Cone‐beam CT for imaging of the head/brain: Development and assessment of scanner prototype and reconstruction algorithms

• Published in: Medical physics (Lancaster) Vol. 47; no. 6; pp. 2392 - 2407
• Main Authors: Wu, P., Sisniega, A., Stayman, J. W., Zbijewski, W., Foos, D., Wang, X., Khanna, N., Aygun, N., Stevens, R. D., Siewerdsen, J. H.
• Format: Journal Article
• Language: English
• Published: United States 01.06.2020
• Subjects: Algorithms; artifact correction; Brain - diagnostic imaging; Cone-Beam Computed Tomography; cone‐beam CT; Head - diagnostic imaging; Humans; Image Processing, Computer-Assisted; image quality; model‐based image reconstruction; Phantoms, Imaging; point‐of‐care neuroimaging; traumatic brain injury; traumatic brain injury; image quality; model-based image reconstruction; point-of-care neuroimaging; cone-beam CT; artifact correction
• DOI: 10.1002/mp.14124
• ISSN: 0094-2405; 2473-4209

Purpose Our aim was to develop a high‐quality, mobile cone‐beam computed tomography (CBCT) scanner for point‐of‐care detection and monitoring of low‐contrast, soft‐tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft‐tissue contrast resolution and evaluation of its technical performance with human subjects. Methods Four configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x‐ray scatter), motion compensation, and three‐dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware‐specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution. Results The progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source‐detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion‐induced streak artifacts via the multi‐motion compensation method; and ~15% improvement in soft‐tissue contrast‐to‐noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point‐of‐care cranial imaging. Conclusions This work presents the first application of a high‐quality, point‐of‐care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point‐of‐care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus.