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Frontiers in Radiology is a peer-reviewed journal published by Frontiers, covering Radiomics and Machine Learning in Medical Imaging, Advanced X-ray and CT Imaging, Artificial Intelligence in Healthcare and Education. Over its lifetime it has published 322 papers, which have received 1,822 citations.
| Publisher | Frontiers |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. Frontiers in Radiology. 2023;12(3):45–58.
Formats any DOI in Frontiers in Radiology style. No sign-up. |
| Publishes research in | Radiomics and Machine Learning in Medical Imaging Advanced X-ray and CT Imaging Artificial Intelligence in Healthcare and Education Glioma Diagnosis and Treatment AI in cancer detection |
| ISSN | 2673-8740 |
| Citation impact (2-yr) | 2.73 |
| h-index | 19 |
| i10-index | 60 |
| Article processing charge | $1,900 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Johns Hopkins University |
| Journal website | www.frontiersin.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.
Introduction: Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible. Methods: We present an integrated platform - uAI Research Portal (uRP), to achieve…
Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction…
Arterial spin labeling (ASL) is a non-invasive and cost-effective MRI technique for brain perfusion measurements. While it has developed into a robust technique for scientific and clinical use, its image processing can still be daunting. The 2019 Ann Arbor ISMRM ASL working group established that education is one of the main areas that can accelerate…
Glioma is a type of severe brain tumor, and its accurate segmentation is useful in surgery planning and progression evaluation. Based on different biological properties, the glioma can be divided into three partially-overlapping regions of interest, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET). Recently, UNet has identified its effectiveness in automatically…
Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic…
