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Radical excision of periorbital skin tumors is difficult without sacrificing excessive healthy tissue. Photoacoustic (PA) imaging is an emerging non-invasive biomedical imagi­­ng modality that has potential for intraoperative micrographic control of surgical margins. This is the first study to assess the feasibility of PA imaging for the detection of periocular skin cancer. Eleven patients underwent surgical excision of periocular skin cancer, one of which was a malignant melanoma (MM), eight were basal cell carcinomas (BCCs), and two squamous cell carcinomas (SCCs). Six tumors were located in the eyelid, and five in periocular skin. The excised samples, as well as healthy eyelid samples, were scanned with PA imaging postoperatively, using 59 wavelengths in the range 680–970 nm, to generate 3D multispectral images. Spectral unmixing was performed using endmember spectra for oxygenated and deoxygenated Hb, melanin, and collagen, to iden­­tify the chromophore composition of tumors and healthy eyelid tissue. After PA scanning, the tumor samples were examined histopathologically using standard hematoxylin and eosin staining. The PA spectra of healthy eyelid tissue were dominated by melanin in the skin, oxygenated and deoxygenated hemoglobin in the orbicularis oculi muscle, and collagen in the tarsal plate. Multiwavelength 3D scanning provided spectral information on the three tumor types. The spectrum from the MM was primarily reconstructed by the endmember melanin, while the SCCs showed contributions primarily from melanin, but also HbR and collagen. BCCs showed contributions from all four endmembers with a predominance of HbO(2) and HbR. PA imaging may be used to distinguish different kinds of periocular skin tumors, paving the way for future intraoperative micrographic control.

The adaptive matched filter (AMF) is a method widely used in spectral unmixing to classify different tissue chromophores in photoacoustic images. However, a threshold needs to be applied to the AMF detection image to distinguish the desired tissue chromophores from the background. In this study, we propose an automatic threshold selection (ATS) algorithm capable of differentiating a target from the background, based on the features of the AMF detection image. The mean difference between the estimated thickness, using the ATS algorithm, and the known values was 0.17 SD (0.24) mm for the phantom inclusions and -0.05 SD (0.21) mm for the tissue samples of malignant melanoma. The evaluation shows that the thickness and the width of the phantom inclusions and the tumors can be estimated using AMF in an automatic way after applying the ATS algorithm.

In translation from preclinical to clinical studies using photoacoustic imaging, motion artifacts represent a major issue. In this study the feasibility of an in-house algorithm, referred to as intensity phase tracking (IPT), for regional motion correction of in vivo human photoacoustic (PA) images was demonstrated. The algorithm converts intensity to phase-information and performs 2D phase-tracking on interleaved ultrasound images. The radial artery in eight healthy volunteers was imaged using an ultra-high frequency photoacoustic system. PA images were motion corrected and evaluated based on PA image similarities. Both controlled measurements using a computerized stepping motor and free-hand measurements were evaluated. The results of the controlled measurements show that the tracking corresponded to 97 ± 6% of the actual movement. Overall, the mean square error between PA images decreased by 52 ± 15% and by 43 ± 19% when correcting for controlled- and free-hand induced motions, respectively. The results show that the proposed algorithm could be used for motion correction in photoacoustic imaging in humans.

Photoacoustic imaging (PAI) is a novel hybrid imaging technique that combines the advantages of optical and ultrasound imaging to produce hyperspectral images of the tissue. The feasibility of measuring oxygen saturation (sO(2)) with PAI has been demonstrated pre-clinically, but has limited use in humans under conditions of ischemia and reperfusion. As an important step towards making PAI clinically available, we present a study in which PAI was used to estimate the spatial distribution of sO(2) in vivo during and after occlusion of the finger of eight healthy volunteers. The results were compared with a commercial oxygen saturation monitor based on diffuse reflectance spectroscopy. We here describe the capability of PAI to provide spatially resolved picture of the evolution of sO(2) during ischemia following vascular occlusion of a finger, demonstrating the clinical viability of PAI as a non-invasive diagnostic tool for diseases indicated by impaired microvascularization.

Overview of the motion tracking algorithm