Literature DB >> 20332373

The skull unfolded: a cranial CT visualization algorithm for fast and easy detection of skull fractures.

Helmut Ringl1, Ruediger E Schernthaner, Gerd Schueller, Csilla Balassy, Daniela Kienzl, Ana Botosaneanu, Michael Weber, Christian Czerny, Stefan Hajdu, Thomas Mang, Christian J Herold, Wolfgang Schima.   

Abstract

PURPOSE: To retrospectively assess the rate of detection of skull fractures at cranial computed tomography (CT) achieved with the use of curved maximum intensity projections (MIPs) compared with that achieved by reading transverse sections only.
MATERIALS AND METHODS: The institutional review board approved this research and waived informed consent. A curved thin (3-mm) MIP of the skull cap and a curved thick (50-mm) MIP of the skull base were obtained from the cranial CT data in 200 consecutive patients with head trauma. Four radiologists (two residents without experience in cranial CT and two consultants) independently evaluated all cases. Each radiologist reported findings in 100 patients by using transverse sections only and findings in the other 100 patients by using the unfolded view. The radiologists were blinded to patient names, and patient and group orders were randomized. The results were compared with a standard of reference established by two experts from all prior reading results, all reconstructions, and high-spatial-resolution multiplanar reformats. Logistic regression with repeated measurements was used for statistical analysis.
RESULTS: The experts found 63 fractures in 30 patients. When transverse sections only were used, the mean patient-based fracture detection rate was 43% (13 of 30) for inexperienced and 70% (21 of 30) for experienced readers; with curved MIPs, the rates were 80% (24 of 30) and 87% (26 of 30), respectively. Overall sensitivity was higher with curved MIPs (P < .001); specificity was higher with transverse sections (P < .001).
CONCLUSION: Curved MIPs enable a significantly higher fracture detection rate than transverse sections. They also considerably close the experience gap in fracture detection rate between residents and experts.

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Year:  2010        PMID: 20332373     DOI: 10.1148/radiol.10091096

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  8 in total

1.  Trainee misinterpretations on pediatric neuroimaging studies: classification, imaging analysis, and outcome assessment.

Authors:  C V A Guimaraes; J L Leach; B V Jones
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2.  The ribs unfolded - a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients.

Authors:  Helmut Ringl; Mathias Lazar; Michael Töpker; Ramona Woitek; Helmut Prosch; Ulrika Asenbaum; Csilla Balassy; Daniel Toth; Michael Weber; Stefan Hajdu; Grzegorz Soza; Andreas Wimmer; Thomas Mang
Journal:  Eur Radiol       Date:  2015-02-14       Impact factor: 5.315

3.  Skull fractures in pediatric patients on computerized tomogram: comparison between routing bone window images and 3D volume-rendered images.

Authors:  Sathish Kumar Dundamadappa; Senthur Thangasamy; Nancy Resteghini; Srinivasan Vedantham; Andrew Chen; Deepak Takhtani
Journal:  Emerg Radiol       Date:  2015-02-20

4.  Effect of Bone Reading CT software on radiologist performance in detecting bone metastases from breast cancer.

Authors:  Ji Y Ha; Kyung N Jeon; Kyungsoo Bae; Bong H Choi
Journal:  Br J Radiol       Date:  2017-03-03       Impact factor: 3.039

5.  Assessment of Rib Fracture in Acute Trauma Using Automatic Rib Segmentation and a Curved, Unfolded View of the Ribs: Is There a Saving of Time?

Authors:  Benedikt Pregler; Lukas Philipp Beyer; Natascha Platz Batista da Silva; Sebastian Steer; Florian Zeman; Daniel Popp; Christian Stroszczynski; René Müller-Wille
Journal:  J Clin Med       Date:  2022-04-29       Impact factor: 4.964

6.  The Comprehensive AOCMF Classification System: Radiological Issues and Systematic Approach.

Authors:  Carlos H Buitrago-Téllez; Carl-Peter Cornelius; Joachim Prein; Christoph Kunz; Antonio di Ieva; Laurent Audigé
Journal:  Craniomaxillofac Trauma Reconstr       Date:  2014-12

7.  The First AO Classification System for Fractures of the Craniomaxillofacial Skeleton: Rationale, Methodological Background, Developmental Process, and Objectives.

Authors:  Laurent Audigé; Carl-Peter Cornelius; Antonio Di Ieva; Joachim Prein
Journal:  Craniomaxillofac Trauma Reconstr       Date:  2014-12

8.  Clinical comparison of the predictive value of the simple skull x-ray and 3 dimensional computed tomography for skull fractures of children.

Authors:  Young-Im Kim; Jong-Woo Cheong; Soo Han Yoon
Journal:  J Korean Neurosurg Soc       Date:  2012-12-31
  8 in total

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