Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction

authors:

avatar Samaneh Mostafapour , avatar Hossein Arabi , *

Koomesh: Vol.23, issue 1; 64-71
published online: December 01, 2021
article type: issue_documents_importer
received: January 02, 2020
accepted: July 01, 2020

how to cite: Mostafapour S, Arabi H. Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction. koomesh. 2021;23(1):e153240. 

Abstract

Introduction: Magnetic Resonance (MR) imaging has emerged as a valuable tool in radiation treatment (RT) planning as well as Positron Emission Tomography (PET) imaging owing to its superior soft-tissue contrast. Due to the fact that there is no direct transformation from voxel intensity in MR images into electron density, it;#39s crucial to generate a pseudo-CT (Computed Tomography) image from MRI for the task of MR-guided attenuation correction and RT planning. This study set out to investigate the performance of two state-of-the-art atlas-based pseudo-CT generation approaches from MR brain images. Materials and Methods: Bone segmentation was performed on 43 brain CT and MR pairs using atlas-based local weighting (Atlas-LW) and atlas registration combined with pattern recognition (AT-PR) techniques. The accuracy of bone extraction performed by these two approaches was investigated for the entire bony structures as well as cortical bone using standard segmentation metrics such as Dice similarity (DSC). Moreover, the accuracy of the CT value estimated from MR images was evaluated using mean absolute error (MAE), and root mean square error (RMSE) metrics. Results: Overall, Atlas-LW technique exhibited higher segmentation accuracy resulting in DSC=0.79±0.61 and 0.84±0.03, while AT-PR method led to DSC=0.72±0.8 and 0.77±0.05 for cortical and total bone, respectively. Moreover, Atlas-LW approach estimated CT values for the total bone tissue with MAE=17.6±138.0 HU and RMSE=466.2±75.0 HU compared to MAE=-10.9±147.0 HU and RMSE=522.5±89.7 HU obtained from AT-PR approach. Conclusion: The Atlas-LW exhibited superior the performance in this study which demonstrates its potential to be employed in PET/MR attenuation correction (AC) and MR-only RT planning

References

  • 1.

    Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in radiation therapy. J Magn Reson Imaging 2018; 48: 1468-1478.

  • 2.

    Ulin K, Urie MM, Cherlow JM. Results of a multi-institutional benchmark test for cranial CT/MR image registration. Int J Radiat Oncol Biol Phys 2010; 77: 1584-1589.

  • 3.

    Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, et al. Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region. Med Phys 2018; 45: 5218-5233.

  • 4.

    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: 1130-1155.

  • 5.

    Chen Y, An H. Attenuation Correction of PET/MR Imaging. Magn Reson Imaging Clin N Am 2017; 25: 245-255.

  • 6.

    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.

  • 7.

    Arabi H, Zaidi H. Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging. Med Phys 2016; 43: 5848.

  • 8.

    Arabi H, Zaidi H. One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI. Eur J Nucl Med Mol Imaging 2016; 43: 2021-2035.

  • 9.

    Arabi H, Rager O, Alem A, Varoquaux A, Becker M, Zaidi H. Clinical assessment of MR-guided 3-class and 4-class attenuation correction in PET/MR. Mol Imaging Biol 2015; 17: 264-276.

  • 10.

    Bortolin K, Arabi H, Zaidi H. Deep learning-guided attenuation and scatter correction in brain PET/MRI without using anatomical images. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019.

  • 11.

    Bahrami A, Karimian A, Fatemizadeh E, Arabi H. Zaidi H. A novel convolutional neural network with high convergence rate: Application to CT synthesis from MR images. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019.

  • 12.

    Arabi H, Zaidi H. Three-dimensional shape completion using deep convolutional neural networks: Application to truncation compensation and metal artifact reduction in PET/MRI attenuation correction. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019.

  • 13.

    Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum Brain Mapp 2020; 41: 3667-3679.

  • 14.

    Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal 2016; 31: 1-15.

  • 15.

    Arabi H, Koutsouvelis N, Rouzaud M, Miralbell R, Zaidi H. Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning. Phys Med Biol 2016; 61: 6531-6552.

  • 16.

    Arabi H, Zaidi H, editors. MRI-based pseudo-CT generation using sorted atlas images in whole-body PET/MRI. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC); 2014.

  • 17.

    Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging 2014; 33: 2332-2341.

  • 18.

    Andreasen D, Van Leemput K, Hansen RH, Andersen JA, Edmund JM. Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain. Med phys 2015; 42: 1596-1605.

  • 19.

    Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J Nucl Med 2008; 49: 1875-1883.

  • 20.

    Sekine T, Ter Voert EE, Warnock G, Buck A, Huellner M, Veit-Haibach P, et al. Clinical evaluation of Zero-Echo-Time attenuation correction for brain 18F-FDG PET/MRI: comparison with Atlas attenuation correction. J Nucl Med 2016; 57: 1927-1932.

  • 21.

    Montazeri M. Machine learning models for predicting the diagnosis of liver disease. Koomesh 2014; 16. (Persian).

  • 22.

    Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, et al. Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning. Int J Radiat Oncol Biol Phys 2019; 105: 1137-1150.

  • 23.

    Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010; 29: 196-205.

  • 24.

    Akbarzadeh A, Gutierrez D, Baskin A, Ay MR, Ahmadian A, Riahi Alam N, et al. Evaluation of whole-body MR to CT deformable image registration. J Appl Clin Med Phys 2013; 14: 4163.

  • 25.

    Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation-and atlas-based methods. J Nucl Med 2011; 52: 1392-1399.

  • 26.

    Haghparast A, Hashemi B, Eivazi MT. An assessment of the factors involved in effective attenuation coefficient of the compensator material for the treatment with 6MV photons using intensity modulated radiation therapy method. Koomesh 2011; 12: 279-184.

  • 27.

    Arabi H, Zaidi H. Comparison of atlas-based techniques for whole-body bone segmentation. Med Image Anal 2017; 36: 98-112.

  • 28.

    Arabi H, Zeng G, Zheng G, Zaidi H. Does deep learning approaches outperform atlas-guided attenuation correction in brain PET/MRI? J Nucl Med 2019; 60: 175.

  • 29.

    Arabi H, Zaidi H. Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data. Med Image Anal 2020; 64: 101718.