Reducing Acoustic Inhomogeneity Based on Speed of Sound Autofocus in Microwave Induced Thermoacoustic Tomography

Microwave induced thermoacoustic tomography is a newly developing non-invasive and non-ionizing modality. In practical applications, such as breast tumor detection and brain imaging, the acoustic properties in the tissue to be detected are usually unknown and spatially non-uniform, which results in distortion and blurring of the buried targets. In this paper, a reconstruction method based on speed of sound (SoS) autofocus is proposed to reduce the effect of acoustic inhomogeneity in different soft tissues. According to this method, the number of tissue types, which are referred to as clusters in this work, can be automatically determined by a decision graph. To distinguish the boundaries of different tissues, a Gaussian Mixture Model (GMM) is fitted to the obtained image data for soft clustering instead of traditional hard clustering. Through fixing the tissue centers which are characterized by corresponding data density peaks as the means of Gaussian parameters rather than choosing them randomly, adaptive and robust reconstruction performance can be guaranteed. After performing an iterative GMM optimization, the SoS autofocus is achieved. Image reconstructed by using the updated SoS distribution is with higher accuracy than that with homogeneous assumption. Compared with the existing similar methods, the proposed method strategy obviates the need of extra experiment costs, and possesses good robustness with respect to hard assignment model errors when the medium is relatively complex. Realistic breast model and brain model simulations combined with experiments of agar phantom and pig's brain are provided to demonstrate the effectiveness of the proposed method.