Calibration techniques for single-sensor ultrasound imaging with a coding mask

We consider a model-based ultrasound imaging scenario using a single transducer with a coding mask, and assume that the pulse-echo model is erroneously estimated, resulting in decreased imaging performance. Although the pulse-echo Green's function to each pixel has to be measured to obtain a good model, typically only forward-field measurements are obtained for better SNR, from which the pulse-echo Green's functions are estimated. However, if the transducer's receive transfer function is different from the transmit transfer function, the forward-field measurements do not incorporate the receive transfer function, resulting in an incorrect pulse-echo model. We propose two calibration techniques that start with this erroneous model, and update it using pulse-echo measurements. In the first technique we assume the calibration phantom is known a priori, whereas in the second technique we use multiple random calibration phantoms of which only the second-order statistics are assumed to be known beforehand. Both methods are able to significantly improve the pulse-echo model, strongly improving imaging performance. Our simulation results show that the first technique works best, since there is no uncertainty about the calibration image, whereas the blind calibration technique requires no exact knowledge of the calibration phantom, making it robust to positioning or manufacturing errors.