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Advances in Magnetic Resonance Imaging (MRI)
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Advances in Magnetic Resonance Imaging (MRI)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 4292

Special Issue Editor

Dr. Beom Sun Chung

E-Mail Website
Guest Editor
Department of Anatomy, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
Interests: magnetic resonance imaging; medical imaging; diagnosis; radiology; 3-dimensional modeling; segmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue presents advances in magnetic resonance imaging for clinicians and researchers. Magnetic resonance imaging, along with related technologies, have been continuously improved to provide diagnostic accuracy and various medical applications. Recent advances, including 3-dimensional modeling and segmentation using artificial intelligence, will enhance the utility of magnetic resonance imaging in numerous fields. Interested radiologists, technicians, doctors, and researchers may contribute their novel techniques and findings to enrich their colleagues. Both hardware and software can be the focus for the papers, as long as it adds new knowledge to the field. This Special Issue might provide a forum for academic discussion among the peer researchers.

Dr. Beom Sun Chung
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • magnetic resonance imaging
  • medical imaging
  • diagnosis
  • radiology
  • 3-dimensional modeling
  • segmentation

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Published Papers (3 papers)

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Research

15 pages, 5976 KiB  
Article
Assessing Pancreatic Fat and Its Correlation with Liver Fat in Suspected MASLD Patients Using Advanced Deep Learning Techniques from MRI Images
by Hay Ching Cherrie Fung, Juan Pablo Meneses, Nirusha Surendran, Numan Kutaiba, Yasmeen George, Enes Makalic and Sergio Uribe
Appl. Sci. 2024, 14(24), 11924; https://doi.org/10.3390/app142411924 - 20 Dec 2024
Viewed by 854
Abstract
Pancreatic steatosis and metabolic-dysfunction-associated steatotic liver disease are characterised by fat accumulation in abdominal organs, but their correlation remains inconclusive. Recently proposed deep learning (DL) for proton density fat fraction (PDFF) estimation, which quantifies organ fat, has primarily been assessed for quantifying liver [...] Read more.
Pancreatic steatosis and metabolic-dysfunction-associated steatotic liver disease are characterised by fat accumulation in abdominal organs, but their correlation remains inconclusive. Recently proposed deep learning (DL) for proton density fat fraction (PDFF) estimation, which quantifies organ fat, has primarily been assessed for quantifying liver fat. This study aims to validate DL models for pancreatic PDFF quantification and compare pancreas and liver fat content. We evaluated three DL models—Non-Linear Variables Neural Network (NLV-Net), U-Net, and Multi-Decoder Water-Fat separation Network—against a reference PDFF measured using a graph-cut-based method. NLV-Net showed a strong correlation (Spearman rho) with the reference PDFF in the six-echo pancreatic head (slope: 1.02, rho: 0.95) and body (slope: 1.04, rho: 0.94) and a moderate correlation in the three-echo pancreatic head (slope: 0.44, rho: 0.40) and body (slope: 0.49, rho: 0.34). Weak correlations were found between liver and pancreatic body PDFF using graph cut in six-echo (slope: −0.041, rho: −0.12) and three-echo images (slope: 0.0014, rho: 0.073) and using NLV-Net in six-echo (slope: −0.053, rho: −0.12) and three-echo images (slope: −0.014, rho: −0.033). In conclusion, NLV-Net showed the best agreement with the reference for pancreatic fat quantification, and no correlation was found between liver and pancreas fat. Full article
(This article belongs to the Special Issue Advances in Magnetic Resonance Imaging (MRI))
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15 pages, 6761 KiB  
Article
Reliability of Automated Intracranial Volume Measurements by Synthetic Brain MRI in Children
by Veronika Weiss, Nathan Vishwanathan, Anja Dutschke, Nikolaus Stranger, Mario Scherkl, Eszter Nagy, Andreea Ciornei-Hoffman and Sebastian Tschauner
Appl. Sci. 2024, 14(11), 4751; https://doi.org/10.3390/app14114751 - 31 May 2024
Viewed by 1532
Abstract
(1) Background: Hydrocephalus poses challenges in pediatric neuroimaging, and conventional MRI methods have limitations regarding its accurate quantification. Synthetic MRI (SyMRI) offers a promising automated solution to assess intracranial compartment volumes. However, its clinical utility in pediatric patients remains underexplored. Our study aims [...] Read more.
(1) Background: Hydrocephalus poses challenges in pediatric neuroimaging, and conventional MRI methods have limitations regarding its accurate quantification. Synthetic MRI (SyMRI) offers a promising automated solution to assess intracranial compartment volumes. However, its clinical utility in pediatric patients remains underexplored. Our study aims to assess the accuracy and reliability of automated CSF volume measurements using SyMRI in children and adolescents, comparing them with manual measurements and human expert ratings. (2) Methods: A single-center retrospective study included 124 pediatric patients undergoing cranial MRI with SyMRI. CSF, brain parenchyma, and intracranial volumes were measured using both automated SyMRI and manual methods. Human radiologists assessed hydrocephalus subjectively. (3) Results: Correlations between manual and SyMRI volume evaluations were significant. Human raters demonstrated good agreement on hydrocephalus ratings among themselves (Fleiss’ kappa = 0.66, p < 0.001) but only moderate agreement with the SyMRI method (Cohen’s kappa = 0.45, p < 0.001). SyMRI volumes were systematically tendentially higher in SyMRI (CSF p = 0.005; BPV and ICV p < 0.001). (4) Conclusions: Our findings highlight SyMRI’s reliability in assessing hydrocephalus and intracranial volumes in pediatric cases. Despite some differences from manual measurements, the strong correlation suggests its clinical viability. Full article
(This article belongs to the Special Issue Advances in Magnetic Resonance Imaging (MRI))
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11 pages, 2193 KiB  
Article
The Influence of Late Gadolinium Enhancement Cardiac Magnetic Resonance Image Analysis Imprecision on Myocardial Damage Quantification in Patients with Myocarditis: A Pilot Study
by Lana Kralj, Andreja Cerne Cercek, Alja Gomišček Novak and Borut Kirn
Appl. Sci. 2024, 14(1), 117; https://doi.org/10.3390/app14010117 - 22 Dec 2023
Cited by 1 | Viewed by 1389
Abstract
Background: Myocardial damage in myocarditis is assessed through late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR). Variability in quantifying myocarditis extent results from imprecise image segmentation and inconclusive data on quantification method selection. To improve analysis precision, segmentation steps are systematically ranked based [...] Read more.
Background: Myocardial damage in myocarditis is assessed through late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR). Variability in quantifying myocarditis extent results from imprecise image segmentation and inconclusive data on quantification method selection. To improve analysis precision, segmentation steps are systematically ranked based on their inherent risks of error. Additionally, data on two distinct quantification methods are presented. Methods: Using newly developed software, four experts analyzed five LGE-CMR left ventricular (LV) short-axis (SAx) images of myocarditis patients in three sessions. Regions of interest (ROIs) (myocardial (ROImyoc), reference (ROIref), and exclusion region (ROIexcl)) were identified and used to calculate LGE extent with 3σ (intensity above three standard deviations (σ) in reference) and the full width at half maximum (FWHM) method (intensity above 50% of maximum signal in reference). The reference LGE extent was calculated and the influence of the ROIs on LGE extent variability was determined. Interobserver and intraobserver variability were evaluated as 1-intraclass correlation coefficient (ICC). Results: LGE extent variability was 6.2 ± 0.6% for 3σ and 4.0 ± 0.6% for FWHM. The contributions of ROImyoc, ROIref, and ROIexcl were 1.5 ± 0.2%, 2.7 ± 0.4%, and 2 ± 0.3%, respectively, for 3σ, and 1.1 ± 0.1%, 1.6 ± 0.4%, and 1.3 ± 0.3%, respectively, for FWHM. LGE extent was lower in FWHM. Interobserver variability was 0.56 for 3σ and 0.43 for FWHM. The intraobserver variability was higher for the 3σ method in all four observers. Conclusion: ROIref selection contributed most to LGE extent variability. FWHM yielded lower LGE extent and lower inter- and intraobserver variability. Due to low statistical significance, the findings are only partially confirmed. Full article
(This article belongs to the Special Issue Advances in Magnetic Resonance Imaging (MRI))
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