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2 May 2019 Task-driven source–detector trajectories in cone-beam computed tomography: I. Theory and methods
Joseph W. Stayman, Sarah Capostagno, Grace J. Gang, Jeffrey H. Siewerdsen
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Journal of Medical Imaging, Vol. 6, Issue 2, 025002 (May 2019). https://doi.org/10.1117/1.JMI.6.2.025002
Abstract
We develop a mathematical framework for the design of orbital trajectories that are optimal to a particular imaging task (or tasks) in advanced cone-beam computed tomography systems that have the capability of general source–detector positioning. The framework allows various parameterizations of the orbit as well as constraints based on imaging system capabilities. To accommodate nonstandard system geometries, a model-based iterative reconstruction method is applied. Such algorithms generally complicate the assessment and prediction of reconstructed image properties; however, we leverage efficient implementations of analytical predictors of local noise and spatial resolution that incorporate dependencies of the reconstruction algorithm on patient anatomy, x-ray technique, and geometry. These image property predictors serve as inputs to a task-based performance metric defined by detectability index, which is optimized with respect to the orbital parameters of data acquisition. We investigate the framework of the task-driven trajectory design in several examples to examine the dependence of optimal source–detector trajectories on the imaging task (or tasks), including location and spatial-frequency dependence. A variety of multitask objectives are also investigated, and the advantages to imaging performance are quantified in simulation studies.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$25.00 © 2019 SPIE
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Joseph W. Stayman, Sarah Capostagno, Grace J. Gang, and Jeffrey H. Siewerdsen "Task-driven source–detector trajectories in cone-beam computed tomography: I. Theory and methods," Journal of Medical Imaging 6(2), 025002 (2 May 2019). https://doi.org/10.1117/1.JMI.6.2.025002
Received: 12 November 2018; Accepted: 29 March 2019; Published: 2 May 2019
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Cited by 32 scholarly publications.
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KEYWORDS
Imaging systems
Data modeling
Computed tomography
Optical spheres
Performance modeling
Image quality
Optimization (mathematics)
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