Fat ray ultrasound transmission tomography: preliminary experimental results with simulated data

Ultrasound transmission tomography is a promising modality for breast cancer diagnosis. For image reconstruction approximations to the acoustic wave equation such as straight or bent rays are commonly used due to their low computational complexity. For sparse apertures the coverage of the volume by rays is very limited, thereby requiring strong regularization in the inversion process. The concept of fat rays reduces the sparseness and includes the contributions to the measured signal originating from the first Fresnel zone. In this work we investigate the application of the fat ray concept to ultrasound transmission tomography. We implement a straight ray, bent ray and fat ray forward model. For the inversion process a least squares solver (LSQR), a simultaneous algebraic reconstruction technique (SART) and a compressive sensing based total variation minimization (TVAL3) is applied. The combination of forward models and inversion processes has been evaluated by synthetic data. TVAL3 outperforms SART and LSQR, especially for sparse apertures. The fat ray concept is able to decrease the error with respect to the ground truth compared to the bent ray method especially for SART and LSQR inversion, and especially for very sparse apertures.