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Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format
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Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format Example of Radiological Physics and Technology format
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Radiological Physics and Technology — Template for authors

Publisher: Springer
Guideline source
Categories Rank Trend in last 3 yrs
Physical Therapy, Sports Therapy and Rehabilitation #65 of 206 up up by 36 ranks
Radiation #26 of 53 up up by 5 ranks
Radiology, Nuclear Medicine and Imaging #145 of 288 up up by 53 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 205 Published Papers | 581 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/07/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

2.8

33% from 2019

CiteRatio for Radiological Physics and Technology from 2016 - 2020
Year Value
2020 2.8
2019 2.1
2018 1.4
2017 1.3
2016 1.6
graph view Graph view
table view Table view

0.41

10% from 2019

SJR for Radiological Physics and Technology from 2016 - 2020
Year Value
2020 0.41
2019 0.456
2018 0.372
2017 0.318
2016 0.382
graph view Graph view
table view Table view

0.698

26% from 2019

SNIP for Radiological Physics and Technology from 2016 - 2020
Year Value
2020 0.698
2019 0.948
2018 0.629
2017 0.715
2016 0.579
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 33% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

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

insights Insights

  • SNIP of this journal has decreased by 26% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Radiological Physics and Technology

Guideline source: View

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Springer

Radiological Physics and Technology

The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear med...... Read More

Medicine

i
Last updated on
22 Jul 2020
i
ISSN
1865-0333
i
Impact Factor
Medium - 0.624
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
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/S12194-017-0406-5 •
Overview of deep learning in medical imaging
Kenji Suzuki1, Kenji Suzuki2 •
Tokyo Institute of Technology1, Illinois Institute of Technology2
08 Jul 2017 - Radiological Physics and Technology

Abstract:

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 201... The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. “Deep learning”, or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades. read more read less
1,004 Citations
Journal Article • DOI: 10.1007/S12194-019-00552-4 •
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first
Hiroshi Fujita1 •
Gifu University1
02 Jan 2020 - Radiological Physics and Technology

Abstract:

The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has... The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has evolved and diversified at a remarkable pace in medical diagnosis, especially in diagnostic imaging. Therefore, this commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it. read more read less
257 Citations
open accessOpen access • Journal Article • DOI: 10.1007/S12194-017-0394-5 •
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.
Bram van Ginneken1 •
Radboud University Nijmegen1
16 Feb 2017 - Radiological Physics and Technology

Abstract:

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in t... Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest. read more read less
View PDF
171 Citations
open accessOpen access • Journal Article • DOI: 10.1007/S12194-019-00520-Y •
Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging.
Shizuo Kaji1, Shizuo Kaji2, Satoshi Kida3 •
Kyushu University1, National Presto Industries2, DTS3
20 Jun 2019 - Radiological Physics and Technology

Abstract:

Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in medical imaging. Every year, many new methods are reported at conferences such as the International Conference on Medical Image Computing and Computer-Assisted Intervention and M... Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in medical imaging. Every year, many new methods are reported at conferences such as the International Conference on Medical Image Computing and Computer-Assisted Intervention and Machine Learning for Medical Image Reconstruction, or published online at the preprint server arXiv. There is a plethora of surveys on applications of neural networks in medical imaging (see [1] for a relatively recent comprehensive survey). This article does not aim to cover all aspects of the field, but focuses on a particular topic, image-to-image translation. Although the topic may not sound familiar, it turns out that many seemingly irrelevant applications can be understood as instances of image-to-image translation. Such applications include (1) noise reduction, (2) super-resolution, (3) image synthesis, and (4) reconstruction. The same underlying principles and algorithms work for various tasks. Our aim is to introduce some of the key ideas on this topic from a uniform viewpoint. We introduce core ideas and jargon that are specific to image processing by use of DNNs. Having an intuitive grasp of the core ideas of applications of neural networks in medical imaging and a knowledge of technical terms would be of great help to the reader for understanding the existing and future applications. Most of the recent applications which build on image-to-image translation are based on one of two fundamental architectures, called pix2pix and CycleGAN, depending on whether the available training data are paired or unpaired (see Sect. 1.3). We provide codes ([2, 3]) which implement these two architectures with various enhancements. Our codes are available online with use of the very permissive MIT license. We provide a hands-on tutorial for training a model for denoising based on our codes (see Sect. 6). We hope that this article, together with the codes, will provide both an overview and the details of the key algorithms and that it will serve as a basis for the development of new applications. read more read less
126 Citations
Journal Article • DOI: 10.1007/S12194-016-0361-6 •
Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging
Rie Tanaka1 •
RMIT University1
13 Jun 2016 - Radiological Physics and Technology

Abstract:

Dynamic chest radiography is a flat-panel detector (FPD)-based functional X-ray imaging, which is performed as an additional examination in chest radiography. The large field of view (FOV) of FPDs permits real-time observation of the entire lungs and simultaneous right-and-left evaluation of diaphragm kinetics. Most important... Dynamic chest radiography is a flat-panel detector (FPD)-based functional X-ray imaging, which is performed as an additional examination in chest radiography. The large field of view (FOV) of FPDs permits real-time observation of the entire lungs and simultaneous right-and-left evaluation of diaphragm kinetics. Most importantly, dynamic chest radiography provides pulmonary ventilation and circulation findings as slight changes in pixel value even without the use of contrast media; the interpretation is challenging and crucial for a better understanding of pulmonary function. The basic concept was proposed in the 1980s; however, it was not realized until the 2010s because of technical limitations. Dynamic FPDs and advanced digital image processing played a key role for clinical application of dynamic chest radiography. Pulmonary ventilation and circulation can be quantified and visualized for the diagnosis of pulmonary diseases. Dynamic chest radiography can be deployed as a simple and rapid means of functional imaging in both routine and emergency medicine. Here, we focus on the evaluation of pulmonary ventilation and circulation. This review article describes the basic mechanism of imaging findings according to pulmonary/circulation physiology, followed by imaging procedures, analysis method, and diagnostic performance of dynamic chest radiography. read more read less
92 Citations
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Frequently asked questions

1. Can I write Radiological Physics and Technology in LaTeX?

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

2. Do you follow the Radiological Physics and Technology guidelines?

Yes, the template is compliant with the Radiological Physics and Technology 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 Radiological Physics and Technology?

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 Radiological Physics and Technology citation style.

4. Can I use the Radiological Physics and Technology 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 Radiological Physics and Technology.

5. Can I use a manuscript in Radiological Physics and Technology 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 Radiological Physics and Technology that you can download at the end.

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

7. Where can I find the template for the Radiological Physics and Technology?

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 Radiological Physics and Technology'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 Radiological Physics and Technology'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. Radiological Physics and Technology an online tool or is there a desktop version?

SciSpace's Radiological Physics and Technology 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 Radiological Physics and Technology?

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 Radiological Physics and Technology?”

11. What is the output that I would get after using Radiological Physics and Technology?

After writing your paper autoformatting in Radiological Physics and Technology, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Radiological Physics and Technology'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 Radiological Physics and Technology?

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 Radiological Physics and Technology. 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 Radiological Physics and Technology?

The 5 most common citation types in order of usage for Radiological Physics and Technology 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 Radiological Physics and Technology?

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 Radiological Physics and Technology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Radiological Physics and Technology 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 Radiological Physics and Technology Endnote style according to Elsevier guidelines.

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