When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-reference IQA (FR-IQA) measures have been primarily developed and optimized for natural images. In many specialized settings, such as medical images, this poses an often-overlooked problem regarding suitability. In previous studies, the FR-IQA measure HaarPSI showed promising behavior for natural and medical images. The measure is based on Haar wavelet representations and the framework allows optimization of two parameters. So far, these parameters have been aligned for natural images. Here, we optimize these parameters for two medical image data sets, a photoacoustic and a chest X-ray data set, with IQA expert ratings. We observe that they lead to similar parameter values and are more sensitive to parameter changes compared to natural image data. We denote the novel optimized setting as HaarPSIMED, which improves the performance of the employed medical images significantly (p < 0.05). Additionally, we include an independent CT test data set that illustrates the generalizability of HaarPSIMED, as well as visual examples that qualitatively demonstrate the improvement. The results suggest that adapting common IQA measures within their frameworks for medical images can provide a valuable, generalizable addition to employment of more specific task-based measures.

Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have been developed and tested for natural images, but not in the medical setting. Reported inconsistencies arising in medical images are not surprising, as they have different properties than natural images. In this study, we test the applicability of common IQA measures for medical image data by comparing their assessment to manually rated chest X-ray (5 experts) and photoacoustic image data (2 experts). Moreover, we include supplementary studies on grayscale natural images and accelerated brain MRI data. The results of all experiments show a similar outcome in line with previous findings for medical images: PSNR and SSIM in the default setting are in the lower range of the result list and HaarPSI outperforms the other tested measures in the overall performance. Also among the top performers in our medical experiments are the full reference measures FSIM, LPIPS and MS-SSIM. Generally, the results on natural images yield considerably higher correlations, suggesting that additional employment of tailored IQA measures for medical imaging algorithms is needed.

Significance: Photoacoustic imaging (PAI) promises to measure spatially-resolved blood oxygen saturation, but suffers from a lack of accurate and robust spectral unmixing methods to deliver on this promise. Accurate blood oxygenation estimation could have important clinical applications, from cancer detection to quantifying inflammation. Aim: This study addresses the inflexibility of existing data-driven methods for estimating blood oxygenation in PAI by introducing a recurrent neural network architecture. Approach: We created 25 simulated training dataset variations to assess neural network performance. We used a long short-term memory network to implement a wavelength-flexible network architecture and proposed the Jensen-Shannon divergence to predict the most suitable training dataset. Results: The network architecture can handle arbitrary input wavelengths and outperforms linear unmixing and the previously proposed learned spectral decolouring method. Small changes in the training data significantly affect the accuracy of our method, but we find that the Jensen-Shannon divergence correlates with the estimation error and is thus suitable for predicting the most appropriate training datasets for any given application. Conclusions: A flexible data-driven network architecture combined with the Jensen-Shannon Divergence to predict the best training data set provides a promising direction that might enable robust data-driven photoacoustic oximetry for clinical use cases.

Accurate measurement of optical absorption coefficients from photoacoustic imaging (PAI) data would enable direct mapping of molecular concentrations, providing vital clinical insight. The ill-posed nature of the problem of absorption coefficient recovery has prohibited PAI from achieving this goal in living systems due to the domain gap between simulation and experiment. To bridge this gap, we introduce a collection of experimentally well-characterised imaging phantoms and their digital twins. This first-of-a-kind phantom data set enables supervised training of a U-Net on experimental data for pixel-wise estimation of absorption coefficients. We show that training on simulated data results in artefacts and biases in the estimates, reinforcing the existence of a domain gap between simulation and experiment. Training on experimentally acquired data, however, yielded more accurate and robust estimates of optical absorption coefficients. We compare the results to fluence correction with a Monte Carlo model from reference optical properties of the materials, which yields a quantification error of approximately 20%. Application of the trained U-Nets to a blood flow phantom demonstrated spectral biases when training on simulated data, while application to a mouse model highlighted the ability of both learning-based approaches to recover the depth-dependent loss of signal intensity. We demonstrate that training on experimental phantoms can restore the correlation of signal amplitudes measured in depth. While the absolute quantification error remains high and further improvements are needed, our results highlight the promise of deep learning to advance quantitative PAI.

There is an urgent need to improve conventional cancer-treatments by preventing detrimental side effects, cancer recurrence and metastases. Recent studies have shown that presence of senescent cells in tissues treated with chemo- or radiotherapy can be used to predict the effectiveness of cancer treatment. However, although the accumulation of senescent cells is one of the hallmarks of cancer, surprisingly little progress has been made in development of strategies for their detection in vivo. To address a lack of detection tools, we developed a biocompatible, injectable organic nanoprobe (NanoJagg), which is selectively taken up by senescent cells and accumulates in the lysosomes. The NanoJagg probe is obtained by self-assembly of indocyanine green (ICG) dimers using a scalable manufacturing process and characterized by a unique spectral signature suitable for both photoacoustic tomography (PAT) and fluorescence imaging. In vitro, ex vivo and in vivo studies all indicate that NanoJaggs are a clinically translatable probe for detection of senescence and their PAT signal makes them suitable for longitudinal monitoring of the senescence burden in solid tumors after chemotherapy or radiotherapy.

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

Cellular senescence is considered an important tumour suppression mechanism in response to damage and oncogenic stress in early lesions. However, when senescent cells are not immune-cleared and persist in the tumour microenvironment, they can drive a variety of tumour-promoting activities, including cancer initiation, progression, and metastasis. Additionally, there is compelling evidence demonstrating a direct connection between chemo(radio)therapy-induced senescence and the development of drug resistance and cancer recurrence. Therefore, detection of senescent cells in tissues holds great promise for predicting cancer occurrence earlier, assessing tumour progression, aiding patient stratification and prognosis, and informing about the efficacy of potential senotherapies. However, effective detection of senescent cells is limited by lack of biomarkers and readout strategies suitable for in vivo clinical imaging. To this end, a nanoprobe composed of biocompatible polydopamine (PDA) nanoparticle doped with FDA-approved indocyanine green (ICG) dye, namely PDA-ICG, was designed as a contrast agent for senescence detection using photoacoustic imaging (PAI). In an in vitro model of chemotherapy-induced senescence, PDA-ICG nanoprobe showed an elevated uptake in senescent cells relative to cancer cells. In addition to its improved photostability, 2.5-fold enhancement in photoacoustic signal relative to ICG was observed. Collectively, the results indicate that the PDA-ICG nanoprobe has the potential to be used as a contrast agent for senescence detection of chemotherapy-induced senescence using PAI.

Significance:Photoacoustic imaging (PAI) provides contrast based on the concentration of optical absorbers in tissue, enabling the assessment of functional physiological parameters such as blood oxygen saturation (sO2). Recent evidence suggests that variation in melanin levels in the epidermis leads to measurement biases in optical technologies, which could potentially limit the application of these biomarkers in diverse populations. Aim:To examine the effects of skin melanin pigmentation on PAI and oximetry. Approach:We evaluated the effects of skin tone in PAI using a computational skin model, two-layer melanin-containing tissue-mimicking phantoms, and mice of a consistent genetic background with varying pigmentations. The computational skin model was validated by simulating the diffuse reflectance spectrum using the adding-doubling method, allowing us to assign our simulation parameters to approximate Fitzpatrick skin types. Monte Carlo simulations and acoustic simulations were run to obtain idealized photoacoustic images of our skin model. Photoacoustic images of the phantoms and mice were acquired using a commercial instrument. Reconstructed images were processed with linear spectral unmixing to estimate blood oxygenation. Linear unmixing results were compared with a learned unmixing approach based on gradient-boosted regression. Results:Our computational skin model was consistent with representative literature for in vivo skin reflectance measurements. We observed consistent spectral coloring effects across all model systems, with an overestimation of sO2 and more image artifacts observed with increasing melanin concentration. The learned unmixing approach reduced the measurement bias, but predictions made at lower blood sO2 still suffered from a skin tone-dependent effect. Conclusion:PAI demonstrates measurement bias, including an overestimation of blood sO2, in higher Fitzpatrick skin types. Future research should aim to characterize this effect in humans to ensure equitable application of the technology.

ABSTRACT Senescent cells accumulate in multiple age-related disorders, including cancer, exacerbating the pathological manifestations, and the eradication of these cells has emerged as a promising therapeutic strategy. Despite the impact of senescence in diseases, the development of tools to monitor the senescent burden in vivo remains a challenge due to their suboptimal specificity, translatability, and tissue penetrance. Here, we have designed a nanostructured organic probe (NanoJaggs) based on biocompatible indocyanine green dye (ICG) building blocks forming J-aggregates, which possess distinct spectral properties allowing both fluorescence and photoacoustic tomography (PAT) detection. We show that NanoJaggs are taken up by an active process of endocytosis and exhibit selective accumulation at the lysosomal compartment in several in vitro models for senescence. Finally, NanoJagg probe is validated in two in vivo studies including live PAT imaging and shows remarkable specificity to tumours with chemotherapy-induced senescence compared to untreated proliferative tumors. In vitro, ex vivo and in vivo all indicate that NanoJaggs are a clinically translatable tool for detection of senescence and their robust PAT signal makes them suitable for longitudinal monitoring of the senescent burden in solid tumors after chemo or radiotherapy.

Radiotherapy is recognized globally as a mainstay of treatment in most solid tumors and is essential in both curative and palliative settings. Ionizing radiation is frequently combined with surgery, either preoperatively or postoperatively, and with systemic chemotherapy. Recent advances in imaging have enabled precise targeting of solid lesions yet substantial intratumoral heterogeneity means that treatment planning and monitoring remains a clinical challenge as therapy response can take weeks to manifest on conventional imaging and early indications of progression can be misleading. Photoacoustic imaging (PAI) is an emerging modality for molecular imaging of cancer, enabling non-invasive assessment of endogenous tissue chromophores with optical contrast at unprecedented spatio-temporal resolution. Preclinical studies in mouse models have shown that PAI could be used to assess response to radiotherapy and chemoradiotherapy based on changes in the tumor vascular architecture and blood oxygen saturation, which are closely linked to tumor hypoxia. Given the strong relationship between hypoxia and radio-resistance, PAI assessment of the tumor microenvironment has the potential to be applied longitudinally during radiotherapy to detect resistance at much earlier time-points than currently achieved by size measurements and tailor treatments based on tumor oxygen availability and vascular heterogeneity. Here, we review the current state-of-the-art in PAI in the context of radiotherapy research. Based on these studies, we identify promising applications of PAI in radiation oncology and discuss the future potential and outstanding challenges in the development of translational PAI biomarkers of early response to radiotherapy.

Spectral colouring severely affects quantification in photoacoustic imaging, impacting the biological interpretation of the output imaging biomarkers. Melanin is a particularly strong optical absorber in the near infrared wavelength range that exhibits substantial variation in concentration according to skin tone. Here, Monte-Carlo simulations of light transport in a computational skin phantom were carried out to establish the effects of quantifying blood oxygenation at different melanin concentrations. These results were validated with a tissue-mimicking phantom. The results demonstrated that raised melanin concentration in the epidermis significantly affects quantification of haemoglobin concentration and oxygenation with photoacoustic imaging.