Accelerating Ultrasound Computer Tomography Data Acquisition Using a Deep Convolutional Neural Network

Ultrasound Computer Tomography (USCT) enables imaging of the spatial distribution of physical parameters, such as speed of sound and acoustic attenuation. These parameters are highly relevant in the context of tissue diagnostics, as pathological regions, for example tumors, often exhibit distinct attenuation and speed-of-sound characteristics compared to healthy tissue. Achieving USCT imaging at a clinically reasonable frame rate presents a number of challenges. The frame rate is constrained by several factors: the time of flight of ultrasound pulses between the source and the receivers, the number of transmit/receive events, the transfer of acquired data to the host computer, and the time required for data processing and image reconstruction. Among these, the physical limitations imposed by the time of flight—necessary for a pulse to fully traverse the imaged object—are particularly critical. While data transfer and processing times may be reduced through hardware acceleration and software optimization, these fundamental physical constraints cannot be circumvented in the same manner. Consequently, the reduction of the number of transmissions remains the only practical strategy to decrease this component of the total acquisition time. Although such a reduction generally results in some deterioration of image quality, this degradation can be mitigated through interpolation. This strategy shortens both the acquisition and data transfer stages, while shifting the computational burden to more intensive data processing.The objective of the present study is to demonstrate a proof of concept for this approach in USCT imaging, employing a neural network–based interpolator to restore image quality.