Advertisement

On the influence of computed tomography's slice thickness on computer tomography based finite element analyses results

      Highlights

      • Autonomous finite elements of femurs based on 1 & 3 mm Computed Tomography scans were compared.
      • For stance positions differences of 11.0 ± 13.4% were observed in neck regions on average.
      • For stance positions differences of −1.5 ± 1.8% were observed in other regions on average
      • Consecutive 3 mm Computed Tomography scans may be used in clinical practice if changes are above 10%.

      Abstract

      Background

      Patient-specific autonomous finite element analyses of femurs, based on clinical computed tomography scans may be used to monitor the progression of bone-related diseases. Some CT scan protocols provide lower resolution (slice thickness of 3 mm) that affects the accuracy. To investigate the impact of low-resolution scans on the CT-based finite element analyses results, identical CT raw data were reconstructed twice to generate a 1 mm (“gold standard”) and a 3 mm slice thickness scans.

      Methods

      CT-based finite element analyses of twenty-four femurs (twelve patients) under stance and sideways fall loads were performed based on 1 and 3 mm slice thickness scans. Bone volume, load direction, and strains were extracted at different locations along the femurs and differences were evaluated.

      Findings

      Average differences in bone volume were 1.0 ± 1.5%. The largest average difference in strains in stance position was in the neck region (11.0 ± 13.4%), whereas in other regions these were much smaller. For sidewise fall loading, the average differences were at most 9.2 ± 16.0%.

      Interpretation

      Whole-body low dose CT scans (3 mm-slice thickness) are suboptimal for monitoring strain changes in patient's femurs but may allow longitudinal studies if larger than 5% in all areas and larger than 12% in the upper neck. CT-based finite element analyses with slice thickness of 3 mm may be used in clinical practice for patients with smoldering myeloma to associate changes in strains with progression to active myeloma if above ~10%.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Clinical Biomechanics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. Applications [WWW Document], n.d. . PerSimiO. URL https://www.persimio.com/new-page-5 (accessed 11.24.20).

        • Ataei A.
        • Eikhout J.
        • van Leeuwen R.G.H.
        • Tanck E.
        • Eggermont F.
        The effect of variations in CT scan protocol on femoral finite element failure load assessment using phantomless calibration.
        PLoS One. 2022; 17e0265524https://doi.org/10.1371/journal.pone.0265524
        • Benca E.
        • Amini M.
        • Pahr D.H.
        Effect of CT imaging on the accuracy of the finite element modelling in bone.
        Eur. Radiol. Exp. 2020; 4: 51https://doi.org/10.1186/s41747-020-00180-3
        • Birnbaum B.A.
        • Hindman N.
        • Lee J.
        • Babb J.S.
        Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom.
        Radiology. 2007; 242: 109-119https://doi.org/10.1148/radiol.2421052066
        • Bolli N.
        • Sgherza N.
        • Curci P.
        • Rizzi R.
        • Strafella V.
        • Delia M.
        • Gagliardi V.P.
        • Neri A.
        • Baldini L.
        • Albano F.
        • Musto P.
        What is new in the treatment of smoldering multiple myeloma?.
        J. Clin. Med. 2021; 10: 421https://doi.org/10.3390/jcm10030421
        • Cohen Y.C.
        • Avivi I.
        • Yosibash Z.
        • Trabelsi N.
        • Sherman H.
        • Sternheim A.
        Novel CT-based bone strength assessment by finite element analysis for monitoring bone involvement in myeloma: a proof of concept study.
        Blood. 2017; 130: 3143https://doi.org/10.1182/blood.V130.Suppl_1.3143.3143
        • Eggermont F.
        • Verdonschot N.
        • van der Linden Y.
        • Tanck E.
        Calibration with or without phantom for fracture risk prediction in cancer patients with femoral bone metastases using CT-based finite element models.
        PLoS One. 2019; 14e0220564https://doi.org/10.1371/journal.pone.0220564
        • Ford J.M.
        • Decker S.J.
        Computed tomography slice thickness and its effects on three-dimensional reconstruction of anatomical structures.
        J. Forensic Radiol. Imag. 2016; 2015: 43-46https://doi.org/10.1016/j.jofri.2015.10.004
        • Gholami M.
        • Karami V.
        Addressing as low as reasonably achievable (ALARA) in pediatric computed tomography (CT) procedures.
        J. Res. Med. Dental Sci. 2018; 6: 104-114
        • Hjorth M.
        • Hellquist L.
        • Holmberg E.
        • Magnusson B.
        • Rödjer S.
        • Westin J.
        Initial versus deferred melphalan-prednisone therapy for asymptomatic multiple myeloma stage I - a randomized study. Myeloma Group of Western Sweden.
        Eur. J. Haematol. 1993; 50: 95-102https://doi.org/10.1111/j.1600-0609.1993.tb00148.x
        • Holzer G.
        • von Skrbensky G.
        • Holzer L.A.
        • Pichl W.
        Hip fractures and the contribution of cortical versus trabecular bone to femoral neck strength.
        J. Bone Miner. Res. 2009; 24: 468-474https://doi.org/10.1359/jbmr.081108
        • Katz Y.
        • Dahan G.
        • Sosna J.
        • Shelef I.
        • Cherniavsky E.
        • Yosibash Z.
        Scanner influence on the mechanical response of QCT-based finite element analysis of long bones.
        J. Biomech. 2019; 86: 149-159
        • Katz Yekutiel
        • Trabelsi Nir
        • Yosibash Zohar
        Automatic Femur Segmentation from CT Scans for Autonomous Finite Element Analyses. Submitted for publication.
        2022
        • Kazley J.M.
        • Banerjee S.
        • Abousayed M.M.
        • Rosenbaum A.J.
        Classifications in brief: garden classification of femoral neck fractures.
        Clin. Orthop. Relat. Res. 2018; 476: 441-445https://doi.org/10.1007/s11999.0000000000000066
        • Keyak J.H.
        • Skinner H.B.
        • Fleming J.A.
        Effect of force direction on femoral fracture load for two types of loading conditions.
        J. Orthop. Res. 2001; 19: 539-544https://doi.org/10.1016/S0736-0266(00)00046-2
        • Kivelson M.G.
        • Russell C.T.
        Introduction to Space Physics [WWW document].
        Higher Education from Cambridge University Press, 1995https://doi.org/10.1017/9781139878296
        • Kristinsson S.Y.
        • Minter A.R.
        • Korde N.
        • Tan E.
        • Landgren O.
        Bone disease in multiple myeloma and precursor disease: novel diagnostic approaches and implications on clinical management.
        Expert. Rev. Mol. Diagn. 2011; 11: 593-603https://doi.org/10.1586/erm.11.44
        • Mateos M.-V.
        • Hernández M.-T.
        • Giraldo P.
        • de la Rubia J.
        • de Arriba F.
        • Corral L.L.
        • Rosiñol L.
        • Paiva B.
        • Palomera L.
        • Bargay J.
        • Oriol A.
        • Prosper F.
        • López J.
        • Olavarría E.
        • Quintana N.
        • García J.-L.
        • Bladé J.
        • Lahuerta J.-J.
        • San Miguel J.-F.
        Lenalidomide plus dexamethasone for high-risk smoldering multiple myeloma.
        N. Engl. J. Med. 2013; 369: 438-447https://doi.org/10.1056/NEJMoa1300439
        • Mateos M.-V.
        • Kumar S.
        • Dimopoulos M.A.
        • González-Calle V.
        • Kastritis E.
        • Hajek R.
        • De Larrea C.F.
        • Morgan G.J.
        • Merlini G.
        • Goldschmidt H.
        • Geraldes C.
        • Gozzetti A.
        • Kyriakou C.
        • Garderet L.
        • Hansson M.
        • Zamagni E.
        • Fantl D.
        • Leleu X.
        • Kim B.-S.
        • Esteves G.
        • Ludwig H.
        • Usmani S.
        • Min C.-K.
        • Qi M.
        • Ukropec J.
        • Weiss B.M.
        • Rajkumar S.V.
        • Durie B.G.M.
        • San-Miguel J.
        International myeloma working group risk stratification model for smoldering multiple myeloma (SMM).
        Blood Cancer J. 2020; 10: 102https://doi.org/10.1038/s41408-020-00366-3
        • Merz M.
        • Hielscher T.
        • Schult D.
        • Mai E.K.
        • Raab M.S.
        • Hillengass J.
        • Seckinger A.
        • Hose D.
        • Granzow M.
        • Jauch A.
        • Goldschmidt H.
        Cytogenetic subclone formation and evolution in progressive smoldering multiple myeloma.
        Leukemia. 2020; 34: 1192-1196https://doi.org/10.1038/s41375-019-0634-2
        • Piao S.
        • Liu J.
        Accuracy improvement of UNet based on dilated convolution.
        J. Phys. Conf. Ser. 2019; 1345052066https://doi.org/10.1088/1742-6596/1345/5/052066
        • Prevrhal S.
        • Fox J.C.
        • Shepherd J.A.
        • Genant H.K.
        Accuracy of CT-based thickness measurement of thin structures: modeling of limited spatial resolution in all three dimensions.
        Med. Phys. 2003; 30: 1-8https://doi.org/10.1118/1.1521940
        • Rajkumar S.V.
        Multiple myeloma: 2020 update on diagnosis, risk-stratification and management.
        Am. J. Hematol. 2020; 95: 548-567https://doi.org/10.1002/ajh.25791
        • Romano A.
        • Cerchione C.
        • Conticello C.
        • Martinelli G.
        • Di Raimondo F.
        How we manage smoldering multiple myeloma.
        Hematol. Rep. 2020; 12: 8951https://doi.org/10.4081/hr.2020.8951
        • Rotman D.
        • Ariel G.
        • Rojas Lievano J.
        • Schermann H.
        • Trabelsi N.
        • Salai M.
        • Yosibash Z.
        • Sternheim A.
        Assessing hip fracture risk in type-2 diabetic patients using CT-based autonomous finite element methods : a feasibility study.
        Bone Joint J. 2021; 103-B: 1497-1504https://doi.org/10.1302/0301-620X.103B9.BJJ-2020-2147.R1
        • Sternheim A.
        • Giladi O.
        • Gortzak Y.
        • Drexler M.
        • Salai M.
        • Trabelsi N.
        • Milgrom C.
        • Yosibash Z.
        Pathological fracture risk assessment in patients with femoral metastases using CT-based finite element methods. A retrospective clinical study.
        Bone. 2018; 110: 215-220https://doi.org/10.1016/j.bone.2018.02.011
        • Sternheim A.
        • Traub F.
        • Trabelsi N.
        • Dadia S.
        • Gortzak Y.
        • Snir N.
        • Gorfine M.
        • Yosibash Z.
        When and where do patients with bone metastases actually break their femurs?.
        Bone Joint J. 2020; 102-B: 638-645https://doi.org/10.1302/0301-620X.102B5.BJJ-2019-1328.R2
        • Trabelsi N.
        • Yosibash Z.
        Patient-specific finite-element analyses of the proximal femur with orthotropic material properties validated by experiments.
        J. Biomech. Eng. 2011; 133
        • Trabelsi N.
        • Yosibash Z.
        • Wutte C.
        • Augat P.
        • Eberle S.
        Patient-specific finite element analysis of the human femur--a double-blinded biomechanical validation.
        J. Biomech. 2011; 44: 1666-1672https://doi.org/10.1016/j.jbiomech.2011.03.024
        • Treece G.M.
        • Poole K.E.S.
        • Gee A.H.
        Imaging the femoral cortex: thickness, density and mass from clinical CT.
        Med. Image Anal. 2012; 16: 952-965https://doi.org/10.1016/j.media.2012.02.008
        • Yosibash Z.
        • Plitman Mayo R.
        • Dahan G.
        • Trabelsi N.
        • Amir G.
        • Milgrom C.
        Predicting the stiffness and strength of human femurs with real metastatic tumors.
        Bone. 2014; 69: 180-190https://doi.org/10.1016/j.bone.2014.09.022
        • Yosibash Z.
        • Myers K.
        • Trabelsi N.
        • Sternheim A.
        Autonomous FEs (AFE) - a stride toward personalized medicine.
        Comput. Math. Appl. 2020; 2019: 2417-2432https://doi.org/10.1016/j.camwa.2020.03.012