Literature DB >> 29457287

Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning.

Florian Wiesinger1, Mikael Bylund2, Jaewon Yang3, Sandeep Kaushik4, Dattesh Shanbhag4, Sangtae Ahn5, Joakim H Jonsson2, Josef A Lundman2, Thomas Hope3, Tufve Nyholm2,6, Peder Larson3, Cristina Cozzini1.   

Abstract

PURPOSE: To describe a method for converting Zero TE (ZTE) MR images into X-ray attenuation information in the form of pseudo-CT images and demonstrate its performance for (1) attenuation correction (AC) in PET/MR and (2) dose planning in MR-guided radiation therapy planning (RTP).
METHODS: Proton density-weighted ZTE images were acquired as input for MR-based pseudo-CT conversion, providing (1) efficient capture of short-lived bone signals, (2) flat soft-tissue contrast, and (3) fast and robust 3D MR imaging. After bias correction and normalization, the images were segmented into bone, soft-tissue, and air by means of thresholding and morphological refinements. Fixed Hounsfield replacement values were assigned for air (-1000 HU) and soft-tissue (+42 HU), whereas continuous linear mapping was used for bone.
RESULTS: The obtained ZTE-derived pseudo-CT images accurately resembled the true CT images (i.e., Dice coefficient for bone overlap of 0.73 ± 0.08 and mean absolute error of 123 ± 25 HU evaluated over the whole head, including errors from residual registration mismatches in the neck and mouth regions). The linear bone mapping accounted for bone density variations. Averaged across five patients, ZTE-based AC demonstrated a PET error of -0.04 ± 1.68% relative to CT-based AC. Similarly, for RTP assessed in eight patients, the absolute dose difference over the target volume was found to be 0.23 ± 0.42%.
CONCLUSION: The described method enables MR to pseudo-CT image conversion for the head in an accurate, robust, and fast manner without relying on anatomical prior knowledge. Potential applications include PET/MR-AC, and MR-guided RTP.
© 2018 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  PET/MR; RTP; RUFIS; ZTE; attenuation correction; pseudo-CT; radiation therapy planning; synthetic CT

Mesh:

Year:  2018        PMID: 29457287     DOI: 10.1002/mrm.27134

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  27 in total

1.  Rapid dual-RF, dual-echo, 3D ultrashort echo time craniofacial imaging: A feasibility study.

Authors:  Hyunyeol Lee; Xia Zhao; Hee Kwon Song; Rosaline Zhang; Scott P Bartlett; Felix W Wehrli
Journal:  Magn Reson Med       Date:  2018-12-18       Impact factor: 4.668

2.  Zero TE MRI for Craniofacial Bone Imaging.

Authors:  A Lu; K R Gorny; M-L Ho
Journal:  AJNR Am J Neuroradiol       Date:  2019-09       Impact factor: 3.825

3.  A Quantitative Evaluation of Joint Activity and Attenuation Reconstruction in TOF PET/MR Brain Imaging.

Authors:  Ahmadreza Rezaei; Georg Schramm; Stefanie M A Willekens; Gaspar Delso; Koen Van Laere; Johan Nuyts
Journal:  J Nucl Med       Date:  2019-04-12       Impact factor: 10.057

Review 4.  Advances in simultaneous PET/MR for imaging neuroreceptor function.

Authors:  Christin Y Sander; Hanne D Hansen; Hsiao-Ying Wey
Journal:  J Cereb Blood Flow Metab       Date:  2020-03-13       Impact factor: 6.200

5.  MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

Authors:  Paul Kyu Han; Debra E Horng; Kuang Gong; Yoann Petibon; Kyungsang Kim; Quanzheng Li; Keith A Johnson; Georges El Fakhri; Jinsong Ouyang; Chao Ma
Journal:  Med Phys       Date:  2020-05-11       Impact factor: 4.071

6.  ZTE MR-based attenuation correction in brain FDG-PET/MR: performance in patients with cognitive impairment.

Authors:  Brian Sgard; Maya Khalifé; Arthur Bouchut; Brice Fernandez; Marine Soret; Alain Giron; Clara Zaslavsky; Gaspar Delso; Marie-Odile Habert; Aurélie Kas
Journal:  Eur Radiol       Date:  2019-11-20       Impact factor: 5.315

7.  Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

Authors:  Jia Guo; Enhao Gong; Audrey P Fan; Maged Goubran; Mohammad M Khalighi; Greg Zaharchuk
Journal:  J Cereb Blood Flow Metab       Date:  2019-11-13       Impact factor: 6.200

8.  Clinical Feasibility of Zero TE Skull MRI in Patients with Head Trauma in Comparison with CT: A Single-Center Study.

Authors:  S B Cho; H J Baek; K H Ryu; B H Choi; J I Moon; T B Kim; S K Kim; H Park; M J Hwang
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-13       Impact factor: 3.825

9.  Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

Authors:  Greg Zaharchuk
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-29       Impact factor: 9.236

Review 10.  Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Authors:  Dana J Lin; Patricia M Johnson; Florian Knoll; Yvonne W Lui
Journal:  J Magn Reson Imaging       Date:  2020-02-12       Impact factor: 4.813

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