Literature DB >> 21216160

MDCT for computerized volumetry of pneumothoraces in pediatric patients.

Wenli Cai1, Edward Y Lee, Abhinav Vij, Soran A Mahmood, Hiroyuki Yoshida.   

Abstract

RATIONALE AND
OBJECTIVES: Our purpose in this study was to develop an automated computer-aided volumetry (CAV) scheme for quantifying pneumothorax in multidetector computed tomography (MDCT) images for pediatric patients and to investigate the imaging parameters that may affect its accuracy.
MATERIALS AND METHODS: Fifty-eight consecutive pediatric patients (mean age 12 ± 6 years) with pneumothorax who underwent MDCT for evaluation were collected retrospectively for this study. All cases were imaged by a 16- or 64-MDCT scanner with weight-based kilovoltage, low-dose tube current, 1.0-1.5 pitch, 0.6-5.0 mm slice thickness, and a B70f (sharp) or B31f (soft) reconstruction kernel. Sixty-three pneumothoraces ≥1 mL were visually identified in the left (n = 30) and right (n = 33) lungs. Each identified pneumothorax was contoured manually on an Amira workstation V4.1.1 (Mercury Computer Systems, Chelmsford, MA) by two radiologists in consensus. The computerized volumes of the pneumothoraces were determined by application of our CAV scheme. The accuracy of our automated CAV scheme was evaluated by comparison between computerized volumetry and manual volumetry, for the total volume of pneumothoraces in the left and right lungs.
RESULTS: The mean difference between the computerized volumetry and the manual volumetry for all 63 pneumothoraces ≥1 mL was 8.2%. For pneumothoraces ≥10 mL, ≥50 mL, and ≥200 mL, the mean differences were 7.7% (n = 57), 7.3% (n = 33), and 6.4% (n = 13), respectively. The correlation coefficient was 0.99 between the computerized volume and the manual volume of pneumothoraces. Bland-Altman analysis showed that computerized volumetry has a mean difference of -5.1% compared to manual volumetry. For all pneumothoraces ≥10 mL, the mean differences for slice thickness ≤1.25 mm, = 1.5 mm, and = 5.0 mm were 6.1% (n = 28), 3.5% (n = 10), and 12.2% (n = 19), respectively. For the two reconstruction kernels, B70f and B31f, the mean differences were 6.3% (n = 42, B70f) and 11.7% (n = 15, B31f), respectively.
CONCLUSION: Our automated CAV scheme provides an accurate measurement of pneumothorax volume in MDCT images of pediatric patients. For accurate volumetric quantification of pneumothorax in children in MDCT images by use of the automated CAV scheme, we recommended reconstruction parameters based on a slice thickness ≤1.5 mm and the reconstruction kernel B70f.
Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

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Mesh:

Year:  2011        PMID: 21216160      PMCID: PMC3072076          DOI: 10.1016/j.acra.2010.11.008

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  21 in total

1.  Incidence, risk factors, and outcomes for occult pneumothoraces in victims of major trauma.

Authors:  Chad G Ball; Andrew W Kirkpatrick; Kevin B Laupland; Daniel I Fox; Savvas Nicolaou; Ian B Anderson; S Morad Hameed; John B Kortbeek; Robert R Mulloy; Stacey Litvinchuk; Bernard R Boulanger
Journal:  J Trauma       Date:  2005-10

2.  Dynamic-threshold level set method for volumetry of porcine kidney in CT images in vivo and ex vivo assessment of the accuracy of volume measurement.

Authors:  Wenli Cai; Nagaraj-Setty Holalkere; Gordon Harris; Dushyant Sahani; Hiroyuki Yoshida
Journal:  Acad Radiol       Date:  2007-07       Impact factor: 3.173

Review 3.  Pneumothorax.

Authors:  Marc Noppen; Tom De Keukeleire
Journal:  Respiration       Date:  2008-06-26       Impact factor: 3.580

4.  Pediatric chest CT after trauma: impact on surgical and clinical management.

Authors:  Rina P Patel; Marta Hernanz-Schulman; Melissa A Hilmes; Chang Yu; Jackie Ray; J Herman Kan
Journal:  Pediatr Radiol       Date:  2010-02-24

5.  Complications of tube thoracostomy in trauma.

Authors:  R C Bailey
Journal:  J Accid Emerg Med       Date:  2000-03

6.  Detection of occult pneumothoraces on abdominal computed tomographic scans in trauma patients.

Authors:  M A Neff; J S Monk; K Peters; A Nikhilesh
Journal:  J Trauma       Date:  2000-08

7.  Incidence and management of occult hemothoraces.

Authors:  Renae E Stafford; John Linn; Lacey Washington
Journal:  Am J Surg       Date:  2006-12       Impact factor: 2.565

8.  MDCT for automated detection and measurement of pneumothoraces in trauma patients.

Authors:  Wenli Cai; Malek Tabbara; Noboru Takata; Hiroyuki Yoshida; Gordon J Harris; Robert A Novelline; Marc de Moya
Journal:  AJR Am J Roentgenol       Date:  2009-03       Impact factor: 3.959

9.  How accurate is the Light index for estimating pneumothorax size?

Authors:  K Hoi; B Turchin; A-M Kelly
Journal:  Australas Radiol       Date:  2007-04

10.  Chest tube complications: how well are we training our residents?

Authors:  Chad G Ball; Jason Lord; Kevin B Laupland; Scott Gmora; Robert H Mulloy; Alex K Ng; Colin Schieman; Andrew W Kirkpatrick
Journal:  Can J Surg       Date:  2007-12       Impact factor: 2.089

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  7 in total

1.  Iterative mesh transformation for 3D segmentation of livers with cancers in CT images.

Authors:  Difei Lu; Yin Wu; Gordon Harris; Wenli Cai
Journal:  Comput Med Imaging Graph       Date:  2015-01-28       Impact factor: 4.790

2.  Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.

Authors:  Sebastian Röhrich; Thomas Schlegl; Constanze Bardach; Helmut Prosch; Georg Langs
Journal:  Eur Radiol Exp       Date:  2020-04-17

Review 3.  Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.

Authors:  Steven Schalekamp; Willemijn M Klein; Kicky G van Leeuwen
Journal:  Pediatr Radiol       Date:  2021-09-01

4.  MDCT quantification is the dominant parameter in decision-making regarding chest tube drainage for stable patients with traumatic pneumothorax.

Authors:  Wenli Cai; June-Goo Lee; Karim Fikry; Hiroyuki Yoshida; Robert Novelline; Marc de Moya
Journal:  Comput Med Imaging Graph       Date:  2012-05-04       Impact factor: 4.790

Review 5.  Artificial intelligence in paediatric radiology: Future opportunities.

Authors:  Natasha Davendralingam; Neil J Sebire; Owen J Arthurs; Susan C Shelmerdine
Journal:  Br J Radiol       Date:  2020-09-17       Impact factor: 3.039

6.  The frequency of reexpansion pulmonary edema after trocar and hemostat assisted thoracostomy in patients with spontaneous pneumothorax.

Authors:  Kyoung Chul Cha; Hyun Kim; Ho Jin Ji; Woo Cheol Kwon; Hyung Jin Shin; Yong Sung Cha; Kang Hyun Lee; Sung Oh Hwang; Christopher C Lee; Adam J Singer
Journal:  Yonsei Med J       Date:  2013-01-01       Impact factor: 2.759

7.  Radiation dosimetry changes in radiotherapy treatment plans for adult patients arising from the selection of the CT image reconstruction kernel.

Authors:  Anne T Davis; Sarah Muscat; Antony L Palmer; David Buckle; James Earley; Matthew G J Williams; Andrew Nisbet
Journal:  BJR Open       Date:  2019-07-30
  7 in total

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