Task-specific adaptive sensing in computed tomography (CT) scan is critical to dose reduction and scanning acceleration. Due to the sequential nature of the CT acquisition process, the information of the objects aggregates as the measurement process progresses. Conventional adaptive sensing methods, aiming to maximize the task-specific information acquisition, formulate the measurement strategy as an optimization problem with assumptions in object distributions (for example, Gaussian mixture model), which requires considerable computational time and resource during the acquisition. In our work, we propose a machine learning approach to learn task-specific data-acquisition policy, with the only assumption on the locality and composition of the objects, which shifts the computation load to the pre-acquisition stage. We analyze our learned method on public dataset comparing to a stochastic policy which plans the acquisition randomly and a uniform policy which plans the acquisition with a fixed interval. Based on our experiments the learned method requires at least 25% fewer acquisition steps than the stochastic and uniform policies.

Date Published: 14 May 2019
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Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990C (14 May 2019); doi: 10.1117/12.2519008

Published in SPIE Proceedings Vol. 10999:
Anomaly Detection and Imaging with X-Rays (ADIX) IV
Amit Ashok; Joel A. Greenberg; Michael E. Gehm, Editor(s)