Literature DB >> 25304701

Iterative reconstruction: how it works, how to apply it.

James Anthony Seibert1.   

Abstract

Computed tomography acquires X-ray projection data from multiple angles though an object to generate a tomographic rendition of its attenuation characteristics. Filtered back projection is a fast, closed analytical solution to the reconstruction process, whereby all projections are equally weighted, but is prone to deliver inadequate image quality when the dose levels are reduced. Iterative reconstruction is an algorithmic method that uses statistical and geometric models to variably weight the image data in a process that can be solved iteratively to independently reduce noise and preserve resolution and image quality. Applications of this technology in a clinical setting can result in lower dose on the order of 20-40% compared to a standard filtered back projection reconstruction for most exams. A carefully planned implementation strategy and methodological approach is necessary to achieve the goals of lower dose with uncompromised image quality.

Mesh:

Year:  2014        PMID: 25304701     DOI: 10.1007/s00247-014-3102-1

Source DB:  PubMed          Journal:  Pediatr Radiol        ISSN: 0301-0449


  15 in total

1.  Iterative reconstruction in head CT: image quality of routine and low-dose protocols in comparison with standard filtered back-projection.

Authors:  A Korn; M Fenchel; B Bender; S Danz; T K Hauser; D Ketelsen; T Flohr; C D Claussen; M Heuschmid; U Ernemann; H Brodoefel
Journal:  AJNR Am J Neuroradiol       Date:  2011-10-27       Impact factor: 3.825

2.  Automatic parameter selection for denoising algorithms using a no-reference measure of image content.

Authors:  Xiang Zhu; Peyman Milanfar
Journal:  IEEE Trans Image Process       Date:  2010-06-14       Impact factor: 10.856

3.  A three-dimensional statistical approach to improved image quality for multislice helical CT.

Authors:  Jean-Baptiste Thibault; Ken D Sauer; Charles A Bouman; Jiang Hsieh
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

4.  NCRP Report No. 160, Ionizing Radiation Exposure of the Population of the United States, medical exposure--are we doing less with more, and is there a role for health physicists?

Authors:  D A Schauer; O W Linton
Journal:  Health Phys       Date:  2009-07       Impact factor: 1.316

5.  Lowering the dose in head CT using adaptive statistical iterative reconstruction.

Authors:  K Kilic; G Erbas; M Guryildirim; M Arac; E Ilgit; B Coskun
Journal:  AJNR Am J Neuroradiol       Date:  2011-08-11       Impact factor: 3.825

6.  Radiation dose reduction with chest computed tomography using adaptive statistical iterative reconstruction technique: initial experience.

Authors:  Priyanka Prakash; Mannudeep K Kalra; Subba R Digumarthy; Jiang Hsieh; Homer Pien; Sarabjeet Singh; Matthew D Gilman; Jo-Anne O Shepard
Journal:  J Comput Assist Tomogr       Date:  2010-01       Impact factor: 1.826

7.  Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise.

Authors:  Samuel L Brady; Bria M Moore; Brian S Yee; Robert A Kaufman
Journal:  Radiology       Date:  2013-10-28       Impact factor: 11.105

8.  Adaptive statistical iterative reconstruction technique for radiation dose reduction in chest CT: a pilot study.

Authors:  Sarabjeet Singh; Mannudeep K Kalra; Matthew D Gilman; Jiang Hsieh; Homer H Pien; Subba R Digumarthy; Jo-Anne O Shepard
Journal:  Radiology       Date:  2011-03-08       Impact factor: 11.105

9.  Reducing abdominal CT radiation dose with the adaptive statistical iterative reconstruction technique in children: a feasibility study.

Authors:  Gregory A Vorona; Rafael C Ceschin; Barbara L Clayton; Tom Sutcavage; Sameh S Tadros; Ashok Panigrahy
Journal:  Pediatr Radiol       Date:  2011-05-19

10.  CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction.

Authors:  Yasutaka Ichikawa; Kakuya Kitagawa; Naoki Nagasawa; Shuichi Murashima; Hajime Sakuma
Journal:  BMC Med Imaging       Date:  2013-08-09       Impact factor: 1.930

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  6 in total

1.  Spatial and contrast resolution of ultralow dose dentomaxillofacial CT imaging using iterative reconstruction technology.

Authors:  Gerlig Widmann; Alexander Bischel; Andreas Stratis; Hilde Bosmans; Reinhilde Jacobs; Eva-Maria Gassner; Wolfgang Puelacher; Ruben Pauwels
Journal:  Dentomaxillofac Radiol       Date:  2017-02-17       Impact factor: 2.419

2.  Comparison of Iterative Model Reconstruction versus Filtered Back-Projection in Pediatric Emergency Head CT: Dose, Image Quality, and Image-Reconstruction Times.

Authors:  R N Southard; D M E Bardo; M H Temkit; M A Thorkelson; R A Augustyn; C A Martinot
Journal:  AJNR Am J Neuroradiol       Date:  2019-04-11       Impact factor: 3.825

3.  The changing use of pediatric CT in Australia.

Authors:  Zoe Brady; Anna V Forsythe; John D Mathews
Journal:  Pediatr Radiol       Date:  2016-03-07

4.  Assessment of Low-Contrast Resolution for the American College of Radiology Computed Tomographic Accreditation Program: What Is the Impact of Iterative Reconstruction?

Authors:  James M Kofler; Lifeng Yu; Shuai Leng; Yi Zhang; Zhoubo Li; Rickey E Carter; Cynthia H McCollough
Journal:  J Comput Assist Tomogr       Date:  2015 Jul-Aug       Impact factor: 1.826

Review 5.  Postnatal Evaluation of Congenital Chest Pathologies Using a Low-Dose Computed Tomography (CT) Protocol - a Pictorial Review.

Authors:  Danuta Roik; Marzena Barczuk; Zofia Burzyńska; Agnieszka Biejat; Maria Żerańska; Magdalena Mierzewska-Schmidt; Tomasz Floriańczyk; Michał Brzewski
Journal:  Pol J Radiol       Date:  2017-08-23

6.  Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study.

Authors:  Chae Jung Park; Ki Wook Kim; Ho-Joon Lee; Myeong-Jin Kim; Jinna Kim
Journal:  Korean J Radiol       Date:  2018-08-06       Impact factor: 3.500

  6 in total

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