Journal Information
IEEE Transactions on Medical Imaging
Impact Factor:

Call For Papers
IEEE TRANSACTIONS ON MEDICAL IMAGING (T-MI) encourages the submission of manuscripts on imaging of body structure, morphology and function, and imaging of microscopic biological entities. The journal publishes original contributions on medical imaging achieved by various modalities, such as ultrasound, X-rays (including CT) magnetic resonance, radionuclides, microwaves, and light, as well as medical image processing and analysis, visualization, pattern recognition, and related methods. Studies involving highly technical perspectives are most welcome. The journal focuses on a unified common ground where instrumentation, systems, components, hardware and software, mathematics and physics contribute to the studies.
Last updated by Dou Sun in 2016-09-08
Special Issues
Special Issue on Low-Dose Computed Tomography
Submission Date: 2017-01-31

Computed tomography (CT) is now a widely used imaging modality for screening and diagnosis, emergency medicine, image-guided interventions, and monitoring of therapeutic responses. As the use of CT has grown, so has concern about the associated radiation dose, and while the biological risk associated with low (mSv) levels of radiation is not established, the concern is sufficient to motivate major efforts from academic, government and industrial researchers to develop CT methodologies for as low dose as possible while achieving the clinical tasks, hereafter called low-dose CT (LdCT). These include the investigation of new detector technologies, such as photon counting detectors, the development of new data acquisition protocols and associated innovative image reconstruction algorithms, and the development of improved metrics for assessing whether clinical performance can be maintained while reducing radiation dose. To review these developments and facilitate further advances in LdCT, this special issue calls for research papers, each of which must comprise all the three major components of: (1) a review of the current state of the topic of interest (for example, hardware design or image reconstruction); (2) the introduction of a new development or improved method, statement of its innovation, and demonstration that it yields a statistically significant improvement over a current method of the reviewed arts for a clinically relevant task (for example, detection of lung nodules at a size threshold); and (3) discussion of the potential for further improvement based on extensions of the presented method. This special issue aims to provide a forum for both established experts and new investigators to share their knowledge and insights for the further development of LdCT. Each paper shall clearly present the above three major components. Topics include, but are not limited to: - New detector material development and/or new system design; - Efficient data acquisition, data calibration and/or correction strategies; - Innovative image reconstruction methods; - Evaluation strategies to assess the diagnostic accuracy of LdCT methods.
Last updated by Dou Sun in 2016-09-08
Special Issue on Simulation and Synthesis in Medical Imaging
Submission Date: 2017-02-01

From very basic digital phantoms all the way to very realistic in silico models of medical imaging and physiology, our community has progressed enormously in terms of the available techniques and their applications. For instance, mechanistic models (imaging simulations) emulating the geometrical and physical aspects of the acquisition process have been used now for a long time. Advances on computational anatomy and physiology have further enhanced the potential of such simulation platforms by incorporating structural and functional realism to the simulations that can now account for complex spatio-temporal dynamics due to changes in anatomy, physiology, disease progression, patient and organ motion, etc. just to name a few. More recently, developments in machine learning together with the growing availability of ever larger scale databases have provided the theoretical underpinning and the practical data access to develop phenomelogical models (image synthesis) that learn models directly from data associations across subjects, time, modalities, resolutions, etc. These are data-driven models that exist only in the weights of a deep neural network, in the words of a learnt dictionary or in the genes of a genetic algorithm, i.e. in the parameter space of whatever machine learning approach is being used. These techniques may provide ways to address tasks in medical image analysis that are difficult to model from first principles like cross-cohort normalization, image imputation in the presence of missing or corrupted data, transfer of knowledge across imaging modalities, views or domains. To this date, these two main research avenues (simulation and synthesis) remain pretty much independent efforts in spite of sharing common challenges. This special issue will overview the state-of-the-art in methods and algorithms at the forefront of synthesis and simulation in/for medical imaging research. We hope this collection will stimulate new ideas leading to theoretical links, practical synergies, and best practices in evaluation and assessment common to these two research directions. In particular, we welcome contributions from cross-disciplinary teams with expertise, among others, on machine learning, statistical modeling, information theory, computational mechanics, computational physics, computer graphics, applied mathematics, etc. Contributions are sought on methods that address, but are not limited to, the following: - Fundamental methods for image-based biophysical modeling and image synthesis - Image synthesis in high dimensional spaces (vectors, tensors, spatio-temporal features, etc.) - High-throughput simulation and synthesis from large-scale image databases - Quantification of uncertainty in imaging biomarkers and handling incomplete data via image simulation and synthesis - Image synthesis and simulation techniques for data normalization, protocol harmonization, and intensity correction - Mechanistic and data-driven predictive imaging models of disease progression or organ development - Biomechanical and data-driven computational imaging models of organ motion and deformation - Physical and data-driven models of image formation and acquisition in clinical and cellular imaging - Methods and tools for cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis and simulation - Methods for automated quality assessment of simulations and synthetic images - Novel evaluation metrics and benchmarking of state-of-the-art approaches in simulation and synthesis - Normative and annotated datasets for benchmarking and learning models
Last updated by Dou Sun in 2016-09-25
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