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IEEE Transactions on Radiation and Plasma Medical Sciences is a peer-reviewed journal published by IEEE, covering Medical Imaging Techniques and Applications, Radiation Detection and Scintillator Technologies, Advanced X-ray and CT Imaging. Over its lifetime it has published 1,300 papers, which have received 14,220 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Radiation and Plasma Medical Sciences style. No sign-up. |
| Abbreviation (ISO-4) | TRPMS |
| Publishes research in | Medical Imaging Techniques and Applications Radiation Detection and Scintillator Technologies Advanced X-ray and CT Imaging Radiation Therapy and Dosimetry Advanced Radiotherapy Techniques |
| ISSN | 2469-7303 |
| Citation impact (2-yr) | 1.66 |
| h-index | 53 |
| i10-index | 360 |
| Top institutions publishing here | Huazhong University of Science and Technology |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and…
Multi-modality medical imaging techniques have been increasingly applied in clinical practice and research studies. Corresponding multi-modal image analysis and ensemble learning schemes have seen rapid growth and bring unique value to medical applications. Motivated by the recent success of applying deep learning methods to medical image processing, we first propose an algorithmic architecture for supervised…
There is an increasing demand for high sensitivity multiparametric medical imaging approaches. High precision time-of-flight positron emission tomography (TOFPET) scanners have a very high potential in this context, providing an improvement in the signal-to-noise ratio of the reconstructed image and the possibility to further increase the already very high sensitivity (at the pico-molar level) of…
Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the…
Positron emission tomography (PET) is a functional imaging modality widely used in clinical diagnosis. In this work, we trained a deep convolutional neural network (CNN) to improve PET image quality. Perceptual loss based on features derived from a pre-trained VGG network, instead of the conventional mean squared error, was employed as the training loss function…
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