Purpose: This work presents a task-driven joint optimization of fluence field modulation (FFM) and regularization in
quadratic penalized-likelihood (PL) reconstruction. Conventional FFM strategies proposed for filtered-backprojection
(FBP) are evaluated in the context of PL reconstruction for comparison.
Methods: We present a task-driven framework that leverages prior knowledge of the patient anatomy and imaging
task to identify FFM and regularization. We adopted a maxi-min objective that ensures a minimum level of
detectability index (d’) across sample locations in the image volume. The FFM designs were parameterized by 2D
Gaussian basis functions to reduce dimensionality of the optimization and basis function coefficients were estimated
using the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. The FFM was jointly optimized
with both space-invariant and spatially-varying regularization strength (β) - the former via an exhaustive search
through discrete values and the latter using an alternating optimization where β was exhaustively optimized locally
and interpolated to form a spatially-varying map.
Results: The optimal FFM inverts as β increases, demonstrating the importance of a joint optimization. For the task
and object investigated, the optimal FFM assigns more fluence through less attenuating views, counter to conventional
FFM schemes proposed for FBP. The maxi-min objective homogenizes detectability throughout the image and
achieves a higher minimum detectability than conventional FFM strategies.
Conclusions: The task-driven FFM designs found in this work are counter to conventional patterns for FBP and yield
better performance in terms of the maxi-min objective, suggesting opportunities for improved image quality and/or
dose reduction when model-based reconstructions are applied in conjunction with FFM.
G. J. Gang, J. H. Siewerdsen, and J. W. Stayman, "Joint optimization of fluence field modulation and regularization in task-driven computed tomography," Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101320E (Presented at SPIE Medical Imaging: February 13, 2017; Published: 9 March 2017); https://doi.org/10.1117/12.2255517.
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