Current research and developments in the technology of imaging have led to explosive growth in the interdisciplinary field of imaging science. This is particularly evident in medical imaging. There is unfortunately no perfect medical imaging technology that can reveal all aspects of disease on the same image, and different medical imaging technologies often reveal different characteristics of disease. For example, x-ray mammography can reveal malignant microcalcifications, but cannot reveal information on tumor vascularity. On the other hand, magnetic resonance imaging (MRI) cannot detect microcalcifications, but relies primarily on differences in vascularity—as depicted by differences in degree and timing of contrast uptake—to detect tumors. However, MRI contrast enhancement in premenopausal patients varies greatly with the menstrual cycle, and MRI suffers from a high false-positive rate in these patients.
The imaging community has great opportunities to further investigate and improve existing technologies, better define their strengths and weaknesses, and develop new technologies or new combinations of existing technologies. Because imaging technologies are designed and developed to address a specific clinical task, or a limited ensemble of tasks in detection, characterization, treatment, planning or monitoring of disease and conditions, technology goes through various stages of development, laboratory/ bench testing, clinical testing, calibration and/ or optimization. Likewise, as new technologies—or new applications of existing technologies—are rolled out into clinical practice, clinicians must go through education and training on the operation and functionalities of these systems so that the ensemble human technology will result in maximal quality of patient care.
The framework for what is typically referred to as “Health Technology Assessment” has been previously described . Such technology assessments are typically performed only after the technology is developed, manufactured, marketed, and introduced into clinical practice (presumably only after some level of evidence has been obtained to demonstrate the ability of the imaging technology to differentiate normal from disease states). All along the way, development, testing, optimization, and calibration will rely on anthropomorphic phantom imaging, but will also require testing on actual patients. Yet, even this will not provide a complete knowledge of the strengths, weaknesses, and limitations of these systems.
In the current issue of Academic Radiology , Chen et al propose optimization of breast imaging technologies using a computer breast phantom capable of modeling varying anatomy across different patients, including the modeling of disease on a normal anatomic background, and with the capability of simulating images from mammography, breast tomosynthesis, and computed tomography (CT) systems . Although the authors only propose using such a phantom for system optimization and dosimetry experiments, their work raises the possibility of using such a phantom for all steps in technology development and testing.
Breast imaging is a challenging specialty of radiology because of the difficulty of detecting abnormalities that are superimposed on a complex anatomic background. This is especially difficult in screening mammography (whose goal is early detection of breast cancer findings) because of the great variety of normal breast anatomic backgrounds across patients, the subtlety of the imaging findings of cancer, and the low prevalence of breast cancer in a screening population. These difficulties lead additional imaging (and radiation exposure), and even unnecessary treatment for normal imaging findings or a condition that was never going to cause harm. Values and limitations of breast imaging systems are never entirely known before such systems being released to the clinical arena, and clinicians must rely on predicted or estimated performance with a degree of uncertainty that can vary greatly. The value of specific imaging technologies (or even specific devices within a generic technology), and their limitations often only become well-known after decades of using these systems in clinical practice. Perhaps it is possible to break through and overcome certain technical limitations at the early stage of the system design and development to better predict the values and limitations of the given imaging system, and thus to ensure “optimal” patient care.
All imaging systems go through the same process including the design, prototyping, development, calibration, optimization, validation, as well as maintenance, quality assurance and control, updates, and improvement over time. At all stages, there are assumptions and decisions that are made and often require validation or modification. Over the past 10 years, research in breast imaging has slowly moved forward using computer simulation and computational models—in other words, virtual reality—that have long been a useful part of mathematical modeling for many domains of physics, astrophysics, chemistry, biology, economic, social science, defense, and engineering. Computer models and simulations offer an incredible opportunity to explore and gain new insights into imaging technologies, and enable the prediction of the behavior of the system, or the human‒system interaction, from a set of parameters and initial conditions that are otherwise too complex or too demanding in resources and time. These powerful tools and techniques are progressively gaining popularity in the breast imaging community. O’Connor et al explained that, “Having the capability of using breast object models and simulation software is clinically significant because prior to a clinical trial of any prototype breast imaging system many parameter tradeoffs can be investigated in a simulation environment. This capability is worthwhile not only for the obvious benefit of improving patient safety during initial clinical trials but also because simulation prior to prototype implementation should result in reduced cost and increased speed of development.”
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References
1. Lim M.E., O’Reilly D., Tarride J.-E., et. al.: Health technology assessment for radiologists: basic principles and evaluation framework. J Am Coll Radiol 2009; 6: pp. 299-306.
2. Chen B., Shorey J., Saunders R.S., et. al.: An anthropomorphic breast model for breast imaging simulation and optimization. Acad Radiol 2011;
3. O’Connor J.M., Das M., Didier C., et. al.: Comparison of two methods to develop breast models for simulation of breast tomosynthesis and CT. Lect Notes Computer Sci 2008; 5116: pp. 417-425.
4. Burgess A.E., Jacobson F.L., Judy P.F.: Lesion detection in digital mammograms. Proc SPIE 2001; 4320: pp. 555-560.
5. Burgess A.E., Jacobson F.L., Judy P.F.: Human observer detection experiments with mammograms and power-law noise. Med Phys 2001; 28: pp. 419-437.
6. Krupinski E.A., Johnson J., Roehrig H., et. al.: Using a human visual system model to optimize soft-copy mammography display: influence of display phosphor. Acad Radiol 2003; 10: pp. 161-166.
7. Krupinski E.A., Johnson J., Roehrig H., et. al.: Using a human visual system model to optimize soft-copy mammography display: influence of MTF compensation. Acad Radiol 2003; 10: pp. 1030-1035.
8. Burgess A., Jacobson F., Judy P.: Mass discrimination in mammography: experiments using hybrid images. Acad Radiol 2003; 10: pp. 1247-1256.
9. Olsen J.B., Skretting A.: Detectability of simulated masses and calcifications in mammography: development of a phantom and a new method for determination of receiver operating characteristics. Acta Radiol 1998; 39: pp. 501-506.
10. Bakic P.R., Albert M., Brzakovic D., et. al.: Mammogram synthesis using a 3D simulation I. Breast tissue model and image acquisition simulation. Med Phys 2002; 29: pp. 2131-2139.
11. Bakic P.R., Albert M., Brzakovic D., et. al.: Mammogram synthesis using a 3D simulation II. Evaluation of synthetic mammogram texture. Med Phys 2002; 29: pp. 2140-2151.
12. Bakic P.R., Albert M., Brzakovic D., et. al.: Mammogram synthesis using a three-dimensional simulation III. Modeling and evaluation of the breast ductal network. Med Phys 2003; 30: pp. 1914-1925.
13. Hoeschen C., Fill U., Zankl M., et. al.: A high-resolution voxel phantom of the breast for dose calculations in mammography. Radiat Prot Dosim 2005; 114: 406-309
14. Bliznakova K., Bliznakov Z., Bravou V., et. al.: A three-dimensional breast software phantom for mammography simulation. Phys Med Biol 2003; 48: pp. 3699-3719.
15. Dance D.R., Hunt R.A., Bakic P.R., et. al.: Breast dosimetry using high-resolution voxel phantoms. Radiat Prot Dosim 2005; 114: pp. 359-363.
16. Hunt R.A., Dance D.R., Bakic P.R., et. al.: Calculation of the properties of digital mammograms using a computer simulation. Radiat Prot Dosim 2005; 114: pp. 395-398.
17. Bliznakova K., Kazakli S., Pallikarakis N.: An optimised 3D breast phantom for x-ray breast imaging techniques. 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proc 2009; 22: pp. 2455-2458.
18. Ma A.K.W., Gunn S., Darambara D.G.: Introducing DeBRa: a detailed breast model for radiological studies. Phys Med Biol 2009; 54: pp. 4533.
19. René Daumal: The Lie of the Truth and Other Parables from the Way of Liberation.1989.Hanuman BooksNew York / Madras