Digital twins enable full-reference quality assessment of photoacoustic image reconstructions
- URL: http://arxiv.org/abs/2505.24514v1
- Date: Fri, 30 May 2025 12:25:36 GMT
- Title: Digital twins enable full-reference quality assessment of photoacoustic image reconstructions
- Authors: Janek Gröhl, Leonid Kunyansky, Jenni Poimala, Thomas R. Else, Francesca Di Cecio, Sarah E. Bohndiek, Ben T. Cox, Andreas Hauptmann,
- Abstract summary: No-reference image quality measures are often inadequate, but full-reference measures require access to an ideal reference image.<n>We tackle this problem by using numerical digital twins of tissue-mimicking phantoms and the imaging system to perform a quantitative calibration.
- Score: 1.5926880411246285
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantitative comparison of the quality of photoacoustic image reconstruction algorithms remains a major challenge. No-reference image quality measures are often inadequate, but full-reference measures require access to an ideal reference image. While the ground truth is known in simulations, it is unknown in vivo, or in phantom studies, as the reference depends on both the phantom properties and the imaging system. We tackle this problem by using numerical digital twins of tissue-mimicking phantoms and the imaging system to perform a quantitative calibration to reduce the simulation gap. The contributions of this paper are two-fold: First, we use this digital-twin framework to compare multiple state-of-the-art reconstruction algorithms. Second, among these is a Fourier transform-based reconstruction algorithm for circular detection geometries, which we test on experimental data for the first time. Our results demonstrate the usefulness of digital phantom twins by enabling assessment of the accuracy of the numerical forward model and enabling comparison of image reconstruction schemes with full-reference image quality assessment. We show that the Fourier transform-based algorithm yields results comparable to those of iterative time reversal, but at a lower computational cost. All data and code are publicly available on Zenodo: https://doi.org/10.5281/zenodo.15388429.
Related papers
- Quantum annealing-based computed tomography using variational approach
for a real-number image reconstruction [0.0]
The study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction.
Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.
arXiv Detail & Related papers (2023-06-03T23:35:10Z) - Ensembling with Deep Generative Views [72.70801582346344]
generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose.
Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification.
We use StyleGAN2 as the source of generative augmentations and investigate this setup on classification tasks involving facial attributes, cat faces, and cars.
arXiv Detail & Related papers (2021-04-29T17:58:35Z) - Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm.
Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
arXiv Detail & Related papers (2021-03-01T19:19:38Z) - Shared Prior Learning of Energy-Based Models for Image Reconstruction [69.72364451042922]
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data.
In the absence of ground truth data, we change the loss functional to a patch-based Wasserstein functional.
In shared prior learning, both aforementioned optimal control problems are optimized simultaneously with shared learned parameters of the regularizer.
arXiv Detail & Related papers (2020-11-12T17:56:05Z) - Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
Blind image restoration is a common yet challenging problem in computer vision.
We propose a novel blind image restoration method, aiming to integrate both the advantages of them.
Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
arXiv Detail & Related papers (2020-08-25T03:30:53Z) - Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid
Learning [48.890709236564945]
A small ISO and a small exposure time are usually used to capture an image in the back or low light conditions.
In this paper, a single image brightening algorithm is introduced to brighten such an image.
The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times.
arXiv Detail & Related papers (2020-07-04T08:23:07Z) - Training Variational Networks with Multi-Domain Simulations:
Speed-of-Sound Image Reconstruction [5.47832435255656]
Variational Networks (VN) have been shown to be a potential learning-based approach for optimizing inverse problems in image reconstruction.
We present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using waves with conventional transducers and single-sided tissue access.
We show that the proposed regularization techniques combined with multi-source domain training yield substantial improvements in the domain adaptation capabilities of VN.
arXiv Detail & Related papers (2020-06-25T13:32:08Z) - Non-iterative Simultaneous Rigid Registration Method for Serial Sections
of Biological Tissue [11.471087682509005]
We propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images.
Our algorithm method is non-iterative, it can simultaneously compute rigid transformation for a large number of serial section images.
arXiv Detail & Related papers (2020-05-11T03:44:10Z) - u-net CNN based fourier ptychography [5.46367622374939]
We propose a new retrieval algorithm that is based on convolutional neural networks.
Experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
arXiv Detail & Related papers (2020-03-16T22:48:44Z) - Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative
Prior [8.712404218757733]
The problem arises in various imaging modalities such as Fourier ptychography, X-ray crystallography, and in visible light communication.
We propose to solve this inverse problem using alternating gradient descent algorithm under two pretrained deep generative networks as priors.
The proposed recovery algorithm strives to find a sharp image and a blur kernel in the range of the respective pre-generators that textitbest explain the forward measurement model.
arXiv Detail & Related papers (2020-02-28T07:36:28Z) - Self-Supervised Linear Motion Deblurring [112.75317069916579]
Deep convolutional neural networks are state-of-the-art for image deblurring.
We present a differentiable reblur model for self-supervised motion deblurring.
Our experiments demonstrate that self-supervised single image deblurring is really feasible.
arXiv Detail & Related papers (2020-02-10T20:15:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.