Evaluation of Three Methods for Delineation and Attenuation Estimation of the Sinus Region in MR-based Attenuation Correction for Brain PET-MR Imaging

Overview

Journal BMC Med Imaging

Publisher Biomed Central

Specialty Radiology

Date 2022 Mar 18

PMID 35300592

Authors

Jani Linden

Jarmo Teuho

Mika Teras

Riku Klen

Affiliations

Soon will be listed here.

Abstract

Background: Attenuation correction is crucial in quantitative positron emission tomography-magnetic resonance (PET-MRI) imaging. We evaluated three methods to improve the segmentation and modelling of the attenuation coefficients in the nasal sinus region. Two methods (cuboid and template method) included a MRI-CT conversion model for assigning the attenuation coefficients in the nasal sinus region, whereas one used fixed attenuation coefficient assignment (bulk method).

Methods: The study population consisted of data of 10 subjects which had undergone PET-CT and PET-MRI. PET images were reconstructed with and without time-of-flight (TOF) using CT-based attenuation correction (CTAC) as reference. Comparison was done visually, using DICE coefficients, correlation, analyzing attenuation coefficients, and quantitative analysis of PET and bias atlas images.

Results: The median DICE coefficients were 0.824, 0.853, 0.849 for the bulk, cuboid and template method, respectively. The median attenuation coefficients were 0.0841 cm, 0.0876 cm, 0.0861 cm and 0.0852 cm, for CTAC, bulk, cuboid and template method, respectively. The cuboid and template methods showed error of less than 2.5% in attenuation coefficients. An increased correlation to CTAC was shown with the cuboid and template methods. In the regional analysis, improvement in at least 49% and 80% of VOI was seen with non-TOF and TOF imaging. All methods showed errors less than 2.5% in non-TOF and less than 2% in TOF reconstructions.

Conclusions: We evaluated two proof-of-concept methods for improving quantitative accuracy in PET/MRI imaging and showed that bias can be further reduced by inclusion of TOF. Largest improvements were seen in the regions of olfactory bulb, Heschl's gyri, lingual gyrus and cerebellar vermis. However, the overall effect of inclusion of the sinus region as separate class in MRAC to PET quantification in the brain was considered modest.

References

Ladefoged C, Law I, Anazodo U, St Lawrence K, Izquierdo-Garcia D, Catana C . A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients. Neuroimage. 2016; 147:346-359. PMC: 6818242. DOI: 10.1016/j.neuroimage.2016.12.010. View

Conti M . Why is TOF PET reconstruction a more robust method in the presence of inconsistent data?. Phys Med Biol. 2010; 56(1):155-68. DOI: 10.1088/0031-9155/56/1/010. View

Ladefoged C, Benoit D, Law I, Holm S, Kjaer A, Hojgaard L . Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging. Phys Med Biol. 2015; 60(20):8047-65. DOI: 10.1088/0031-9155/60/20/8047. View

Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N . Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002; 15(1):273-89. DOI: 10.1006/nimg.2001.0978. View

Ranta I, Teuho J, Linden J, Klen R, Teras M, Kapanen M . Assessment of MRI-Based Attenuation Correction for MRI-Only Radiotherapy Treatment Planning of the Brain. Diagnostics (Basel). 2020; 10(5). PMC: 7278007. DOI: 10.3390/diagnostics10050299. View

Yu H, Caldwell C, Balogh J, Mah K . Toward magnetic resonance-only simulation: segmentation of bone in MR for radiation therapy verification of the head. Int J Radiat Oncol Biol Phys. 2014; 89(3):649-57. DOI: 10.1016/j.ijrobp.2014.03.028. View

Merida I, Reilhac A, Redoute J, Heckemann R, Costes N, Hammers A . Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR. Phys Med Biol. 2017; 62(7):2834-2858. DOI: 10.1088/1361-6560/aa5f6c. View

Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi M . Physical performance of the new hybrid PET∕CT Discovery-690. Med Phys. 2011; 38(10):5394-411. DOI: 10.1118/1.3635220. View

Zaidi H, Ojha N, Morich M, Griesmer J, Hu Z, Maniawski P . Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys Med Biol. 2011; 56(10):3091-106. PMC: 4059360. DOI: 10.1088/0031-9155/56/10/013. View

10.

Chen K, Izquierdo-Garcia D, Poynton C, Chonde D, Catana C . On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners. Eur J Nucl Med Mol Imaging. 2016; 44(3):398-407. PMC: 5285302. DOI: 10.1007/s00259-016-3489-z. View

11.

Hitz S, Habekost C, Furst S, Delso G, Forster S, Ziegler S . Systematic Comparison of the Performance of Integrated Whole-Body PET/MR Imaging to Conventional PET/CT for ¹⁸F-FDG Brain Imaging in Patients Examined for Suspected Dementia. J Nucl Med. 2014; 55(6):923-31. DOI: 10.2967/jnumed.113.126813. View

12.

Catana C, van der Kouwe A, Benner T, Michel C, Hamm M, Fenchel M . Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med. 2010; 51(9):1431-8. PMC: 3801218. DOI: 10.2967/jnumed.109.069112. View

13.

Mehranian A, Zaidi H, Reader A . MR-guided joint reconstruction of activity and attenuation in brain PET-MR. Neuroimage. 2017; 162:276-288. DOI: 10.1016/j.neuroimage.2017.09.006. View

14.

Yang J, Wiesinger F, Kaushik S, Shanbhag D, Hope T, Larson P . Evaluation of Sinus/Edge-Corrected Zero-Echo-Time-Based Attenuation Correction in Brain PET/MRI. J Nucl Med. 2017; 58(11):1873-1879. PMC: 6944168. DOI: 10.2967/jnumed.116.188268. View

15.

Sousa J, Appel L, Merida I, Heckemann R, Costes N, Engstrom M . Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [C]PE2I PET-MR brain imaging. EJNMMI Phys. 2020; 7(1):77. PMC: 7769756. DOI: 10.1186/s40658-020-00347-2. View

16.

Mehranian A, Arabi H, Zaidi H . Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities. Med Phys. 2016; 43(3):1130-55. DOI: 10.1118/1.4941014. View

17.

Ouyang J, Chun S, Petibon Y, Bonab A, Alpert N, El Fakhri G . Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR. IEEE Trans Nucl Sci. 2014; 60(5):3373-3382. PMC: 4067048. DOI: 10.1109/TNS.2013.2278624. View

18.

Martinez-Moller A, Nekolla S . Attenuation correction for PET/MR: problems, novel approaches and practical solutions. Z Med Phys. 2012; 22(4):299-310. DOI: 10.1016/j.zemedi.2012.08.003. View

19.

Son Y, Kim H, Kim S, Kim N, Kim Y, Cho Z . Analysis of biased PET images caused by inaccurate attenuation coefficients. J Nucl Med. 2010; 51(5):753-60. DOI: 10.2967/jnumed.109.070326. View

20.

Mehranian A, Arabi H, Zaidi H . Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI. Neuroimage. 2016; 130:123-133. DOI: 10.1016/j.neuroimage.2016.01.060. View