Photoacoustic imaging (PAI) is an emerging tool that bridges the traditional depth limits of ballistic optical imaging and the resolution limits of diffuse optical imaging. Using the acoustic waves generated in response to the absorption of pulsed laser light, it provides noninvasive images of absorbed optical energy density at depths of several centimeters with a resolution of ∼100 μm. This versatile and scalable imaging modality has now shown potential for molecular imaging, which enables visualization of biological processes with systemically introduced contrast agents. Understanding the relative merits of the vast range of contrast agents available, from small-molecule dyes to gold and carbon nanostructures to liposome encapsulations, is a considerable challenge. Here we critically review the physical, chemical and biochemical characteristics of the existing photoacoustic contrast agents, highlighting key applications and present challenges for molecular PAI.
Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300
Optoacoustic tomography (OT) is now widely used in preclinical imaging; however, the precision (repeatability and reproducibility) of OT has yet to be determined. We used a commercial small-animal OT system. Measurements in stable phantoms were used to independently assess the impact of system variables on precision (using coefficient of variation, COV), including acquisition wavelength, rotational position, and frame averaging. Variables due to animal handling and physiology, such as anatomic placement and anesthesia conditions, were then assessed in healthy nude mice using the left kidney and spleen as reference organs. Temporal variation was assessed by repeated measurements over hours and days both in phantoms and in vivo. Sensitivity to small-molecule dyes was determined in phantoms and in vivo; precision was assessed in vivo using IRDye800CW. OT COV in a stable phantom was less than 2.8% across all wavelengths over 30 d. The factors with the greatest impact on signal repeatability in phantoms were rotational position and user experience, both of which still resulted in a COV of less than 4% at 700 nm. Anatomic region-of-interest size showed the highest variation, at 12% and 18% COV in the kidney and spleen, respectively; however, functional SO measurements based on a standard operating procedure showed an exceptional reproducibility of less than 4% COV. COV for repeated injections of IRDye800CW was 6.6%. Sources of variability for in vivo data included respiration rate, degree of user experience, and animal placement. Data acquired with our small-animal OT system were highly repeatable and reproducible across subjects and over time. Therefore, longitudinal OT studies may be performed with high confidence when our standard operating procedure is followed.
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