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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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General Review Article

Single-photon Emission CT Combined with Spiral CT for Early Detection and Localization of Bone Metastasis: A Review

Author(s): Berna Okudan, Pelin Arıcan and Bedri Seven*

Volume 16, Issue 5, 2020

Page: [507 - 512] Pages: 6

DOI: 10.2174/1573405615666181224143010

Price: $65

Abstract

Background: Bone metastasis is common in cancer. Evaluating the metastatic status in cancer is of utmost importance in order to provide the best patient’s management.

Discussion: Bone scintigraphy is widely used for evaluation of bone metastasis. It has high sensitivity with limited specificity. Planar bone scintigraphy has been shown to have increased radiotracer uptake without accurate anatomic localization and characterization. Hybrid Single-Photon Emission Computed Tomography/Computerized Tomography (SPECT/CT) system has been developed by combination of SPECT and CT. Accurate lesion localization and discrimination of equivocal bone lesions is an advantage in this hybrid technique. It improves diagnostic accuracy by differentiation of benign bone lesions from malignant ones due to their morphological changes. So, SPECT/CT improves the specificity of bone scintigraphy leading to better outcomes in diagnosis and treatment outcomes of bone metastatic cancer patients.

Conclusion: In here, we discussed the prognostic value of bone scintigraphy and SPECT/CT in bone metastasis with our clinical experience and review of the literature.

Keywords: Bone metastasis, bone scintigraphy, cancer, hybrid imaging, SPECT, CT.

Graphical Abstract

[1]
Kelemen A, Székely G, Gerig G. Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Trans Med Imaging 1999; 18(10): 828-39.
[http://dx.doi.org/10.1109/42.811260] [PMID: 10628943]
[2]
Leventon ME, Grimson WEL, Faugeras O. Statistical shape influence in geodesic active contours. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, SC, USA; IEEE 2000; pp. 316-23.
[http://dx.doi.org/10.1109/CVPR.2000.855835]
[3]
Cremers D, Schnörr C, Weickert J. Diffusion-snakes combining statistical shape knowledge and image information in a variational framework. In: Workshop on Variational and Level Set Methods in Computer Vision. Vancouver, BC, Canada; IEEE 2001; pp. 137-44.
[http://dx.doi.org/10.1109/VLSM.2001.938892]
[4]
Wang Y, Staib LH. Elastic model-based nonrigid registration incorporating statistical shape information. In: Medical Image Computing and Computer-Assisted Intervention- MICCAI’98. Cambridge, MA, USA: Springer 1998; pp. 1162-73.
[5]
Styner M, Gerig G. Medical models incorporating object variability for 3-D shape analysis. In: Information Processing in Medical Imaging. Davis, CA, USA: Springer 2001; pp. 502-16.
[http://dx.doi.org/10.1007/3-540-45729-1_53]
[6]
Golland P, Grimson WEL, Shenton ME, Kikinis R. Small sample size learning for shape analysis of anatomical structures. In: Medical Image Computing and Computer-Assisted Intervention. Pittsburgh, PA, USA; Springer 2000; pp. 72-82.
[http://dx.doi.org/10.1007/978-3-540-40899-4_8]
[7]
Davis A. Deformation analysis for shape based classification. In: Information Processing in Medical Imaging. Davis, CA, USA: Springer 2005; pp. 17-30.
[8]
Frangi AF, Rueckert D, Schnabel JA, Niessen WJ. Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging 2002; 21(9): 1151-66.
[http://dx.doi.org/10.1109/TMI.2002.804426] [PMID: 12564883]
[9]
Davis A, Fletcher P, Bullitt E, Joshi S. Population shape regression from random design data. In: 11th International Conference on Computer Vision. Rio de Janeiro, Brazil, IEEE 2007; pp. 1-7.
[http://dx.doi.org/10.1109/ICCV.2007.4408977]
[10]
Ericsson P. Construction of a patient specific atlas of the brain: application to normal aging. In: International Symposium on Biomedical Imaging: From Nano to Macro. Paris, France; IEEE 2008; pp. 480-3.
[11]
Kishimoto M, Saito A, Osaka M, Takakuwa T, Yamada S, Shimizu A. A spatiotemporal statistical model for landmarks of oral and maxillofacial area during the human embryonic period. In: The International Forum on Medical Imaging in Asia. Japan 2017; pp. 5-3.
[12]
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. Int J Comput Vis 2013; 103(1): 22-59.
[http://dx.doi.org/10.1007/s11263-012-0592-x] [PMID: 23956495]
[13]
Lynch M, Ghita O, Whelan PF. Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model. IEEE Trans Med Imaging 2008; 27(2): 195-203.
[http://dx.doi.org/10.1109/TMI.2007.904681] [PMID: 18334441]
[14]
Davis BC, P. Fletcher T, Bullitt E, Joshi S. Population shape regression from random design data. Int J Comput Vis 2010; 90: 255-66.
[http://dx.doi.org/10.1007/s11263-010-0367-1]
[15]
Joshi SC. Large deformation diffeomorphisms and Gaussian random fields for statistical characterization of brain sub-manifolds. PhD dissertation 1998.
[16]
Gee JC, Bajcsy RK. Elastic matching: continuum mechanical and probabilistic analysis in Brain Warping. New York: Academic 1999.
[http://dx.doi.org/10.1016/B978-012692535-7/50087-2]
[17]
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17(3): 143-55.
[http://dx.doi.org/10.1002/hbm.10062] [PMID: 12391568]
[18]
Jenkinson M, Pechaud M, Smith S. BET2: MR-based estimation of brain, skull and scalp surfaces. In: Eleventh Annual Meeting of the Organization for Human Brain Mapping. Oxford, UK 2017.
[19]
Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on hamilton–jacobi formulation. J Comput Phys 1988; 79: 12-49.
[http://dx.doi.org/10.1016/0021-9991(88)90002-2]
[20]
Malladi R, Sethian J, Vemuri B. Shape modeling with front propagation: A level set approach. IEEE Trans Pattern Anal Mach Intell 1995; 17(2): 158-74.
[http://dx.doi.org/10.1109/34.368173]
[21]
Pohl KM, Fisher J, Shenton M, et al. Logarithm odds maps for shape representation. In: Medical Image Computing and Computer- Assisted Intervention. Copenhagen, Denmark 2006; pp. 955-63.
[http://dx.doi.org/10.1007/11866763_117]
[22]
Rousson CM, Deriche R. A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int J Comput Vis 2007; 72(2): 195-215.
[http://dx.doi.org/10.1007/s11263-006-8711-1]
[23]
Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 2009; 13(4): 543-63.
[http://dx.doi.org/10.1016/j.media.2009.05.004] [PMID: 19525140]
[24]
Alam SB, Nakano R, Kobashi S. Brain age estimation using multiple regression analysis in brain MR image. Int J Innov Comput, Inf Control 2016; 12(4): 2-18.
[25]
Bailey S. Principal component analysis with noisy and/or missing data 1 Cyclotron Rd, Berkeley, CA 94720. USA: Lawrence Berkeley National Lab 2012.
[http://dx.doi.org/10.1086/668105]
[26]
Bilmes J. A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models Univ Berkeley, Berkeley, CA, Tech Rep TR-97-021. 1998.
[27]
OASIS Brains. Available from:. http://www.oasis-brains.org/

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