Journal article
Pitfalls on PET/MRI
Seminars in nuclear medicine, Vol.51(5), pp.529-539
09/01/2021
DOI: 10.1053/j.semnuclmed.2021.04.003
PMID: 34020770
Abstract
A decade of PET/MRI clinical imaging has passed and many of the pitfalls are similar to those on earlier studies. However, techniques to overcome them have emerged and continue to develop. Although clinically significant lung nodules are demonstrable, smaller nodules may be detected using ultrashort/zero echo-time (TE) lung MRI. Fast reconstruction ultrashort TE sequences have also been used to achieve high-resolution lung MRI even with free-breathing.
The introduction and improvement of time-of-flight scanners and increasing the axial length of the PET detector arrays have more than doubled the sensitivity of the PET part of the system. MRI for attenuation correction has provided many potential pitfalls, including misclassifica-tion of tissue classes based on MRI information for attenuation correction. Although the use of short echo times have helped to address these pitfalls, one of the most exciting developments has been the use of deep learning algorithms and computational neural net-works to rapidly provide soft tissue, fat, bone and air information for the attenuation correc-tion as a supplement to the attenuation correction information from fat-water imaging.
Challenges with motion correction, particularly respiratory and cardiac remain but are being addressed with respiratory monitors and using PET data.
In order to address truncation artefacts, the system manufacturers have developed methods to extend the MR field-of-view for the purpose of the attenuation and scatter corrections. General pitfalls like stitching of body sections for individual studies, optimum delivery of images for viewing and reporting, and resource implications for the sheer volume of data generated remain
Methods to overcome these pitfalls serve as a strong foundation for the future of PET/MRI. Advances in the underlying technology with significant evolution in hard-ware and software and the exiting developments in use of deep learning algorithms and computational neural networks will drive the next decade of PET/MRI imaging. (c) 2021 Elsevier Inc. All rights reserved.
Details
- Title: Subtitle
- Pitfalls on PET/MRI
- Creators
- Asim Afaq - Roy J. and Lucille A. Carver College of MedicineDavid Faul - Medical SolutionsVenkata Veerendranadh Chebrolu - Siemens Medical Solutions, USA, Inc; Department of Radiology, Mayo Clinic, Rochester, MN.Simon Wan - Institute of Nuclear Medicine, UCL/ UCLH London, UKThomas A. Hope - University of California SystemPatrick Veit Haibach - Toronto Joint Dept. Medical Imaging, University Health Network, Sinai Health System, Women's College University of Toronto, CanadaJamshed Bomanji - Institute of Nuclear Medicine, UCL/ UCLH London, UK
- Resource Type
- Journal article
- Publication Details
- Seminars in nuclear medicine, Vol.51(5), pp.529-539
- Publisher
- Elsevier
- DOI
- 10.1053/j.semnuclmed.2021.04.003
- PMID
- 34020770
- ISSN
- 0001-2998
- eISSN
- 1558-4623
- Number of pages
- 11
- Language
- English
- Date published
- 09/01/2021
- Academic Unit
- Radiology
- Record Identifier
- 9984318714502771
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