Call for papers: Medical Image Reconstruction with Low SNR
While signal processing and image recovery can achieve high-quality images from measured dataset with low SNR, medical image reconstruction can generate tomography image for disease diagnosis. In fact, there are various image features of medical images, such as piecewise constant, non-local similarity, low-rank, and so on. The regularization-based, multiscale transforms-based, Learning-based and machine learning-based (especially deep learning-based) methods are being actively developed worldwide for image reconstruction. Hence, in order to efficiently extract the useful information within medical image, advanced image features should be explored. Incorporating the medical image feature prior to formulating optimized models and deep-learning network can improve reconstructed image quality from measured datasets with low SNR. In this respect, this special issue titled as "Medical Image Reconstruction with Low SNR" can serve as a platform to preform better reconstruction results. To evaluate and validate the outperformances of the proposed algorithm, it is neccessary to compare quantitatively the reconstrution results in a systematic and reproducible strategy.
Submissions may include methods in medical imaging, the topics include but are limited to:
- Regularization methods in ill-posed inverse imaging/reconstruction;
- Application of multiscale transforms in medical imaging;
- Deep learning based image reconstruction;
- Learning-based algorithms/techniques in improving ill-posed inverse imaging;
- Machine learning/pattern recognition algorithms (e.g., Perception, Bayesian network, support vector machine, fuzzy logic, etc.) in inverse imaging/reconstruction;
- Learning based signal processing techniques in medical image processing/analysis (segmentation/registration/restoration/recognition, etc.);
- New Quality Assessment algorithms for Medical/Biological Images or signals.
Since machine learning for image reconstruction is a new area, we especially encourage submissions about deep learning based methods in medical reconstruction. We appreciate you sharing this call for papers with colleagues or collaborators who might be interested in submitting a manuscript.
Submission is permitted only if the paper has not been submitted, accepted, published, or copyrighted in another journal. Papers that have been published in conference and workshop proceedings may be submitted for consideration provided that (i) the citation for their earlier work; (ii) the papers are not identical; and (iii) the journal publication includes novel elements (e.g., more comprehensive experiments). For submission details, please refer to the authors guidelines at https://asp-eurasipjournals.springeropen.com/submission-guidelines.
Initial Paper Submission: September 2, 2019
1st Review Completed: November 15, 2019
Revised Manuscript Due: December 15, 2019
2nd Review Completed: Feb 10, 2020
Final Manuscript Due: Mar 5, 2020
Lead Guest Editor:
Yang Chen, Southeast University, China
Jean-Claude Nunes, University of Rennes 1, France
Yudong Zhang, University of Leicester, UK
Weiwen Wu, Chongqing University, China
Hao Zhang, Stanford University, USA
Jiri Jan, Brno University of Technology, Czech
Annual Journal Metrics
101 days to first decision for reviewed manuscripts only
64 days to first decision for all manuscripts
223 days from submission to acceptance
26 days from acceptance to publication
86 Altmetric mentions
Funding your APC
- ISSN: 1687-6180 (electronic)