Multiscale Multispectral Optoacoustic Tomography by a Stationary Wavelet Transform Prior to Unmixing

source: © 2014  IEEE Transactions on Medical Imaging. 

Multispectral optoacoustic tomography (MSOT) utilizes broadband ultrasound detection for imaging biologically-relevant optical absorption features at a range of scales. Due to the multiscale and multispectral features of the technology, MSOT comes with distinct requirements in implementation and data analysis. In this work, we investigate the interplay between scale, which depends on ultrasonic detection frequency, and optical multispectral spectral analysis, two dimensions that are unique to MSOT and represent a previously unexplored challenge. We show that ultrasound frequency-dependent artifacts suppress multispectral features and complicate spectral analysis. In response, we employ a wavelet decomposition to perform spectral unmixing on a per-scale basis (or per ultrasound frequency band) and showcase imaging of fine-scale features otherwise hidden by low frequency components. We explain the proposed algorithm by means of simple simulations and demonstrate improved performance in imaging data of blood vessels in human subjects.  [Read more…]

Fig. 5 A simulated example. Each of the single wavelength images (a), which contain negative values, is decomposed by SWT and individually reconstructed (inverse SWT=ISWT ), as shown in (b). It can be observed in this particular case that the fine scale features are mainly captured in the detail coefficients, while the coarse scale features are mainly captured in the level three approximation. NNLS is applied per scale and the resulting oxyhemoglobin and deoxyhemoglobin images are shown (c). These images compare favorably with the “ground truth” images (d), which are the HbO2 and Hb distributions used to generate the data, but with negative values truncated. The images obtained by the conventional approach (e), NNLS without SWT, do not resemble the “ground truth” because of the effect of negative value artifacts. The example applies the Daubechies wavelet with two vanishing moments and a three level decomposition.

Adrian Taruttis , Amir Rosenthal , Marcin Kacprowicz , Neal C. Burton , Vasilis Ntziachristos, “Multiscale multispectral optoacoustic tomography by a stationary wavelet transform prior to unmixing,” IEEE transactions on medical imaging, Volume 33, Issue 5, Pages 1194-1202