Literature DB >> 26956288

CT synthesis in the head & neck region for PET/MR attenuation correction: an iterative multi-atlas approach.

Ninon Burgos1, M Jorge Cardoso1,2, Marc Modat1,2, Shonit Punwani3,4, David Atkinson4, Simon R Arridge5, Brian F Hutton6, Sébastien Ourselin1,2.   

Abstract

Entities:  

Year:  2015        PMID: 26956288      PMCID: PMC4798679          DOI: 10.1186/2197-7364-2-S1-A31

Source DB:  PubMed          Journal:  EJNMMI Phys        ISSN: 2197-7364


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In this work, we propose to tackle the problem of attenuation correction in the head and neck by synthesising CT from MR images using an iterative multi-atlas approach. The proposed method relies on pre-acquired T2-weighted MRI and CT images of the neck. For each subject, the MRI is non-rigidly mapped to the CT. To synthesise a pseudo CT, all the MRIs in the database are first registered to the target MRI. This registration consists of a robust affine followed by a non-rigid registration. The pseudo CT is obtained by fusing the mapped atlases according to their morphological similarity to the target. In contrast to CTs, T2 images do not provide a good estimate of the bone location. Combining multiple modalities at both the registration and image similarity stages is expected to provide more realistic mappings and to reduce the bias. An initial pseudo CT (pCT) is combined with the target MRI to form a MRI-pCT pair. The MRI-pCT pair is registered to all the MRI-CT pairs from the database. An improved pseudo CT is obtained by fusing the mapped MRI-CT pairs according to their morphological similarity to the target MRI-pCT pair. Results showed that the proposed CT synthesis algorithm based on a multi-atlas information propagation scheme and iterative process is able to synthesise pseudo CT images in a region challenging for registration algorithms. The results also demonstrate that the robust affine decreases the absolute error compared to the classic approach and that the bone refinement process reduces the bias in the bone region. The proposed method could be used to correct for attenuation PET/MR data, but also for dosimetry calculations in the context of MR-based radiotherapy treatment planning.
  4 in total

1.  Synthesized b0 for diffusion distortion correction (Synb0-DisCo).

Authors:  Kurt G Schilling; Justin Blaber; Yuankai Huo; Allen Newton; Colin Hansen; Vishwesh Nath; Andrea T Shafer; Owen Williams; Susan M Resnick; Baxter Rogers; Adam W Anderson; Bennett A Landman
Journal:  Magn Reson Imaging       Date:  2019-05-07       Impact factor: 2.546

2.  MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Magn Reson Med       Date:  2021-09-04       Impact factor: 3.737

Review 3.  EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies.

Authors:  Nicolas Aide; Charline Lasnon; Patrick Veit-Haibach; Terez Sera; Bernhard Sattler; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-06-16       Impact factor: 9.236

4.  Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development.

Authors:  Christopher A Lane; Thomas D Parker; Dave M Cash; Kirsty Macpherson; Elizabeth Donnachie; Heidi Murray-Smith; Anna Barnes; Suzie Barker; Daniel G Beasley; Jose Bras; David Brown; Ninon Burgos; Michelle Byford; M Jorge Cardoso; Ana Carvalho; Jessica Collins; Enrico De Vita; John C Dickson; Norah Epie; Miklos Espak; Susie M D Henley; Chandrashekar Hoskote; Michael Hutel; Jana Klimova; Ian B Malone; Pawel Markiewicz; Andrew Melbourne; Marc Modat; Anette Schrag; Sachit Shah; Nikhil Sharma; Carole H Sudre; David L Thomas; Andrew Wong; Hui Zhang; John Hardy; Henrik Zetterberg; Sebastien Ourselin; Sebastian J Crutch; Diana Kuh; Marcus Richards; Nick C Fox; Jonathan M Schott
Journal:  BMC Neurol       Date:  2017-04-18       Impact factor: 2.474

  4 in total

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