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Soft-tissue imaging in low-dose, C-arm cone-beam CT using statistical image reconstruction
Author(s) -
Adam Wang,
Sebastian Schäfer,
J. Webster Stayman,
Yoshi Otake,
Marc Sussman,
A. Jay Khanna,
Gary L. Gallia,
Jeffrey H. Siewerdsen
Publication year - 2013
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.2008421
Subject(s) - imaging phantom , image quality , iterative reconstruction , computer science , image resolution , artificial intelligence , contrast to noise ratio , computer vision , cone beam computed tomography , biomedical engineering , nuclear medicine , medicine , radiology , image (mathematics) , computed tomography
C-arm cone-beam CT (CBCT) is an emerging tool for intraoperative imaging, but current embodiments exhibit modest soft-tissue imaging capability and are largely constrained to high-contrast imaging tasks. A major advance in image quality is facilitated by statistical iterative reconstruction techniques. This work adapts a general penalized likelihood (PL) reconstruction approach with variable penalties and regularization to C-arm CBCT and investigates performance in imaging of large (>10 mm), low-contrast (<100 HU) tasks pertinent to soft-tissue surgical guidance. Experiments involved a mobile C-arm for CBCT with phantoms and cadavers presenting soft-tissue structures imaged using 3D filtered backprojection (FBP), quadratic, and non-quadratic PL reconstruction. Polyethylene phantoms with various tissue-equivalent inserts were used to quantity contrast-to-noise / resolution tradeoffs in low-contrast (~40 HU) structures, and the optimal reconstruction parameters were translated to imaging an anthropomorphic head phantom with low-contrasts targets and a cadaveric torso. Statistical reconstruction - especially non-quadratic PL variants - boosted soft-tissue image quality through reduction of noise and artifacts (e.g., a ~2-4 fold increase in contrast-to-noise ratio (CNR) at equivalent spatial resolution). For tasks relating to large, low-contrast tissues, even greater gains were possible using non-quadratic penalties and strong regularization that sacrificed spatial resolution in a manner still consistent with the imaging task. The advances in image quality offered by statistical reconstruction present promise and new challenges for interventional imaging, with high-speed computing facilitating realistic application. Careful investigation of performance relative to specific imaging tasks permits knowledgeable application of such techniques in a manner that overcomes conventional tradeoffs in noise, resolution, and dose and could extend application of CBCT-capable C-arms to soft-tissue interventions in neurosurgery as well as thoracic and abdominal interventions.

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