13 citations · 35 across the 15 of their papers we have counts for
17 papers
Latent Score-Based Bayesian Cramér-Rao Bound Estimation for High-Dimensional Imaging Systems
Evan Scope Crafts, Thomas Wynn, Seonyeong Park +2
We propose a data-driven framework for estimating the Bayesian Cramér-Rao bound (CRB) in high-dimensional imaging systems with complex, analytically intractable priors. Direct CRB…
A Learning-based Framework for Spatial Impulse Response Compensation in 3D Photoacoustic Computed Tomography
Kaiyi Yang, Seonyeong Park, Gangwon Jeong +4
Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing ultrasound transducer…
Benchmarking Deep Learning-Based Reconstruction Methods for Photoacoustic Computed Tomography with Clinically Relevant Synthetic Datasets
Panpan Chen, Seonyeong Park, Gangwon Jeong +3
Deep learning (DL)-based image reconstruction methods for photoacoustic computed tomography (PACT) have developed rapidly in recent years. However, most existing methods have not e…
Stochastic numerical head phantoms to enable virtual imaging studies of transcranial photoacoustic computed tomography
Hsuan-Kai Huang, Joseph Kuo, Seonyeong Park +3
Transcranial photoacoustic computed tomography (PACT) is an emerging neuroimaging modality, but skull-induced aberrations can result in severe image artifacts if not compensated fo…
Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography
Refik Mert Cam, Seonyeong Park, Umberto Villa +1
Quantitative photoacoustic computed tomography (qPACT) is a promising imaging modality for estimating physiological parameters such as blood oxygen saturation. However, developing…
LensPlus: A High Space-bandwidth Optical Imaging Technique
Neha Goswami, Mark A. Anastasio
The space-bandwidth product (SBP) imposes a fundamental limitation in achieving high-resolution and large field-of-view image acquisitions simultaneously. High-NA objectives provid…
Learned Correction Methods for Ultrasound Computed Tomography Imaging Using Simplified Physics Models
Luke Lozenski, Hanchen Wang, Fu Li +4
Ultrasound computed tomography (USCT) is an emerging modality for breast imaging. Image reconstruction methods that incorporate accurate wave physics produce high resolution quanti…
Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers
Kaiyan Li, Prabhat Kc, Hua Li +3
Medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging mea…
Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography with Hemispherical Measurement Geometries
Panpan Chen, Seonyeong Park, Refik Mert Cam +4
In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that encl…
Optimizing Quantitative Photoacoustic Imaging Systems: The Bayesian Cramér-Rao Bound Approach
Evan Scope Crafts, Mark A. Anastasio, Umberto Villa
Quantitative photoacoustic computed tomography (qPACT) is an emerging medical imaging modality that carries the promise of high-contrast, fine-resolution imaging of clinically rele…
Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging
Youzuo Lin, Shihang Feng, James Theiler +7
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include…
Ortho-positronium Lifetime For Soft-tissue Classification
Ashish V. Avachat, Kholod H. Mahmoud, Anthony G. Leja +4
The objective of this work is to showcase the ortho-positronium lifetime as a probe for soft-tissue characterization. We employed positron annihilation lifetime spectroscopy to exp…
Identifying Functional Brain Networks of Spatiotemporal Wide-Field Calcium Imaging Data via a Long Short-Term Memory Autoencoder
Xiaohui Zhang, Eric C Landsness, Lindsey M Brier +6
Wide-field calcium imaging (WFCI) that records neural calcium dynamics allows for identification of functional brain networks (FBNs) in mice that express genetically encoded calciu…
Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data
Xiaohui Zhang, Eric C. Landsness, Hanyang Miao +6
Background: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the stu…
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics
Xiaohui Zhang, Mimi Tan, Mansour Nabil +5
Significance: Endoscopic screening for esophageal cancer may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving sp…
The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts
A Badano, M Lago, E Sizikova +4
Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trial…
A signal detection model for quantifying over-regularization in non-linear image reconstruction
Emil Y. Sidky, John Paul Phillips, Weimin Zhou +5
Purpose: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for non-linear image reconstruction.…