Artifact removal in photoacoustic tomography with an unsupervised method

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.

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction

Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, practical implementations encounter inevitable trade-offs between cost and performance due to the expensive nature of employing additional channels for accessing more measurements. Here, we propose a masked cross-domain self-supervised (CDSS) reconstruction strategy to overcome the lack of ground truth labels from limited PA measurements. We implement the self-supervised reconstruction in a model-based form. Simultaneously, we take advantage of self-supervision to enforce the consistency of measurements and images across three partitions of the measured PA data, achieved by randomly masking different channels. Our findings indicate that dynamically masking a substantial proportion of channels, such as 80%, yields meaningful self-supervisors in both the image and signal domains. Consequently, this approach reduces the multiplicity of pseudo solutions and enables efficient image reconstruction using fewer PA measurements, ultimately minimizing reconstruction error. Experimental results on in-vivo PACT dataset of mice demonstrate the potential of our self-supervised framework. Moreover, our method exhibits impressive performance, achieving a structural similarity index (SSIM) of 0.87 in an extreme sparse case utilizing only 13 channels, which outperforms the performance of the supervised scheme with 16 channels (0.77 SSIM). Adding to its advantages, our method can be deployed on different trainable models in an end-to-end manner, further enhancing its versatility and applicability.

Removing Artifacts in Transcranial Photoacoustic Imaging With Polarized Self-Attention Dense-UNet

OBJECTIVE

Photoacoustic imaging (PAI) is a promising transcranial imaging technique. However, the distortion of photoacoustic signals induced by the skull significantly influences its imaging quality. We aimed to use deep learning for removing artifacts in PAI.

METHODS

In this study, we propose a polarized self-attention dense U-Net, termed PSAD-UNet, to correct the distortion and accurately recover imaged objects beneath bone plates. To evaluate the performance of the proposed method, a series of experiments was performed using a custom-built PAI system.

RESULTS

The experimental results showed that the proposed PSAD-UNet method could effectively implement transcranial PAI through a one- or two-layer bone plate. Compared with the conventional delay-and-sum and classical U-Net methods, PSAD-UNet can diminish the influence of bone plates and provide high-quality PAI results in terms of structural similarity and peak signal-to-noise ratio. The 3-D experimental results further confirm the feasibility of PSAD-UNet in 3-D transcranial imaging.

CONCLUSION

PSAD-UNet paves the way for implementing transcranial PAI with high imaging accuracy, which reveals broad application prospects in preclinical and clinical fields.

Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning

OBJECTIVE

Bone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues. However, the spatiotemporal trade-off limits the application of OR-PAM. The purpose of this study was to improve the quality of OR-PAM images without sacrificing temporal resolution.

METHODS

In this study, we proposed the Photoacoustic Dense Attention U-Net (PADA U-Net) model, which was used for reconstructing full-scanning images from under-sampled images. Thereby, this approach breaks the trade-off between imaging speed and spatial resolution.

RESULTS

The proposed method was validated on resolution test targets and bovine cancellous bone samples to demonstrate the capability of PADA U-Net in recovering full-scanning images from under-sampled OR-PAM images. With a down-sampling ratio of [4, 1], compared to bilinear interpolation, the Peak Signal-to-Noise Ratio and Structural Similarity Index Measure values (averaged over the test set of bovine cancellous bone) of the PADA U-Net were improved by 2.325 dB and 0.117, respectively.

CONCLUSION

The results demonstrate that the PADA U-Net model reconstructed the OR-PAM images well with different levels of sparsity. Our proposed method can further facilitate early diagnosis and treatment of bone diseases using OR-PAM.

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.

Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view

Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains.

Optimization on artifacts in photoacoustic images based on spectrum analyses and signal extraction

Photoacoustic (PA) imaging is a promising technology for functional imaging of biological tissues, offering optical contrast and acoustic penetration depth. However, the presence of signal aliasing from multiple PA sources within the same imaging object can introduce artifacts and significantly impact the quality of the PA tomographic images. In this study, an optimized method is proposed to suppress these artifacts and enhance image quality effectively. By leveraging signal time-frequency spectrum, signals from each PA source can be extracted. Subsequently, the images are reconstructed using these extracted signals and fused together to obtain an optimized image. To verify this proposed method, PA imaging experiments were conducted on two phantoms and two in vitro samples and the distribution relative error and root mean square error of the images obtained through conventional and optimized methods were calculated. The results demonstrate that the proposed method successfully suppresses the artifacts and substantially improves the image quality.

Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors

Photoacoustic tomography (PAT) commonly works in sparse view due to data acquisition limitations. However, reconstruction suffers from serious deterioration (e.g., severe artifacts) using traditional algorithms under sparse view. Here, a novel accelerated model-based iterative reconstruction strategy for sparse-view PAT aided by multi-channel autoencoder priors was proposed. A multi-channel denoising autoencoder network was designed to learn prior information, which provides constraints for model-based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The performance of the proposed method was evaluated using blood vessel simulation data and experimental data. The results show that the proposed method can achieve superior sparse-view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition (e.g., 32 projections) compared with the U-Net method, with an improvement of 48% in PSNR and 12% in SSIM for in vivo experimental data.

Photoacoustic imaging plus X: a review

Significance

Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability.

Aim

The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications.

Approach

An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities.

Results

After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively.

Conclusions

The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

Multikernel positional embedding convolutional neural network for photoacoustic reconstruction with sparse data

Photoacoustic imaging (PAI) is an emerging noninvasive imaging modality that merges the high contrast of optical imaging with the high resolution of ultrasonic imaging. Low-quality photoacoustic reconstruction with sparse data due to sparse spatial sampling and limited view detection is a major obstacle to the popularization of PAI for medical applications. Deep learning has been considered as the best solution to this problem in the past decade. In this paper, we propose what we believe to be a novel architecture, named DPM-UNet, which consists of the U-Net backbone with additional position embedding block and two multi-kernel-size convolution blocks, a dilated dense block and dilated multi-kernel-size convolution block. Our method was experimentally validated with both simulated data and in vivo data, achieving a SSIM of 0.9824 and a PSNR of 33.2744 dB. Furthermore, the reconstructed images of our proposed method were compared with those obtained by other advanced methods. The results have shown that our proposed DPM-UNet has a great advantage in PAI over other methods with respect to the imaging effect and memory consumption.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging. Recently, deep-learning-based image deconvolution approaches have shown promise for image recovery. However, PAT imaging presents unique challenges, including spatially varying resolution and the absence of ground truth data. Consequently, there is a pressing need for a novel learning strategy specifically tailored for PAT imaging. Herein, we propose a configurable network model named Deep hybrid Image-PSF Prior (DIPP) that builds upon the physical image degradation model of PAT. DIPP is an unsupervised and deeply learned network model that aims to extract the ideal PAT image from complex system degradation. Our DIPP framework captures the degraded information solely from the acquired PAT image, without relying on ground truth or labeled data for network training. Additionally, we can incorporate the experimentally measured Point Spread Functions (PSFs) of the specific PAT system as a reference to further enhance performance. To evaluate the algorithm's effectiveness in addressing multiple degradations in PAT, we conduct extensive experiments using simulation images, publicly available datasets, phantom images, and in vivo small animal imaging data. Comparative analyses with classical analytical methods and state-of-the-art deep learning models demonstrate that our DIPP approach achieves significantly improved restoration results in terms of image details and contrast.

Photoacoustic imaging with limited sampling: a review of machine learning approaches

Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.

Photoacoustic image synthesis with generative adversarial networks

Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Probabilistic generation of realistic tissue morphology, and (2) pixel-wise assignment of corresponding optical and acoustic properties. The former is achieved with Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data. According to a validation study on a downstream task our approach yields more realistic synthetic images than the traditional model-based approach and could therefore become a fundamental step for deep learning-based quantitative PAT (qPAT).

Feasibility of a Generative Adversarial Network for Artifact Removal in Experimental Photoacoustic Imaging

Photoacoustic tomography (PAT) reconstruction is an expeditiously growing interest among biomedical researchers because of its possible transition from laboratory to clinical pre-eminence. Nonetheless, the PAT inverse problem is yet to achieve an optimal solution in rapid and precise reconstruction under practical constraints. Precisely, the sparse sampling problem and random noise are the main impediments to attaining accuracy but in support of rapid PAT reconstruction. The limitations are associated with acquiring undersampled artifacts that deteriorate the optimality of the reconstruction task. Therefore, the former achievements of fast image formation limit the modality for clinical settings. Delving into the problem, here we explore a deep learning-based generative adversarial network (GAN) to improve the image quality by denoising and removing these artifacts. The specially designed attributes and unique manner of optimizing the problem, such as incorporating the data set limitations and providing stable training performance, constitute the main motivation behind the employment of GAN. Moreover, exploitation of the U-net variant as a generator network offers robust performance in terms of quality and computational cost, which is further validated with the detailed quantitative and qualitative analysis. The quantitatively evaluated structured similarity indexing method = 0.980 ± 0.043 and peak signal-to-noise ratio = 31 ± 0.002 dB state that the proposed solution provides the high-resolution image at the output, even training with a low-quality data set.