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WE‐EF‐207‐01: FEATURED PRESENTATION and BEST IN PHYSICS (IMAGING): Task‐Driven Imaging for Cone‐Beam CT in Interventional Guidance
Author(s) -
Gang G,
Stayman J,
Ouadah S,
Ehtiati T,
Siewerdsen J
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4926008
Subject(s) - cone beam computed tomography , computer science , imaging phantom , iterative reconstruction , medical imaging , kernel (algebra) , orbit (dynamics) , artificial intelligence , computer vision , optics , physics , mathematics , computed tomography , medicine , engineering , combinatorics , radiology , aerospace engineering
Purpose: This work introduces a task‐driven imaging framework that utilizes a patient‐specific anatomical model, mathematical definition of the imaging task, and a model of the imaging system to prospectively design acquisition and reconstruction techniques that maximize task‐based imaging performance. Utility of the framework is demonstrated in the joint optimization of tube current modulation and view‐dependent reconstruction kernel in filtered‐backprojection reconstruction and non‐circular orbit design in model‐based reconstruction. Methods: The system model is based on a cascaded systems analysis of cone‐beam CT capable of predicting the spatially varying noise and resolution characteristics as a function of the anatomical model and a wide range of imaging parameters. Detectability index for a non‐prewhitening observer model is used as the objective function in a task‐driven optimization. The combination of tube current and reconstruction kernel modulation profiles were identified through an alternating optimization algorithm where tube current was updated analytically followed by a gradient‐based optimization of reconstruction kernel. The non‐circular orbit is first parameterized as a linear combination of bases functions and the coefficients were then optimized using an evolutionary algorithm. The task‐driven strategy was compared with conventional acquisitions without modulation, using automatic exposure control, and in a circular orbit. Results: The task‐driven strategy outperformed conventional techniques in all tasks investigated, improving the detectability of a spherical lesion detection task by an average of 50% in the interior of a pelvis phantom. The non‐circular orbit design successfully mitigated photon starvation effects arising from a dense embolization coil in a head phantom, improving the conspicuity of an intracranial hemorrhage proximal to the coil. Conclusion: The task‐driven imaging framework leverages a knowledge of the imaging task within a patient‐specific anatomical model to optimize image acquisition and reconstruction techniques, thereby improving imaging performance beyond that achievable with conventional approaches. 2R01‐CA‐112163; R01‐EB‐017226; U01‐EB‐018758; Siemens Healthcare (Forcheim, Germany)