Preclinical small animal imaging platform providing co-registered 3D maps of photoacoustic response and fluorescence

Dumani, Diego S.; Yu, Anthony M.; Thompson, Weylan; Brecht, Hans-Peter; Ivanov, Vassili; Anastasio, Mark A.; Cook, Jason R.; Ermilov, Sergey A.; Emelianov, Stanislav

doi:10.1117/12.2514489

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…At present, the registration studies involved in photoacoustic imaging are mostly about real-time registration or PAI image registration with other imaging modalities [44][45][46][47][48], and there is no comparative study on different registration schemes for PAI images taken at different times with different transducers and optical wavelengths. In this study, we tested different automatic registration schemes using four categories of image datasets collected under three optical wavelengths and two transducer center frequencies.…”

Section: Introductionmentioning

confidence: 99%

Registration of photoacoustic tomography vascular images: Comparison and analysis of automatic registration approaches

Liao

Liu

et al. 2022

Front. Phys.

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Image registration is crucial in the clinical application of photoacoustic tomography (PAT) for vascular growth monitoring. Aiming to find an optimized registration scheme for PAT vascular images acquired at different times and with varying imaging conditions, we compared and analyzed different commonly used intensity-based and feature-based automatic registration schemes. To further improve the registration performance, we proposed a new scheme that combines phase correlation with these commonly used intensity-based registration methods and compared their performances. The objective evaluation measures: peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), root mean square error (RMSE), and quantitative visual perception (jump percentage P), as well as subjective evaluation using mean opinion score (MOS), were combined to evaluate the registration performance. Results show that the feature-based approaches in this study were not suitable for PAT image registration. And by adding phase correlation as rough registration, the overall registration performance was improved significantly. Among these methods, the proposed scheme of phase correlation combined with mean square error (MSE) similarity measure and regular-step-gradient-descent optimizer provides the best visual effect, accuracy, and efficiency in PAT vascular image registration.

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Section: Introductionmentioning

confidence: 99%