A.A.T. Bui et al institution via a display workstation, and multiple individuals could simultaneous view the same study. Preliminary work also highlighted the need to integrate PACS with other aspects of the healthcare environment and for common data standards to be adopted. Development of the latter was spearheaded by a joint commission of the ACR in conjunction with the National Electrical Manufacturer's Association(NEMA later leading to establishment of the now well-known DICOM(Digital Imaging and Communication in Medicine) standard. While some academic research in PACS is still being performed today, arguably much of this work has transitioned to industry and information technology (IT) suppor Teleradiology: Standardizing Data and Communications In 1994, DICOM version 3.0 was released, setting the stage for digital imaging and PAcS to be embraced across a broader section of the healthcare arena. At the same time, MR and CT scanners were becoming widespread tools for clinical diagnosis ecognizing early on the potential for data networks to transmit imaging studies between sites, and partly in response to a shortage of(subspecialist) radiologists to provide interpretation, the next major step came with teleradiology applications. [18 describes the genesis of teleradiology and its later growth in the mid-1990s. Key tech- nical developments during this era include the exploration of distributed healthcare information systems through standardized data formats and communication protocols methods to efficiently compress/transmit imaging data, and analysis of the ensuing workflow(e.g, within a hospital and between local/remote sites). Legal policies and egulations were also enacted to support teleradiology. From a clinical viewpoint, the power of teleradiology brought about consolidation of expertise irrespective of (physi cal) geographic constraints. These forays provided proof positive for the feasibility of telemedicine, and helped create the backbone infrastructure for today s imaging-based multi-site clinical trials. Although DICOM provided the beginnings of standardization, there was a continued need to extend and enhance the standard given the rapid changes in medical imaging. Moreover, researchers began to appreciate the need to normalize the meaning and content of data fields as information was being transmitted between sites[15]. Newer endeavors in this area continue to emerge given changes in underly ing networking technology and ideas in distributed architectures. For instance, more recent work has applied grid computing concepts to image processing and repositories Integrating Patient Data Alongside teleradiology, medical informatics efforts started to gain further pro- minence, launching a(renewed) push towards EMRs. It became quickly evident that while many facets of the patient record could be combined into a single application, incorporating imaging remained a difficultly because of its specialized viewing requirements(both because of the skill needed to interpret the image, and because of ts multimedia format). Conversely, PACS vendors encountered similar problems radiologists using imaging workstations needed better access to the Emr in order to provide proper assessment. Hence in this next major phase of development, processes that were originally conceived of as radiology-centric were opened up to the breadth of healthcare activities, sparking a cross-over with informatics. For example, the Integrating the Healthcare Enterprise(IHE) initiative was spawned in 1998 througl HIMSS and RSNA (Healthcare Information and Management Systems Society, Radio- logical Society of North America), looking to demonstrate data flow between HL7 and DICOM systems. Additionally, drawing from informatics, researchers began to tackle the problems of integration with respect to content standardization: the onset of
10 A.A.T. Bui et al. institution via a display workstation, and multiple individuals could simultaneously view the same study. Preliminary work also highlighted the need to integrate PACS with other aspects of the healthcare environment and for common data standards to be adopted. Development of the latter was spearheaded by a joint commission of the ACR in conjunction with the National Electrical Manufacturer’s Association (NEMA), later leading to establishment of the now well-known DICOM (Digital Imaging and Communication in Medicine) standard. While some academic research in PACS is still being performed today, arguably much of this work has transitioned to industry and information technology (IT) support. Teleradiology: Standardizing Data and Communications In 1994, DICOM version 3.0 was released, setting the stage for digital imaging and PACS to be embraced across a broader section of the healthcare arena. At the same time, MR and CT scanners were becoming widespread tools for clinical diagnosis. Recognizing early on the potential for data networks to transmit imaging studies between sites, and partly in response to a shortage of (subspecialist) radiologists to provide interpretation, the next major step came with teleradiology applications. [18] describes the genesis of teleradiology and its later growth in the mid-1990s. Key technical developments during this era include the exploration of distributed healthcare information systems through standardized data formats and communication protocols, methods to efficiently compress/transmit imaging data, and analysis of the ensuing workflow (e.g., within a hospital and between local/remote sites). Legal policies and regulations were also enacted to support teleradiology. From a clinical viewpoint, the power of teleradiology brought about consolidation of expertise irrespective of (physical) geographic constraints. These forays provided proof positive for the feasibility of telemedicine, and helped create the backbone infrastructure for today’s imaging-based multi-site clinical trials. Although DICOM provided the beginnings of standardization, there was a continued need to extend and enhance the standard given the rapid changes in medical imaging. Moreover, researchers began to appreciate the need to normalize the meaning and content of data fields as information was being transmitted between sites [15]. Newer endeavors in this area continue to emerge given changes in underlying networking technology and ideas in distributed architectures. For instance, more recent work has applied grid computing concepts to image processing and repositories. Integrating Patient Data Alongside teleradiology, medical informatics efforts started to gain further prominence, launching a (renewed) push towards EMRs. It became quickly evident that while many facets of the patient record could be combined into a single application, incorporating imaging remained a difficultly because of its specialized viewing requirements (both because of the skill needed to interpret the image, and because of its multimedia format). Conversely, PACS vendors encountered similar problems: radiologists using imaging workstations needed better access to the EMR in order to provide proper assessment. Hence in this next major phase of development, processes that were originally conceived of as radiology-centric were opened up to the breadth of healthcare activities, sparking a cross-over with informatics. For example, the Integrating the Healthcare Enterprise (IHE) initiative was spawned in 1998 through HIMSS and RSNA (Healthcare Information and Management Systems Society, Radiological Society of North America), looking to demonstrate data flow between HL7 and DICOM systems. Additionally, drawing from informatics, researchers began to tackle the problems of integration with respect to content standardization: the onset of
1 Introduction structured repor reation and use of controlled vocabularies/ontologies to describe image findings: development of medical natural language processing were all pursued within as aids towards being able to search and index textual reports (and hence the related imaging). Though great strides have been made in these areas, research efforts are still very active: within routine clinical care, the process of docu menting observations largely remains ad hoc and rarely meets the standards associated with a scientific investigation, let alone making such data"computer understandable. Understanding Images: Todays Challenge The modern use of the adage, "A picture is worth ten thousand words, "is attributed to a piece by Fred Barnard in 1921; and its meaning is a keystone of medical imaging informatics. The current era of medical imaging informatics has turned to the question of how to manage the content within images. Presently, research is driven by three basic questions: 1)what is in an image; 2)what can the image tell us from a quantita ive view; and 3) what can an image now correlated with other clinical data, tell us about a specific individuals disease and response to treatment? Analyses are looking to the underlying physics of the image and biological phenomena to derive new knowledge, and combined with work in other areas(genomics/proteomics, clinical informatics), are leading to novel diagnostic and prognostic biomarkers. While efforts in medical image processing and content-based image retrieval were made in the 1990s(eg, image segmentation; computer-aided detection/diagnosis, CAD), it has only been more recently that applications have reached clinical standards of accept ability. Several forces are driving this shift towards computer understanding of images: the increasing amount and diversity of imaging, with petabytes of additional image data accrued yearly; the formulation of new mathematical and statistical tech- niques in image processing and machine learning, made amenable to the medical domain; and the prevalence of computing power. As a result, new imaging-based models of normal anatomy and disease processes are now being formed Knowledge creation. Clinical imaging evidence, which is one of the most important means of in vivo monitoring for many patient conditions, has been used in only a limited fashion(e.g, gross tumor measurements) and the clinical translation of derived quantitative imaging features remains a difficulty. And, in some cases, imaging remains the only mechanism for routine measurement of treatment response. For example, a recent study suggests that while common genetic pathways may be uncovered for high-grade primary brain tumors (glioblastoma multiforme, GBM), the highly hetero- geneous nature of these cancers may not fully lend themselves to be sufficiently prog- nostic [17]; rather, other biomarkers, including imaging, may provide better guidance In particular, as the regional heterogeneity and the rate of mutation of GBMs is high [13], imaging correlation could be important, providing a continuous proxy to assess gene expression, with subsequent treatment modification as needed. In the short-term, the utilization of imaging data can be improved: by standardizing image data, pre-and post-acquisition(e.g, noise reduction, intensity signal normalization/calibration, con- sistent registration of serial studies to ensure that all observed changes arise from physiological differences rather than acquisition); by(automatically) identifying and segmenting pathology and anatomy of interest; by computing quantitative imaging features characterizing these regions; and by integrating these imaging-derived fea tures into ac ehensive disease model
1 Introduction 11 image findings; and the development of medical natural language processing were all pursued within radiology as aids towards being able to search and index textual reports (and hence the related imaging). Though great strides have been made in these areas, research efforts are still very active: within routine clinical care, the process of documenting observations largely remains ad hoc and rarely meets the standards associated with a scientific investigation, let alone making such data “computer understandable.” Understanding Images: Today’s Challenge The modern use of the adage, “A picture is worth ten thousand words,” is attributed to a piece by Fred Barnard in 1921; and its meaning is a keystone of medical imaging informatics. The current era of medical imaging informatics has turned to the question of how to manage the content within images. Presently, research is driven by three basic questions: 1) what is in an image; 2) what can the image tell us from a quantitative view; and 3) what can an image, now correlated with other clinical data, tell us about a specific individual’s disease and response to treatment? Analyses are looking to the underlying physics of the image and biological phenomena to derive new knowledge; and combined with work in other areas (genomics/proteomics, clinical informatics), are leading to novel diagnostic and prognostic biomarkers. While efforts in medical image processing and content-based image retrieval were made in the 1990s (e.g., image segmentation; computer-aided detection/diagnosis, CAD), it has only been more recently that applications have reached clinical standards of acceptability. Several forces are driving this shift towards computer understanding of images: the increasing amount and diversity of imaging, with petabytes of additional image data accrued yearly; the formulation of new mathematical and statistical techniques in image processing and machine learning, made amenable to the medical domain; and the prevalence of computing power. As a result, new imaging-based models of normal anatomy and disease processes are now being formed. Knowledge creation. Clinical imaging evidence, which is one of the most important means of in vivo monitoring for many patient conditions, has been used in only a limited fashion (e.g., gross tumor measurements) and the clinical translation of derived quantitative imaging features remains a difficulty. And, in some cases, imaging remains the only mechanism for routine measurement of treatment response. For example, a recent study suggests that while common genetic pathways may be uncovered for high-grade primary brain tumors (glioblastoma multiforme, GBM), the highly heterogeneous nature of these cancers may not fully lend themselves to be sufficiently prognostic [17]; rather, other biomarkers, including imaging, may provide better guidance. In particular, as the regional heterogeneity and the rate of mutation of GBMs is high [13], imaging correlation could be important, providing a continuous proxy to assess gene expression, with subsequent treatment modification as needed. In the short-term, the utilization of imaging data can be improved: by standardizing image data, pre- and post-acquisition (e.g., noise reduction, intensity signal normalization/calibration, consistent registration of serial studies to ensure that all observed changes arise from physiological differences rather than acquisition); by (automatically) identifying and segmenting pathology and anatomy of interest; by computing quantitative imaging features characterizing these regions; and by integrating these imaging-derived features into a comprehensive disease model. structured reporting; the creation and use of controlled vocabularies/ontologies to describe
A.A.T. Bui et al One can assume that every picture including medical images- contain a huge amount of information and knowledge that must be extracted and organized. Knowl edge can be conveniently categorized twofold [16]: implicit, which represents a given individual's acumen and experience; and explicit, which characterizes generally accepted facts. Clearly, im owledge is advanced through current informatics endeavors, as employed by the individual scientist and clinician. But informatics can further serve to create explicit knowledge by combining together the implicit knowl edge from across a large number of sources. In the context of healthcare, individual physician practices and the decisions made in routine patient care can be brought together to generate new scientific insights. That is to say that medical imaging informatics can provide the transformative process through which medical practice nvolving imaging can lead to new explicit knowledge. Informatics research can lead to means to standardize image content, enabling comparisons across populations and facilitate new ways of thinking References 1. Aberegg SK, Terry PB(2004)Medical decision-making and healthcare disparities: The physicians role. J Lab Clin Med, 144(1): 11-17. 2. American College of Radiology(ACr)(2005)ACR chair tells House Committee unneces- sary and inferior medical imaging lowers quality of care, costs taxpayers. American CollegeofRadiology.http:/www.acr.org.AccessedApril23,2009 3. Berbaum KS, Franken EA, Jr, Dorfman DD, Lueben KR(1994) Influence of clinical his- tory on perception of abnormalities in pediatric radiographs. Acad Radiol, 1(3): 217-223 4. Bui AA, Taira RK, Goldman D, Dionisio JD, Aberle DR, El-Saden S, Sayre J, Rice T, Kangarloo H(2004) Effect of an imaging-based streamlined electronic healthcare process on quality and costs. Acad Radiol, 11(1): 13-20 5. Clayman CB, Curry RH (1992) The American Medical Association Guide to Your Familys Symptoms. 1st updated pbk. edition. Random House, New York 6. Croskerry P(2002)Achieving quality in clinical decision making: Cognitive strategies and detection of bias. Acad Emerg Med, 9(11): 1184-1204. 7. DeWalt DA, Rothrock N, Yount S, Stone AA(2007)Evaluation of item candidates: The PROMIS qualitative item review. Med Care, 45(5 Suppl 1): S12-21 8. Dwyer Ill SJ(2000)A personalized view of the history of PACS in the USA. Medical Imaging 2000: PACS Design and Evaluation: Engineering and Clinical Issues, vol 3980 SPIE, San Diego, CA, USA, pp 2-9 Edep ME, Shah NB, Tateo IM, Massie BM (1997) Differences between primary care phy sicians and cardiologists in management of congestive heart failure: Relation to practice :518-526. 10. Greenes RA (2007)A brief history of clinical decision support: Technical, social, cultural, economic, and governmental perspectives. In: Greenes RA (ed) Clinical Decision Support The road ahead. elsevier academic press. Boston ma. 11. Huang HK (2004)PACS and Imaging Informatics: Basic Principles and Applications. 2nd edition. Wiley-Liss, Hoboken, NJ 12. Kangarloo H, Valdez JA, Yao L, Chen S, Curran J, Goldman D, Sinha U, Dionisio JD, Taira R, Sayre J, Seeger L, Johnson R, Barbaric Z, Steckel R(2000) Improving the quality of care through routine teleradiology consultation. Acad Radiol, 7(3): 149-155 13. Kansal AR, Torquato S, Harsh GI, Chiocca EA, Deisboeck TS(2000) Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor BioL, 203(4):367-382
12 A.A.T. Bui et al. One can assume that every picture – including medical images – contain a huge amount of information and knowledge that must be extracted and organized. Knowledge can be conveniently categorized twofold [16]: implicit, which represents a given individual’s acumen and experience; and explicit, which characterizes generally accepted facts. Clearly, implicit knowledge is advanced through current informatics endeavors, as employed by the individual scientist and clinician. But informatics can further serve to create explicit knowledge by combining together the implicit knowledge from across a large number of sources. In the context of healthcare, individual physician practices and the decisions made in routine patient care can be brought together to generate new scientific insights. That is to say that medical imaging informatics can provide the transformative process through which medical practice involving imaging can lead to new explicit knowledge. Informatics research can lead to means to standardize image content, enabling comparisons across populations and facilitate new ways of thinking. References 1. Aberegg SK, Terry PB (2004) Medical decision-making and healthcare disparities: The physician’s role. J Lab Clin Med, 144(1):11-17. 2. American College of Radiology (ACR) (2005) ACR chair tells House Committee unnecessary and inferior medical imaging lowers quality of care, costs taxpayers. American College of Radiology. http://www.acr.org. Accessed April 23, 2009. 3. Berbaum KS, Franken EA, Jr., Dorfman DD, Lueben KR (1994) Influence of clinical history on perception of abnormalities in pediatric radiographs. Acad Radiol, 1(3):217-223. 4. Bui AA, Taira RK, Goldman D, Dionisio JD, Aberle DR, El-Saden S, Sayre J, Rice T, Kangarloo H (2004) Effect of an imaging-based streamlined electronic healthcare process on quality and costs. Acad Radiol, 11(1):13-20. 5. Clayman CB, Curry RH (1992) The American Medical Association Guide to Your Family’s Symptoms. 1st updated pbk. edition. Random House, New York. 6. Croskerry P (2002) Achieving quality in clinical decision making: Cognitive strategies and detection of bias. Acad Emerg Med, 9(11):1184-1204. 7. DeWalt DA, Rothrock N, Yount S, Stone AA (2007) Evaluation of item candidates: The PROMIS qualitative item review. Med Care, 45(5 Suppl 1):S12-21. 8. Dwyer III SJ (2000) A personalized view of the history of PACS in the USA. Medical Imaging 2000: PACS Design and Evaluation: Engineering and Clinical Issues, vol 3980. SPIE, San Diego, CA, USA, pp 2-9. 9. Edep ME, Shah NB, Tateo IM, Massie BM (1997) Differences between primary care physicians and cardiologists in management of congestive heart failure: Relation to practice guidelines. J Am Coll Cardiol, 30(2):518-526. 10. Greenes RA (2007) A brief history of clinical decision support: Technical, social, cultural, economic, and governmental perspectives. In: Greenes RA (ed) Clinical Decision Support: The Road Ahead. Elsevier Academic Press, Boston, MA. 11. Huang HK (2004) PACS and Imaging Informatics: Basic Principles and Applications. 2nd edition. Wiley-Liss, Hoboken, NJ. 12. Kangarloo H, Valdez JA, Yao L, Chen S, Curran J, Goldman D, Sinha U, Dionisio JD, Taira R, Sayre J, Seeger L, Johnson R, Barbaric Z, Steckel R (2000) Improving the quality of care through routine teleradiology consultation. Acad Radiol, 7(3):149-155. 13. Kansal AR, Torquato S, Harsh GI, Chiocca EA, Deisboeck TS (2000) Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor Biol, 203(4):367-382
1 Introduction 14. Kulikowski C, Ammenwerth E, Bohne A, Ganser K, Haux R, Knaup P. Maier C, Michel A ger R, Wolff AC(2002) Medical imaging informatics and medical informatics Opportunities and constraints. Findings from the IMIA Yearbook of Medical Informatics 2002. Methods Inf Med, 41(2): 183-189 15. Kulikowski CA (1997) Medical imaging informatics: Challenges of definition and integration J Am Med Inform Assoc, 4(3): 252-253. 16. Pantazi SV, Arocha JF, Moehr JR(2004)Case-based medical informatics. BMC Med Inform Decis Mak. 4: 19 17. The Cancer Genome Atlas Research Network(2008)Comprehensive genomic char- acterization defines human glioblastoma genes and core pathways. Nature, 455(7216): 1061 1068. 18. Thrall JH (2007) Teleradiology: Part L. History and clinical applications. Radiology, 243(3):613-617
1 Introduction 13 14. Kulikowski C, Ammenwerth E, Bohne A, Ganser K, Haux R, Knaup P, Maier C, Michel A, Singer R, Wolff AC (2002) Medical imaging informatics and medical informatics: Opportunities and constraints. Findings from the IMIA Yearbook of Medical Informatics 2002. Methods Inf Med, 41(2):183-189. 15. Kulikowski CA (1997) Medical imaging informatics: Challenges of definition and integration. J Am Med Inform Assoc, 4(3):252-253. 16. Pantazi SV, Arocha JF, Moehr JR (2004) Case-based medical informatics. BMC Med Inform Decis Mak, 4:19. 17. The Cancer Genome Atlas Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216):1061- 1068. 18. Thrall JH (2007) Teleradiology: Part I. History and clinical applications. Radiology, 243(3):613-617
Chapter 2 A Primer on Imaging Anatomy and Physiology DENISE ABERLE, SUZIE EL-SADEN, PABLO ABBONA, ANA GOMEZ KAMBIZ MOTAMEDL NAGESH RAGAVENDRA. LAWRENCE BASSETT LEANNE SEEGER, MATTHEW BROWN. KATHLEEN BROWN. ALEX A.T. BUI AND HOOSHANG KANGARLOO A n understanding of medical imaging informatics begins with knowledge of medical imaging and its application toward diagnostic and therapeutic clinical assessment. This chapter is divided into two sections: a review of current imaging modalities; and a primer on imaging anatomy and physiology. In the first half, we introduce the major imaging modalities that are in use today: projectional imaging, computed tomography, magnetic resonance, and ultrasound. The core physics concepts behind each modality; the parameters and algorithms driving image formation and variants and newer advances in each of these areas are briefly covered to familiarize the reader with the capabilities of each technique. From this foundation, in the second half of the chapter we describe several anatomical and physiologic systems from the erspective of imaging. Three areas are covered in detail: 1)the respiratory system 2)the brain; and 3)breast imaging. Additional coverage of musculoskeletal, cardiac, rinary, and upper gastrointestinal systems is included. Each anatomical section begins with a general description of the anato physiology, discusses the use of different imaging modalities, and concludes with a description of common medical problems/ conditions and their appearance on imaging. From this chapter, the utility of imaging and its complexities becomes apparent and will serve to ground discussion in future chapters. A Review of Basic Imaging Modalitie The crucial role of imaging in illuminating both the human condition and disease is largely self-evident, with medical imaging being a routine tool in the diagnosis and the treatment of most medical problems. Imaging provides an objective record for docu- menting and communicating in vivo findings at increasingly finer levels of detail. This section focuses on a review of the current major imaging modalities present in the clinical environment. As it is beyond the ability of a single chapter to comprehensively cover all aspects of medical imaging. we Aismethode tne scope of this field, we omit a discussion of nuclear medicine, and newer methods such as molecular and optical imaging that are still largely seen in research environments Projectional Imaging The genesis of medical imaging and radiography started in 1895 with the discovery of x-rays by Roentgen. Today, the use of x-ray projectional imaging comes only second to the use of laboratory tests as a clinical diagnostic tool. Core Physical Concepts A thorough handling of x-ray physics can be found in [15, 19]. X-rays are a form of electromagnetic(EM) radiation, with a wavelength ranging from 0. 1-10 nm, which A A.T. Bui and R K. Taira (eds ) Medical Imaging Informatics DOI 10.1007/978-1-4419-0385-3_2, o Springer Science Business Media, LLC 2010
A.A.T. Bui and R.K. Taira (eds.), Medical Imaging Informatics, 15 DOI 10.1007/978-1-4419-0385-3_2, © Springer Science + Business Media, LLC 2010 Chapter 2 A Primer on Imaging Anatomy and Physiology DENISE ABERLE, SUZIE EL-SADEN, PABLO ABBONA, ANA GOMEZ, KAMBIZ MOTAMEDI, NAGESH RAGAVENDRA, LAWRENCE BASSETT, LEANNE SEEGER, MATTHEW BROWN, KATHLEEN BROWN, ALEX A.T. BUI, AND HOOSHANG KANGARLOO n understanding of medical imaging informatics begins with knowledge of medical imaging and its application toward diagnostic and therapeutic clinical assessment. This chapter is divided into two sections: a review of current imaging modalities; and a primer on imaging anatomy and physiology. In the first half, we introduce the major imaging modalities that are in use today: projectional imaging, computed tomography, magnetic resonance, and ultrasound. The core physics concepts behind each modality; the parameters and algorithms driving image formation; and variants and newer advances in each of these areas are briefly covered to familiarize the reader with the capabilities of each technique. From this foundation, in the second half of the chapter we describe several anatomical and physiologic systems from the perspective of imaging. Three areas are covered in detail: 1) the respiratory system; 2) the brain; and 3) breast imaging. Additional coverage of musculoskeletal, cardiac, urinary, and upper gastrointestinal systems is included. Each anatomical section begins with a general description of the anatomy and physiology, discusses the use of different imaging modalities, and concludes with a description of common medical problems/ conditions and their appearance on imaging. From this chapter, the utility of imaging and its complexities becomes apparent and will serve to ground discussion in future chapters. A Review of Basic Imaging Modalities The crucial role of imaging in illuminating both the human condition and disease is largely self-evident, with medical imaging being a routine tool in the diagnosis and the treatment of most medical problems. Imaging provides an objective record for documenting and communicating in vivo findings at increasingly finer levels of detail. This section focuses on a review of the current major imaging modalities present in the clinical environment. As it is beyond the ability of a single chapter to comprehensively cover all aspects of medical imaging, we aim only to cover key points: references to seminal works are provided for the reader. Also, given the scope of this field, we omit a discussion of nuclear medicine, and newer methods such as molecular and optical imaging that are still largely seen in research environments. Projectional Imaging The genesis of medical imaging and radiography started in 1895 with the discovery of x-rays by Roentgen. Today, the use of x-ray projectional imaging comes only second to the use of laboratory tests as a clinical diagnostic tool. Core Physical Concepts A thorough handling of x-ray physics can be found in [15, 19]. X-rays are a form of electromagnetic (EM) radiation, with a wavelength ranging from 0.1-10 nm, which A