《医学影像信息学概论》课程参考资源：Medical Imaging Informatics

Medical Imaging Informatics Alex A.T. Bui, Ricky K. Taira(eds ② pringer

Medical Imaging Informatics Alex A.T. Bui, Ricky K. Taira (eds.)

editors Alex A.T. Bui Ricky K. Taira Medical Imaging Informatics Medical Imaging Informatics Group Department of Radiological Department of Radiological Sciences David Geffen School of M David Geffen School of Medicine University of Califomia, Los University of California, Los Angeles 924 Westwood Blvd 24W od Blvd Los Angeles, CA 90024 Los Angeles, CA 9002 Suite 420 Suite 420 USA buia@miL. ucla.edu taira@mii. ucla. edu ISBN978-1-4419-0384-6 e-ISBN978-1-4419-0385-3 DOI10.1007/978-1-4419-0385-3 Springer New York Dordrecht Heidelberg London Control Number: 2009939431 e Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part orm of information storage and retrieval, electronic adaptation, computer software, or nethodology now known or hereafter developed is forbidden. he use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not lentified as such, is not to be taken as an expression of opinion as to whether or not they are subject to While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein SpringerispartofSpringerSciencetBusinessMedia(www.springer.com

Editors Alex A.T. Bui Ricky K. Taira Medical Imaging Informatics Group Medical Imaging Informatics Group Department of Radiological Sciences Department of Radiological Sciences David Geffen School of Medicine David Geffen School of Medicine University of California, Los Angeles University of California, Los Angeles 924 Westwood Blvd. 924 Westwood Blvd. Los Angeles, CA 90024 Los Angeles, CA 90024 Suite 420 Suite 420 USA USA buia@mii.ucla.edu rtaira@mii.ucla.edu ISBN 978-1-4419-0384-6 e-ISBN 978-1-4419-0385-3 DOI 10.1007/978-1-4419-0385-3 Springer New York Dordrecht Heidelberg London © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written per￾mission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Library of Congress Control Number: 2009939431

For our mentor and friend, Hoosh, who has the wisdom and leadership to realize a vision;and to our students past, present, and future, for helping to pave a path forward

For our mentor and friend, Hoosh, who has the wisdom and leadership to realize a vision; and to our students past, present, and future, for helping to pave a path forward

Foreword Imaging is considered as one of the most effective- if not the most effective- in vivo sampling techniques applicable to chronic serious illnesses like cancer. This simple yet comprehensive textbook in medical imaging informatics(Mil) promotes and facili- tates two different areas of innovation: the innovations in technology that improve the field of biomedical informatics itself, and the application of these novel technologies to medicine, thus, improving health. Aside from students in imaging disciplines such as radiological sciences(vs. radiology as a service), this book is also very pertinent to other disciplines such as cardiology and surgery. Faculty and students familiar with this book will come to have their own ideas how to innovate. whether it be in core technologies or in applications to biomedicine Organizationally, the book follows a very sensible structure related to the process of are, which can in principle be summarized in three questions: what is wrong, how serious is it; and what to do? The first question( what is wrong) focuses mostly on diagnosis (i.e, what studies should be obtained). In this way, issues such as individu- ally-tailored image protocol selection are addressed so that the most appropriate and correct study is obtained -as opposed to the traditional sequential studies. For example, a patient with knee pain and difficulty going up stairs or with minor trauma to the knee and evidence of effusion is directly sent for an MRI (magnetic resonance imaging) study rather than first going to x-ray, or in a child suspected of having abnormal(or even normal) brain development, MRI studies are recommended rather than traditional insurance-required computed tomography (CT). The role of not only in mproving diagnosis but reducing health costs is highlighted. The second question (how serious is it)relates to how we can standardize and document image findings, on the way to providing truly objective, quantitative assessment from an imaging study as opposed to today's norm of largely qualitative descriptors. Finally, the third question in regard to how we can act upon the information we obtain clinically, from imaging and other sources: how can decisions be made rationally and how can we assess the impact of either research or an intervention? The textbook has been edited by two scientists, an Associate Professor and a Professor in Mll who are both founders of this discipline at our institution. Contributions come from various specialists in medical imaging, informatics, computer science, and bio- statistics. The book is not focused on image acquisition techniques or image process- ing, which are both well-known and described elsewhere in other texts: rather, it focuses on how to extract knowledge and information from imaging studies and related data. The material in this textbook has been simplified eloquently, one of the most difficult tasks by any teacher to simplify difficult material so that it is under- In short, this textbook is highly recommended for students in any discipline dealing with imaging as well as faculty interested in disciplines of medical imaging and informatics Hooshang Kangaroo, MD Professor Emeritus of Radiological Sciences, Pediatrics, and Bioengineering University of California at Los Angeles

vii Foreword Imaging is considered as one of the most effective – if not the most effective – in vivo sampling techniques applicable to chronic serious illnesses like cancer. This simple yet comprehensive textbook in medical imaging informatics (MII) promotes and facili￾tates two different areas of innovation: the innovations in technology that improve the field of biomedical informatics itself; and the application of these novel technologies to medicine, thus, improving health. Aside from students in imaging disciplines such as radiological sciences (vs. radiology as a service), this book is also very pertinent to other disciplines such as cardiology and surgery. Faculty and students familiar with this book will come to have their own ideas how to innovate, whether it be in core technologies or in applications to biomedicine. Organizationally, the book follows a very sensible structure related to the process of care, which can in principle be summarized in three questions: what is wrong; how serious is it; and what to do? The first question (what is wrong) focuses mostly on diagnosis (i.e., what studies should be obtained). In this way, issues such as individu￾ally-tailored image protocol selection are addressed so that the most appropriate and correct study is obtained – as opposed to the traditional sequential studies. For example, a patient with knee pain and difficulty going up stairs or with minor trauma to the knee and evidence of effusion is directly sent for an MRI (magnetic resonance imaging) study rather than first going to x-ray; or in a child suspected of having abnormal (or even normal) brain development, MRI studies are recommended rather than traditional insurance-required computed tomography (CT). The role of imaging, not only in improving diagnosis but reducing health costs is highlighted. The second question (how serious is it) relates to how we can standardize and document image findings, on the way to providing truly objective, quantitative assessment from an imaging study as opposed to today’s norm of largely qualitative descriptors. Finally, the third question is in regard to how we can act upon the information we obtain clinically, from imaging and other sources: how can decisions be made rationally and how can we assess the impact of either research or an intervention? The textbook has been edited by two scientists, an Associate Professor and a Professor in MII who are both founders of this discipline at our institution. Contributions come from various specialists in medical imaging, informatics, computer science, and bio￾statistics. The book is not focused on image acquisition techniques or image process￾ing, which are both well-known and described elsewhere in other texts; rather, it focuses on how to extract knowledge and information from imaging studies and related data. The material in this textbook has been simplified eloquently, one of the most difficult tasks by any teacher to simplify difficult material so that it is under￾standable at all levels. In short, this textbook is highly recommended for students in any discipline dealing with imaging as well as faculty interested in disciplines of medical imaging and informatics. Hooshang Kangarloo, MD Professor Emeritus of Radiological Sciences, Pediatrics, and Bioengineering University of California at Los Angeles

Foreword With the advancement of picture archiving and communications systems(PACs)into mainstream use in healthcare facilities. there is a natural transition from the dis- ciplines of engineering research and technology assessment to clinical operations While much research in PACS-related areas continues, commercial systems are widely available. The burgeoning use of PACs in a range of healthcare facility sizes has created entirely new employment opportunities for"PACS managers, " modality managers,""interface analysts, and others who are needed to get these systems implemented, keep them operating, and expand them as necessary. The field of medical imaging informatics is often described as the discipline encompassing the subject areas that these new specialists need to understand. As the Society of Imaging Infor matics in Medicine(SliM)defines it Imaging informatics is a relatively new multidisciplinary field that intersects with the biological sciences, health services, information sciences and com- puting, medical physics, and engineering. Imaging informatics touches every aspect of the imaging chain and forms a bridge with imaging and other medical disciplines. Because the technology of PACS continues to evolve, imaging informatics is also important for the researcher. Each of the areas comprising the field of imaging infor- matics has aspects that make for challenging research topics. Absent the research these challenges foster and PACs would stagnate For the student of medical imaging informatics, there is a wealth of literature available for study. However, much of this is written for trainees in a particular discipline Anatomy, for example, is typically aimed at medical, dental, veterinary, and physical therapy students, not at engineers. Texts on networks or storage systems are not designed for physicians. Even primers on such topics tend not to provide a cross- disciplinary perspective of the subject The authors of Medical Imaging Informatics have accepted the challenge of creating a textbook that provides the student of medical imaging informatics with the broad range of topical areas necessary for the field and doing so without being superficial Unusual for a text on informatics, the book contains a chapter, A Primer on Imaging Anatomy and Physiology, subject material this writer knows is important, but is often lacking in the knowledge-base of the information technology (IT) people he works with. Similarly, many informatics-oriented physicians this writer knows do not have the in-depth understanding of information systems and components that IT experts have. Such is the subject matter of the"middle chapters of the book -Chapter 3. Information Systems Architectures, Chapter 4: Medical Data Visualization: Toward Integrated Clinical Workstations, and Chapter 5. Characterizing Imaging Data. The succeeding chapters are directed towards integrating IT theory and infrastructure with medical practice topics-Chapter 6: Natural Language Processing of Medical Reports, Chapter 7: Organizing Observations: Data Models, Chapter 8: Disease Models, Part 1. Graphical Models, and Chapter 9: Disease Models, Part 1 Querying Applications Finally, because a practitioner of medical imaging informatics is expected to keep up with the current literature and to know the bases of decision making. the authors have included a chapter on Evaluation. With the statistical methods and technology assess- ment areas covered, the reader will gain the understanding needed to be a critical iSocietyofImagingiNformaticsinMedicinewebsitehttp://nwwsimmveb.org

viii Foreword With the advancement of picture archiving and communications systems (PACS) into “mainstream” use in healthcare facilities, there is a natural transition from the dis￾ciplines of engineering research and technology assessment to clinical operations. While much research in PACS-related areas continues, commercial systems are widely available. The burgeoning use of PACS in a range of healthcare facility sizes has created entirely new employment opportunities for “PACS managers,” “modality managers,” “interface analysts,” and others who are needed to get these systems implemented, keep them operating, and expand them as necessary. The field of medical imaging informatics is often described as the discipline encompassing the subject areas that these new specialists need to understand. As the Society of Imaging Infor￾matics in Medicine (SIIM) defines it: Imaging informatics is a relatively new multidisciplinary field that intersects with the biological sciences, health services, information sciences and com￾puting, medical physics, and engineering. Imaging informatics touches every aspect of the imaging chain and forms a bridge with imaging and other medical disciplines.1 Because the technology of PACS continues to evolve, imaging informatics is also important for the researcher. Each of the areas comprising the field of imaging infor￾matics has aspects that make for challenging research topics. Absent the research these challenges foster and PACS would stagnate. For the student of medical imaging informatics, there is a wealth of literature available for study. However, much of this is written for trainees in a particular discipline. Anatomy, for example, is typically aimed at medical, dental, veterinary, and physical therapy students, not at engineers. Texts on networks or storage systems are not designed for physicians. Even primers on such topics tend not to provide a cross￾disciplinary perspective of the subject. The authors of Medical Imaging Informatics have accepted the challenge of creating a textbook that provides the student of medical imaging informatics with the broad range of topical areas necessary for the field and doing so without being superficial. Unusual for a text on informatics, the book contains a chapter, A Primer on Imaging Anatomy and Physiology, subject material this writer knows is important, but is often lacking in the knowledge-base of the information technology (IT) people he works with. Similarly, many informatics-oriented physicians this writer knows do not have the in-depth understanding of information systems and components that IT experts have. Such is the subject matter of the “middle” chapters of the book – Chapter 3: Information Systems & Architectures, Chapter 4: Medical Data Visualization: Toward Integrated Clinical Workstations, and Chapter 5: Characterizing Imaging Data. The succeeding chapters are directed towards integrating IT theory and infrastructure with medical practice topics – Chapter 6: Natural Language Processing of Medical Reports, Chapter 7: Organizing Observations: Data Models, Chapter 8: Disease Models, Part I: Graphical Models, and Chapter 9: Disease Models, Part II: Querying & Applications. Finally, because a practitioner of medical imaging informatics is expected to keep up with the current literature and to know the bases of decision making, the authors have included a chapter on Evaluation. With the statistical methods and technology assess￾ment areas covered, the reader will gain the understanding needed to be a critical 1 Society of Imaging Informatics in Medicine website: http://www.siimweb.org

reader of scientific publications and to understand how systems are evaluated during development and after deployment Structured in this way, this book forms a unique and valuable resource both for the trainee who intends to become an expert in medical imaging informatics and a refer ence for the established practitioner Steven C. Horii. MD. FACR. FSIIM Professor of Radiolog Clinical Director, Medical Informatics Group, and Department of Radiolog University of Pennsylvania Medical Center

Foreword ix development and after deployment. Structured in this way, this book forms a unique and valuable resource both for the trainee who intends to become an expert in medical imaging informatics and a refer￾ence for the established practitioner. Steven C. Horii, MD, FACR, FSIIM Professor of Radiology, Clinical Director, Medical Informatics Group, and Modality Chief for Ultrasound Department of Radiology University of Pennsylvania Medical Center reader of scientific publications and to understand how systems are evaluated during

Preface This book roughly follows the process of care, illustrating the techniques involved in medical imaging informatics. Our intention in this text is to provide a roadmap for the different topics that are involved in this field: in many cases, the topics covered in the ensuing chapters are themselves worthy of lengthy descriptions, if not an entire book As a result, when possible the authors have attempted to provide both seminal and current references for the reader to pursue additional details For the imaging novice and less experienced informaticians, in Part I of this book Performing the Imaging Exam, we cover the current state of medical imaging and set the foundation for understanding the role of imaging and informatics in routine clinical Chapter 1(Introduction) provides an introduction to the field of medical imaging informatics and its role in transforming healthcare research and delivery. The interwoven nature of imaging with preventative, diagnostic, and therapeutic elements of patient care are touched upon relative to the process of care. a brief historic perspective is provided to illustrate both past and current challenges of the discipline Chapter 2(An Introduction to Imaging Anatomy Physiology) starts with a review of clinical imaging modalities(i.e, projectional x-ray, computed tomography (CT), magnetic resonance(MR), ultrasound) and a primer on imaging anatomy and physiology. The modality review encompasses core physics principles and image formation techniques, along with brief descriptions of present and future directions for each imaging modality. To familiarize non-radiologists with medical imaging and the human body, the second part of this chapter presents an overview of anatomy and physiology from the perspective of projectional and cross- sectional imaging. A few systems(neurological, respiratory, breast) are covered in detail, with additional examples from other major systems(gastrointestinal urinary,cardiac, musculoskeletal More experienced readers will likely benefit from starting with Part Il of this book Integrating Imaging into the Patient Record, which examines topics related to communicating and presenting imaging data alongside the growing wealth of clinical Once imaging and other clinical data are acquired, Chapter 3(Information Systems Architectures) tackles the question of how we store and access imaging and other patient information as part of an increasingly distributed and heterogeneous EMr. A description of major information systems(.g, PACS; hospital informa- tion systems, HIS; etc. )as well as the different data standards employed today to represent and communicate data(e. g, HL7, DICOM) are provided. A discussion of newer distributed architectures as they apply to clinical databases(peer-to-peer grid computing) and information processing is given, examining issues of scal- ability and searching. Different informatics-driven applications are used to high- light ongoing efforts with respect to the development of information architectures, including telemedicine, IHE, and collaborative clinical research involving imaging After the data is accessed, the challenge is to integrate and to present patient information in such a way to support the physicians cognitive tasks. The longitud- inal EMR, in conjunction with the new types of information available to clinicians, has created an almost overwhelming flow of data that must be fully understood to

xi Preface This book roughly follows the process of care, illustrating the techniques involved in medical imaging informatics. Our intention in this text is to provide a roadmap for the different topics that are involved in this field: in many cases, the topics covered in the ensuing chapters are themselves worthy of lengthy descriptions, if not an entire book. As a result, when possible the authors have attempted to provide both seminal and current references for the reader to pursue additional details. For the imaging novice and less experienced informaticians, in Part I of this book, Performing the Imaging Exam, we cover the current state of medical imaging and set the foundation for understanding the role of imaging and informatics in routine clinical practice: ƒ Chapter 1 (Introduction) provides an introduction to the field of medical imaging informatics and its role in transforming healthcare research and delivery. The interwoven nature of imaging with preventative, diagnostic, and therapeutic elements of patient care are touched upon relative to the process of care. A brief historic perspective is provided to illustrate both past and current challenges of the discipline. ƒ Chapter 2 (An Introduction to Imaging Anatomy & Physiology) starts with a review of clinical imaging modalities (i.e., projectional x-ray, computed tomography (CT), magnetic resonance (MR), ultrasound) and a primer on imaging anatomy and physiology. The modality review encompasses core physics principles and image formation techniques, along with brief descriptions of present and future directions for each imaging modality. To familiarize non-radiologists with medical imaging and the human body, the second part of this chapter presents an overview of anatomy and physiology from the perspective of projectional and cross￾sectional imaging. A few systems (neurological, respiratory, breast) are covered in detail, with additional examples from other major systems (gastrointestinal, urinary, cardiac, musculoskeletal). More experienced readers will likely benefit from starting with Part II of this book, Integrating Imaging into the Patient Record, which examines topics related to communicating and presenting imaging data alongside the growing wealth of clinical information: ƒ Once imaging and other clinical data are acquired, Chapter 3 (Information Systems & Architectures) tackles the question of how we store and access imaging and other patient information as part of an increasingly distributed and heterogeneous EMR. A description of major information systems (e.g., PACS; hospital informa￾tion systems, HIS; etc.) as well as the different data standards employed today to represent and communicate data (e.g., HL7, DICOM) are provided. A discussion of newer distributed architectures as they apply to clinical databases (peer-to-peer, grid computing) and information processing is given, examining issues of scal￾ability and searching. Different informatics-driven applications are used to high￾light ongoing efforts with respect to the development of information architectures, including telemedicine, IHE, and collaborative clinical research involving imaging. ƒ After the data is accessed, the challenge is to integrate and to present patient information in such a way to support the physician’s cognitive tasks. The longitud￾inal EMR, in conjunction with the new types of information available to clinicians, has created an almost overwhelming flow of data that must be fully understood to

Preface properly inform decision making. Chapter 4(Medical Data visualization Toward Integrated Clinical Workstations) presents works related to the visualiz- ation of medical data. A survey of graphical metaphors (lists and tables, plots and charts, graphs and trees, and pictograms)is given, relating their use to convey clinical concepts. A discussion of portraying temporal, spatial, multidimensional nd causal relationships is provided, using the navigation of images as an example application. Methods to combine these visual components are illustrated, based on a definition of(task) context and user modeling, resulting in a means of creating an adaptive graphical user interface to accommodate the range of different user Part Ill, Documenting Imaging Findings, discusses techniques for automatically nt fro In Chapter 5( Characterizing Imaging Data), an introduction to medical image understanding is presented. Unlike standard image processing, techniques within medical imaging informatics focus on how imaging studies, alongside other clinical data, can be standardized and their content(automatically) extracted to guide medical decision making processes. Notably, unless medical images are standard- ized, quantitative comparisons across studies is subject to various sources of bias/ artifacts that negatively influence assessment. From the perspective of creating scientific-quality imaging databases, this chapter starts with the groundwork for understanding what exactly an image captures, and commences to outline the dif- ferent aspects encompassing the standardization process: intensity normalization denoising, and both linear and nonlinear image registration methods are covered Subsequently, a discussion of commonly extracted imaging features is given, divided amongst appearance-and shape-based descriptors. with the wide array of image features that can be computed, an overview of image feature selection and dimensionality reduction methods is provided. Lastly, this chapter concludes with a description of increasingly popular imaging-based anatomical atlases, detailing their construction and usage as a means for understanding population-based norms and differences arising due to a disease process Absent rigorous methods to automatically analyze and quantify image findings, radiology reports are the sole source of expert image interpretation. In point of fact, a large amount of information about a patient remains locked within clinical documents, and as with images, the concepts therein are not readily computer un- derstandable. Chapter 6(Natural Language Processing of Medical Reports) deals with the structuring and standardization of free-text medical reports via natural language processing(NLP). Issues related to medical NLP representation. computation, and evaluation are presented. An overview of the NLP task is first described to frame the problem, providing an analysis of past efforts and applica- tions of NLP. A sequence of subtasks is then related: structural analysis (e.g,section and sentence boundary detection), lexical analysis(e. g, logical word sequences, disambiguation, concept coding), phrasal chunking, and parsing are covered. For each subtask, a description of the challenges and the range of approaches are given to familiarize the reader with the field Core to informatics endeavors is a systematic method to organize both data and knowledge, representing original(clinical) observations, derived data, and conclu sions in a logical manner. Chapter 7(Organizing Observations: Data Models) describes the different types of relationships between healthcare entities, particularly focusing on those relations commonly encountered in medical imaging. Often in

xii Preface properly inform decision making. Chapter 4 (Medical Data Visualization: Toward Integrated Clinical Workstations) presents works related to the visualiz￾ation of medical data. A survey of graphical metaphors (lists and tables; plots and charts; graphs and trees; and pictograms) is given, relating their use to convey clinical concepts. A discussion of portraying temporal, spatial, multidimensional, and causal relationships is provided, using the navigation of images as an example an adaptive graphical user interface to accommodate the range of different user goals involving patient data. Part III, Documenting Imaging Findings, discusses techniques for automatically extracting content from images and related data in order to objectify findings: ƒ In Chapter 5 (Characterizing Imaging Data), an introduction to medical image understanding is presented. Unlike standard image processing, techniques within medical imaging informatics focus on how imaging studies, alongside other clinical data, can be standardized and their content (automatically) extracted to guide medical decision making processes. Notably, unless medical images are standard￾ized, quantitative comparisons across studies is subject to various sources of bias/ artifacts that negatively influence assessment. From the perspective of creating scientific-quality imaging databases, this chapter starts with the groundwork for understanding what exactly an image captures, and commences to outline the dif￾ferent aspects encompassing the standardization process: intensity normalization; denoising; and both linear and nonlinear image registration methods are covered. Subsequently, a discussion of commonly extracted imaging features is given, divided amongst appearance- and shape-based descriptors. With the wide array of image features that can be computed, an overview of image feature selection and dimensionality reduction methods is provided. Lastly, this chapter concludes with a description of increasingly popular imaging-based anatomical atlases, detailing their construction and usage as a means for understanding population-based norms and differences arising due to a disease process. ƒ Absent rigorous methods to automatically analyze and quantify image findings, radiology reports are the sole source of expert image interpretation. In point of fact, a large amount of information about a patient remains locked within clinical documents; and as with images, the concepts therein are not readily computer un￾derstandable. Chapter 6 (Natural Language Processing of Medical Reports) deals with the structuring and standardization of free-text medical reports via natural language processing (NLP). Issues related to medical NLP representation, computation, and evaluation are presented. An overview of the NLP task is first described to frame the problem, providing an analysis of past efforts and applica￾tions of NLP. A sequence of subtasks is then related: structural analysis (e.g., section and sentence boundary detection), lexical analysis (e.g., logical word sequences, disambiguation, concept coding), phrasal chunking, and parsing are covered. For each subtask, a description of the challenges and the range of approaches are given to familiarize the reader with the field. ƒ Core to informatics endeavors is a systematic method to organize both data and knowledge, representing original (clinical) observations, derived data, and conclu￾sions in a logical manner. Chapter 7 (Organizing Observations: Data Models) describes the different types of relationships between healthcare entities, particularly focusing on those relations commonly encountered in medical imaging. Often in on a definition of (task) context and user modeling, resulting in a means of creating application. Methods to combine these visual components are illustrated, based

Preface XIIL clinical practice, a disease is studied from a specific perspective (e.g, genetic, pathologic, radiologic, clinical). But disease is a phenomenon of nature, and is thus typically multifaceted in its presentation. The goal is to aggregate the observations for a single patient to characterize the state and behavior of the patient's disease, both in terms of its natural course and as the result of (therapeutic)interventions The chapter divides the organization of such information along spatial (e.g physical and anatomical relations, such as between objects in space), temporal (e.g, sequences of clinical events, episodes of care), and clinically-oriented models (i. e, those models specific to representing a healthcare abstraction a discussion of the motivation behind what drives the design of a medical data model is given, leading to the description of a phenomenon-centric data model to support healthcare research Finally, in Part IV, Toward Medical Decision Making, we reflect on issues pertain- ng to reasoning with clinical observations derived from imaging and other data sources in order to reach a conclusion about patient care and the value of our decision A variety of formalisms are used to represent disease models, of these, probabilistic graphical models have become increasingly popular given their ability to reason in light of missing data, and their relatively intuitive representation. Chapter 8 Disease Models, Part 1: Graphical Models) commences with a review of key concepts in probability theory as the basis for understanding these graphical models and their different formulations. In particular, the first half of the chapter handles Bayesian belief networks(BBNS), appraising past and current efforts to apply these models to the medical environment. The latter half of this chapter addresses he burgeoning exploration of causal models, and the implications for analysis and positing questions to such networks. Throughout, a discussion of the practical considerations in the building of these models and the assumptions that must be Following the discussion of the creation of the models, in Chapter 9(Disease Models, Part 1: Querying Applications), we address the algorithms and tools that enable us to query BBNs. Two broad classes of queries are considered: belief updating, and abductive reasoning. The former entails the re-computation of pos- terior probabilities in a network given some specific evidence; the latter involves calculating the optimal configuration of the bBn in order to maximize some specified criteria. Brief descriptions of exact and approximate inference methods are provided. Special types of belief networks(naive Bayes classifiers, influence diagrams, probabilistic relational models)are covered, illustrating their potential usage in medicine. Importantly, issues related to the evaluation of belief networks are discussed in this chapter, looking to standard technical accuracy metrics, but also ideas in parametric sensitivity analysis. Lastly, the chapter concludes with some example applications of BBNs in medicine, including to support case-based retrieval and image processing tasks Chapter 10(Evaluation) concludes by considering how to assess informatics endeavors. A primer on biostatistics and study design starts this chapter, including a review of basic concepts(e.g, confidence intervals, significance and hypothesis testing)and the statistical tests that are used to evaluate hypotheses under differ- ent circumstances and assumptions. a discussion of error and performance assessment is then introduced, including sensitivity/specificity and receiver opera- tive characteristic analysis Study design encompasses a description of the differ ent types of experiments that can be formed to test a hypothesis, and goes over the

Preface xiii clinical practice, a disease is studied from a specific perspective (e.g., genetic, pathologic, radiologic, clinical). But disease is a phenomenon of nature, and is thus typically multifaceted in its presentation. The goal is to aggregate the observations for a single patient to characterize the state and behavior of the patient’s disease, both in terms of its natural course and as the result of (therapeutic) interventions. The chapter divides the organization of such information along spatial (e.g., physical and anatomical relations, such as between objects in space), temporal (e.g., sequences of clinical events, episodes of care), and clinically-oriented models (i.e., those models specific to representing a healthcare abstraction). A discussion of the motivation behind what drives the design of a medical data model is given, leading to the description of a phenomenon-centric data model to support healthcare research. Finally, in Part IV, Toward Medical Decision Making, we reflect on issues pertain￾ing to reasoning with clinical observations derived from imaging and other data sources in order to reach a conclusion about patient care and the value of our decision: ƒ A variety of formalisms are used to represent disease models; of these, probabilistic graphical models have become increasingly popular given their ability to reason in light of missing data, and their relatively intuitive representation. Chapter 8 (Disease Models, Part I: Graphical Models) commences with a review of key concepts in probability theory as the basis for understanding these graphical models and their different formulations. In particular, the first half of the chapter handles Bayesian belief networks (BBNs), appraising past and current efforts to apply these models to the medical environment. The latter half of this chapter addresses the burgeoning exploration of causal models, and the implications for analysis and positing questions to such networks. Throughout, a discussion of the practical considerations in the building of these models and the assumptions that must be made, are given. ƒ Following the discussion of the creation of the models, in Chapter 9 (Disease Models, Part II: Querying & Applications), we address the algorithms and tools that enable us to query BBNs. Two broad classes of queries are considered: belief updating, and abductive reasoning. The former entails the re-computation of pos￾terior probabilities in a network given some specific evidence; the latter involves calculating the optimal configuration of the BBN in order to maximize some specified criteria. Brief descriptions of exact and approximate inference methods are provided. Special types of belief networks (naïve Bayes classifiers, influence diagrams, probabilistic relational models) are covered, illustrating their potential usage in medicine. Importantly, issues related to the evaluation of belief networks are discussed in this chapter, looking to standard technical accuracy metrics, but also ideas in parametric sensitivity analysis. Lastly, the chapter concludes with some example applications of BBNs in medicine, including to support case-based retrieval and image processing tasks. ƒ Chapter 10 (Evaluation) concludes by considering how to assess informatics endeavors. A primer on biostatistics and study design starts this chapter, including a review of basic concepts (e.g., confidence intervals, significance and hypothesis testing) and the statistical tests that are used to evaluate hypotheses under differ￾ent circumstances and assumptions. A discussion of error and performance assessment is then introduced, including sensitivity/specificity and receiver opera￾tive characteristic analysis. Study design encompasses a description of the differ￾ent types of experiments that can be formed to test a hypothesis, and goes over the

Preface process of variable selection and sample size/power calculations. Sources of study bias/error are briefly described, as are statistical tools for decision making. The second part of this chapter uses the foundation set out by the primer to focus specifically on informatics-related evaluations. Two areas serve as focal points evaluating information retrieval (IR) systems, including content-based image retrieval; and assessing(system)usability

xiv Preface process of variable selection and sample size/power calculations. Sources of study bias/error are briefly described, as are statistical tools for decision making. The second part of this chapter uses the foundation set out by the primer to focus specifically on informatics-related evaluations. Two areas serve as focal points: evaluating information retrieval (IR) systems, including content-based image retrieval; and assessing (system) usability