Robotic C-arms like the Artis Zeego (Siemens Healthineers) open new possibilities for cone-beam CT (CBCT) scanning beyond conventional circular orbits of the x-ray source and detector. Such motion capabilities permit scan orbits that expand the CBCT field of view, improve image quality, and reduce artifacts. A recent set of papers (1 and 2) co-authored by Sarah Capostagno (PhD student in Biomedical Engineering at Johns Hopkins University) and J. Webster Stayman (Associate Professor in Biomedical Engineering at Johns Hopkins University) leverages the capabilities of such robotic C-arm gantries to improve image quality using non-circular orbits computed to maximize imaging performance with respect to a particular imaging task.

Part I presents a mathematical framework to compute source-detector trajectories in CBCT that are optimal to a particular imaging task. Given a model of the patient (for example, a prior CT) and a specification of the structure of interest (namely, its location and spatial-frequency content), the framework solves for the scan orbit that maximizes performance of the task. The paper (1) gives a comprehensive introduction to the analytical framework, discusses various objective functions and practical optimization methods, and presents simulations in phantoms that provide intuition on the optimization process and demonstrate the potential for improved image quality.

Part II develops and applies the methodology from Part I to specific clinical scenarios in neurointerventional radiology, including embolization of neurovascular aneurysms and ablation of arteriovenous malformations (AVMs). The paper (2) tests the task-driven orbits concept on a laboratory test-bench for CBCT and translates the methodology for the first time to a clinical robotic C-arm (Artis Zeego) at Johns Hopkins Hospital.

The papers were published in the Journal of Medical Imaging, 2019.

(1) J. Webster Stayman, Sarah Capostagno, Grace J. Gang, Jeffrey H. Siewerdsen, “Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods,” J. Med. Imag. 6(2), 025002 (May 2 2019), doi: 10.1117/1.JMI.6.2.025002.

(2) Sarah Capostagno, J. Webster Stayman, Matthew Jacobson, Tina Ehtiati, Clifford R. Weiss, Jeffrey H. Siewerdsen, “Task-driven source-detector trajectories in cone-beam computed tomography: II. Application to neuroradiology,” J. Med. Imag. 6(2), 025004 (May 9 2019), doi: 10.1117/1.JMI.6.2.025004.