Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2310.00529

Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers

Refik M. Cam·Chao Wang·Weylan Thompson·Sergey A. Ermilov·Mark A. Anastasio·Umberto Villa
View original source ↗Read source document ↗Export metadata ↓

Abstract

Significance: Dynamic photoacoustic computed tomography (PACT) is a valuable technique for monitoring physiological processes. However, current dynamic PACT techniques are often limited to 2D spatial imaging. While volumetric PACT imagers are commercially available, these systems typically employ a rotating gantry in which the tomographic data are sequentially acquired. Because the object varies during the data-acquisition process, the sequential data-acquisition poses challenges to image reconstruction associated with data incompleteness. The proposed method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing imagers. Aim: The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. Approach: A low-rank matrix estimation-based STIR method (LRME-STIR) is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies to accurately reconstruct a 4D spatiotemporal image. Results: The numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from measurements acquired with a rotating gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent at a frame rate of 0.1 s even when only a single tomographic measurement per frame is available. Conclusions: The LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric imagers. By enabling accurate 4D reconstruction, this method has the potential to advance preclinical research.

Explore related subjects

eess.SPeess.IVphysics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Refik M. Cam, Chao Wang, Weylan Thompson, Sergey A. Ermilov, Mark A. Anastasio, Umberto Villa. 2023-10-01. Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers. https://arxiv.org/abs/2310.00529

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Circulant ADMM-Net for Fast High-resolution DoA Estimation

This paper introduces CADMM-Net and CHADMM-Net, two deep neural networks for direction of arrival estimation within the least-absolute shrinkage and selection operator (LASSO) framework. These two networks are based on a structured deep unfolding of the alternating direction method of multipliers (ADMM) algorithm through the use of circulant as well as Hermitian-circulant matrices. Along with a computational complexity of $\mathcal{O}(N\log(N))$ per layer for the inference, where $N$ is the length of the dictionary $\mathbf{A}$, they additionally exhibit a memory footprint of $N$ and approximately half of $N$ for CADMMNet and CHADMM-Net, respectively, compared with $N^{2}$ for ADMM-Net. Furthermore, these structured networks exhibit a competitive performance against ADMM-Net, LISTA, TLISTA, and THLISTA with respect to the detection rate, the angular root-mean square error, and the normalized mean squared error.

eess.SP↗
arXiv2026

BASIIS: Bistatic Angular Sampling and Interpolation for ISAC Setups

Integrated Sensing and Communications (ISAC) is a defining feature of 6G, extending cellular networks with radar-like sensing at limited additional overhead. In bistatic deployments, sensing requires coordinating the transmitter (TX) and receiver (RX) arrays to scan the Cartesian product of angle of departure and arrival, resulting in a four-dimensional sampling problem in the angular domain. This work establishes a complete angular sampling framework for bistatic ISAC, extending the DFT-based optimal-sampling methodology to the full azimuth and elevation domains of both arrays. We show that the bistatic geometry couples the TX and RX elevation angles, and represent this coupling through the ortho-baseline coarray, a virtual array that captures the joint elevation aperture of the array pair. From the coarray we derive a minimal sampling and interpolation scheme, near-lossless and realizable with any beamforming architecture. Monte Carlo simulations confirm the proposed minimal acquisition essentially equalizes the detection accuracy of dense oversampled imaging while acquiring 3 to 5 times fewer TX-RX direction pairs. This allows having bistatic operations with drastically reduced overhead on the radio resource usage of ISAC systems.

eess.SP↗
arXiv2026

Centroid Angle Estimation of Multiple Scatterers Using Monopulse Radar with Frequency Diversity

The monopulse technique determines the angle of a target by comparing signals from two narrow beams, yielding a precise angular estimate with low complexity. However, it struggles to resolve multiple closely spaced scatterers within the same resolution cell. Existing methods for estimating multiple scatterer angles involve complex signal processing and system modifications. We propose an effective method to estimate the angular centroid of scatterers using the mode of monopulse angle estimates. A semi-analytic expression for the angle estimate distribution is derived, confirming that its mode aligns with the centroid. To enhance estimation accuracy, we employ frequency diversity to reduce sample correlation. Numerical results validate the advantages of the proposed method, demonstrating superior performance over conventional techniques with low complexity.

eess.SP↗