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser's repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (i.e., undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to improve sparsely sampled PAM images; however, these methods often require time-consuming pre-training and large training dataset with ground truth. Here, we propose the use of deep image prior (DIP) to improve the image quality of undersampled PAM images. Unlike other DL approaches, DIP requires neither pre-training nor fully-sampled ground truth, enabling its flexible and fast implementation on various imaging targets. Our results have demonstrated substantial improvement in PAM images with as few as 1.4 % of the fully sampled pixels on high-speed PAM. Our approach outperforms interpolation, is competitive with pre-trained supervised DL method, and is readily translated to other high-speed, undersampling imaging modalities.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1016/j.pacs.2021.100266

Publication Info

Vu, Tri, Anthony DiSpirito, Daiwei Li, Zixuan Wang, Xiaoyi Zhu, Maomao Chen, Laiming Jiang, Dong Zhang, et al. (2021). Deep image prior for undersampling high-speed photoacoustic microscopy. Photoacoustics, 22. p. 100266. 10.1016/j.pacs.2021.100266 Retrieved from https://hdl.handle.net/10161/30709.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Horstmeyer

Roarke Horstmeyer

Assistant Professor of Biomedical Engineering

Roarke Horstmeyer is an assistant professor within Duke's Biomedical Engineering Department. He develops microscopes, cameras and computer algorithms for a wide range of applications, from forming 3D reconstructions of organisms to detecting neural activity deep within tissue. His areas of interest include optics, signal processing, optimization and neuroscience. Most recently, Dr. Horstmeyer was a guest professor at the University of Erlangen in Germany and an Einstein postdoctoral fellow at Charitè Medical School in Berlin. Prior to his time in Germany, Dr. Horstmeyer earned a PhD from Caltech’s electrical engineering department in 2016, a master of science degree from the MIT Media Lab in 2011, and a bachelors degree in physics and Japanese from Duke University in 2006.

Yao

Junjie Yao

Associate Professor of Biomedical Engineering

Our mission at PI-Lab is to develop state-of-the-art photoacoustic tomography (PAT) technologies and translate PAT advances into diagnostic and therapeutic applications, especially in functional brain imaging and early cancer theranostics. PAT is the most sensitive modality for imaging rich optical absorption contrast over a wide range of spatial scales at high speed, and is one of the fastest growing biomedical imaging technologies. Using numerous endogenous and exogenous contrasts, PAT can provide high-resolution images at scales covering organelles, cells, tissues, organs, small-animal organisms, up to humans, and can reveal tissue’s anatomical, functional, metabolic, and even histologic properties, with molecular and neuronal specificity.

At PI-Lab, we develop PAT technologies with novel and advanced imaging performance, in terms of spatial resolutions, imaging speed, penetration depth, detection sensitivity, and functionality. We are interested with all aspects of PAT technology innovations, including efficient light illumination, high-sensitivity ultrasonic detection, super-resolution PAT, high-speed imaging acquisition, novel PA genetic contrast, and precise image reconstruction. On top of the technological advancements, we are devoted to serve the broad life science and medical communities with matching PAT systems for various research and clinical needs. With its unique contrast mechanism, high scalability, and inherent functional and molecular imaging capabilities, PAT is well suited for a variety of pre-clinical applications, especially for studying tumor angiogenesis, cancer hypoxia, and brain disorders; it is also a promising tool for clinical applications in procedures such as cancer screening, melanoma staging, and endoscopic examination.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.