Photoacoustic imaging (PAI) is rapidly moving from the laboratory to the clinic, increasing the need to understand confounders which might adversely affect patient care. Over the past five years, landmark studies have shown the clinical utility of PAI, leading to regulatory approval of several devices. In this article, we describe the various causes of artifacts in PAI, providing schematic overviews and practical examples, simulated as well as experimental. This work serves two purposes: (1) educating clinical users to identify artifacts, understand their causes, and assess their impact, and (2) providing a reference of the limitations of current systems for those working to improve them. We explain how two aspects of PAI systems lead to artifacts: their inability to measure complete data sets, and embedded assumptions during reconstruction. We describe the physics underlying PAI, and propose a classification of the artifacts. The paper concludes by discussing possible advanced mitigation strategies.

The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker–Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag^®-D70, Synomag^®-D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation.

Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring. We investigate the performance of domain-dependent and general state-of-the-art language models and their alignment with domain expertise. To this end, eXplainable Artificial Intelligence (XAI) techniques are used to acquire insight into the inner workings of the model, which are verified on their trustworthiness. The verified XAI explanations are then compared with explanations from a domain expert, to indirectly determine the reliability of the model. BERTje, a Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperforms RobBERT and MedRoBERTa.nl in both accuracy and reliability. The latter model (MedRoBERTa.nl) is a domain-specific model, while BERTje is a generic model, showing that domain-specific models are not always superior. Our validation of BERTje in a small prospective study shows promising results for the potential uptake of the model in a practical setting.

Photoacoustic imaging offers optical contrast images of human tissue at acoustic resolution, making it valuable for diverse clinical applications. However, quantifying tissue composition via optical contrast remains challenging due to the unknown light fluence within the tissue. Here, we propose a method that leverages known chromophores (e.g., arterial blood) to improve the accuracy of quantitative photoacoustic imaging. By using the optical properties of a known chromophore as a fluence marker and integrating it into the optical inversion process, we can estimate the unknown fluence within the tissue. Experimentally, we demonstrate that this approach successfully recovers both the spectral shape and magnitude of the optical absorption coefficient of an unknown chromophore. Additionally, we show that the fluence marker method enhances conventional optical inversion techniques, specifically (i) a straightforward iterative approach and (ii) a gradient-based method. Our results indicate an improvement in accuracy of up to 24.4% when comparing optical absorption recovery with and without the fluence marker. Finally, we present the method’s performance and illustrate its applications in carotid plaque quantification.

SignificanceThyroid nodules have a high incidence, but a low malignancy rate, placing demands on imaging specificity. Photoacoustic imaging is being studied as a method that could potentially help increase the unsatisfactory specificity of ultrasound, the current first-line imaging method, which would decrease the number of unnecessary clinical interventions.AimWe employ a digital thyroid phantom and numerical simulations to evaluate photoacoustic probe designs on their suitability for thyroid nodule risk estimation.ApproachUsing the SIMPA toolkit, we examined 1080 probe configurations with varying illumination geometry, detector shape, pitch, and wavelength combinations. The performance of each probe was quantified on its sensitivity and its accuracy of oxygen saturation estimation.ResultsOptimal performance was found using wavelengths 757 and 800 nm, and a curved detector (0.5 split, 0.40 mm pitch) for external illumination. However, performance increases significantly with endotracheal illumination, using wavelengths of 700 and 800 nm, and a curved detector (0.25 split, 0.40 mm pitch).ConclusionsThese results could provide design guidance for optimizing photoacoustic hardware, assisting with the clinical adoption of PAI, which could improve the diagnosis of thyroid nodules.