According to the STEP research roadmap the Virtual Physiological Human (VPH) is a methodological and technological framework that once established will enable the investigation of the human body as a single complex system. Underlying the VPH concept, the International Union for Physiological Sciences (IUPS) has been sponsoring for more than a decade now the IUPS Physiome Project, which is a worldwide public domain effort to provide a computational framework for understanding human physiology. It aims to develop integrative models at all levels of biological organization, from genes to the whole organism via gene regulatory networks, protein pathways, integrative cell function, and tissue and whole organ structure/function relations.
In this context, the roles of medical imaging and image computing play, and will continue to play, an increasingly important role as they provide systems and methods to image, quantify and fuse both structural and functional information of the human being in vivo. These two main research areas include the transformation of generic computational models to represent specific subjects thus paving the way for personalized computational models. Individualization of generic computational models through imaging can be realized in three complementary directions: a) definition of the subject-specific computational domain (anatomy) and related subdomains (tissue types); b) definition of boundary and initial conditions from (dynamic) imaging; and c) characterization of the structural and functional tissue properties. In addition, imaging has also a pivotal role in the evaluation and validation of such models both in human and in animal models, and in the translation of such models to the clinical setting with both diagnostic and therapeutic applications.
The applications of image-based VPH/Physiome models in basic and clinical domains are vast but, broadly speaking, they hold the promise to become new virtual imaging techniques. Effectively more, and often non-observable, parameters will be imaged in silico based on the integration of observable but sometimes sparse and inconsistent multimodal images and physiological measurements. Computational models will serve to engender interpretation of the measurements in a way compliant with the underlying biophysical, biochemical or biological laws of the physiological or pathophysiological processes under investigation. Ultimately, such investigative tools and systems will help our understanding of disease processes, the natural history of disease evolution, and the influence on the course of a disease of pharmacological and/or interventional therapeutic procedures.
We invite submission of papers describing new methods and tools for image-based approaches to the VPH/Physiome. The special issue will give particular attention to contributions describing methods and tools combined with a thorough clinical evaluation. Suggested topics include but are not restricted to:
T-MI seeks high quality research papers for this special issue. This special issue will welcome full-paper submissions. Authors should submit their manuscripts electronically, by the deadline above, through the ScholarOne Manuscripts following the IEEE TMI instructions for authors and indicating in the author's cover letter that the manuscript be considered for the special issue on Medical Imaging in Computational Physiology. Articles received after the due date will be reviewed, but may not be reviewed in time for inclusion in the special issue. Accepted papers not included in the special issue will be published in regular issues of TMI. Authors intending to submit articles are encouraged to discuss their submissions with the Guest Editors first to determine suitability for this special issue. The Guest Editors will screen submitted papers for suitability for this special issue. Papers not deemed suitable for the special issue will be forwarded to the Editor-in-Chief of TMI for possible consideration as regular submissions to TMI.
In recent years, imaging is increasingly moving from being a primarily diagnostic modality towards a therapeutic and interventional aid, facilitated by advances in minimal access and robotic assisted surgery, along with the emergence of novel drugs and other forms of treatment. The first medical imaging device was the human eye coupled to the human brain, and this imaging system has been used both for diagnosis and intervention since before the time of Imhotep. Technological advances have greatly extended our ability to image patient anatomy and physiological processes, to diagnose disease, to plan and monitor medical interventions, and to assess therapeutic outcomes. Modalities now include X-rays, computed tomography, ultrasound, MRI, PET/SPECT, video endoscopy and microscopy, emerging biophotonics techniques, and more. As devices and systems have become more sophisticated, their use has become more specialized, based in part on where they are used in the "diagnose-plan-treat-assess" cycle. Diagnostic systems frequently emphasize exquisite image quality, but generally do not impose real-time imaging performance requirements. Interventional imaging systems, on the other hand, have to satisfy a number of design requirements, even at the expense of image detail, particularly in terms of real-time/interactive responses and the ease of being integrated with interventional workflows. For example, these systems must be able to operate within the complex environment of the operating room or intervention suite, working side-by-side with the clinical team. They must provide timely information needed for real-time intraoperative decision-making, monitoring, and control. As a result, the development of these systems poses new challenges to the medical imaging community.
This special issue is intended to highlight the state-of-the-art, emerging challenges, and innovative approaches in interventional imaging, analysis, and visualization. Topics may include, but are not limited to:
Authors should submit their manuscripts electronically, by the deadline above, through the ScholarOne Manuscripts following the IEEE TMI instructions for authors and indicating in the author's cover letter that the manuscript be considered for the special issue on Interventional Imaging. Articles received after the due date will be reviewed, but may not be reviewed in time for inclusion in the special issue. Accepted papers not included in the special issue will be published in regular issues of TMI. Authors intending to submit articles are encouraged to discuss their submissions with the Guest Editors first to determine suitability for this special issue. The Guest Editors will screen submitted papers for suitability for this special issue. Papers not deemed suitable for the special issue will be forwarded to the Editor-in-Chief of TMI for possible consideration as regular submissions to TMI.
While it is common knowledge that most digital images can be greatly compressed, compressive sensing (CS) theory has established that such compression can be produced by the data acquisition process itself and uncompressed by optimization algorithms such as L1-norm minimization. In the field of biomedical imaging, CS is exciting for several reasons. First, it allows accurate recovery of an image from far fewer measurements than the number of unknowns. Second, it does not require a close match between the sampling pattern and characteristic image structures. Most importantly, CS solutions can often efficiently deliver practical results in terms of increased acquisition speed, reduced radiation dose, enhanced image quality, or other benefits. Given its transformative potential in major aspects of system design, algorithm development, and preclinical and clinical applications, CS has recently become a hot topic of biomedical imaging.
This special issue is intended as a forum with high visibility and synergy to report cutting-edge results in the application of compressive sensing to biomedical imaging, and elaborate the methodologies behind such applications. In this context, we encourage both established experts and new investigators to contribute high-quality papers targeting one or more established or emerging biomedical imaging modalities, with an emphasis on robust image quality assessment with much more than one dataset. As related to biomedical imaging, topics of interest include but are not limited to
Authors should submit their manuscripts electronically, by the deadline above, through the ScholarOne Manuscripts following the IEEE TMI instructions for authors and indicating in the author's cover letter that the manuscript be considered for the special issue on Compressive Sensing for Biomedical Imaging. Authors intending to submit articles are encouraged to discuss their submissions with the Guest Editors to determine suitability for this special issue.
Analysis of microscopy images of samples has numerous applications in a wide range of areas such as molecular biology, life sciences, pharmacology and medicine. Recently, new protocols based on molecular labeling and imaging automation are spurring a revolution in microscopy techniques since they allow to capture the trans- or co-localization of proteins in multivariate microscopy image (MMI) data. In MMI, each pixel at a location may be associated with more than one intensity value, to an array of multiple intensities. These intensities can encode protein location, co-location or translocation over time, as seen by incident light of multiple wavelengths. In histopathology, for example, diagnosis and grading of cancer and other diseases can be improved by analyzing localization patterns in MMI obtained with multi-staining and/or multi-spectral techniques. As a consequence, there is a rapidly growing interest in the processing and analysis of MMI not only in the classic fields listed above but also in the new and rapidly evolving field of systems biology since the MMI data unfolds the spatial information on the molecular level that cannot be evaluated using the classic "omics" methods. Although there has been great progress in the development and application of image analysis in biomedicine over the recent years, there are a number of significant challenges involving the MMI data. These challenges include acquisition, efficient storage, registration, segmentation, classification, semantic annotation and visualization of the MMI data. Recent advances in other related areas such as image processing, computer vision, pattern recognition, and machine learning as well as the availability of high-performance computing equipment at a relatively affordable cost are seemingly fueling the development of computational methods to deal with these challenges.
This Special Issue will highlight new research directions in Multivariate Microscopy Image Analysis by collecting selected papers in all relevant areas including, but not limited to, the following topics:
The IEEE Transactions on Medical Imaging seeks high-quality original research papers for this Special Issue. Authors should submit their manuscripts electronically, by the deadline below, through the IEEE ScholarOne Manuscripts Office (http://mc.manuscriptcentral.com/tmi-ieee/) following the TMI Instructions for Authors and indicating in the Author Comments to the Editor-in-Chief that the manuscript be considered for the special issue on Multivariate Microscopy Image Analysis. Authors intending to submit articles are encouraged to discuss their submissions with the Guest Editors to determine suitability for this Special Issue.