Modeling the variation in speed of sound between couplant and tissue improves the spectral accuracy of multispectral optoacoustic tomography

Even though the speed of sound (SoS) is non-homogeneous in biological tissue, most reconstruction algorithms for optoacoustic imaging neglect its variation. In addition, when heavy water is used as coupling medium to enable imaging of certain biological chromophores such as lipids and proteins, the SoS also differs significantly between couplant and tissue. While the assumption of uniform SoS is known to introduce visible deformations of features in single-wavelength optoacoustic images, the spectral error introduced by the assumption of uniform SoS is not fully understood. In this work, we provide an in-depth spectral analysis of multi-spectral optoacoustic imaging artifacts that result from the assumption of uniform SoS in situations where SoS changes substantially. We propose a dual-SoS model to incorporate the SoS variation between the couplant and the sample. Tissue-mimicking phantom experiments and in vivo measurements show that uniform SoS reconstruction causes spectral smearing, which dual-SoS modeling can largely eliminate. Due to this increased spectral accuracy, the method has the potential to improve clinical studies that rely on quantitative optoacoustic imaging of biomolecules like hemoglobin or lipids.

[1]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[2]  Lihong V. Wang,et al.  Universal back-projection algorithm for photoacoustic computed tomography. , 2005 .

[3]  Vasilis Ntziachristos,et al.  Acceleration of Optoacoustic Model-Based Reconstruction Using Angular Image Discretization , 2012, IEEE Transactions on Medical Imaging.

[4]  Vasilis Ntziachristos,et al.  Fast Semi-Analytical Model-Based Acoustic Inversion for Quantitative Optoacoustic Tomography , 2010, IEEE Transactions on Medical Imaging.

[5]  Vasilis Ntziachristos,et al.  In-vivo handheld optoacoustic tomography of the human thyroid , 2016, Photoacoustics.

[6]  V. Ntziachristos,et al.  Imaging of fatty tumors: appearance of subcutaneous lipomas in optoacoustic images , 2017, Journal of biophotonics.

[7]  Brian W Pogue,et al.  Near-infrared tomography of breast cancer hemoglobin, water, lipid, and scattering using combined frequency domain and cw measurement. , 2010, Optics letters.

[8]  V. Ntziachristos,et al.  Molecular imaging by means of multispectral optoacoustic tomography (MSOT). , 2010, Chemical reviews.

[9]  Haim Azhari,et al.  Appendix A: Typical Acoustic Properties of Tissues , 2010 .

[10]  Junjie Yao,et al.  Single-impulse Panoramic Photoacoustic Computed Tomography of Small-animal Whole-body Dynamics at High Spatiotemporal Resolution , 2017, Nature Biomedical Engineering.

[11]  J. G. Bayly,et al.  The absorption spectra of liquid phase H2O, HDO and D2O from 0·7 μm to 10 μm , 1963 .

[12]  David A Bluemke,et al.  Carotid artery plaque morphology and composition in relation to incident cardiovascular events: the Multi-Ethnic Study of Atherosclerosis (MESA). , 2014, Radiology.

[13]  Wiendelt Steenbergen,et al.  Speed-of-sound compensated photoacoustic tomography for accurate imaging. , 2012, Medical physics.

[14]  Vasilis Ntziachristos,et al.  Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT). , 2009, Medical physics.

[15]  Vasilis Ntziachristos,et al.  Effects of small variations of speed of sound in optoacoustic tomographic imaging. , 2014, Medical physics.

[16]  Vasilis Ntziachristos,et al.  Real-time handheld multispectral optoacoustic imaging. , 2013, Optics letters.

[17]  Vasilis Ntziachristos,et al.  Multispectral optoacoustic tomography at 64, 128, and 256 channels , 2014, Journal of biomedical optics.