Molecular imaging and the unification of multilevel mechanisms and data in medical physics

Molecular imaging (MI) constitutes a recently developed approach of imaging, where modalities and agents have been reinvented and used in novel combinations in order to expose and measure biologic processes occurring at molecular and cellular levels. It is an approach that bridges the gap between modalities acquiring data from high (e.g., computed tomography, magnetic resonance imaging, and positron-emitting isotopes) and low (e.g., PCR, microarrays) levels of a biological organization. While data integration methodologies will lead to improved diagnostic and prognostic performance, interdisciplinary collaboration, triggered by MI, will result in a better perception of the underlying biological mechanisms. Toward the development of a unifying theory describing these mechanisms, medical physicists can formulate new hypotheses, provide the physical constraints bounding them, and consequently design appropriate experiments. Their new scientific and working environment calls for interventions in their syllabi to educate scientists with enhanced capabilities for holistic views and synthesis.     

Medical image management: practical, legal and ethical considerations

The data acquired by the new medical imaging techniques, in many ways, exceeded our ability to properly store, transmit and use the images produced. As diagnostic imaging procedures become progressively less invasive and traumatic, they are being applied to a much larger patient population. The decrease in memory and other instrumentation costs, along with expanded technological capability of computer systems, has provided medicine an opportunity to create network systems for the storage, processing, recall, and remote location of these diagnostic images. Therefore, problems of access and confidentiality have become increasingly important. This communication will consider certain medical, legal, and ethical aspects of these technologies of data acquisition, storage, manipulation and retrieval.     

Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology

Over the last 50 years, diagnostic imaging has grown from a state of infancy to a high level of maturity. Many new imaging modalities have been developed. However, modern medical imaging includes not only image production but also image processing, computer-aided diagnosis (CAD), image recording and storage, and image transmission, most of which are included in a picture archiving and communication system (PACS). The content of this paper includes a short review of research and development in medical imaging science and technology, which covers (a) diagnostic imaging in the 1950s, (b) the importance of image quality and diagnostic performance, (c) MTF, Wiener spectrum, NEQ and DQE, (d) ROC analysis, (e) analogue imaging systems, (f) digital imaging systems, (g) image processing, (h) computer-aided diagnosis, (i) PACS, (j) 3D imaging and (k) future directions. Although some of the modalities are already very sophisticated, further improvements will be made in image quality for MRI, ultrasound and molecular imaging. The infrastructure of PACS is likely to be improved further in terms of its reliability, speed and capacity. However, CAD is currently still in its infancy, and is likely to be a subject of research for a long time.     

Technology, governance and patient safety: systems issues in technology and patient safety

Technology and equipment are often identified as contributors to adverse medical events, however technology is seldom the focal point of investigation as a source of medical error or adverse event. It is often seen as both a means of reducing error (e.g., automated drug dispensing machines) or as a major contributing factor to adverse events (e.g., through cognitive overload). Here we review literature about the governance of technology in health settings, which is addressed in relation to patient safety. We outline the challenges of addressing technology governance issues in the health sector, provide an overview of governance processes, and suggest that technology related adverse events have been largely conceptualized as device and user problems rather than system or socio-technical problems, which is reflected in governance processes associated with medical devices. A recognition of the situatedness of medical practices implies that new forms of governance may be required that place greater emphasis on socio-technical and systems issues.     

Evolving rule-based systems in two medical domains using genetic programming

OBJECTIVE: To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. MATERIAL: Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. METHODS: Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. RESULTS: Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. CONCLUSION: The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.     

Managing Biomedical Image Metadata for Search and Retrieval of Similar Images

Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations (“semantic” metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system’s “match observations” function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.     

PACS and multimodality in medical imaging.

A PACS (Picture Archiving and Communication System) is a system that is able to store, exchange, display and manipulate images and associated diagnoses from any modality within a hospital in a timely and cost-effective way. Several developments, such as the DICOM standard, fast and convenient networking, and new storage solutions for large amounts of data, make the setup of such a PACS system possible. As the information acquired with various imaging modalities is then available and often complementary, it is desirable for the clinician to have a point-by-point spatial co-registration of images from different modalities in order to enable a synergistic use of the multimodality imaging of a patient for increased diagnostic accuracy. Various types of algorithms are available for the matching of medical images from the same or from different modalities. Co-registration algorithms based on voxel properties consist of a similarity or dissimilarity measure and an iterative or non-iterative method minimizing the dissimilarity or maximizing the similarity between the two images by a transformation of one image relative to the other.     

Evaluation in the design of health information systems: application of approaches emerging from usability engineering

This paper examines the role of evaluation in the design of health care information systems. A framework is presented for considering evaluation in the context of software development processes, in particular, the systems development life cycle (SDLC). Variations on standard design methodologies are then discussed, including methods based on rapid development and continual evaluation of prototype systems. Usability testing is presented as a key method for conducting evaluations during iterative system development. The emergence of design methodologies, where evaluation is viewed as a central part of the development cycle is also discussed. Evaluation methodologies are then considered along a continuum, ranging from studies involving a high degree of experimental control to observational approaches. A full cycle approach to evaluation of health care systems is argued for, involving deployment of new methods across the SDLC. Implications for future work exploring the integration of design and evaluation processes in health informatics are discussed.     

Data representation for joint kinematics simulation of the lower limb within an educational context

Three-dimensional (3D) visualization is becoming increasingly frequent in both qualitative and quantitative biomechanical studies of anatomical structures involving multiple data sources (e.g. morphological data and kinematics data). For many years, this kind of experiment was limited to the use of bi-dimensional images due to a lack of accurate 3D data. However, recent progress in medical imaging and computer graphics has forged new perspectives. Indeed, new techniques allow the development of an interactive interface for the simulation of human motions combining data from both medical imaging (i.e., morphology) and biomechanical studies (i.e., kinematics). Fields of application include medical education, biomechanical research and clinical research. This paper presents an experimental protocol for the development of anatomically realistic joint simulation within a pedagogical context. Results are shown for the lower limb. Extension to other joints is straightforward. This work is part of the Virtual Animation of the Kinematics of the Human project (VAKHUM) (http://www.ulb.ac.be/project/vakhum).     

An update on "special stain" histochemistry with emphasis on automation

For nearly 100 years, pathologists have utilized "special histochemical stains" to assist in tissue-based diagnosis. As illustrated in Figures 1 and 2, histochemical stains have been used to identify infectious microorganisms (e.g., Mycobacterium tuberculosis with acid-fast bacillus (AFB) stain), to detail inflammatory stromal or structural alterations (e.g., fibrosis in liver cirrhosis with Masson trichrome), to identify microanatomic sites of disease (e.g., basement membrane in glomerulonephritis with Jones methenamine silver), to identify abnormal chemical deposits (e.g., iron in hemochromatosis with Prussian blue stain), or abnormal immune deposits (e.g., amyloid via Congo red stain). The current surgical pathology laboratory may employ a repertoire of 20 to 25 "special stains" to ensure the full diagnostic complement. While the diagnostic repertoire and the biochemical recipes for the stains are now a well-established, codified part of surgical pathology, there is an ever-moving, leading edge of new developments including new reagents, applications, and methods. This review seeks to update the reader on some of the new applications including both new reagents and methods. Particular emphasis will be placed on the recent technologic advance of automating special stains in kinetic-mode (1-4). The authors consider in turn: 1. In brief, the "news" (recent literature review) of new staining applications; 2. In greater detail, two new applications for detection of Microsporidia and Helicobacer pylori; 3. The new technologic advancement of kinetic mode automation of special stains.     

Emerging ultrasound technologies for early markers of disease

Ultrasound has been a popular clinical imaging modality for decades. It is well established as a means of displaying the macroscopic anatomy of soft-tissue structures. While conventional ultrasound methodologies (i.e., B-mode and Doppler methods) are well proven and continue to advance technically on a daily basis, e.g. by extending into higher frequencies and taking advantage of harmonic phenomena in tissues, fundamentally new ultrasound technologies also are emerging and offer exciting promise for making significant improvements in the clinical imaging of disease. These emerging methods include spectrum analysis, elasticity imaging, contrast-agent methods, and advanced flow detection and measurement techniques. Each provides independent information and, used alone, each can provide powerful new imaging capabilities; combined with each other, their capabilities may be even greater in many applications; and all in principle can be used in concert with other imaging modalities to offer the possibility of further improvements in disease detection, evaluation, and monitoring.     

Computation of semantic similarity within an ontology of breast pathology to assist inter-observer consensus

Computer-assisted consensus in medical imaging involves automatic comparison of morphological abnormalities observed by physicians in images. We built an ontology of morphological abnormalities in breast pathology to assist inter-observer consensus. Concepts of morphological abnormalities extracted from existing terminologies, published grading systems and medical reports were organized in an taxonomic hierarchy and furthermore linked by the relation "is a diagnostic criterion of" according to diagnostic meaning. We implemented position-based, content-based and mixed semantic similarity measures between concepts in this ontology and compared the results with experts' judgment. The position-based similarity measure using both taxonomic and non-taxonomic relations performed as well as the other measures and was used for automatic comparison of morphological abnormalities within the IDEM computer-assisted consensus platform.     

Integrating clinician-educators into Academic Medical Centers: challenges and potential solutions.

During the last decade academic medical centers (AMCs) have hired large numbers of clinician-educators to teach and provide clinical care. However, these clinician-educators often do not advance in academic rank, since excellence in clinical care and teaching alone is not adequate justification for advancement. The authors articulate the problems with the present system of recognition for clinician-educators-i.e., the requirement for regional and national reputation, the lack of reliable measures of clinical and teaching excellence, and the lack of training opportunities for young clinician-educators. They call for solutions, including fundamental changes in promotion criteria (e.g., focus criteria for promotion on clinician-educators' accomplishments within their institutions) and the development of valid and feasible methods to measure outcomes of teaching programs. Further, they recommend the development of a new faculty position, a "clinician-educator researcher," to foster the scholarship of discovery in medical education and clinical practice. Investments in clinician-educator researchers will ultimately help AMCs to achieve their threefold mission-excellence in patient care, teaching, and research.     

Ensuring the success of women faculty at AMCs: lessons learned from the National Centers of Excellence in Women's Health.

Since the early 1970s, the numbers of women entering medical school and, subsequently, academic medicine have increased substantially. However, women faculty have not advanced at the expected rate to senior academic ranks or positions of leadership. In 1996, to counter this trend, the U.S. Department of Health and Human Services (DHHS) Office on Women's Health included women's leadership as a required component of the nationally funded Centers of Excellence in Women's Health to identify effective strategies and initiate model programs to advance women faculty in academic medicine. The authors describe the experience of Centers at seven U.S. medical schools in initiating and sustaining leadership programs for women. The processes used for program formation, the current programmatic content, and program evaluation approaches are explained. Areas of success (e.g., obtaining support from the institution's leaders) and difficulties faced in maintaining an established program (such as institutional fiscal constraints and the diminishing time available to women to participate in mentoring and leadership activities) are reviewed. Strategies to overcome these and other difficulties (e.g., prioritize and tightly focus the program with the help of an advisory group) are proposed. The authors conclude by reviewing issues that programs for women in academic medicine will increasingly need to focus on (e.g., development of new kinds of skills; issues of recruitment and retention of faculty; and increasing faculty diversity).     

Biomedical image visualization research using the Visible Human Datasets

The practice of medicine and conduct of research in major segments of the biologic sciences have always relied on visualizations to study the relationship of anatomic structure to biologic function. Traditionally, these visualizations have either been direct, via vivisection and postmortem examination, or have required extensive mental reconstruction. The revolutionary capabilities of 3-D and 4-D medical imaging modalities, together with computer reconstruction and rendering of multidimensional medical and histological volume image data, obviate the need for physical dissection or abstract assembly. The availability of the Visible Human Datasets from the National Library of Medicine, coupled with the development of advanced computer algorithms to accurately and rapidly process, segment, register, measure, and display high resolution 3-D images, has provided a rich opportunity to help advance these important new imaging, visualization, and analysis methodologies from scientific theory to clinical practice.     

Practicing pediatric pathology without a microscope.

This article highlights changes in the field of pediatric pathology that have resulted from technical advances in prenatal diagnostics, immunohistochemistry, cytogenetics, and molecular genetics. The relatively new and growing need for specialized training in fetal pathology is used as an example. Comprehensive evaluation of human fetuses has become a requisite skill for many diagnostic pathologists, in part because contemporary prenatal diagnostic techniques have shifted the demographics of many congenital conditions from spontaneous term delivery to mid-gestation termination of pregnancy. The information provided by the pathologist has a tremendous impact for families and clinicians as they consider recurrence risks in future pregnancies. As most specimens from therapeutic terminations have gross dysmorphology, which may or may not constitute a recognizable pattern of human malformation, their analysis requires additional skills and methods that were traditionally the domain other specialists (e.g., medical geneticists). The pathologist must learn to identify syndromes, to be aware of their underlying etiology and pathogenesis, and to utilize advanced cytogenetic methods (e.g., fluorescence in situ hybridization), flow cytometry, or specific mutational analysis when appropriate. At a minimum, important anatomic details must be well documented and appropriate tissue samples should be obtained and stored to facilitate more specific diagnostic testing in the future.     

Quantitative Imaging Biomarkers: The Application of Advanced Image Processing and Analysis to Clinical and Preclinical Decision Making

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.     

Multimedia electronic medical record systems.

A wide range of imaging technologies are becoming increasingly important to the practice of medicine. In addition, many medical specialties are highly visual, independent of their use of new imaging modalities. Because today's medical record contains text, images, and physiologic signals, it is inherently multimedia in nature. However, most electronic medical record systems handle only the textual portion of the patient record, resulting in a fragmentation of the database that physicians need to make timely, effective clinical decisions. Advances in database-, storage-, data-compression, and networking technologies will facilitate the development of multimedia electronic medical record systems for the 21st century. These systems will become widely used over the next decade, and in addition to enhancing patient care, will also present new opportunities for using clinical imaging data for biomedical research and education.     

Intraoperative ultrasound for guidance and tissue shift correction in image-guided neurosurgery

We present a surgical guidance system that incorporates pre-operative image information (e.g., MRI) with intraoperative ultrasound (US) imaging to detect and correct for brain tissue deformation during image-guided neurosurgery (IGNS). Many interactive IGNS implementations employ pre-operative images as a guide to the surgeons throughout the procedure. However, when a craniotomy is involved, tissue movement during a procedure can be a significant source of error in these systems. By incorporating intraoperative US imaging, the target volume can be scanned at any time, and two-dimensional US images may be compared directly to the corresponding slice from the pre-operative image. Homologous points may be mapped from the intraoperative to the pre-operative image space with an accuracy of better than 2 mm, enabling the surgeon to use this information to assess the accuracy of the guidance system along with the progress of the procedure (e.g., extent of lesion removal) at any time during the operation. Anatomical features may be identified on both the pre-operative and intraoperative images and used to generate a deformation map, which can be used to warp the pre-operative image to match the intraoperative US image. System validation is achieved using a deformable multi-modality imaging phantom, and preliminary clinical results are presented.     

Dual-energy imaging of the chest: optimization of image acquisition techniques for the 'bone-only' image

Experiments were conducted to determine optimal acquisition techniques for bone image decompositions for a prototype dual-energy (DE) imaging system. Technique parameters included kVp pair (denoted [kVp(L)/kVp(H)]) and dose allocation (the proportion of dose in low- and high-energy projections), each optimized to provide maximum signal difference-to-noise ratio in DE images. Experiments involved a chest phantom representing an average patient size and containing simulated ribs and lung nodules. Low- and high-energy kVp were varied from 60-90 and 120-150 kVp, respectively. The optimal kVp pair was determined to be [60/130] kVp, with image quality showing a strong dependence on low-kVp selection. Optimal dose allocation was approximately 0.5-i.e., an equal dose imparted by the low- and high-energy projections. The results complement earlier studies of optimal DE soft-tissue image acquisition, with differences attributed to the specific imaging task. Together, the results help to guide the development and implementation of high-performance DE imaging systems, with applications including lung nodule detection and diagnosis, pneumothorax identification, and musculoskeletal imaging (e.g., discrimination of rib fractures from metastasis).