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      Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?

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          Abstract

          Objective

          Osteoporosis diagnosis using multidetector CT (MDCT) is limited to relatively high radiation exposure. We investigated the effect of simulated ultra-low-dose protocols on in-vivo bone mineral density (BMD) and quantitative trabecular bone assessment.

          Materials and methods

          Institutional review board approval was obtained. Twelve subjects with osteoporotic vertebral fractures and 12 age- and gender-matched controls undergoing routine thoracic and abdominal MDCT were included (average effective dose: 10 mSv). Ultra-low radiation examinations were achieved by simulating lower tube currents and sparse samplings at 50%, 25% and 10% of the original dose. BMD and trabecular bone parameters were extracted in T10–L5.

          Results

          Except for BMD measurements in sparse sampling data, absolute values of all parameters derived from ultra-low-dose data were significantly different from those derived from original dose images (p<0.05). BMD, apparent bone fraction and trabecular thickness were still consistently lower in subjects with than in those without fractures (p<0.05).

          Conclusion

          In ultra-low-dose scans, BMD and microstructure parameters were able to differentiate subjects with and without vertebral fractures, suggesting osteoporosis diagnosis is feasible. However, absolute values differed from original values. BMD from sparse sampling appeared to be more robust. This dose-dependency of parameters should be considered for future clinical use.

          Key Points

          • BMD and quantitative bone parameters are assessable in ultra-low-dose in vivo MDCT scans.

          • Bone mineral density does not change significantly when sparse sampling is applied.

          • Quantitative trabecular bone microstructure measurements are sensitive to dose reduction.

          • Osteoporosis subjects could be differentiated even at 10% of original dose.

          • Radiation exposure should be considered when comparing quantitative bone parameters.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s00330-017-4904-y) contains supplementary material, which is available to authorized users.

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          Most cited references23

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          Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study.

          The incidence of all non-vertebral fractures, as well as the relation to bone mineral density (BMD), was quantified in 7806 men and women from the Rotterdam Study, a prospective, population-based cohort study of men and women aged 55 years and older. In addition, the sensitivity of using a T-score at or below -2.5 for identifying subjects at risk for fractures was assessed. At baseline, between 1990 and 1993, femoral neck BMD was measured by dual energy X-ray absorptiometry (DXA). Subsequently, gender-specific T-scores were calculated using the NHANES reference population. During a mean follow-up of 6.8 years, information on incident non-vertebral fractures was gathered. In general, hip, wrist and upper humerus fractures are the most frequent fractures in both men and women. Femoral neck BMD appears to be an equally important risk factor in both genders, and is especially related to hip fractures. For all non-vertebral fractures, the age-adjusted hazard ratio (95% confidence interval) per standard deviation decrease in femoral neck BMD was 1.5 (1.4-1.6) for women and 1.4 (1.2-1.6) for men. For hip fractures, the hazard ratios were 2.1 (1.7-2.5) for women and 2.3 (1.6-3.3) for men. Only 44% of all non-vertebral fractures occurred in women with a T-score below -2.5; in men, this percentage was even lower (21%). Thus, there is a clear need for the development of more sensitive risk assessment tools, using not only BMD, but also other clinical predictors of fractures.
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            Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study.

            The purpose of this study was to evaluate the image noise, low-contrast resolution, image quality, and spatial resolution of adaptive statistical iterative reconstruction in low-dose body CT. Adaptive statistical iterative reconstruction was used to scan the American College of Radiology phantom at the American College of Radiology reference value and at one-half that value (12.5 mGy). Test objects in low- and high-contrast and uniformity modules were evaluated. Low-dose CT with adaptive statistical iterative reconstruction was then tested on 12 patients (seven men, five women; average age, 67.5 years) who had previously undergone routine-dose CT. Two radiologists blinded to scanning technique evaluated images of the same patients obtained with routine-dose CT and low-dose CT with and without adaptive statistical iterative reconstruction. Image noise, low-contrast resolution, image quality, and spatial resolution were graded on a scale of 1 (best) to 4 (worst). Quantitative noise measurements were made on clinical images. In the phantom, low- and high-contrast and uniformity assessments showed no significant difference between routine-dose imaging and low-dose CT with adaptive statistical iterative reconstruction. In patients, low-dose CT with adaptive statistical iterative reconstruction was associated with CT dose index reductions of 32-65% compared with routine imaging and had the least noise both quantitatively and qualitatively (p < 0.05). Low-dose CT with adaptive statistical iterative reconstruction and routine-dose CT had identical results for low-contrast resolution and nearly identical results for overall image quality (grade 2.1-2.2). Spatial resolution was better with routine-dose CT (p = 0.004). These preliminary results support body CT dose index reductions of 32-65% when adaptive statistical iterative reconstruction is used. Studies with larger statistical samples are needed to confirm these findings.
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              Iterative reconstruction methods in X-ray CT.

              Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray computed tomography (CT). They were successfully used in the early years of CT, but given up when the amount of measured data increased because of the higher computational demands of IR compared to analytical methods. The availability of large computational capacities in normal workstations and the ongoing efforts towards lower doses in CT have changed the situation; IR has become a hot topic for all major vendors of clinical CT systems in the past 5 years. This review strives to provide information on IR methods and aims at interested physicists and physicians already active in the field of CT. We give an overview on the terminology used and an introduction to the most important algorithmic concepts including references for further reading. As a practical example, details on a model-based iterative reconstruction algorithm implemented on a modern graphics adapter (GPU) are presented, followed by application examples for several dedicated CT scanners in order to demonstrate the performance and potential of iterative reconstruction methods. Finally, some general thoughts regarding the advantages and disadvantages of IR methods as well as open points for research in this field are discussed. Copyright © 2012 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                + 49 89 4140 7244 , kai.mei@tum.de
                Journal
                Eur Radiol
                Eur Radiol
                European Radiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0938-7994
                1432-1084
                21 June 2017
                21 June 2017
                2017
                : 27
                : 12
                : 5261-5271
                Affiliations
                [1 ]Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany
                [2 ]ISNI 0000 0001 2248 7639, GRID grid.7468.d, Philips GmbH Innovative Technologies, , Research Laboratories, ; Hamburg, Germany
                [3 ]ISNI 0000000123222966, GRID grid.6936.a, TUM Institute for Advanced Studies, , Technische Universität München, ; Garching b. Munich, Germany
                [4 ]ISNI 0000000123222966, GRID grid.6936.a, Lehrstuhl für Biomedizinische Physik, , Technische Universität München, ; Garching b. Munich, Germany
                [5 ]Section of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
                Article
                4904
                10.1007/s00330-017-4904-y
                5674130
                28639046
                e8a7bb01-56c3-44ee-a6d0-6c98388f2001
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 17 March 2017
                : 16 May 2017
                : 18 May 2017
                Categories
                Computed Tomography
                Custom metadata
                © European Society of Radiology 2017

                Radiology & Imaging
                computed tomography,radiation dose,sparse sampling,osteoporosis,trabecular microstructure

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