AccScience Publishing / IJB / Online First / DOI: 10.36922/IJB025080064
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RESEARCH ARTICLE

Evaluation of 3D-printed silicone phantoms with controllable MRI signal properties

Sepideh Hatamikia1,2,3* ,  Olgica Zaric4,5 ,  Laszlo Jaksa3 ,  Florian Schwarzhans4 ,  Siegfried Trattnig5,6 ,  Sebastian Fitzek7 ,  Gernot Kronreif3 ,  Ramona Woitek4† ,  Andrea Lorenz3†
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1 Clinical AI-Research in Omics and Medical Data Science (CAROM) group, Department of Medicine, Krems an der Donau, Austria
2 Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
3 Austrian Center for Medical Innovation and Technology (ACMIT GmbH), Wiener Neustadt, Austria
4 Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria
5 Institute for Clinical Molecular MRI in Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
6 High-field MR Centre, Medical University of Vienna, Vienna, Austria
7 Health Services Research Group, Medical Images Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
†These authors contributed equally to this work.
IJB, 025080064 https://doi.org/10.36922/IJB025080064
Received: 23 February 2025 | Accepted: 25 March 2025 | Published online: 26 March 2025
© 2025 by the Author(s).. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

3D printing technology is widely used for creating magnetic resonance imaging (MRI) phantoms, mimicking tissue, and contrast levels found in real patients. Traditionally, 3D-printed structures were filled with gels containing contrast agents. Recently, studies have shown that some 3D-printed materials can be directly used to create MRI phantoms. However, each material typically produces a unique MRI signal, requiring specific materials for desired contrasts, or a single material can produce various contrasts, but these often do not match the properties of different soft tissues. In this study, we aimed to investigate MRI signal properties of 3D-printed phantoms made of silicone in MRI. We determined the MRI relaxation times of extrusion silicone 3D-printed phantoms from different materials with different infill densities and correlated them with the reference values in soft tissues. We also evaluated the performance of our approach using realistic tumor phantoms. A reproducibility analysis as well as longitudinal stability analysis was also performed. The experimental results showed that the 3D-printed silicone phantoms could achieve MRI signal properties with good correspondence to a range of soft tissues and organs (T1 relaxation time range from 850.8 to 1113.3 ms and T2 relaxation time range from 22.6 to 140.7 ms). Our results demonstrated good stability of the T1 and T2 values over time and also good agreement for the replicas compared to the original samples, confirming the reproducibility of the printed materials. A good agreement was observed between the MRI signal property in tumor phantoms and the reference values of invasive ductal carcinoma of the breast in patients.

Graphical abstract
Keywords
3D printing
Adjustable contrast
MRI
Silicone phantoms
Funding
This work was supported by ACMIT – Austrian Center for Medical Innovation and Technology, which is funded within the scope of the COMET program and funded by Austrian BMVIT and BMWFW and the governments of Lower Austria and Tyrol. This work was also supported by the Provincial Government of Lower Austria (Land Niederösterreich) under grant assignment number WST3-F2- 528983/005-2018.
Conflict of interest
The authors declare they have no competing interests.
References
  1. van der Heide UA, Frantzen-Steneker M, Astreinidou E, Nowee ME, van Houdt PJ. MRI basics for radiation oncologists. Clin Transl Radiat Oncol. 2019;18:74-79. doi: 10.1016/j.ctro.2019.04.008
  2. Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020;49:74-80. doi: 10.1016/j.breast.2019.10.018
  3. Hatamikia S, George G, Schwarzhans F, Mahbod A, Woitek R. Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - how are they affected by variations in tumor delineation? Comput Struct Biotechnol J. 2023;23:52-63. doi: 10.1016/j.csbj.2023.11.016
  4. Bianchini L, Botta F, Origgi D, et al. PETER PHAN: an MRI phantom for the optimisation of radiomic studies of the female pelvis. Phys Med. 2020;71:71-81. doi: 10.1016/j.ejmp.2020.02.003
  5. Gallivanone F, D’Ambrosio D, Carne I, et al. A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics. Phys Med. 2022;98:28-39. doi: 10.1016/j.ejmp.2022.04.007
  6. Lee J, Steinmann A, Ding Y, et al. Radiomics feature robustness as measured using an MRI phantom. Sci Rep. 2021;11(1):3973. doi: 10.1038/s41598-021-83593-3
  7. Filippou V, Tsoumpas C. Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound. Med Phys. 2018;45(9):e740-e760. doi: 10.1002/mp.13058
  8. Kut C, Kao T, Morcos M, Kim Y, Boctor E, Viswanathan AN. 3D-printed magnetic resonance (MR)-based gynecological phantom for image-guided brachytherapy training. Brachytherapy. 2022;21(6):799-805. doi: 10.1016/j.brachy.2022.07.005
  9. Altermatt A, Santini F, Deligianni X, et al. Design and construction of an innovative brain phantom prototype for MRI. Magn Reson Med. 2019;81(2):1165-1171. doi: 10.1002/mrm.27464
  10. Cox BL, Ludwig KD, Adamson EB, Eliceiri KW, Fain SB. An open source, 3D printed preclinical MRI phantom for repeated measures of contrast agents and reference standards. Biomed Phys Eng Express. 2018;4(2):027005. doi: 10.1088/2057-1976/aa9491
  11. Cho HM, Cheolpyo H, Changwoo L, Ding H, Taeho K, Ahn B. LeGo-compatible modular mapping phantom for magnetic resonance imaging. Sci Rep. 2020;10:14755. doi: 10.1038/s41598-020-71279-1
  12. Mitsouras D, Lee TC, Liacouras P, et al. Three-dimensional printing of MRI-visible phantoms and MR image-guided therapy simulation. Magn Reson Med. 2017;77(2):613-622. doi: 10.1002/mrm.26136
  13. Rai R, Holloway LC, Brink C, et al. Multicenter evaluation of MRI-based radiomic features: a phantom study. Med Phys. 2020;47(7):3054-3063. doi: 10.1002/mp.14173
  14. Rausch I, Valladares A, Sundar LKS, et al. Standard MRI-based attenuation correction for PET/MRI phantoms: a novel concept using MRI-visible polymer. EJNMMI Phys. 2021;8(1):18. doi: 10.1186/s40658-021-00364-9
  15. Rai R, Wang YF, Manton D, Dong B, Deshpande S, Liney GP. Development of multi-purpose 3D printed phantoms for MRI. Phys Med Biol. 2019;64(7):075010. doi: 10.1088/1361-6560/ab0b49
  16. Valladares A, Oberoi G, Berg A, Beyer T, Unger E, Rausch I. Additively manufactured, solid object structures for adjustable image contrast in magnetic resonance imaging. Z Med Phys. 2022;32(4):466-476. doi: 10.1016/j.zemedi.2022.03.003
  17. Yunker BE, Stupic KF, Wagner JL, et al. Characterization of 3-dimensional printing and casting materials for use in magnetic resonance imaging phantoms at 3 T. J Res Natl Inst Stand Technol. 2020;vol:125028. doi: 10.6028/jres.125.028
  18. Rai R, Manton D, Jameson MG, et al. 3D printed phantoms mimicking cortical bone for the assessment of ultrashort echo time magnetic resonance imaging. Med Phys. 2018;45(2):758-766. doi: 10.1002/mp.12727
  19. Jiangfeng Q, Kun H, Brandon AD, et al. Constructing customized multimodal phantoms through 3D printing: a preliminary evaluation. Front Phys. 2021;9:605630. doi: 10.3389/fphy.2021.605630
  20. Woletz M, Chalupa‐Gantner F, Hager B, et al. Toward printing the brain: a microstructural ground truth phantom for MRI. Adv Mater Technol. 2024;9(3):2300176. doi: 10.1002/admt.202300176
  21. Hatamikia S, Jaksa L, Kronreif G, et al. Silicone phantoms fabricated with multi-material extrusion 3D printing technology mimicking imaging properties of soft tissues in CT. Z Med Phys. 2023;S0939-3889,26(23)00076-4. doi: 10.1016/j.zemedi.2023.05.007
  22. Jaksa L, Aryeetey OJ, Hatamikia S, et al. 3D-Printed multi-material liver model with simultaneous mechanical and radiological tissue-mimicking features for improved realism. Int J Bioprint. 2023;9(4):721. doi: 10.18063/ijb.721
  23. Hatamikia S, Kronreif G, Unger A, et al. 3D printed patient-specific thorax phantom with realistic heterogenous bone radiopacity using filament printer technology. Z Med Phys. 2022;32(4):438-452. doi: 10.1016/j.zemedi.2022.02.001
  24. Chen Y, Panda A, Pahwa S, et al. Three-dimensional MR fingerprinting for quantitative breast imaging. Radiology. 2019;290(1):33-40. doi: 10.1148/radiol.2018180836
  25. Fram EK, Herfkens RJ, Johnson GA, et al. Rapid calculation of T1 using variable flip angle gradient refocused imaging. Magn Reson Imaging. 1987;5(3):201-208. doi: 10.1016/0730-725x(87)90021-x
  26. Poon CS, Henkelman RM. Practical T2 quantitation for clinical applications. J Magn Reson Imaging. 1992;2(5):541-553. doi: 10.1002/jmri.1880020512
  27. Bojorquez JZ, Bricq S, Acquitter C, Brunotte F, Walker PM, Lalande A. What are normal relaxation times of tissues at 3 T? Magn Reson Imaging. 2017;35:69-80. doi: 10.1016/j.mri.2016.08.021
  28. Carr ME, Keenan KE, Rai R, Metcalfe P, Walker A, Holloway L. Determining the longitudinal accuracy and reproducibility of T1 and T2 in a 3T MRI scanner. J Appl Clin Med Phys. 2021;22(11):143-150. doi: 10.1002/acm2.13432
  29. De Bazelaire CMJ, Duhamel GD, Rofsky NM, Alsop DC. MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology. 2004;230(3):652-659. doi: 10.1148/radiol.2303021331
  30. Gold GE, Han E, Stainsby J, Wright G, Brittain J, Beaulieu C. Musculoskeletal MRI at 3.0 T: relaxation times and image contrast. AJR Am J Roentgenol. 2004;183(2):343-351. doi: 10.2214/ajr.183.2.1830343
  31. Chen Y, Jiang Y, Pahwa S, et al. MR fingerprinting for rapid quantitative abdominal imaging. Radiology. 2016;279(1):278-286. doi: 10.1148/radiol.2016152037
  32. Fennessy FM, Fedorov A, Gupta SN, Schmidt EJ, Tempany CM, Mulkern RV. QIN: practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses. Magn Reson Imaging. 2012;30(9):1224-1233. doi: 10.1016/j.mri.2012.06.011
  33. Piechnik SK, Ferreira VM, Dall’Armellina E, et al. Shortened modified Look–Locker inversion recovery (shmolli) for clinical myocardial t1-mapping at 1.5 and 3 twithin a 9 heartbeat breathhold. J Cardiovasc Magn Reson. 2010;12(1):69. doi: 10.1186/1532-429X-12-69
  34. Shin W, Gu H, Yang Y. Fast high-resolution T1 mapping using inversion-recovery look–locker echo-planar imaging at steady state: optimization for accuracy and reliability. Magn Reson Med. 2009;61(4):899-906. doi: 10.1002/mrm.21836
  35. Chen L, Bernstein M, Huston J, Fain S. Measurements of T1 relaxation times at 3.0 T: implications for clinical MRA. Proceedings International Society for. Magn Reson Med. 2001:1.
  36. Bojorquez JZ, Bricq S, Brunotte F, Walker PM, Lalande AA. A novel alternative to classify tissues from T 1 and T 2 relaxation times for prostate MRI. Magn Reason Mater Phys Biol Med. 2016;29(5):1–12. doi: 10.1007/s10334-016-0562-3
  37. Van Heeswijk RB, Feliciano H, Bongard C, et al. Free-breathing 3 T magnetic resonance T2-mapping of the heart. J Am Coll Cardiol Imaging. 2012;5(12):1231-1239. doi: 10.1016/j.jcmg.2012.06.010
  38. Rakow-Penner R, Daniel B, Yu H, Sawyer-Glover A, Glover GH. Relaxation times of breast tissue at 1.5 T and 3 T measured using IDEAL. J Magn Reson Imaging. 2006;23(1):87-91. doi: 10.1002/jmri.20469
  39. Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold MAMR. Fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reason Med. 2015;74(6):1621-1631. doi: 10.1002/mrm.25559
  40. Bidhult S, Kantasis G, Aletras AH, Arheden H, Heiberg E, Hedström E. Validation of T1 and T2 algorithms for quantitative MRI: performance by a vendor-independent software. BMC Med Imaging. 2016;16(1):46. doi: 10.1186/s12880-016-0148-6
  41. Jaksa L, Pahr D, Kronreif G, Lorenz A. Development of a multi-material 3D printer for functional anatomic models. Int J Bioprint. 2021;7(4):420. doi: 10.18063/ijb.v7i4.420
  42. James Aryeetey O, Jaksa l, Bittner-Frank M, Lorenz A, Pahr DH. Development of 3D printed tissue-mimicking materials: Combining fiber reinforcement and fluid content for improved surgical rehearsal. Materialia. 2024; 34:102088. doi: 10.1016/j.mtla.2024.102088
  43. Gillaspie EA, Matsumoto JS, Morris NE, et al. From 3-dimensional printing to 5-dimensional printing: enhancing thoracic surgical planning and resection of complex tumors. Ann Thorac Surg. 2016;101(5):1958-1962. doi: 10.1016/j.athoracsur.2015.12.075
  44. Kokkinis D, Schaffner M, Studart A. Multimaterial magnetically assisted 3D printing of composite materials. Nat Commun. 2015;6:8643. doi: 10.1038/ncomms9643
  45. Foresti R, Rossi S, Pinelli S, et al. In-vivo vascular application via ultra-fast bioprinting for future 5D personalised nanomedicine. Sci Rep. 2020;10:3205. doi: 10.1038/s41598-020-60196-y
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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