The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios. Graphical

This work compares photoacoustic images of a tooth by analyzing the signals generated with wavelengths 532 and 355 nm. This comparison addresses the differences in the optical properties of dental tissue for these wavelengths that affect the resulting photoacoustic images. A pulsed Nd:YAG laser was used to illuminate a complete extracted tooth sample, and 2D photoacoustic images (PAIs) were reconstructed using the single-sensor scanning synthetic aperture focusing technique (SSC-SAFT), which is a suitable method for our experimental system with forward detection mode. Signal comparison was conducted using sinogram, signal-to-noise ratio (SNR), root mean square (RMS), arrival time, maximum amplitude, and fast Fourier transform (FFT). PAI comparison utilized intensity profile, edge correlation, and image composition tools. The signal analysis revealed that at 532 nm, the signals exhibited longer decay time and a wider distribution of vibration frequencies due to higher laser pulse energy and greater optical penetration depth. Conversely, at 355 nm, the signals had shorter decay times and a lower frequency distribution, which was attributed to lower energy but improved optical absorption, resulting in reconstructed images with better sharpness and contour definition. This study contributes to the advancement of photoacoustic imaging technology in dentistry by providing insights that could optimize signal generation and image reconstruction for dental tissue.

Photoacoustic tomography is an innovative non-ionizing imaging technique that combines optical contrast with ultrasound resolution for 3D object characterization. While promising, its broader adoption is limited by challenges such as shallow penetration depth and strong optical scattering. To address these issues, this study introduces a dual illumination and detection photoacoustic tomography method, specifically designed for symmetrical objects like hollow metallic cylinders. The illumination system plays a critical role in determining the quality of photoacoustic signals and, thus, the final image. This approach enhances spatial resolution and contrast by using complementary light delivery and signal detection. In industrial settings, where accurate and efficient non-destructive testing is essential, traditional techniques often lack the precision required. The dual illumination and detection strategy offers significant improvements in effective resolution, contrast, defect detection, and artifact reduction, surpassing the limitations of unidirectional approaches. This technique not only strengthens the characterization of metal structures but also contributes to a deeper understanding of their physical behavior. Applications extend across various fields, including aerospace and biomedical engineering. This paper explores the underlying principles and potential of this advanced imaging modality, highlighting its value in modern diagnostic and inspection technologies.

Photoacoustic tomography (PAT) is a promising imaging technique that combines the high spatial resolution of ultrasound with the high contrast of optical imaging. One of the challenges in PAT is the ill-posed nature of the inverse problem, where limited measurement data and noise often lead to inaccurate reconstructions. This work introduces a kernel-based regularization (KBR) approach for model-based reconstruction algorithms in photoacoustic (PA) imaging. The proposed method leverages kernel-induced feature space to enforce smoothness and spatial coherence in the reconstructed images, thereby improving the robustness to noise and data sparsity. By incorporating prior knowledge of the signal dynamics for solving the PA inverse problem, KBR enhances the reconstruction fidelity, especially in regions with low signal-to-noise ratio. Numerical experiments and phantom studies demonstrate that the proposed algorithm outperforms traditional regularization techniques, such as Tikhonov and total variation regularization, regarding reconstruction accuracy and computation speed. The results suggest KBR provides a powerful tool for addressing the inherent challenges in PA image reconstruction, offering potential improvements in several applications.

Photoacoustic tomography (PAT), which combines optical absorption and ultrasonic detection, enables the monitoring of dehydration-driven structural changes in extracted teeth over time. In this proof-of-concept study, 2D photoacoustic images of a wisdom tooth were generated on the same scanning plane at days 0, 1, 3, 6, 10, 15, 21, and 28 post-extraction, using day 0 as the reference. Measurements were performed in forward-detection mode with a single ultrasound transducer and a 532 nm pulsed laser. For the comparative analysis of variations between images, four metrics were used: Pearson correlation coefficient, Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Structural changes were also examined through radial intensity profiles extracted from each image. The results revealed marked differences in the central region, evidencing progressive structural and acoustic modifications within the tooth. The most significant change occurred on day 1, followed by small but consistent variations on subsequent days. These differences are associated with dehydration-induced changes in tissue density, which affect sound propagation. This study highlights the value of PAT for noninvasive monitoring of post-extraction dental changes, with implications for diagnosis, treatment guidance, and biomaterials research in dentistry.