Literature DB >> 34140278

Head CT: Toward Making Full Use of the Information the X-Rays Give.

K A Cauley1, Y Hu2, S W Fielden3.   

Abstract

Although clinical head CT images are typically interpreted qualitatively, automated methods applied to routine clinical head CTs enable quantitative assessment of brain volume, brain parenchymal fraction, brain radiodensity, and brain radiomass. These metrics gain clinical meaning when viewed relative to a reference database and expressed as quantile regression values. Quantitative imaging data can aid in objective reporting and in the identification of outliers, with possible diagnostic implications. The comparison to a reference database necessitates standardization of head CT imaging parameters and protocols. Future research is needed to learn the effects of virtual monochromatic imaging on the quantitative characteristics of head CT images.
© 2021 by American Journal of Neuroradiology.

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Year:  2021        PMID: 34140278      PMCID: PMC8367614          DOI: 10.3174/ajnr.A7153

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   4.966


  50 in total

1.  Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers.

Authors:  E Courchesne; H J Chisum; J Townsend; A Cowles; J Covington; B Egaas; M Harwood; S Hinds; G A Press
Journal:  Radiology       Date:  2000-09       Impact factor: 11.105

2.  Decomposing the Hounsfield unit: probabilistic segmentation of brain tissue in computed tomography.

Authors:  A Kemmling; H Wersching; K Berger; S Knecht; C Groden; I Nölte
Journal:  Clin Neuroradiol       Date:  2012-01-21       Impact factor: 3.649

3.  Radiopacity of intracerebral hemorrhage correlates with perihemorrhagic edema.

Authors:  I Wagner; B Volbers; M J Hilz; S Schwab; A Doerfler; D Staykov
Journal:  Eur J Neurol       Date:  2011-09-26       Impact factor: 6.089

4.  The apical artifact: elevated attenuation values toward the apex of the skull.

Authors:  G Di Chiro; R A Brooks; L Dubal; E Chew
Journal:  J Comput Assist Tomogr       Date:  1978-01       Impact factor: 1.826

5.  Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Multiple Sclerosis Collaborative Research Group.

Authors:  R A Rudick; E Fisher; J C Lee; J Simon; L Jacobs
Journal:  Neurology       Date:  1999-11-10       Impact factor: 9.910

6.  The effect of skull volume and density on differentiating gray and white matter on routine computed tomography scans of the head.

Authors:  Carter Craddock; Michael Y Chen; Robert L Dixon; Christopher A Schlarb; Daniel W Williams
Journal:  J Comput Assist Tomogr       Date:  2006 Sep-Oct       Impact factor: 1.826

7.  The effects of the skull on CT imaging of the brain: a skull and brain phantom study.

Authors:  Keith A Cauley; Patrick J Yorks; Sarah Flora; Samuel W Fielden
Journal:  Br J Radiol       Date:  2021-02-03       Impact factor: 3.039

8.  Pediatric Head CT: Automated Quantitative Analysis with Quantile Regression.

Authors:  K A Cauley; Y Hu; S W Fielden
Journal:  AJNR Am J Neuroradiol       Date:  2020-12-10       Impact factor: 3.825

9.  Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain.

Authors:  Lucas D Eggert; Jens Sommer; Andreas Jansen; Tilo Kircher; Carsten Konrad
Journal:  PLoS One       Date:  2012-09-18       Impact factor: 3.240

10.  Quantitative analysis of computed tomography images and early detection of cerebral edema for pediatric traumatic brain injury patients: retrospective study.

Authors:  Hakseung Kim; Gwang-dong Kim; Byung C Yoon; Keewon Kim; Byung-Jo Kim; Young Hun Choi; Marek Czosnyka; Byung-Mo Oh; Dong-Joo Kim
Journal:  BMC Med       Date:  2014-10-22       Impact factor: 8.775

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  2 in total

Review 1.  Computational Approaches for Acute Traumatic Brain Injury Image Recognition.

Authors:  Emily Lin; Esther L Yuh
Journal:  Front Neurol       Date:  2022-03-09       Impact factor: 4.003

2.  Medical image fusion quality assessment based on conditional generative adversarial network.

Authors:  Lu Tang; Yu Hui; Hang Yang; Yinghong Zhao; Chuangeng Tian
Journal:  Front Neurosci       Date:  2022-08-09       Impact factor: 5.152

  2 in total

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