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Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial

  • Breast
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach.

Materials and methods

Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers.

Results

For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography.

Discussion

A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography.

Clinical relevance statement

This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol.

Key Points

• In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images.

• A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups.

• Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections.

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Abbreviations

2D:

Two dimensional

AEC:

Automatic exposure control

DBT:

Digital breast tomosynthesis

DM:

Digital mammography

FOM:

Figure of merit

JAFROC:

Jackknife-alternative free-response receiver operating characteristic

mAs:

Tube current–time product

MLO:

Medio-lateral oblique

ROI:

Region of interest

SM:

Synthetic mammography

VOI:

Volume of interest

References

  1. Skaane P, Bandos AI, Eben EB et al (2014) Two-view digital breast tomosynthesis screening with synthetically reconstructed projection images: comparison with digital breast tomosynthesis with full-field digital mammographic images. Radiology 271:655–663

    Article  PubMed  Google Scholar 

  2. Zuley ML, Guo B, Catullo VJ et al (2014) Comparison of two-dimensional synthesized mammograms versus original digital mammograms alone and in combination with tomosynthesis images. Radiology 271:664–671

    Article  PubMed  Google Scholar 

  3. Bernardi D, Macaskill P, Pellegrini M et al (2016) Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study. Lancet Oncol 17:1105–1113

    Article  PubMed  Google Scholar 

  4. Zuckerman SP, Sprague BL, Weaver DL, Herschorn SD, Conant EF (2020) Multicenter evaluation of breast cancer screening with digital breast tomosynthesis in combination with synthetic versus digital mammography. Radiology 297:545–553

    Article  PubMed  Google Scholar 

  5. Vancoillie L, Cockmartin L, Marshall N, Bosmans H (2021) The impact on lesion detection via a multi-vendor study: a phantom-based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography. Med Phys 48:6270–6292

    Article  CAS  PubMed  Google Scholar 

  6. Ikejimba LC, Salad J, Graff CG et al (2021) Assessment of task-based performance from five clinical DBT systems using an anthropomorphic breast phantom. Med Phys 48:1026–1038

    Article  PubMed  Google Scholar 

  7. Mackenzie A, Thomson EL, Mitchell M et al (2022) Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging. Eur Radiol 32:806–814

    Article  PubMed  Google Scholar 

  8. Choi JS, Han BK, Ko EY et al (2019) Comparison of synthetic and digital mammography with digital breast tomosynthesis or alone for the detection and classification of microcalcifications. Eur Radiol 29:319–329

    Article  PubMed  Google Scholar 

  9. Chikarmane S (2022) Synthetic Mammography: Review of Benefits and Drawbacks in Clinical Use. J Breast Imaging 4:124–134

    Article  PubMed  Google Scholar 

  10. Vancoillie L, Marshall N, Cockmartin L et al (2020) Verification of the accuracy of a hybrid breast imaging simulation framework for virtual clinical trial applications. J Med Imaging 7:042804

    Google Scholar 

  11. Marshall NW, Bosmans H (2022) Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data. Phys Med Biol 67:22TR03. https://doi.org/10.1088/1361-6560/ac9a35

  12. Das M, Gifford HC, O’Connor JM, Glick SJ (2009) Evaluation of a variable dose acquisition technique for microcalcification and mass detection in digital breast tomosynthesis. Med Phys 36:1976–1984

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hu YH, Zhao W (2011) The effect of angular dose distribution on the detection of microcalcifications in digital breast tomosynthesis. Med Phys 38:2455–2466

    Article  PubMed  PubMed Central  Google Scholar 

  14. Vecchio S, Albanese A, Vignoli P, Taibi A (2011) A novel approach to digital breast tomosynthesis for simultaneous acquisition of 2D and 3D images. Eur Radiol 21:1207–1213

    Article  PubMed  Google Scholar 

  15. Abadi E, Segars WP, Tsui BMW et al (2020) Virtual clinical trials in medical imaging: a review. J Med Imaging 7:042805-042801–042840

    Article  Google Scholar 

  16. Barufaldi B, Maidment ADA, Dustler M et al (2021) Virtual clinical trials in medical imaging system evaluation and optimisation. Radiat Prot Dosimetry 195:363–371

    Article  PubMed  PubMed Central  Google Scholar 

  17. Marshall NW, Bosmans H (2022) Performance evaluation of digital breast tomosynthesis systems: comparison of current virtual clinical trial methods. Phys Med Biol 67:22TR04. https://doi.org/10.1088/1361-6560/ac9a34

  18. Badano A, Graff CG, Badal A et al (2018) Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in silico imaging trial. JAMA Netw Open 1:e185474

    Article  PubMed  PubMed Central  Google Scholar 

  19. Barufaldi B, Acciavatti RJ, Conant EF, Maidment ADA (2024) Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis. Eur Radiol 34:193–203

    Article  CAS  PubMed  Google Scholar 

  20. Mackenzie A, Kaur S, Thomson EL et al (2021) Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial. Med Phys 48:6859–6868

    Article  CAS  PubMed  Google Scholar 

  21. Barufaldi B, Vent TL, Bakic PR, Maidment ADA (2022) Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials. Med Phys 49:2220–2232

    Article  PubMed  Google Scholar 

  22. Horvat JV, Keating DM, Rodrigues-Duarte H, Morris EA, Mango VL (2019) Calcifications at digital breast tomosynthesis: imaging features and biopsy techniques. Radiographics 39:307–318

    Article  PubMed  Google Scholar 

  23. Shaheen E, Van Ongeval C, Zanca F et al (2011) The simulation of 3D microcalcification clusters in 2D digital mammography and breast tomosynthesis. Med Phys 38:6659–6671

    Article  PubMed  Google Scholar 

  24. Warren LM, Mackenzie A, Dance DR, Young KC (2013) Comparison of the x-ray attenuation properties of breast calcifications, aluminium, hydroxyapatite and calcium oxalate. Phys Med Biol 58:N103-113

    Article  CAS  PubMed  Google Scholar 

  25. Mackenzie A, Dance DR, Workman A et al (2012) Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system. Med Phys 39:2721–2734

    Article  PubMed  Google Scholar 

  26. Hakansson M, Svensson S, Zachrisson S et al (2010) VIEWDEX: an efficient and easy-to-use software for observer performance studies. Radiat Prot Dosimetry 139:42–51

    Article  PubMed  Google Scholar 

  27. Chakraborty DP (2006) Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. Acad Radiol 13:1187–1193

    Article  PubMed  Google Scholar 

  28. Zackrisson S, Lang K, Rosso A et al (2018) One-view breast tomosynthesis versus two-view mammography in the Malmo Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study. Lancet Oncol 19:1493–1503

    Article  PubMed  Google Scholar 

  29. Gilbert FJ, Tucker L, Young KC (2016) Digital breast tomosynthesis (DBT): a review of the evidence for use as a screening tool. Clin Radiol 71:141–150

    Article  PubMed  Google Scholar 

  30. van Winkel SL, Rodriguez-Ruiz A, Appelman L et al (2021) Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol 31:8682–8691

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chan HP, Helvie MA (2021) Using single-view wide-angle DBT with AI for breast cancer screening. Radiology 300:537–538

    Article  PubMed  Google Scholar 

  32. Durand MA (2018) Synthesized mammography: clinical evidence, appearance, and implementation. Diagnostics (Basel) 8:22. https://doi.org/10.3390/diagnostics8020022

    Article  CAS  PubMed  Google Scholar 

  33. Re A, Park JM, Philpotts LE et al (2013) Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. Radiology 266:104–113

    Article  Google Scholar 

  34. Monnin P, Gnesin S, Verdun FR, Marshall NW (2019) Generalized SDNR analysis based on signal and noise power. Phys Med 64:10–15

    Article  CAS  PubMed  Google Scholar 

  35. Hadjipanteli A, Elangovan P, Mackenzie A et al (2017) The effect of system geometry and dose on the threshold detectable calcification diameter in 2D-mammography and digital breast tomosynthesis. Phys Med Biol 62:858–877

    Article  PubMed  Google Scholar 

  36. Koetzier LR, Mastrodicasa D, Szczykutowicz TP et al (2023) Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology 306:e221257

    Article  PubMed  Google Scholar 

  37. Marshall NW, Bosmans H (2012) Measurements of system sharpness for two digital breast tomosynthesis systems. Phys Med Biol 57:7629–7650

    Article  CAS  PubMed  Google Scholar 

  38. Zhou J, Zhao B, Zhao W (2007) A computer simulation platform for the optimization of a breast tomosynthesis system. Med Phys 34:1098–1109

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank Katrien Houbrechts, Karen Merken, Stoyko Marinov, and Hannelore Verhoeven for reading the images in this study. We thank Kristin Buelens for acquiring the patient images and informed consent.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Imaging and Pathology, Division of Medical Physics, KU Leuven, Herestraat 49, 3000, Leuven, Belgium

    Liesbeth Vancoillie, Nicholas Marshall, Chantal Van Ongeval & Hilde Bosmans

  2. CVIT, Duke University, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA

    Liesbeth Vancoillie

  3. Department of Radiology, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium

    Lesley Cockmartin, Nicholas Marshall, Machteld Keupers, Chantal Van Ongeval & Hilde Bosmans

  4. Siemens Healthcare GmbH, Siemensstraße 1, 91301, Forchheim, Germany

    Ferdinand Lueck, Ralf Nanke & Steffen Kappler

Authors
  1. Liesbeth Vancoillie
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  2. Lesley Cockmartin
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  3. Ferdinand Lueck
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  4. Nicholas Marshall
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  5. Machteld Keupers
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  6. Ralf Nanke
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  7. Steffen Kappler
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  8. Chantal Van Ongeval
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  9. Hilde Bosmans
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Corresponding author

Correspondence to Liesbeth Vancoillie.

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Guarantor

The scientific guarantor of this publication is Dr. Hilde Bosmans.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: The UZ Leuven Department of Radiology has a research agreement with Siemens Healthineers. Authors FL, RN, and SK are employees of Siemens. However, we wish to emphasize that Siemens Healthineers was not involved in the design, execution, analysis, or interpretation of the research findings presented in this manuscript. We affirm that this potential conflict of interest has not influenced the objectivity, integrity, or validity of our research.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Only if the study is on human subjects:

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

  • prospective

  • experimental

  • performed at one institution

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Vancoillie, L., Cockmartin, L., Lueck, F. et al. Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10712-9

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  • DOI: https://doi.org/10.1007/s00330-024-10712-9

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