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J. Imaging
Special Issues
Spatio-Temporal Biomedical Image Analysis
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Spatio-Temporal Biomedical Image Analysis

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 6687

Special Issue Editor

Dr. Ahmed Ashraf

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: deep learning; artificial intelligence; biomedical image analysis

Special Issue Information

Dear Colleagues,

The application of biomedical imaging continues to grow, e.g., in relation to diagnostics, understanding of disease progression, molecular subtyping, to treatment planning, therapy response prediction, treatment prognosis assessment, as well as phenotypic assays of gene expression through imaging genetics. Advanced imaging modalities typically result in a series of multidimensional imaging volumes capturing structural/anatomical and functional aspects, both at the spatial and the temporal level. These modalities include the traditional imaging, such as CT, PET, MRI (i.e., perfusion MRI, dynamic susceptibility contrast (DSC) MRI, dynamic contrast enhanced (DCE) MRI), diffusion weighted imaging (DWI), and diffusion tensor imaging (DTI). This Special Issue of Journal of Imaging invites reports on recent advances in algorithms for analyzing multidimensional spatiotemporal imaging volumes, including both deep-learning-based data-driven methods and medical physics-based methods.

Dr. Ahmed Ashraf
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • machine learning
  • artificial intelligence
  • deep neural networks
  • biomedical imaging
  • spatio-temporal
  • medical physics

Published Papers (2 papers)

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Research

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21 pages, 6664 KiB  
Article
3D Dynamic Spatiotemporal Atlas of the Vocal Tract during Consonant–Vowel Production from 2D Real Time MRI
by Ioannis K. Douros, Yu Xie, Chrysanthi Dourou, Karyna Isaieva, Pierre-André Vuissoz, Jacques Felblinger and Yves Laprie
J. Imaging 2022, 8(9), 227; https://doi.org/10.3390/jimaging8090227 - 25 Aug 2022
Cited by 1 | Viewed by 1773
Abstract
In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps [...] Read more.
In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps of the proposed method are the temporal alignment of the real-time MR images acquired in several sagittal planes and their combination with adaptive kernel regression. As a preprocessing step, a reference space was created to be used in order to remove anatomical information of the speakers and keep only the variability in speech production for the construction of the atlas. The adaptive kernel regression makes the choice of atlas time points independently of the time points of the frames that are used as an input for the construction. The evaluation of this atlas construction method was made by mapping two new speakers to the atlas and by checking how similar the resulting mapped images are. The use of the atlas helps in reducing subject variability. The results show that the use of the proposed atlas can capture the dynamic behavior of the articulators and is able to generalize the speech production process by creating a universal-speaker reference space. Full article
(This article belongs to the Special Issue Spatio-Temporal Biomedical Image Analysis)
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Review

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13 pages, 1893 KiB  
Review
Multimodality Imaging of the Neglected Valve: Role of Echocardiography, Cardiac Magnetic Resonance and Cardiac Computed Tomography in Pulmonary Stenosis and Regurgitation
by Pietro Costantini, Francesco Perone, Agnese Siani, Léon Groenhoff, Giuseppe Muscogiuri, Sandro Sironi, Paolo Marra, Serena Carriero, Anna Giulia Pavon and Marco Guglielmo
J. Imaging 2022, 8(10), 278; https://doi.org/10.3390/jimaging8100278 - 10 Oct 2022
Cited by 9 | Viewed by 4494
Abstract
The pulmonary valve (PV) is the least imaged among the heart valves. However, pulmonary regurgitation (PR) and pulmonary stenosis (PS) can occur in a variety of patients ranging from fetuses, newborns (e.g., tetralogy of Fallot) to adults (e.g., endocarditis, carcinoid syndrome, complications of [...] Read more.
The pulmonary valve (PV) is the least imaged among the heart valves. However, pulmonary regurgitation (PR) and pulmonary stenosis (PS) can occur in a variety of patients ranging from fetuses, newborns (e.g., tetralogy of Fallot) to adults (e.g., endocarditis, carcinoid syndrome, complications of operated tetralogy of Fallot). Due to their complexity, PR and PS are studied using multimodality imaging to assess their mechanism, severity, and hemodynamic consequences. Multimodality imaging is crucial to plan the correct management and to follow up patients with pulmonary valvulopathy. Echocardiography remains the first line methodology to assess patients with PR and PS, but the information obtained with this technique are often integrated with cardiac magnetic resonance (CMR) and computed tomography (CT). This state-of-the-art review aims to provide an updated overview of the usefulness, strengths, and limits of multimodality imaging in patients with PR and PS. Full article
(This article belongs to the Special Issue Spatio-Temporal Biomedical Image Analysis)
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