Enhancing optoacoustic mesoscopy through calibration-based iterative reconstruction

Ultrasound-Aided Large-Scale Optoacoustic Microscopy for Volumetric Angiography and Oximetry

Given its direct relationship to tissue metabolism and various pathological processes, 3D mapping of blood oxygen saturation (sO2) is essential for advancing our knowledge on oxygen delivery to tissues and evaluating therapeutic efficacy. Optoacoustic microscopy has enabled label-free estimation of sO2 values by exploiting the spectrally distinctive absorption of hemoglobin in its oxygenated and deoxygenated forms. However, quantitative 3D mapping of sO2 distribution over large heterogenous tissue regions is commonly hindered due to the strong spatial and spectral variability of the excitation light fluence. Herein, we capitalize on hybridization between pulse-echo ultrasound and large-scale spectroscopic optoacoustic microscopy readings to accurately delineate the tissue surface, achieve depth-resolved tissue layer segmentation, and comprehensively evaluate the main causes behind inaccurate sO2 estimations with optoacoustic microscopy. Compensation for wavelength-dependent light fluence variations due to relative reflectance and attenuation through multiple tissue layers is further shown to remove spectral noise and restore physiologically relevant sO2 values in the images recorded from the mouse ear and the dorsal murine skin. The ultrasound-aided large-scale optoacoustic microscopy (uLSOM) approach is thus expected to enhance applicability of optoacoustic microscopy for quantitative label-free imaging of tissue oxygenation and metabolism.

The Environmental Impact of Medical Imaging Agents and the Roadmap to Sustainable Medical Imaging

Medical imaging agents, i.e., contrast agents for magnetic resonance imaging (MRI) and radiopharmaceuticals, play a vital role in the diagnosis of diseases. Yet, they mostly contain harmful and non-biodegradable substances, such as per- and polyfluoroalkyl substances (PFAS), heavy metals or radionuclides. As a result of their increasing clinical use, these agents are entering various water bodies and soil, posing risks to environment and human health. Here, the environmental effects of the application of imaging agents are outlined for the major imaging modalities, and the respective chemistry of the contrast agents with environmental implications is linked. Recommendations are introduced for the design and application of contrast agents: the 3Cs of imaging agents: control, change, and combine; and recent approaches for more sustainable imaging strategies are highlighted. This combination of measures should engage an open discussion, inspire solutions to reduce pollution by imaging agents, and increase awareness for the impact of toxic waste related to imaging agents.

Image restoration for ring-array photoacoustic tomography system based on blind spatially rotational deconvolution

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

Spatially-variant image deconvolution for photoacoustic tomography

Photoacoustic tomography (PAT) system can reconstruct images of biological tissues with high resolution and contrast. However, in practice, the PAT images are usually degraded by spatially variant blur and streak artifacts due to the non-ideal imaging conditions and chosen reconstruction algorithms. Therefore, in this paper, we propose a two-phase restoration method to progressively improve the image quality. In the first phase, we design a precise device and measuring method to obtain spatially variant point spread function samples at preset positions of the PAT system in image domain, then we adopt principal component analysis and radial basis function interpolation to model the entire spatially variant point spread function. Afterwards, we propose a sparse logarithmic gradient regularized Richardson-Lucy (SLG-RL) algorithm to deblur the reconstructed PAT images. In the second phase, we present a novel method called deringing which is also based on SLG-RL to remove the streak artifacts. Finally, we evaluate our method with simulation, phantom and in vivo experiments, respectively. All the results show that our method can significantly improve the quality of PAT images.