Journal of Biomedical Graphics and Computing
https://www.sciedu.ca/journal/index.php/jbgc
<img style="float: right; padding: 10px;" src="/journal/public/site/images/jbgc/jbgc.jpg" alt="" /><strong>>> The journal is under re-structure, and will not accept new submissions. Thanks. </strong><br /><p>Journal of Biomedical Graphics and Computing (PRINT ISSN 1925-4008, ONLINE ISSN 1925-4016) is a peer-reviewed international scientific and open access journal published by Sciedu Press. JBGC's primary goal is to publish advances in Medical/Diagnostic imaging, intervention, and development of imaging, graphics and computing in biomedical areas. It is published in both online and printed versions.</p><p><strong>JBGC is included in:</strong></p><ul><li>EBSCOhost</li><li><a href="https://scholar.google.com/citations?user=VaVWavkAAAAJ&hl=en">Google Scholar Citations</a></li><li>Lockss</li><li><a href="https://www.ncbi.nlm.nih.gov/nlmcatalog/101585682">NLM Catalog</a> (ID: 101585682) </li><li>PKP Open Archives Harvester</li><li>ProQuest</li><li><a href="https://www.ncbi.nlm.nih.gov/pubmed?term=%22Journal+of+biomedical+graphics+and+computing%22%5BJournal%3A__jrid40352%5D&cmd=detailssearch">PubMed</a>: Selected citations only</li><li>SHERPA/RoMEO</li><li>The Standard Periodical Directory</li></ul><div><strong>Areas include but are not limited to:</strong></div><ul><li>Medical/Diagnostic Imaging</li><li>Magnetic Resonance Imaging (MRI)</li><li>Radiology</li><li>Computer-aided Diagnosis</li><li><div>Ultrasonic Imaging</div></li><li>Augmented-reality Medical Visualization</li><li>Molecular Imaging</li><li>Nuclear Medicine Imaging (PET, etc.)</li><li>Genomics Imaging</li><li>Image-guided Therapy</li><li>Confocal and Multiphoton Microscopy Imaging</li><li>Optical Micro Endoscope Imaging</li><li>Photoacoustic Imaging</li><li>Infrared Radiation</li><li>Scanning Techniques (CT, X ray, etc.)</li></ul><p>To facilitate rapid publication and minimize administrative costs, The Journal accepts <a href="/journal/index.php/jbgc/about/submissions"><strong>Online submission</strong></a> and <strong><a href="mailto:jbgc@sciedupress.com">Email submission</a></strong>. All manuscripts and any supplementary material can be submitted via the journal’s Online Submission and peer-review system or email to <strong><a href="mailto:jbgc@sciedupress.com">jbgc@sciedupress.com</a></strong>. For online submission, please register and then follow the instructions given.</p><p><strong>Sections</strong></p><p>Original Research, Reviews, Case Reports, Case Studies.</p>Sciedu Pressen-USJournal of Biomedical Graphics and Computing1925-4008Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication. <br />Copyrights for articles published in our journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.Automated segmentation of cardiac adipose tissue in Dixon magnetic resonance images
https://www.sciedu.ca/journal/index.php/jbgc/article/view/12413
<p><strong>Objective: </strong>Increasing evidence suggests a strong link between excess cardiac adipose tissue (CAT) and the risk of a cardiovascular event. Multi-echo Dixon magnetic resonance imaging (MRI), providing fat-only and water-only images, is a useful tool for quantification but requires the segmentation of CAT from a large number of images. The intent of this study was to evaluate an automated technique for CAT segmentation from Dixon MRI by comparing the contours identified by the automated algorithm to those manually traced by an observer. </p><p><strong>Methods: </strong>An automated segmentation algorithm, based on optimal thresholds and custom morphological processing, was applied to the registered fat-only and water-only images to identify CAT in the volume scans. CAT contours in 446 images, from 10 MRI scans, were selected for validation analysis. Cross-sectional area (CSA) and volume were computed and compared using Bland-Altman analysis. In addition, Hausdorff distance and Dice Similarity Coefficient (DSC) were used for assessment.</p><p><strong>Results: </strong>Linear regression analysis yielded correlation of <em>R</em><sup>2</sup> = 0.381 for CSA and <em>R</em><sup>2</sup> = 0.879 for volume. When compared to the observer, the computer algorithm under-estimated CSA by 27.5 ± 40.0% and volume by 26.4 ± 10.4%. The average bidirectional Hausdorff distance was 26.2 ± 16.0 mm while the average unidirectional Hausdorff distances were 24.5 ± 15.7 mm and 12.4 ± 11.7 mm. The average DSC was 0.561 ± 0.100. The time required for manual tracing was 15.84 ± 3.73 min and the time required for the computer algorithm was 2.81 ± 0.12 min.</p><p><strong>Conclusions: </strong>This study provided a technique, faster and less tedious than manual tracing (<em>p</em> < 0.00001), for quantification of CAT in Dixon MRI data, demonstrating feasibility of this approach for cardiac risk stratification.</p>Jon D. KlingensmithAddison L. ElliottMaria Fernandez-del-ValleSunanda Mitra
Copyright (c) 2017 Jon D. Klingensmith, Addison L. Elliott, Maria Fernandez-del-Valle, Sunanda Mitra
http://creativecommons.org/licenses/by/4.0
2017-12-122017-12-1281110.5430/jbgc.v8n1p1Three-dimensional modeling and assessment of cardiac adipose tissue distribution.
https://www.sciedu.ca/journal/index.php/jbgc/article/view/12661
<p><strong>Objective: </strong>The layer of fat that accumulates around the heart, called cardiac adipose tissue (CAT), can influence the development of coronary disease and is indicative of cardiovascular risk. While volumetric assessment of magnetic resonance imaging (MRI) can quantify CAT, volume alone gives no information about its distribution across the myocardial surface, which may be an important factor in risk assessment. In this study, a three-dimensional (3D) modeling technique is developed and used to quantify the distribution of the CAT across the surface of the heart.</p><p><strong>Methods:</strong> Dixon MRI scans, which produce a registered 3D set of fat-only and water-only images, were acquired in 10 subjects for a study on exercise intervention. A previously developed segmentation algorithm was used to identify the heart and CAT. Extracted contours were used to build 3D models. Procrustes analysis was used to register the heart models and an iterative closest point algorithm was used to register and align the CAT models for calculation of CAT thickness. Rays were cast in directions specified by a spherical parameterization of elevation and azimuthal angles, and intersections of the ray with the CAT surface were used to calculate the thickness at each location. To evaluate the effects of the spherical parameterization on the thickness estimates, a set of synthetic models were created with increasing major-to-minor axis ratios.</p><p><strong>Results:</strong> Based on the validation in the synthetic models, the average error in CAT thickness ranged from 1.25% to 17.3% for increasing major-to-minor axis ratio.<strong></strong></p><p><strong>Conclusions:</strong> A process was developed, based on Dixon MRI data, to provide 3D models of the myocardial surface and the cardiac fat. The models can be used in future segmentation algorithm development and for studies on changes in cardiac fat as a result of various interventions.</p>Jon D. KlingensmithSaygin SopMete NazMaria Fernandez-del-ValleH. Felix Lee
Copyright (c) 2018 Jon D. Klingensmith, Saygin Sop, Mete Naz, Maria Fernandez-del-Valle, H. Felix Lee
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2018-01-142018-01-14811410.5430/jbgc.v8n1p14Analysis of the specific vibration modes of goethite (α-FeOOH) by terahertz spectroscopy and calculations of the vibration frequencies of a single molecule using density functional theory
https://www.sciedu.ca/journal/index.php/jbgc/article/view/13097
<p class="Text"><span lang="EN-US">Steel sheet with an insulator to prevent corrosion is used for various purposes including in building and car manufacture. Terahertz waves, for which insulators are highly permeable and metal surfaces are highly reflective, have been studied in order to establish a new inspection technology for these steel plates. In our previous research, spectroscopic measurements in the 1.0-4.0 THz range, generated by a GaP crystal, were carried out in order to collect information on the infrared activity of the metal corrosion products formed on Zn-Al hot-dip galvanized steel sheet. In the previous work, the infrared activity of Fe-based corrosion products was not examined. To examine these products, we conducted THz spectroscopy on goethite (α-FeOOH) in the range from 8.4 to 11.0 THz, generated by a GaSe crystal. The results of Attenuated Total Reflectance (ATR) FTIR spectral measurements and molecular vibration calculations were analyzed, on the basis of which the natural vibration modes of α-FeOOH in the THz frequency range were assigned.</span></p>Ryo HasegawaTakashi KimuraTadao TanabeKatsuhiro NishiharaAkira TaniyamaYutaka Oyama
Copyright (c) 2018 Ryo Hasegawa, Takashi Kimura, Tadao Tanabe, Katsuhiro Nishihara, Akira Taniyama, Yutaka Oyama
http://creativecommons.org/licenses/by/4.0
2018-02-182018-02-18812910.5430/jbgc.v8n1p29