Evidence map›Paper›PMID 27025783›Full record

ArticlePhysics in medicine and biology2016

Modeling and design of a cone-beam CT head scanner using task-based imaging performance optimization.

J Xu, A Sisniega, W Zbijewski, H Dang, J W Stayman, X Wang, D H Foos, N Aygun, V E Koliatsos, J H Siewerdsen

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
5.7field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Personalization of X-Ray Tube Motion in Digital Breast Tomosynthesis Using Virtual Defrise Phantoms.Proceedings of SPIE--the International Society for Optical Engineering · 2019
    Article
  6. Article
  7. Spatial Resolution and Noise Prediction in Flat-Panel Cone-Beam CT Penalized-likelihood Reconstruction.Proceedings of SPIE--the International Society for Optical Engineering · 2018
    Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

J XuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
A SisniegaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
W ZbijewskiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
H DangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
J W StaymanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
X WangCarestream Health, Rochester, NY 14608, USA.
D H FoosCarestream Health, Rochester, NY 14608, USA.
N AygunRussell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA.
V E KoliatsosDepartment of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA.
J H SiewerdsenDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
Johns Hopkins University · USCarestream (United States) · US

Funding

Image Science for the New X-ray: Taking NEQ to TaskR01CA112163 · NCI · UNIVERSITY HEALTH NETWORK · PI SIEWERDSEN, JEFFREY H · 2006 to 2014
$2.1M
NCI NIH HHS R01 CA112163
6 · The paper itself

Abstract

Detection of acute intracranial hemorrhage (ICH) is important for diagnosis and treatment of traumatic brain injury, stroke, postoperative bleeding, and other head and neck injuries. This paper details the design and development of a cone-beam CT (CBCT) system developed specifically for the detection of low-contrast ICH in a form suitable for application at the point of care. Recognizing such a low-contrast imaging task to be a major challenge in CBCT, the system design began with a rigorous analysis of task-based detectability including critical aspects of system geometry, hardware configuration, and artifact correction. The imaging performance model described the three-dimensional (3D) noise-equivalent quanta using a cascaded systems model that included the effects of scatter, scatter correction, hardware considerations of complementary metal-oxide semiconductor (CMOS) and flat-panel detectors (FPDs), and digitization bit depth. The performance was analyzed with respect to a low-contrast (40-80 HU), medium-frequency task representing acute ICH detection. The task-based detectability index was computed using a non-prewhitening observer model. The optimization was performed with respect to four major design considerations: (1) system geometry (including source-to-detector distance (SDD) and source-to-axis distance (SAD)); (2) factors related to the x-ray source (including focal spot size, kVp, dose, and tube power); (3) scatter correction and selection of an antiscatter grid; and (4) x-ray detector configuration (including pixel size, additive electronics noise, field of view (FOV), and frame rate, including both CMOS and a-Si:H FPDs). Optimal design choices were also considered with respect to practical constraints and available hardware components. The model was verified in comparison to measurements on a CBCT imaging bench as a function of the numerous design parameters mentioned above. An extended geometry (SAD = 750 mm, SDD  = 1100 mm) was found to be advantageous in terms of patient dose (20 mGy) and scatter reduction, while a more isocentric configuration (SAD = 550 mm, SDD  = 1000 mm) was found to give a more compact and mechanically favorable configuration with minor tradeoff in detectability. An x-ray source with a 0.6 mm focal spot size provided the best compromise between spatial resolution requirements and x-ray tube power. Use of a modest anti-scatter grid (8:1 GR) at a 20 mGy dose provided slight improvement (~5-10%) in the detectability index, but the benefit was lost at reduced dose. The potential advantages of CMOS detectors over FPDs were quantified, showing that both detectors provided sufficient spatial resolution for ICH detection, while the former provided a potentially superior low-dose performance, and the latter provided the requisite FOV for volumetric imaging in a centered-detector geometry. Task-based imaging performance modeling provides an important starting point for CBCT system design, especially for the challenging task of ICH detection, which is somewhat beyond the capabilities of existing CBCT platforms. The model identifies important tradeoffs in system geometry and hardware configuration, and it supports the development of a dedicated CBCT system for point-of-care application. A prototype suitable for clinical studies is in development based on this analysis.

Indexed as

Models, TheoreticalPhantoms, ImagingAlgorithmsArtifactsCone-Beam Computed TomographyFluoroscopyHeadHumansImage Processing, Computer-AssistedTomography Scanners, X-Ray ComputedX-Rays

Identifiers

PMID27025783
PMCPMC13135668
OpenAlexW2312593591

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.