3 years ago

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography.

J Webster Stayman, Grace J Gang, Jeffrey H Siewerdsen
This work presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood (PL) reconstruction that maximizes a taskbased imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index (d0) throughout the image. The optimization algorithm alternates between FFM (represented by lowdimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction as well as a taskdriven TCM strategy for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the taskdriven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared to all strategies, the task-driven FFM strategy not only improved minimum d0 by at least 17.8%, but yielded higher d0 over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally supports the performance estimates based on computed d0. The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

Publisher URL: http://doi.org/10.1109/TMI.2017.2763538

DOI: 10.1109/TMI.2017.2763538

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