Quantification of vascular networks in photoacoustic mesoscopy

Brown, Emma; Lefebvre, Thierry L.; Sweeney, Paul W.; Stolz, Bernadette J.; Gröhl, Janek; Hacker, Lina; Huang, Ziqiang; Couturier, Dominique‐Laurent; Harrington, Heather A.; Byrne, Helen; Bohndiek, Sarah E.

doi:10.1016/j.pacs.2022.100357

Cited by 19 publications

(35 citation statements)

References 59 publications

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“…First, illumination artefacts arise due to the limited top illumination of the field of view, leading to optical excitation and acoustic wave generation only in the upper part of absorbing structures that face the illumination and transducer array (Figure 3A). This results in inaccurate diameter estimations in the XZ plane compared to the XY plane 34 . For example, in the string phantom module, where the true diameter of each string is d act = 126 μm, the measurements from the reconstructed images agree only in the XY plane (d xy =122 ± 7.8 μm) and substantially underestimate the value in the XZ plane (d xz =27 ± 3.2 μm).…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, the impact of motion correction, image reconstruction algorithms and data post-processing has not been investigated. First steps towards analysis of these computational variation sources in mesoscopic imaging have been taken elsewhere 34 , but future work should explore these technical factors of variability in more depth for further user guidance. With this work, we hope to guide data acquisition for similar PAI setups, thereby maximizing information retrieval and accuracy for future preclinical and clinical PAI studies.…”

Section: Discussionmentioning

confidence: 99%

“…For the in vivo repeatability studies, the blood volume was chosen as a comparison metric, as it is a commonly used variable of interest in preclinical studies, and sometimes used as a precursor for further downstream analysis of morphological features 34 . An existing pipeline 34 was used to quantify the results. Briefly, image data was filtered in the Fourier domain in the XY plane to remove reflection lines, before being reconstructed using a backprojection algorithm in viewRSOM software (v2.3.5.2 iThera Medical GmbH, Germany) with motion correction and a voxel size of 20 × 20 × 4 μm 3 (X,Y,Z).…”

Section: Methodsmentioning

confidence: 99%

Section: In Vivomentioning

confidence: 99%

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Performance evaluation of mesoscopic photoacoustic imaging

Hacker

Brown

Lefebvre

et al. 2022

Preprint

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Photoacoustic mesoscopy visualises vascular architecture and associated tissue structures at high resolution at up to 3 mm depth. The technique has shown promise in both preclinical and clinical imaging studies, with possible applications in oncology and dermatology, however, the accuracy and precision of photoacoustic mesoscopy has not been well established. Here, we present a performance evaluation of a commercial photoacoustic mesoscopy system for imaging vascular structures. Typical artefact types are first highlighted and limitations due to non-isotropic illumination and detection are evaluated with respect to rotation, angularity, and depth of the target. Then, using tailored phantoms and mouse models we demonstrate high system precision, with acceptable coefficients of variation (COV) between repeated scans (short term (1h): COV=1.2%; long term (25 days): COV=9.6%), from target repositioning (without: COV=1.2%, with: COV=4.1%), or from varyingin vivouser experience (experienced: COV=15.9%, unexperienced: COV=20.2%). While our findings support the robustness of the technique, they also underscore the general challenges of limited field-of-view photoacoustic systems in accurately imaging vessel-like structures, thereby guiding users to correctly interpret biologically-relevant information.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: In Vivomentioning

confidence: 99%

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Performance evaluation of mesoscopic photoacoustic imaging

Hacker

Brown

Lefebvre

et al. 2022

Preprint

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“…The different light absorption coefficients of hemoglobin and various tissue chromophores at different wavelengths give PAI unique advantages in structural and functional vascular imaging [16][17][18][19][20][21][22]. Numerous studies have proved the effectiveness of PAI in vasculature imaging [23][24][25][26][27], which can be used to visualize the development of single blood vessels around growing tumors, changes in blood oxygen concentration within tumors, and the depth growth of neovascularization areas [28][29][30][31][32][33]. Comparing and analyzing the changes of blood vessels around tumors at different stages, including neovascularization and irregular changes in vascular morphology, can offer essential knowledge for monitoring the development and treatment of the disease, and realizing early screening and postoperative evaluation of cancer [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Registration of photoacoustic tomography vascular images: Comparison and analysis of automatic registration approaches

Liao

Liu

et al. 2022

Front. Phys.

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Image registration is crucial in the clinical application of photoacoustic tomography (PAT) for vascular growth monitoring. Aiming to find an optimized registration scheme for PAT vascular images acquired at different times and with varying imaging conditions, we compared and analyzed different commonly used intensity-based and feature-based automatic registration schemes. To further improve the registration performance, we proposed a new scheme that combines phase correlation with these commonly used intensity-based registration methods and compared their performances. The objective evaluation measures: peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), root mean square error (RMSE), and quantitative visual perception (jump percentage P), as well as subjective evaluation using mean opinion score (MOS), were combined to evaluate the registration performance. Results show that the feature-based approaches in this study were not suitable for PAT image registration. And by adding phase correlation as rough registration, the overall registration performance was improved significantly. Among these methods, the proposed scheme of phase correlation combined with mean square error (MSE) similarity measure and regular-step-gradient-descent optimizer provides the best visual effect, accuracy, and efficiency in PAT vascular image registration.

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Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning

Sweeney,

Hacker,

Lefebvre

et al. 2024

Advanced Science

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Mesoscopic photoacoustic imaging (PAI) enables label‐free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time‐consuming and error‐prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human‐annotated ground‐truth labels. To address this, an unsupervised image‐to‐image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN‐GAN). VAN‐GAN integrates synthetic blood vessel networks that closely resemble real‐life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient‐derived breast cancer xenograft models and 3D clinical angiograms, VAN‐GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN‐GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high‐quality blood vessel segmentation (F1 score: VAN‐GAN vs. U‐Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure and function.

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Quantification of vascular networks in photoacoustic mesoscopy

Cited by 19 publications

References 59 publications

Performance evaluation of mesoscopic photoacoustic imaging

Performance evaluation of mesoscopic photoacoustic imaging

Registration of photoacoustic tomography vascular images: Comparison and analysis of automatic registration approaches

Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning

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