51 citations · 75 across the 7 of their papers we have counts for
7 papers
Deep Learning-Driven Quantitative Spectroscopic Photoacoustic Imaging for Segmentation and Oxygen Saturation Estimation
Ruibo Shang, Sidhartha Jandhyala, Yujia Wu +4
Spectroscopic photoacoustic (sPA) imaging can potentially estimate blood oxygenation saturation (sO2) in vivo noninvasively. However, quantitatively accurate results require accura…
Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning
Ruibo Shang, Geoffrey P. Luke, Matthew O'Donnell
Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is…
Multiplex ultrasound imaging of perfluorocarbon nanodroplets enabled by decomposition of post-vaporization dynamics
Austin Van Namen, Sidhartha Jandhyala, Catalina-Paula Spatarelu +3
Among the various molecular imaging modalities, ultrasound imaging benefits from its real-time, nonionizing, and cost-effective nature. Despite its benefits, there is a dearth of m…
Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximation
Ruibo Shang, Mikaela A. O'Brien, Geoffrey P. Luke
Single-pixel imaging (SPI) has the advantages of high-speed acquisition over a broad wavelength range and system compactness, which are difficult to achieve by conventional imaging…
Repeated Acoustic Vaporization of Perfluorohexane Nanodroplets for Contrast-Enhanced Ultrasound Imaging
Austin Van Namen, Sidhartha Jandhyala, Tomas Jordan +1
Superheated perfluorocarbon nanodroplets are emerging ultrasound imaging contrast agents boasting biocompatible components, unique phase-change dynamics, and therapeutic loading ca…
A Two-step-training Deep Learning Framework for Real-time Computational Imaging without Physics Priors
Ruibo Shang, Kevin Hoffer-Hawlik, Geoffrey P. Luke
Deep learning (DL) is a powerful tool in computational imaging for many applications. A common strategy is to reconstruct a preliminary image as the input of a neural network to ac…
O-Net: A Convolutional Neural Network for Quantitative Photoacoustic Image Segmentation and Oximetry
Geoffrey P. Luke, Kevin Hoffer-Hawlik, Austin C. Van Namen +1
Estimation of blood oxygenation with spectroscopic photoacoustic imaging is a promising tool for several biomedical applications. For this method to be quantitative, it relies on a…