Abstract
The effectiveness of cardiac imaging system is valued from its reliability, reproducibility, accuracy, and usefulness in the clinical settings. These parameters are often evaluated, validated, and justified during the optimization process of the system. A large number of calibration techniques have been used in this process to provide values that specify how standardized the imaging system is. The most common technique is using physical imaging phantom. This device can clarify the degree of image quality and object detectability produced by the imaging system. However, even various imaging phantoms have been widely available, it is still difficult to obtain the phantoms that mimic the realistic biological tissues and functions, particularly for cardiac imaging applications. As cardiac imaging systems capture and analyse dynamic cardiac morphology and function in motions, the main issue in cardiac imaging phantoms is how close the phantom properties to those of realistic biological tissues so that the phantom can guarantee for a reproducible measurement. As cardiac imaging phantom materials play vital roles in the standardized validation for cardiac imaging systems, it is important to study Tissue Mimicking Materials (TMMs) for cardiac imaging systems, materials, and their properties that build the phantom structures. This review study is divided into two parts. Part 1 highlights on preparation processes in phantom development that consist of conception, design, simulation, and materials selection stages, while part 2 concentrates on realization processes from fabrication to optimization stages. This part 1 is aimed to briefly review the current state of knowledge regarding TMMs and their uses for cardiac imaging phantoms. Introduction to systematic processes in the phantom development is also presented to provide an understanding on how to generate the physical phantom step by step.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Emanuel, E.J.: Ethical and Regulatory Aspects of Clinical Research: Readings and Commentary. philpapers.org (2003)
Budoff, M., Cohen, M., Garcia, M., et al.: ACCF/AHA clinical competence statement on cardiac imaging with computed tomography and magnetic resonance. Circulation 112(4), 598–617 (2005)
Hubbard, P.L., Zhou, F.‐L., Eichhorn, S.J., Parker, G.J.M.: Biomimetic phantom for the validation of diffusion magnetic resonance imaging. Magn. Reson. Med. 73(1), 299–305 (2015)
Koonce, J.D., Vliegenthart, R., Schoepf, U.J., et al.: Accuracy of dual-energy computed tomography for the measurement of iodine concentration using cardiac CT protocols: validation in a phantom model. Eur. Radiol. 24, 512 (2014). https://doi.org/10.1007/s00330-013-3040-6
Hill, A.J., Iaizzo, P.A.: Comparative cardiac anatomy. In: Iaizzo, P. (ed.) Handbook of Cardiac Anatomy, Physiology, and Devices. Springer, Cham (2015)
Mathur, A., Ma, Z., Loskill, P., Jeeawoody, S., Healy, K.E.: In vitro cardiac tissue models: current status and future prospects. Adv. Drug Deliv. Rev. 96(15), 203–213 (2016)
Garrett, J., Fear, E.: Stable and flexible materials to mimic the dielectric properties of human soft tissues. IEEE Antennas Wirel. Propag. Lett. 13, 599–602 (2014)
Vannelli, C., Moore, J., McLeod, J., Ceh, D., Peters, T.: Dynamic heart phantom with functional mitral and aortic valves. In: Proceedings, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 9415, p. 941503 (2015). https://doi.org/10.1117/12.2082277
Avendi, M.R., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)
Shahzad, R., Bos, D., Budde, R.P.J., Pellikaan, K., Niessen, W.J., van der Lugt, A., van Walsum, T.: Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans. Phys. Med. Biol. 62(9)
Xu, R., Athavale, P., Nachman, A., Wright, G.A.: Multiscale registration of real-time and prior MRI data for image-guided cardiac interventions. IEEE Trans. Biomed. Eng. 61(10), 2621–2633 (2014)
Abi-Jaoudeh, N., Kruecker, J., Kadoury, S., et al.: Multimodality image fusion-guided procedures: technique, accuracy, and applications. Cardiovasc. Intervent. Radiol. 35, 986 (2012). https://doi.org/10.1007/s00270-012-0446-5
Turner, L.R., et al.: Cardiovascular disease screening in general practice: general practitioner recording of common risk factors. Prev. Med. 99, 282–285 (2017)
Duffy, J.Y., et al.: Cardiovascular disease screening. Semin. Perinatol. 39(4), 264–267 (2015)
Bekar, L., et al.: The preference of the physicians in diagnosis and treatment of cardiovascular diseases. Int. J. Cardiovasc. Acad. 3(1–2), 11–15 (2017)
Liu, Y., et al.: Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces. Sci. Adv. 2(11), e1601185 (2016)
Danad, I., et al.: Diagnostic performance of cardiac imaging methods to diagnose ischaemia-causing coronary artery disease when directly compared with fractional flow reserve as a reference standard: a meta-analysis. Eur. Heart J. 38(13), 991–998 (2017)
Bountry, C.M., et al.: A sensitive and biodegradable pressure sensor array for cardiovascular monitoring. Adv. Mater. 27, 6954–6961 (2015)
Sun, J., et al.: Carotid magnetic resonance imaging for monitoring atherosclerotic plaque progression: a multicenter reproducibility study. Int. J. Cardiovasc. Imaging 31, 95 (2015)
Noc, M., et al.: Invasive coronary treatment strategies for out-of-hospital cardiac arrest: a consensus statement from the European Association for Percutaneous Cardiovascular Interventions (EAPCI)/Stent for Life (SFL) groups. EuroIntervention 10, 31–37 (2014)
Piepoli, M.F., et al.: 2016 European guidelines on cardiovascular disease prevention in clinical practice. Atherosclerosis 252, 207–274 (2016)
Catapano, A.L., et al.: 2016 ESC/EAS guidelines for the management of dyslipidaemias. Atherosclerosis 253, 281–344 (2016)
Arena, R., et al.: Healthy lifestyle interventions to combat noncommunicable disease—a novel nonhierarchical connectivity model for key stakeholders: a policy statement from the American Heart Association, European Society of Cardiology, European Association for Cardiovascular Prevention and Rehabilitation, and American College of Preventive Medicine. Mayo Clin. Proc. 90(8), 1082–1103 (2015)
Kersten-Oertel, M., Jannin, P., Collins, D.L.: The state of the art of visualization in mixed reality image guided surgery. Comput. Med. Imaging Graph. 37(2), 98–112 (2013). https://doi.org/10.1016/j.compmedimag.2013.01.009
Badano, L.P., Miglioranza, M.H., Edvardsen, T., Colafranceschi, A.S., Muraru, D., Bacal, F., Nieman, K., Zoppellaro, G., Marcondes Braga, F.G., Binder, T., Habib, G., Lancellotti, P., Document reviewers Sicari, R., Cosyns, B., Donal, E., Lombardi, M., Sarvari, S.: European Association of Cardiovascular Imaging/Cardiovascular Imaging Department of the Brazilian Society of Cardiology recommendations for the use of cardiac imaging to assess and follow patients after heart transplantation. Eur. Heart J. Cardiovasc. Imaging 16(9), 919–948 (2015). https://doi.org/10.1093/ehjci/jev139
Swift, A.J., Rajaram, S., Condliffe, R., Capener, D., Hurdman, J., Elliot, C.A., Wild, J.M., Kiely, D.G.: Diagnostic accuracy of cardiovascular magnetic resonance imaging of right ventricular morphology and function in the assessment of suspected pulmonary hypertension results from the ASPIRE registry. J. Cardiovasc. Magn. Reson. 14, 40 (2012). https://doi.org/10.1186/1532-429X-14-40
Safavi, K.C., Li, S., Dharmarajan, K., et al.: Hospital variation in the use of noninvasive cardiac imaging and its association with downstream testing, interventions, and outcomes. JAMA Int. Med. 174(4), 546–553 (2014). https://doi.org/10.1001/jamainternmed.2013.14407
Hasan, M.A., Lee, S.-L., Kim, D.-H., Lim, M.-K.: Automatic evaluation of cardiac hypertrophy using cardiothoracic area ratio in chest radiograph images. Comput. Methods Programs Biomed. 105(2), 95–108 (2012)
Dimopoulos, K., Giannakoulas, G., Bendayan, I., Liodakis, E., Petraco, R., Diller, G.-P., Piepoli, M.F., Swan, L., Mullen, M., Best, N., Poole-Wilson, P.A., Francis, D.P., Rubens, M.B., Gatzoulis, M.A.: Cardiothoracic ratio from postero-anterior chest radiographs: a simple, reproducible and independent marker of disease severity and outcome in adults with congenital heart disease. Int. J. Cardiol. 166(2), 453–457 (2013). https://doi.org/10.1016/j.ijcard.2011.10.125
Venugopal, A.N., Koshy, R.C., Koshy, S.M.: Role of chest X-ray in citing central venous catheter tip: a few case reports with a brief review of the literature. J. Anaesthesiol. Clin. Pharmacol. 29(3), 397–400 (2013). https://doi.org/10.4103/0970-9185.117114
Ubeda, C., Vano, E., Gonzalez, L., Miranda, P.: Influence of the antiscatter grid on dose and image quality in pediatric interventional cardiology X‐ray systems. Cathet. Cardiovasc. Intervent. 82(1), 51–57 (2013). https://doi.org/10.1002/ccd.24602
Schoenhagen, P., Halliburton, S.S., Stillman, A.E., Kuzmiak, S.A., Nissen, S.E., Tuzcu, E.M., White, R.D.: Noninvasive imaging of coronary arteries: current and future role of multi–detector row CT. Radiology 232(1) (2004). https://doi.org/10.1148/radiol.2321021803
Wilson, J., Saremi, F., Narula, J., Narayan, S.M.: CT in the management of cardiac arrhythmias. In: Budoff, M., Achenbach, S., Hecht, H., Narula, J. (eds.) Atlas of Cardiovascular Computed Tomography. Springer, London (2018)
Sun, Z., Ng, K.H.: Diagnostic value of coronary CT angiography with prospective ECG-gating in the diagnosis of coronary artery disease: a systematic review and meta-analysis. Int. J. Cardiovasc. Imaging 28, 2109 (2012). https://doi.org/10.1007/s10554-011-0006-0
Morin, R.L., Gerber, T.C., McCollough, C.H.: Radiation dose in computed tomography of the heart. Circulation 107, 917–922 (2003). https://doi.org/10.1161/01.CIR.0000048965.56529.C2
Ulzheimer, S., Kalender, W.A.: Assessment of calcium scoring performance in cardiac computed tomography. Eur. Radiol. 13, 484–497 (2003). https://doi.org/10.1007/s00330-002-1746-y
Sun, Z., Al Moudi, M., Cao, Y.: CT angiography in the diagnosis of cardiovascular disease: a transformation in cardiovascular CT practice. Quant. Imaging Med. Surg. 4(5), 376–396 (2014). https://doi.org/10.3978/j.issn.2223-4292.2014.10.02
Scholtz, J.-E., Ghoshhajra, B.: Advances in cardiac CT contrast injection and acquisition protocols. Cardiovasc. Diagn. Ther. 7(5), 439–451 (2017). https://doi.org/10.21037/cdt.2017.06.07
Baker, J.E., Moulder, J.E., Hopewell, J.W.: Radiation as a risk factor for cardiovascular disease. Antioxid. Redox Signal. 15(7), 1945–1956 (2011). https://doi.org/10.1089/ars.2010.3742
Thai, W., Wai, B., Lin, K., et al.: Pulmonary venous anatomy imaging with low-dose, prospectively ECG-triggered, high-pitch 128-slice dual source computed tomography. Circ. Arrhythm. Electrophysiol. 5(3), 521–530 (2012). https://doi.org/10.1161/CIRCEP.111.968313
Muraru, D., Niero, A., Rodriguez-Zanella, H., Cherata, D., Badano, L.: Three-dimensional speckle-tracking echocardiography: benefits and limitations of integrating myocardial mechanics with three-dimensional imaging. Cardiovasc. Diagn. Ther. 8(1), 101–117 (2018). https://doi.org/10.21037/cdt.2017.06.01
Armstrong, A.C., Ricketts, E.P., Cox, C., et al.: Quality control and reproducibility in M-mode, two-dimensional, and speckle tracking echocardiography acquisition and analysis: the CARDIA study, year-25 examination experience. Echocardiography (Mount Kisco, NY) 32(8), 1233–1240 (2015). https://doi.org/10.1111/echo.12832
Badano, L.P.: The clinical benefits of adding a third dimension to assess the left ventricle with echocardiography. Scientifica 2014, 897431 (2014). https://doi.org/10.1155/2014/897431
Bencsik, G.: Novel strategies in the ablation of typical atrial flutter: role of intracardiac echocardiography. Curr. Cardiol. Rev. 11(2), 127–133 (2015). https://doi.org/10.2174/1573403X10666141013121843
Soloperto, G., Casciaro, S.: Progress in atherosclerotic plaque imaging. World J. Radiol. 4(8), 353–371 (2012). https://doi.org/10.4329/wjr.v4.i8.353
Vignali, L., Solinas, E., Emanuele, E.: Research and clinical applications of optical coherence tomography in invasive cardiology: a review. Curr. Cardiol. Rev. 10, 369–376 (2014). https://doi.org/10.2174/1573403X10666140604120753
Suter, M.J., Nadkarni, S.K., Weisz, G., et al.: Intravascular optical imaging technology for investigating the coronary artery (2011). https://doi.org/10.1016/j.jcmg.2011.03.020
Peterzan, M.A., Rider, O.J., Anderson, L.J.: The role of cardiovascular magnetic resonance imaging in heart failure. Card. Fail. Rev. 2(2), 115–122 (2016). https://doi.org/10.15420/cfr.2016.2.2.115
Parsai, C., O’Hanlon, R., Prasad, S.K., Mohiaddin, R.H.: Diagnostic and prognostic value of cardiovascular magnetic resonance in non-ischaemic cardiomyopathies. J. Cardiovasc. Magn. Reson. 14(1), 54 (2012). https://doi.org/10.1186/1532-429X-14-54
Sherrah, A.G., Grieve, S.M., Jeremy, R.W., Bannon, P.G., Vallely, M.P., Puranik, R.: MRI in chronic aortic dissection: a systematic review and future directions. Front. Cardiovasc. Med. 2, 5 (2015). https://doi.org/10.3389/fcvm.2015.00005
Gulenchyn, K., McEwan, A., Freeman, M., Kiess, M., O’Neill, B., Beanlands, R.: Treating the right patient at the right time: access to cardiovascular nuclear imaging. Can. J. Cardiol. 22(10), 827–833 (2006)
Boogers, M.J., Fukushima, K., Bengel, F.M., Bax, J.J.: The role of nuclear imaging in the failing heart: myocardial blood flow, sympathetic innervation, and future applications. Heart Fail. Rev. 16, 411–423 (2011). https://doi.org/10.1007/s10741-010-9196-0
Merhige, M.E., Breen, W.J., Shelton, V., et al.: Impact of myocardial perfusion imaging with PET and (82)Rb on downstream invasive procedure utilization, costs, and outcomes in coronary disease management. J. Nucl. Med. 48, 1069–1076 (2007). https://doi.org/10.2967/jnumed.106.038323
Kircher, M., Lapa, C.: Novel noninvasive nuclear medicine imaging techniques for cardiac inflammation. Curr. Cardiovasc. Imaging Rep. 10(2), 6 (2017). https://doi.org/10.1007/s12410-017-9400-x
Li, T., Ao, E.C.I., Lambert, B., Brans, B., Vandenberghe, S., Mok, G.S.P.: Quantitative imaging for targeted radionuclide therapy dosimetry—technical review. Theranostics 7(18), 4551–4565 (2017). https://doi.org/10.7150/thno.19782
Pan, J.A., Salerno, M.: Clinical utility and future applications of PET/CT and PET/CMR in cardiology. In: Kjaer, A. (ed.) Diagnostics 6(3), 32 (2016). https://doi.org/10.3390/diagnostics6030032
Zhuang, H., Codreanu, I.: Growing applications of FDG PET-CT imaging in non-oncologic conditions. J. Biomed. Res. 29(3), 189–202 (2015). https://doi.org/10.7555/JBR.29.20140081
Zhenzhen, X., Tao, B., Li, Y., et al.: 3D fusion framework for infarction and angiogenesis analysis in a myocardial infarct minipig model. Mol. Imaging 16, 1536012117708735 (2017). https://doi.org/10.1177/1536012117708735
Woo, J., Stone, M., Prince, J.L.: Multimodal registration via mutual information incorporating geometric and spatial context. IEEE Trans. Image Process. 24(2), 757–769 (2015). https://doi.org/10.1109/TIP.2014.2387019
Nordenfur, T., Babic, A., Bulatovic, I., Giesecke, A., Günyeli, E., Ripsweden, J., Samset, E., Winter, R., Larsson, M.: Method comparison for cardiac image registration of coronary computed tomography angiography and 3-D echocardiography. J. Med. Imaging 5(1), 014001 (2018). https://doi.org/10.1117/1.JMI.5.1.014001
Shrestha, U.M., Seo, Y., Botvinick, E.H., Gullberg, G.T.: Image reconstruction in higher dimensions: myocardial perfusion imaging of tracer dynamics with cardiac motion due to deformation and respiration. Phys. Med. Biol. 60(21), 8275–8301 (2015). https://doi.org/10.1088/0031-9155/60/21/8275
Lopez-Perez, A., Sebastian, R., Ferrero, J.M.: Three-dimensional cardiac computational modelling: methods, features and applications. BioMed. Eng. OnLine 14, 35 (2015). https://doi.org/10.1186/s12938-015-0033-5
Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R.D., Chung, J.D., Cowan, B.R., Dinov, I.D., Finn, J.P., Hunter, P.J., Kadish, A.H., Lee, D.C., Lima, J.A.C., Medrano-Gracia, P., Shivkumar, K., Suinesiaputra, A., Tao, W., Young, A.A.: The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011). https://doi.org/10.1093/bioinformatics/btr360
Wong, M.D., Wu, X., Liu, H.: Image quality and dose efficiency of high energy phase sensitive X-ray imaging: phantom studies. J. X-ray Sci. Technol. 22(3), 321–334 (2014). https://doi.org/10.3233/XST-140428
Ceh, J., Youd, T., Mastrovich, Z., et al.: Bismuth infusion of ABS enables additive manufacturing of complex radiological phantoms and shielding equipment. In: Choi, J.-W., Engeberg, E.D. (eds.) Sensors (Basel, Switzerland) 17(3), 459 (2017). https://doi.org/10.3390/s17030459
Negron, L.A., Viola, F., Black, E.P., Toth, C.A., Walker, W.F.: Development and characterization of a vitreous mimicking material for radiation force imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(11), 1543–1551 (2002)
Rozenkrantz, A.B., Mendiratta-Lala, M., Bartholmai, B.J., Ganeshan, D., Abransom, R.G., Burton, K.R., Yu, J.-P.J., Scalzetti, E.M., Yankeelov, T.E., Subramaniam, R.M., Lenchik, L.: Clinical utility of quantitative imaging. Acad. Radiol. 22, 33–49 (2015)
De Grand, A.M., Lomnes, S.J., Lee, D.S., et al.: Tissue-like phantoms for near-infrared fluorescence imaging system assessment and the training of surgeons. J. Biomed. Opt. 11(1), 014007 (2006). https://doi.org/10.1117/1.2170579
Boltz, T., Pavlicek, W., Paden, R., Renno, M., Jensen, A., Akay, M.: An anthropomorphic beating heart phantom for cardiac X‐ray CT imaging evaluation. J. Appl. Clin. Med. Phys. (2010). https://doi.org/10.1120/jacmp.v11i1.3129
Nattagh, K., Siauw, T., Pouliot, J., Hsu, I.C., Cunha, J.A.: A training phantom for ultrasound-guided needle insertion and suturing. Brachytherapy 13(4), 413–419 (2014). https://doi.org/10.1016/j.brachy.2014.01.003. Epub 12 Feb 2014
Seegenschmiedt, M.H., et al.: Thermoradiotherapy and Thermochemotherapy, Volume 1: Biology, Physiology, Physics. Springer-Verlag, Berlin (1995)
Boutchko, R., Balakrishnan, K., Gullberg, G.T., O’Neil, J.P.: Human torso phantom for imaging of heart with realistic modes of cardiac and respiratory motion. US8535061B2, US Patent 2007
Verkerke, G.J., van der Houwen, E.B.: Design of biomedical products. In: Rakhorst, G., Ploeg, R. (eds.) Biomaterials in Modern Medicine: The Groningen Perspective, pp. 23–38. Biomechanical Engineering, World Scientific Publishing (2008)
Jones, P., Bowes, J.: Rendering systems visible for design: synthesis maps as constructivist design narratives. She Ji J. Des. Econ. Innov. 3(3), 229–248 (2017). https://doi.org/10.1016/j.sheji.2017.12.001
Garcia, J., Yang, Z., Mongrain, R., et al.: 3D printing materials and their use in medical education: a review of current technology and trends for the future. BMJ Simul. Technol. Enhanc. Learn. (2017). https://doi.org/10.1136/bmjstel-2017-000234
Zainon, R.: Design and fabrication of multipurpose smart phantom for positron emission tomography/computed tomography imaging (2008)
Shikhaliev, P.M.: Dedicated phantom materials for spectral radiography and CT. Phys. Med. Biol. 57(6), 1575–1593 (2012). https://doi.org/10.1088/0031-9155/57/6/1575. Epub 7 Mar 2012
Park, S., Lee, J.K., Kim, J.I., Lee, Y.J., Lim, Y.K., Kim, C.S., Lee, C.: In vivo organ mass of Korean adults obtained from whole-body magnetic resonance data. Radiat. Prot. Dosimetry 118(3), 275–279 (2006). https://doi.org/10.1093/rpd/nci340
Bosgra, S., van Eijkeren, J., Bos, P., Zeilmaker, M., Slob, W.: An improved model to predict physiologically based model parameters and their inter-individual variability from anthropometry. Crit. Rev. Toxicol. 42(9) (2012)
Del Bianco, S., Martelli, F., Cignini, F., Zaccanti, G., Pifferi, A., Torricelli, A., Bassi, A., Taroni, P., Cubeddu, R.: Liquid phantom for investigating light propagation through layered diffusive media. Opt. Express 12, 2102–2111 (2004)
Fieseler, M., Kugel, H., Gigengack, F., Kösters, T., Büther, F., Quick, H.H., Faber, C., Jiang, X., Schäfers, K.P.: A dynamic thorax phantom for the assessment of cardiac and respiratory motion correction in PET/MRI: a preliminary evaluation. Nucl. Instrum. Methods Phys. Res. A 702, 59–63 (2013)
Boote, E., Fent, G., Kattumuri, V., Casteel, S., Katti, K., Chanda, N., Kannan, R., Katti, K., Churchill, R.: Gold nanoparticle contrast in a phantom and juvenile swine: models for molecular imaging of human organs using X-ray computed tomography. Acad. Radiol. 17(4), 410–417 (2010)
Abdullah, K.A., McEntee, M.F., Reed, W., Kench, P.L.: Development of an organ-specific insert phantom generated using a 3D printer for investigations of cardiac computed tomography protocols. J. Med. Radiat. Sci. (2018). https://doi.org/10.1002/jmrs.279
Lubis, L.E., Craig, L.A., Bosmans, H., Soejoko, D.S.: Task-based phantom evaluation of cardiac catheterization imaging modes. Phys. Med. 46, 114–123 (2018). https://doi.org/10.1016/j.ejmp.2018.02.002. Epub 5 Feb 2018
Tavakoli, V., Kendrick, M., Shakeri, M., Alshaher, M., Stoddard, M.F., Amini, A.: A multimodal (MRI/ultrasound) cardiac phantom for imaging experiments. In: Proceedings SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, p. 867202, 29 Mar 2013. https://doi.org/10.1117/12.2008783
Thakur, Y., Nikolov, H.N., Gulka, B., Holdsworth, D.W., Drangova, M.: Design and construction of a multipath vessel phantom for interventional training. Br. J. Radiol. 83(995), 979–982 (2010). https://doi.org/10.1259/bjr/91767642
Ventola, C.L.: Medical applications for 3D printing: current and projected uses. P&T 39(10), 704–711 (2014)
Abayazid, M., Kemp, M., Misra, S.: 3D flexible needle steering in soft-tissue phantoms using fiber bragg grating sensors. In: 2013 IEEE International Conference on Robotics and Automation, 6–10 May 2013. https://doi.org/10.1109/icra.2013.6631418
Yokoyama, K., Nakagawa, H., Shah, D.C., Lambert, H., Leo, G., Aeby, N., Ikeda, A., Pitha, J.V., Sharma, T., Lazzara, R., et al.: Novel contact force sensor incorporated in irrigated radiofrequency ablation catheter predicts lesion size and incidence of steam pop and thrombus. Circ. Arrhythm. Electrophysiol. 1, 354–362 (2008)
Magnetic free MRI phantom: Madsen, E.L., Fullerton, G.D.: Prospective tissue-mimicking materials for use in NMR imaging phantoms. Magn. Reson. Imaging 1(3), 135–141 (1982)
Cygan, S., Werys, K., Błaszczyk, Ł., Kubik, T., Kałużyński, K.: Left ventricle phantom and experimental setup for MRI and echocardiography—preliminary results of data acquisitions. Biocybern. Biomed. Eng. 34(1), 19–24 (2014). https://doi.org/10.1016/j.bbe.2013.12.002
Stabin, M.G., Xu, X.G., Emmons, M.A., Segars, W.P., Shi, C., Fernald, M.J.: RADAR reference adult, pediatric, and pregnant female phantom series for internal and external dosimetry. J. Nucl. Med. 53(11), 1807–1813 (2012). https://doi.org/10.2967/jnumed.112.106138
Kim, J.I.: Physical phantom of typical Korean male for radiation protection purpose. Radiat. Prot. Dosimetry 118, 131–136 (2005). https://doi.org/10.1093/rpd/nci338
Bolwin, K., Czekalla, B., Frohwein, L.J., Büther, F., Schäfers, K.P.: Anthropomorphic thorax phantom for cardio-respiratory motion simulation in tomographic imaging. Phys. Med. Biol. 63, 035009 (2018)
Jan, S., Benoit, D., Becheva, E., Carlier, T., Cassol, F., Descourt, P., Frisson, T., Grevillot, L., Guigues, L., Maigne, L.: GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys. Med. Biol. 56, 881 (2011)
Veress, A.I., Segars, W.P., IEEE Member, Tsui, B.M.W., IEEE Fellow, Gullberg, G.T.: Incorporation of a left ventricle finite element model defining infarction into the XCAT imaging phantom. IEEE Trans. Med. Imaging 30(4), 915 (2011)
Paganetti, H.: Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys. Med. Biol. 57, R99 (2012)
Fang, Q.: Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates. Biomed. Opt. Express 1(1), 165–175 (2010). https://doi.org/10.1364/BOE.1.000165
Dobre, A.A., Morega, A.M., Morega, M.: The investigation of flow—structural interaction in an arterial branching by numerical simulation. Proc. IEEE/EMBS Reg. 8 Int. Conf. Inf. Technol. Appl. Biomed. ITAB, 4–7 (2010). https://doi.org/10.1109/itab.2010.5687648
Kung, E.O., Les, A.S., Figueroa, C.A., et al.: In vitro validation of finite element analysis of blood flow in deformable models. Ann. Biomed. Eng. 39, 1947 (2011). https://doi.org/10.1007/s10439-011-0284-7
Tada, M., Nagai, N., Maeno, T.: Material properties estimation of layered soft tissue based on MR observation and iterative FE simulation. Med. Image Comput. Comput. (2005)
Han, L., Noble, J.A., Burcher, M.: A novel ultrasound indentation system for measuring biomechanical properties of in vivo soft tissue. Ultrasound Med. Biol. 29, 813–823 (2003). https://doi.org/10.1016/S0301-5629(02)00776-7
Harrison, S.M., Bush, M.B., Petros, P.E.: A pinch elastometer for soft tissue. Med. Eng. Phys. 29, 307–315 (2007). https://doi.org/10.1016/j.medengphy.2006.03.011
Zhang, M.G., Cao, Y.P., Li, G.Y., Feng, X.Q.: Spherical indentation method for determining the constitutive parameters of hyperelastic soft materials. Biomech. Model. Mechanobiol. 13, 1–11 (2014). https://doi.org/10.1007/s10237-013-0481-4
Dewerd, L.A.: The Phantoms of Medical and Health Physics. M. Kissick (ed.). Springer, Berlin (2014)
Johnson, P.B., Geyer, A., Borrego, D., et al.: The impact of anthropometric patient-phantom matching on organ dose: a hybrid phantom study for fluoroscopy guided interventions. Med. Phys. 38, 1008–1017 (2011). https://doi.org/10.1118/1.3544353
Akhlaghi, P., Miri, H., Motavalli, L.R.: Determination of tissue equivalent materials of a physical 8-year-old phantom for use in computed tomography. Radiat. Phys. Chem. 112, 169–176 (2015). https://doi.org/10.1016/j.radphyschem.2015.03.030
Brundle, C.R., Evans, C.A., Wilson, S.: Encyclopedia of Materials Characterization: Surfaces, Interfaces, Thin Films. Elsevier (1992)
Zalba, B., Marın, J.M., Cabeza, L.F., Mehling, H.: Review on thermal energy storage with phase change: materials, heat transfer analysis and applications. Appl. Therm. Eng. 23(3), 251–283 (2003)
Madsen, E.L., Zagzebski, J.A., Banjavie, R.A., Jutila, R.E.: Tissue mimicking materials for ultrasound phantoms. Med. phy. 5(5), 391–394 (1978)
Yusof, N.S.M., Dewi, D.E.O., Faudzi, A.A.M., Salih, N.M., Bakar, N.A., Hamid, H.A.: Ultrasound imaging characterization on tissue mimicking materials for cardiac tissue phantom: texture analysis perspective. MJFAS (2017)
Vogt, W.C., Jia, C., Wear, K.A., Garra, B.S., Pfefer, T.J.: Biologically relevant photoacoustic imaging phantoms with tunable optical and acoustic properties. J. Biomed. Opt. 21(10), 101405 (2016). https://doi.org/10.1117/1.JBO.21.10.101405
Kawaguchi, Y., Iwazaki, H., Ida, T., Nishi, T., Tanikawa, Y., Nitta, N.: New polymer-based phantom for photoacoustic imaging. In: Proceedings Volume 8945, Design and Performance Validation of Phantoms Used in Conjunction with Optical Measurement of Tissue, vol. VI, p. 89450A (2014) https://doi.org/10.1117/12.2037517
Hron, P.: Hydrophilisation of silicone rubber for medical applications. Polym. Int. 52, 1531–1539 (2003). https://doi.org/10.1002/pi.1273
Latorre, R., Bainbridge, D., Tavernor, A., López Albors, O.: Plastination in anatomy learning: an experience at Cambridge University. J. Vet. Med. Educ. 43(3). https://doi.org/10.3138/jvme.0715-113r1
Yoon, S., Henry, R., Bouley, D., Bennett, N., Fahrig, R.: Characterization of a novel anthropomorphic plastinated lung phantom. Med. Phys. 35, 5934–5943 (2008)
Shih, C.-T., Hsu, J.-T., Han, R.-P., Hsieh, B.-T., Chang, S.-J., Wu, J.: A novel method of estimating dose responses for polymer gels using texture analysis of scanning electron microscopy images. PLoS One 8(7), e67281 (2013). https://doi.org/10.1371/journal.pone.0067281
Jiang, S., Liu, S., Feng, W.: PVA hydrogel properties for biomedical application. J. Mech. Behav. Biomed. Mater. 4(7), 1228–1233 (2011). https://doi.org/10.1016/j.jmbbm.2011.04.005
Wang, R.-M., Zheng, S.-R.: Polymer Matrix Composites and Technology. Woodhead Publishing (2011)
Maitz, M.F.: Applications of synthetic polymers in clinical medicine. Biosurf. Biotribol. 1, 161–176 (2015). https://doi.org/10.1016/j.bsbt.2015.08.002
Fisher, R.F., Hintenlang, D.E.: Super-size me: adipose tissue-equivalent additions for anthropomorphic phantoms. J. Appl. Clin. Med. Phys. 15(6), 306–312 (2014). https://doi.org/10.1120/jacmp.v15i6.5007
Haddad, R., Clarysse, P., Orkisz, M., Croisille, P., Revel, D., Magnin, I.E.: A realistic anthropomorphic dynamic heart phantom. Comput. Cardiol. (2005). https://doi.org/10.1109/cic.2005.1588226
Dąbrowska, A.K., Rotaru, G.M., Derler, S., Spano, F., Camenzind, M., Annaheim, S., Rossi, R.M. et al.: Materials used to simulate physical properties of human skin. Skin Res. Technol, 22(1), 3–14 (2016)
Ayers, F., et al.: Fabrication and characterization of silicone-based tissue phantoms with tunable optical properties in the visible and near infrared domain. Proc. SPIE 6870, 1–9 (2008)
Yoda, R.: Elastomers for biomedical applications. J. Biomater. Sci., 37–41 (2012)
Manik, S.P., Banerjee, S.: Determination of chemical cross-links in rubbers. Macromol. Mater. Eng. 6(71), 171–178 (1979)
Jiang, H., Campbell, G., Boughner, D., et al.: Design and manufacture of a polyvinyl alcohol (PVA) cryogel tri-leaflet heart valve prosthesis. Med. Eng. Phys. 26, 269–277 (2004). https://doi.org/10.1016/j.medengphy.2003.10.007
Surry, K.J.M., Austin, H.J.B., Fenster, A., Peters, T.M.: Poly(vinyl alcohol) cryogel phantoms for use in ultrasound and MR imaging. Phys. Med. Biol. 49, 5529–5546 (2004). https://doi.org/10.1088/0031-9155/49/24/009
Zhou, X., Kenwright, D.A., Wang, S., Hossack, J.A., Hoskins, P.R.: Fabrication of two flow phantoms for Doppler ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 64(1), 53–65 (2017). https://doi.org/10.1109/tuffc.2016.2634919
Culjat, M.O., Goldenberg, D., Tewari, P., Singh, R.S.: A review of tissue substitutes for ultrasound imaging. Ultrasound Med. Biol. 36, 861–873 (2010). https://doi.org/10.1016/j.ultrasmedbio.2010.02.012
Zell, K., Sperl, J.I., Vogel, M.W., et al.: Acoustical properties of selected tissue phantom materials for ultrasound imaging. Phys. Med. Biol. 52, N475–N484 (2007). https://doi.org/10.1088/0031-9155/52/20/N02
Manickam, K., Machireddy, R.R., Seshadri, S.: Study of ultrasound stiffness imaging methods using tissue mimicking phantoms. Ultrasonics 54, 621–631 (2014). https://doi.org/10.1016/j.ultras.2013.08.018
Chen, R., Shih, A.: Multi-modality gellan gum-based tissue-mimicking phantom with targeted mechanical, electrical, and thermal properties. Phys. Med. Biol. 58, 5511–5525 (2013). https://doi.org/10.1088/0031-9155/58/16/5511
Wang, Y., Tai, B.L., Yu, H., Shih, A.J.: Silicone-based tissue-mimicking phantom for needle insertion simulation. J. Med. Devices 8, 021001 (2014). https://doi.org/10.1115/1.4026508
Fallis, A.: Polyurethane as a base for a family of tissue equivalent materials. J. Chem. Inf. Model. 53, 1689–1699 (2013). https://doi.org/10.1017/CBO9781107415324.004
Fromstein, J.D., Woodhouse, K.A.: Elastomeric biodegradable polyurethane blends for soft tissue applications. J. Biomater. Sci. Polym. Ed. 13, 391–406 (2002). https://doi.org/10.1163/156856202320253929
Pogue, B.W., Patterson, M.S.: Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry. J. Biomed. Opt. 11, 041102-1–041102-16 (2006). https://doi.org/10.1117/1.2335429
Cafarelli, A., Miloro, P., Verbeni, A., et al.: Speed of sound in rubber-based materials for ultrasonic phantoms. J. Ultrasound 19, 251–256 (2016). https://doi.org/10.1007/s40477-016-0204-7
Martins, P.A.L.S., Jorge, R.M.N., Ferreira, A.J.M.: A comparative study of several material models for prediction of hyperelastic properties: application to silicone-rubber and soft tissues. Strain, 135–147 (2006)
Maggi, L.E., Von Krüger, M.A., Pereira, W.C.A., Monteiro, E.E.C.: Development of silicon-based materials for ultrasound biological phantoms. Proc. IEEE Ultrason. Symp., 1962–1965 (2009). https://doi.org/10.1109/ultsym.2009.5441472
Jia, C., Kim, K., Kolias, T.J., et al.: 4D elasticity imaging of PVA LV phantom integrated with pulsatile circulation system using 2D phased array. Proc. IEEE Ultrason. Symp., 876–879 (2007). https://doi.org/10.1109/ultsym.2007.224
Hebden, J.C., Price, B.D., Gibson, A.P., Royle, G.: A soft deformable tissue-equivalent phantom for diffuse optical tomography. Phys. Med. Biol. 51, 5581–5590 (2006). https://doi.org/10.1088/0031-9155/51/21/013
Lamouche, G., Kennedy, B.F., Kennedy, K.M., et al.: Review of tissue simulating phantoms with controllable optical, mechanical and structural properties for use in optical coherence tomography. Biomed. Opt. Express 3, 1381–1398 (2012). https://doi.org/10.1364/BOE.3.001381
Chan, R., Manzke, R., Dalal, S., et al.: Image-Based Speckle Tracking for Tissue Motion Characterization in a Deformable Cardiovascular Phantom, vol. 6920, pp. 69200U-1–69200U-7 (2008). https://doi.org/10.1117/12.770631
Acknowledgements
The authors are grateful for funding supports by Universiti Teknologi Malaysia and Ministry of Higher Education Malaysia under FRGS Grant R.J130000.7845.4F764 and GUP Tier 1 Grant Q.J130000.2545.20H36.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Dewi, D.E.O., Yusof, N.S.M. (2020). Tissue-Mimicking Materials for Cardiac Imaging Phantom—Section 1: From Conception to Materials Selection. In: Dewi, D., Hau, Y., Khudzari, A., Muhamad, I., Supriyanto, E. (eds) Cardiovascular Engineering. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-8405-8_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-8405-8_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8404-1
Online ISBN: 978-981-10-8405-8
eBook Packages: EngineeringEngineering (R0)