Thyroid cancer is one of the most common cancers, with a global increase in incidence rate for both genders. Ultrasound-guided fine-needle aspiration is the current gold standard to diagnose thyroid cancers, but the results are inaccurate, leading to repeated biopsies and unnecessary surgeries. To reduce the number of unnecessary biopsies, we explored the use of multiparametric photoacoustic (PA) analysis in combination with the American Thyroid Association (ATA) Guideline (ATAP). In this study, we performed in vivo multispectral PA imaging on thyroid nodules from 52 patients, comprising 23 papillary thyroid cancer (PTC) and 29 benign cases. From the multispectral PA data, we calculated hemoglobin oxygen saturation level in the nodule area, then classified the PTC and benign nodules with multiparametric analysis. Statistical analyses showed that this multiparametric analysis of multispectral PA responses could classify PTC nodules. Combining the photoacoustically indicated probability of PTC and the ATAP led to a new scoring method that achieved a sensitivity of 83% and a specificity of 93%. This study is the first multiparametric analysis of multispectral PA data of thyroid nodules with statistical significance. As a proof of concept, the results show that the proposed new ATAP scoring can help physicians examine thyroid nodules for fine-needle aspiration biopsy, thus reducing unnecessary biopsies. Significance: This report highlights a novel photoacoustic scoring method for risk stratification of thyroid nodules, where malignancy of the nodules can be diagnosed with 83% sensitivity and 93% specificity.

Three-dimensional bimodal photoacoustic/US imaging provided volumetric and multiparametric information about morphologic and physiologic features of the human foot, potentially useful in diagnosing... Background Monitoring the microcirculation in human feet is crucial in assessing peripheral vascular diseases, such as diabetic foot. However, conventional imaging modalities are more focused on di...

Photoacoustic imaging (PAI) has attracted great attention as a medical imaging method. Typically, photoacoustic (PA) images are reconstructed via beamforming, but many factors still hinder the beamforming techniques in reconstructing optimal images in terms of image resolution, imaging depth, or processing speed. Here, we demonstrate a novel deep learning PAI that uses multiple speed of sound (SoS) inputs. With this novel method, we achieved SoS aberration mitigation, streak artifact removal, and temporal resolution improvement all at once in structural and functional in vivo PA images of healthy human limbs and melanoma patients. The presented method produces high-contrast PA images in vivo with reduced distortion, even in adverse conditions where the medium is heterogeneous and/or the data sampling is sparse. Thus, we believe that this new method can achieve high image quality with fast data acquisition and can contribute to the advance of clinical PAI.

The standard-of-care for evaluating lymph node status in breast cancers and melanoma metastasis is sentinel lymph node (SLN) assessment performed with a handheld gamma probe and radioisotopes. However, this method inevitably exposes patients and physicians to radiation, and the special facilities required limit its accessibility. Here, we demonstrate a non-ionizing, cost-effective, handheld photoacoustic finder (PAF) fully integrated with a solid-state dye laser and transparent ultrasound transducer (TUT). The solid-state dye laser handpiece is coaxially aligned with the spherically focused TUT. The integrated finder readily detected photoacoustic signals from a tube filled with methylene blue (MB) beneath a 22 mm thick layer of chicken tissue. In live animals, we also photoacoustically detected both SLNs injected with MB and subcutaneously injected melanomas. We believe that our radiation-free and inexpensive PAF can play a vital role in SLN assessment.