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9 May 2019 Task-driven source–detector trajectories in cone-beam computed tomography: II. Application to neuroradiology
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Abstract
We apply the methodology detailed in “Task-driven source–detector trajectories in cone-beam computed tomography: I. Theory and methods” by Stayman et al. for task-driven optimization of source–detector orbits in cone-beam computed tomography (CBCT) to scenarios emulating imaging tasks in interventional neuroradiology. The task-driven imaging framework is used to optimize the CBCT source–detector trajectory by maximizing the detectability index, d  ′  . The approach was applied to simulated cases of endovascular embolization of an aneurysm and arteriovenous malformation and was translated to real data first using a CBCT test bench followed by implementation on an interventional robotic C-arm. Task-driven trajectories were found to generally favor higher fidelity (i.e., less noisy) views, with an average increase in d  ′   ranging from 7% to 28%. Visually, this resulted in improved conspicuity of particular stimuli by reducing the noise and altering the noise correlation to a form distinct from the spatial frequencies associated with the imaging task. The improvements in detectability and the demonstration of the task-driven workflow using a real interventional imaging system show the potential of the task-driven imaging framework to improve imaging performance on motorized, multiaxis C-arms in neuroradiology.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$25.00 © 2019 SPIE
Sarah Capostagno, Joseph W. Stayman, Matthew W. Jacobson, Tina Ehtiati, Clifford R. Weiss, and Jeffrey H. Siewerdsen "Task-driven source–detector trajectories in cone-beam computed tomography: II. Application to neuroradiology," Journal of Medical Imaging 6(2), 025004 (9 May 2019). https://doi.org/10.1117/1.JMI.6.2.025004
Received: 20 November 2018; Accepted: 4 April 2019; Published: 9 May 2019
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Cited by 16 scholarly publications.
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KEYWORDS
Robotics

Imaging systems

Aneurysms

Image quality

Visualization

Optical spheres

3D image processing

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