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Multidetector Computed Tomography Imaging

Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure

Mookiah, Muthu Rama Krishnan, PhD*; Subburaj, Karupppasamy, PhD*; Mei, Kai, MSc; Kopp, Felix K., MSc; Kaesmacher, Johannes, MD; Jungmann, Pia M., MD; Foehr, Peter, MSc§; Noel, Peter B., PhD; Kirschke, Jan S., MD; Baum, Thomas, MD

Journal of Computer Assisted Tomography: May/June 2018 - Volume 42 - Issue 3 - p 441–447
doi: 10.1097/RCT.0000000000000710
Musculoskeletal Imaging

Multidetector computed tomography–based trabecular bone microstructure analysis ensures promising results in fracture risk prediction caused by osteoporosis. Because multidetector computed tomography is associated with high radiation exposure, its clinical routine use is limited. Hence, in this study, we investigated in 11 thoracic midvertebral specimens whether trabecular texture parameters are comparable derived from (1) images reconstructed using statistical iterative reconstruction (SIR) and filtered back projection as criterion standard at different exposures (80, 150, 220, and 500 mAs) and (2) from SIR-based sparse sampling projections (12.5%, 25%, 50%, and 100%) and equivalent exposures as criterion standard. Twenty-four texture features were computed, and those that showed similar values between (1) filtered back projection and SIR at the different exposure levels and (2) sparse sampling and equivalent exposures and reconstructed with SIR were identified. These parameters can be of equal value in determining trabecular bone microstructure with lower radiation exposure using sparse sampling and SIR.

From the *Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore; Departments of


Neuroradiology, and

§Orthopaedics and Sports Orthopaedics, Biomechanical Laboratory, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.

Received for publication August 17, 2017; accepted November 27, 2017.

Correspondence to: Karupppasamy Subburaj, PhD, Engineering Product Development Pillar, Singapore University of Technology and Design, 8 Somapah Rd, 487372, Singapore (e-mail:

This work was supported by the following research grants: Deutsche Forschungsgemeinschaft (D.F.G.), BA 4085/2-1 (J.S.K.), ERC-StG-2014 637164 iBack (J.S.K.), German Department of Education and Research under grant 01EX1021D (P.B.N.), DFG BA 4906/1-1 and 2-1 (T.B.), and TUM Faculty of Medicine KKF grant H01 (T.B.); Singapore University of Technology and Design Start-up Research Grant SRG EPD 2015 093 (K.S.). The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

The authors declare no conflict of interest.

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