Speaker
Description
Ultrasound Computed Tomography (USCT) is a promising complement to traditional mammography for breast cancer detection. 3D USCT systems, which allow for good illumination of the breasts, are currently limited by the long data acquisition (DAQ) times. Conventionally, sources are activated sequentially, requiring the signal to dissipate before the next wavelet is emitted. These extended DAQ times cause lower image resolution due to patient motion. Here we present an exploratory study on diverse methods to reduce DAQ time, such as source encoding and random wavefield tomography.Regarding source encoding, we consider source apparition, vortex encoding, and compressive sensing. For source apparition and compressive sensing, we encode multiple simultaneous sources with specific time shifts to allow the isolation of the contributions of the individual sources from the mixed signal. Alternatively, with vortex encoding, all sources emit simultaneously. Using full-waveform inversion, we directly invert the blended signals without prior source separation by modifying the misfit accordingly.Random wavefield tomography mimics the concept of ambient noise tomography from geophysics. We actively create the noise by activating all the sources simultaneously, each with a random source time function. By cross-correlating the signals from different receiver pairs, we obtain deterministic travel time information that we can use for inversion. If the duration of simultaneous random sources is shorter than the time needed for sequential emission, we get a reduced DAQ time that is independent of the number of transducers, enabling the scaling to denser measuring systems.We provide a comparison of the different approaches and determine their advantages for later implementation in actual USCT systems.