Example of Japanese Journal of Radiology format
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Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format
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Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format Example of Japanese Journal of Radiology format
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open access Open Access

Japanese Journal of Radiology — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Radiology, Nuclear Medicine and Imaging #139 of 288 up up by 21 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 372 Published Papers | 1130 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/06/2020
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FAQ

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Quality:  
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CiteRatio: 9.3
SJR: 2.628
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.439

4% from 2018

Impact factor for Japanese Journal of Radiology from 2016 - 2019
Year Value
2019 1.439
2018 1.5
2017 1.044
2016 0.982
graph view Graph view
table view Table view

3.0

15% from 2019

CiteRatio for Japanese Journal of Radiology from 2016 - 2020
Year Value
2020 3.0
2019 2.6
2018 2.1
2017 1.9
2016 1.7
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 4% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 15% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.616

25% from 2019

SJR for Japanese Journal of Radiology from 2016 - 2020
Year Value
2020 0.616
2019 0.492
2018 0.458
2017 0.468
2016 0.425
graph view Graph view
table view Table view

0.914

29% from 2019

SNIP for Japanese Journal of Radiology from 2016 - 2020
Year Value
2020 0.914
2019 0.707
2018 0.768
2017 0.633
2016 0.615
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 25% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 29% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Japanese Journal of Radiology

Guideline source: View

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Springer

Japanese Journal of Radiology

Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in m...... Read More

Medicine

i
Last updated on
19 Jun 2020
i
ISSN
1867-1071
i
Impact Factor
Medium - 0.728
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/S11604-017-0617-Z
Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases

Abstract:

The activity of the glymphatic system is impaired in animal models of Alzheimer’s disease (AD). We evaluated the activity of the human glymphatic system in cases of AD with a diffusion-based technique called diffusion tensor image analysis along the perivascular space (DTI-ALPS). Diffusion tensor images were acquired to calcu... The activity of the glymphatic system is impaired in animal models of Alzheimer’s disease (AD). We evaluated the activity of the human glymphatic system in cases of AD with a diffusion-based technique called diffusion tensor image analysis along the perivascular space (DTI-ALPS). Diffusion tensor images were acquired to calculate diffusivities in the x, y, and z axes of the plane of the lateral ventricle body in 31 patients. We evaluated the diffusivity along the perivascular spaces as well as projection fibers and association fibers separately, to acquire an index for diffusivity along the perivascular space (ALPS-index) and correlated them with the mini mental state examinations (MMSE) score. We found a significant negative correlation between diffusivity along the projection fibers and association fibers. We also observed a significant positive correlation between diffusivity along perivascular spaces shown as ALPS-index and the MMSE score, indicating lower water diffusivity along the perivascular space in relation to AD severity. Activity of the glymphatic system may be evaluated with diffusion images. Lower diffusivity along the perivascular space on DTI-APLS seems to reflect impairment of the glymphatic system. This method may be useful for evaluating the activity of the glymphatic system. read more read less

Topics:

Diffusion MRI (52%)52% related to the paper, Glymphatic system (50%)50% related to the paper
246 Citations
Journal Article DOI: 10.1007/S11604-018-0726-3
Deep learning with convolutional neural network in radiology
Koichiro Yasaka1, Hiroyuki Akai1, Akira Kunimatsu1, Shigeru Kiryu2, Osamu Abe1

Abstract:

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically le... Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques. read more read less

Topics:

Deep learning (61%)61% related to the paper, Convolutional neural network (54%)54% related to the paper, Feature extraction (53%)53% related to the paper
238 Citations
open accessOpen access Journal Article DOI: 10.1007/S11604-018-0758-8
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.
Dongsheng Jiang1, Weiqiang Dou2, Weiqiang Dou3, Luc P. J. Vosters4, Xiayu Xu5, Yue Sun4, Tao Tan4

Abstract:

To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly. Multi-channel DnCNN (MCDnCNN) method with two training strategies was developed to denoise MR images... To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly. Multi-channel DnCNN (MCDnCNN) method with two training strategies was developed to denoise MR images with and without a specific noise level, respectively. To evaluate our method, three datasets from two public data sources of IXI dataset and Brainweb, including T1 weighted MR images acquired at 1.5 and 3 T as well as MR images simulated with a widely used MR simulator, were randomly selected and artificially added with different noise levels ranging from 1 to 15%. For comparison, four other state-of-the-art denoising methods were also tested using these datasets. In terms of the highest peak-signal-to-noise-ratio and global of structure similarity index, our proposed MCDnCNN model for a specific noise level showed the most robust denoising performance in all three datasets. Next to that, our general noise-applicable model also performed better than the rest four methods in two datasets. Furthermore, our training model showed good general applicability. Our proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise. read more read less

Topics:

Noise reduction (52%)52% related to the paper, Noise (51%)51% related to the paper, Deep learning (51%)51% related to the paper, Convolutional neural network (50%)50% related to the paper
153 Citations
open accessOpen access Journal Article DOI: 10.1007/S11604-016-0588-5
Neuroimaging findings of Zika virus infection: a review article.
Mohammad Zare Mehrjardi1, Elham Keshavarz1, Andrea Poretti2, Adriano Nassri Hazin

Abstract:

Zika virus (ZIKV) is an arbovirus from the Flaviviridae family. It is usually transmitted by mosquito bite. There have been no reports of severe symptoms caused by ZIKV infection up until the last few years. In October 2013 an outbreak was reported in French Polynesia with severe neurological complications in some affected ca... Zika virus (ZIKV) is an arbovirus from the Flaviviridae family. It is usually transmitted by mosquito bite. There have been no reports of severe symptoms caused by ZIKV infection up until the last few years. In October 2013 an outbreak was reported in French Polynesia with severe neurological complications in some affected cases. In November 2015, the Ministry of Health of Brazil attributed the increased number of neonatal microcephaly cases in northeastern Brazil to congenital ZIKV infection. The rapid spread of the virus convinced the World Health Organization to announce ZIKV infection as a “Public Health Emergency of International Concern” in February 2016. The main neuroimaging findings in congenital ZIKV infection include microcephaly which is the hallmark of the disease, other malformations of cortical development (e.g., lissencephaly, heterotopia, etc.), parenchymal calcifications, unilateral or bilateral ventriculomegaly, enlarged extra-axial CSF spaces, dysgenesis of the corpus callosum, agenesis of the cavum septum pellucidum, cerebellar and brainstem hypoplasia, and ocular abnormalities. ZIKV infection may also cause Guillain-Barre syndrome and acute disseminated encephalomyelitis in adults. Familiarity with neuroimaging findings of congenital and acquired ZIKV infection is crucial to suspect this disease in residents of endemic regions and travelers to these areas. read more read less

Topics:

Zika virus (55%)55% related to the paper
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148 Citations
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Japanese Journal of Radiology format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write Japanese Journal of Radiology in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Japanese Journal of Radiology guidelines and auto format it.

2. Do you follow the Japanese Journal of Radiology guidelines?

Yes, the template is compliant with the Japanese Journal of Radiology guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Japanese Journal of Radiology?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Japanese Journal of Radiology citation style.

4. Can I use the Japanese Journal of Radiology templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Japanese Journal of Radiology.

5. Can I use a manuscript in Japanese Journal of Radiology that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Japanese Journal of Radiology that you can download at the end.

6. How long does it usually take you to format my papers in Japanese Journal of Radiology?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Japanese Journal of Radiology.

7. Where can I find the template for the Japanese Journal of Radiology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Japanese Journal of Radiology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Japanese Journal of Radiology's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Japanese Journal of Radiology an online tool or is there a desktop version?

SciSpace's Japanese Journal of Radiology is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Japanese Journal of Radiology?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Japanese Journal of Radiology?”

11. What is the output that I would get after using Japanese Journal of Radiology?

After writing your paper autoformatting in Japanese Journal of Radiology, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Japanese Journal of Radiology's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Japanese Journal of Radiology?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Japanese Journal of Radiology. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Japanese Journal of Radiology?

The 5 most common citation types in order of usage for Japanese Journal of Radiology are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Japanese Journal of Radiology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Japanese Journal of Radiology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Japanese Journal of Radiology in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Japanese Journal of Radiology Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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