Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data

Computed tomographic (CT) scanning is becoming a popular research tool across earth and life sciences. However, despite its prominence, there have not been systematic investigations into how CT scan parameters affect data quality and reproducibility. Here we conduct two sets of trials to test how exposure time, the number of x-ray radiographs averaged per view, and overall scan time affect the quality of CT scan data, assessed using signal and contrast to noise ratios and the repeatability of measurements derived from these data, in this case the calculated volume of pteropod shells. We find that contrast to noise ratio and calculated shell volume increase and the variability in shell volume measurements decrease with increasing overall scan time. However, the benefits of increased overall scan time diminish considerably at scan times of 50 minutes or more. Furthermore, as overall scan time increases, scans are at greater risk of being affected by sample movement, which can make the data unusable. By balancing exposure time and the number of x-ray radiographs averaged per view, the image quality in a 50-minute scan can be comparable to, or better than, that collected in a 75-minute scan. By selecting a 50-minute rather than a 75-minute scan, data collection can be increased by between 66 and 75%, maximizing both the quantity and quality of CT data collected. Rosie L. Oakes. Academy of Natural Sciences of Drexel University, Philadelphia, Pennsylvania, USA. roakes@drexel.edu Morgan Hill Chase. American Museum of Natural History, New York, New York, USA. mchase@amnh.org Mark E. Siddall. American Museum of Natural History, New York, New York, USA. siddall@amnh.org Jocelyn A. Sessa. Academy of Natural Sciences of Drexel University, Philadelphia, Pennsylvania, USA. jsessa@drexel.edu

[1]  J. Wind,et al.  Computerized x-ray tomography of fossil hominid skulls. , 1984, American journal of physical anthropology.

[2]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[3]  C. Manno,et al.  Degradation of Internal Organic Matter is the Main Control on Pteropod Shell Dissolution After Death , 2019, Global Biogeochemical Cycles.

[4]  Imran A. Rahman,et al.  Techniques for Virtual Palaeontology , 2014 .

[5]  Imran A. Rahman,et al.  Virtual Fossils: a New Resource for Science Communication in Paleontology , 2012, Evolution: Education and Outreach.

[6]  Impact of preservation techniques on pteropod shell condition , 2018, Polar Biology.

[7]  R. Feely,et al.  New ocean, new needs: Application of pteropod shell dissolution as a biological indicator for marine resource management , 2017 .

[8]  R. Feely,et al.  Limacina helicina shell dissolution as an indicator of declining habitat suitability owing to ocean acidification in the California Current Ecosystem , 2014, Proceedings of the Royal Society B: Biological Sciences.

[9]  P. B. Duffy,et al.  Anthropogenic carbon and ocean pH , 2001 .

[10]  E. Maier‐Reimer,et al.  Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms , 2005, Nature.

[11]  Stephan Lautenschlager,et al.  Beyond the print—virtual paleontology in science publishing, outreach, and education , 2014, Journal of paleontology.

[12]  Richard J. Matear,et al.  Southern Ocean acidification: A tipping point at 450-ppm atmospheric CO2 , 2008, Proceedings of the National Academy of Sciences.

[13]  Peter Wenig,et al.  Examination of the Measurement Uncertainty on Dimensional Measurements by X-ray , 2006 .

[14]  S. Goldstein,et al.  The direct examination of three‐dimensional bone architecture in vitro by computed tomography , 1989, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[15]  Xiangyang Tang,et al.  Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain. , 2012, Medical physics.

[16]  V. Poletti,et al.  CT Scan of Thirteen Natural Mummies Dating Back to the XVI-XVIII Centuries: An Emerging Tool to Investigate Living Conditions and Diseases in History , 2016, PloS one.

[17]  Chris Broeckhoven,et al.  Laboratory x-ray micro-computed tomography: a user guideline for biological samples , 2017, GigaScience.

[18]  A. Mucci The solubility of calcite and aragonite in seawater at various salinities , 1983 .

[19]  Andrew Steele,et al.  Radar-Enabled Recovery of the Sutter’s Mill Meteorite, a Carbonaceous Chondrite Regolith Breccia , 2012, Science.

[20]  G. Tarling,et al.  Pteropods counter mechanical damage and dissolution through extensive shell repair , 2018, Nature Communications.

[21]  B. Metscher MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues , 2009, BMC Physiology.

[22]  Sankar Chatterjee,et al.  Neuroanatomy of flying reptiles and implications for flight, posture and behaviour , 2003, Nature.

[23]  S. Inoué,et al.  Suture pattern formation in ammonites and the unknown rear mantle structure , 2016, Scientific Reports.

[24]  Jesús Marugán-Lobón,et al.  Open data and digital morphology , 2017, Proceedings of the Royal Society B: Biological Sciences.

[25]  Tobias Wang,et al.  Inside Out: Modern Imaging Techniques to Reveal Animal Anatomy , 2011, PloS one.

[26]  Julia F. Barrett,et al.  Artifacts in CT: recognition and avoidance. , 2004, Radiographics : a review publication of the Radiological Society of North America, Inc.

[27]  Wei Wei,et al.  Comparing CNR, SNR, and Image Quality of CT Images Reconstructed with Soft Kernel, Standard Kernel, and Standard Kernel plus ASIR 30% Techniques: Bhosale P. Image quality using different reconstruction algorithms with ASIR , 2015 .

[28]  Imran A. Rahman,et al.  From clergymen to computers—the advent of virtual palaeontology , 2010 .

[29]  J. Gattuso,et al.  Comparison of Mediterranean Pteropod Shell Biometrics and Ultrastructure from Historical (1910 and 1921) and Present Day (2012) Samples Provides Baseline for Monitoring Effects of Global Change , 2017, PloS one.

[30]  A. Plessis,et al.  Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species , 2016 .

[31]  J. Sessa,et al.  Pteropoda (Mollusca, Gastropoda, Thecosomata) from the Paleocene-Eocene Thermal Maximum (United States Atlantic Coastal Plain) , 2016 .

[32]  S. Zachow,et al.  Non-invasive imaging methods applied to neo- and paleo-ontological cephalopod research , 2013 .

[33]  M. Siddall,et al.  Description of a soft‐bodied invertebrate with microcomputed tomography and revision of the genus Chtonobdella (Hirudinea: Haemadipsidae) , 2016 .

[34]  Randy D Ernst,et al.  Paleoradiology: advanced CT in the evaluation of nine Egyptian mummies. , 2002, Radiographics : a review publication of the Radiological Society of North America, Inc.

[35]  A. Timmermann,et al.  Abrupt onset and prolongation of aragonite undersaturation events in the Southern Ocean , 2016 .

[36]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[37]  Mark D Sutton,et al.  Tomographic techniques for the study of exceptionally preserved fossils , 2008, Proceedings of the Royal Society B: Biological Sciences.

[38]  R. Ketcham,et al.  Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences , 2001 .

[39]  E. Rayfield,et al.  A virtual world of paleontology. , 2014, Trends in ecology & evolution.