Photoacoustic imaging reconstruction using combined nonlocal patch and total-variation regularization for straight-line scanning

Multiple diffusion models-enhanced extremely limited-view reconstruction strategy for photoacoustic tomography boosted by multi-scale priors

Photoacoustic tomography (PAT) is an innovative biomedical imaging technology, which has the capacity to obtain high-resolution images of biological tissue. In the extremely limited-view cases, traditional reconstruction methods for photoacoustic tomography frequently result in severe artifacts and distortion. Therefore, multiple diffusion models-enhanced reconstruction strategy for PAT is proposed in this study. Boosted by the multi-scale priors of the sinograms obtained in the full view and the limited-view case of 240°, the alternating iteration method is adopted to generate data for missing views in the sinogram domain. The strategy refines the image information from global to local, which improves the stability of the reconstruction process and promotes high-quality PAT reconstruction. The blood vessel simulation dataset and the in vivo experimental dataset were utilized to assess the performance of the proposed method. When applied to the in vivo experimental dataset in the limited-view case of 60°, the proposed method demonstrates a significant enhancement in peak signal-to-noise ratio and structural similarity by 23.08 % and 7.14 %, respectively, concurrently reducing mean squared error by 108.91 % compared to the traditional method. The results indicate that the proposed approach achieves superior reconstruction quality in extremely limited-view cases, when compared to other methods. This innovative approach offers a promising pathway for extremely limited-view PAT reconstruction, with potential implications for expanding its utility in clinical diagnostics.

Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography

Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT reconstruction strategy that combines model-based iteration with score-based generative model was proposed. By incrementally adding noise to the training samples, prior knowledge can be learned from the complex probability distribution. The acquired prior is then utilized as constraint in model-based iteration. The information of missing views can be gradually compensated by cyclic iteration to achieve high-quality reconstruction. The performance of the proposed method was evaluated with the circular phantom and in vivo experimental data. Experimental results demonstrate the outstanding effectiveness of the proposed method in limited-view cases. Notably, the proposed method exhibits excellent performance in limited-view case of 70° compared with traditional method. It achieves a remarkable improvement of 203% in PSNR and 48% in SSIM for the circular phantom experimental data, and an enhancement of 81% in PSNR and 65% in SSIM for in vivo experimental data, respectively. The proposed method has capability of reconstructing PAT images in extremely limited-view cases, which will further expand the application in clinical scenarios.

Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo. Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, in vivo animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.

Photoacoustic image reconstruction with an objective function using TGV and ESTGV as a regularization functional

Photoacoustic tomographic imaging is a non-invasive medical diagnostic technology for visualizing biological tissue. However, the inverse problem and noise in photoacoustic signals often cause blurred images. Existing regularization methods struggle with staircasing artifacts and edge preservation. To overcome this, an objective function incorporating total generalized variation (TGV) is proposed. However, it failed with high-density Gaussian noise. To address this, an extended version called edge-guided second-order TGV (ESTGV) is introduced. For sparsification, wavelet transform and discrete cosine transform are introduced, while the fast-composite-splitting algorithm is employed for the inverse problem solution. Experimental validation demonstrates the potential of these approaches.

A Mathematical Model for Simulating Photoacoustic Signal Generation Process in Biological Tissues.. [DOI: 10.21203/rs.3.rs-2928563/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background: Biomedical photoacoustic imaging (PAI) is a hybrid imaging modality based on the laser-generated ultrasound waves due to the photoacoustic (PA) effect physical phenomenon that has been reported firstly by A. G. Bell in 1880. Numerical modeling-based simulation for the PA signal generation process in biological tissues helps researchers for decreasing error trials in-vitro and hence decreasing error rates for in-vivo experiments. Numerical modeling methods help in obtaining a rapid modeling procedure comparable to pure mathematics. However, if a proper simplified mathematical model can be founded before applying numerical modeling techniques, it will be a great advantage for the overall numerical model. Most scientific theories, equations, and assumptions, been proposed to mathematically model the complete PA signal generation and propagation process in biological tissues, are so complicated. Hence, the researchers, especially the beginners, will find a hard difficulty to explore and obtain a proper simplified mathematical model describing the process. That’s why this paper is introduced. Methods: In this paper we have tried to simplify understanding for the biomedical PA wave’s generation and propagation process, deducing a simplified mathematical model for the whole process. The proposed deduced model is based on three steps: a- pulsed laser irradiance, b- diffusion of light through biological tissue, and c- acoustic pressure wave generation and propagation from the target tissue to the ultrasound transducer surface. COMSOL Multiphysics, which is founded due to the finite element method (FEM) numerical modeling principle, has been utilized to validate the proposed deduced mathematical model on a simulated biological tissue including a tumor inside. Results and Conclusion: The time-dependent study been applied by COMSOL has assured that the proposed deduced mathematical model may be considered as a simplified, easy, and fast startup base for scientific researchers to numerically model and simulate biomedical PA signals’ generation and propagation process utilizing any proper software like COMSOL.

A Mathematical Model for Simulating Photoacoustic Signal Generation Process in Biological Tissues.. [DOI: 10.21203/rs.3.rs-2928563/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background Biomedical photoacoustic imaging (PAI) is a hybrid imaging modality based on the laser-generated ultrasound waves due to the photoacoustic (PA) effect physical phenomenon that has been reported firstly by A. G. Bell in 1880. Numerical modeling based simulation for PA signal generation process in biological tissues helps researchers for decreasing error trials in-vitro and hence decreasing error rates for in-vivo experiments. Numerical modeling methods help in obtaining a rapid modeling procedure comparable to pure mathematics. However, if a proper simplified mathematical model can be founded before applying numerical modeling techniques, it will be a great advantage for the overall numerical model. More scientific theories, equations, and assumptions through the biomedical PA imaging research literature have been proposed trying to mathematically model the complete PA signal generation and propagation process in biological tissues. However, most of them have so complicated details. Hence, the researchers, especially the beginners, will find a hard difficulty to explore and obtain a proper simplified mathematical model describing the process. That’s why this paper is introduced. Methods In this paper we have tried to simplify understanding for the biomedical PA wave’s generation and propagation process, deducing a simplified mathematical model for the whole process. The proposed deduced model is based on three steps: a- pulsed laser irradiance, b- diffusion of light through biological tissue, and c- acoustic pressure wave generation and propagation from the target tissue to the ultrasound transducer surface.

Multispectral interlaced sparse sampling photoacoustic tomography based on directional total variation

BACKGROUND AND OBJECTIVE

Photoacoustic tomography (PAT) is capable of obtaining cross-sectional images of small animals that represent the optical absorption of biological tissues. The multispectral Interlaced Sparse Sampling PAT, or ISS-PAT, is a previously proposed PAT imaging method that offered high quality images with much sparser transducer angular coverage. Although it provides superior imaging performance, the original ISS-PAT method suffered from a heavy computation burden, which hinders its practical application.

METHODS

Here, we propose a new regularization scheme based on the directional total variation (dTV) for ISS-PAT. This method efficiently imposes the structural information by considering both the edge position and direction information of the anatomical prior image in ISS-PAT. It does not require image segmentation, and can be conveniently solved by a modified alternating direction of multipliers (ADMM) algorithm.

RESULTS

We perform simulation, tissue mimicking phantom and in vivo small animal experiments to evaluate the proposed scheme. The reconstructed PAT images showed image quality and spectral un-mixing accuracy close to those obtained by non-local means based ISS-PAT, but with much shorter image reconstruction time. For a 1/6 sparse sampling rate, the average efficiency improvement is nearly 16-folds.

CONCLUSIONS

The experimental results demonstrate the feasibility of the dTV regularization scheme for ISS-PAT. Its efficient image reconstruction performance facilitates the potential of the hardware realization and practical applications of the ISS-PAT.