Novel MRI Techniques and Biomedical Image Processing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 7270

Special Issue Editor

Department of Bioengineering, University of California Riverside, Riverside, CA 92521, USA,
Interests: MRI; perfusion imaging; arterial spin labeling; machine learning; image processing

Special Issue Information

Dear Colleagues,

Since the first picture of Magnetic Resonance Imaging published in 1973 by Lauterbur, 50 years have passed witnessing numerous important technical breakthroughs made possible by pioneers and researchers in this field. MRI has been widely used in various research and clinical applications, and is continuing to advance thanks to the effort from researchers and clinicians. At the same time, more advanced analyzing tools have become available and been adopted by research communities to help better understand and interpret the ever-increasing amount of image data.

This Special Issue on Novel MRI Techniques and Biomedical Image Processing welcomes original research papers and comprehensive reviews with a focus on two important aspects in biomedical imaging: 1) MR image generation; and 2) image processing. The image generation line includes, but is not limited to, novel MRI contrast mechanisms, acquisition and reconstruction methods; while the image processing includes processing and understanding image data obtained from a wide range of imaging modalities, such as CT, nuclear medicine, optical imaging, etc. One particular area of interest is the application of machine (deep) learning-based methods in MR image generation and biomedical image processing in general.

Dr. Jia Guo
Guest Editor

Manuscript Submission Information

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Published Papers (6 papers)

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Research

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16 pages, 4118 KiB  
Article
Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network
by Heejoo Lim, Yoonji Joo, Eunji Ha, Yumi Song, Sujung Yoon and Taehoon Shin
Bioengineering 2024, 11(3), 265; https://doi.org/10.3390/bioengineering11030265 - 08 Mar 2024
Viewed by 614
Abstract
Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this [...] Read more.
Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson’s correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing)
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18 pages, 13118 KiB  
Article
Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging
by Wei Xu, Sen Jia, Zhuo-Xu Cui, Qingyong Zhu, Xin Liu, Dong Liang and Jing Cheng
Bioengineering 2023, 10(9), 1107; https://doi.org/10.3390/bioengineering10091107 - 21 Sep 2023
Viewed by 1184
Abstract
Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of [...] Read more.
Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing)
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14 pages, 2538 KiB  
Article
MRI-Based Deep Learning Method for Classification of IDH Mutation Status
by Chandan Ganesh Bangalore Yogananda, Benjamin C. Wagner, Nghi C. D. Truong, James M. Holcomb, Divya D. Reddy, Niloufar Saadat, Kimmo J. Hatanpaa, Toral R. Patel, Baowei Fei, Matthew D. Lee, Rajan Jain, Richard J. Bruce, Marco C. Pinho, Ananth J. Madhuranthakam and Joseph A. Maldjian
Bioengineering 2023, 10(9), 1045; https://doi.org/10.3390/bioengineering10091045 - 05 Sep 2023
Cited by 1 | Viewed by 1521
Abstract
Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI [...] Read more.
Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin–Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing)
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32 pages, 12976 KiB  
Article
A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique
by Saad M. Darwish, Lina J. Abu Shaheen and Adel A. Elzoghabi
Bioengineering 2023, 10(7), 819; https://doi.org/10.3390/bioengineering10070819 - 09 Jul 2023
Viewed by 1517
Abstract
Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that [...] Read more.
Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing)
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Review

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27 pages, 15749 KiB  
Review
Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review
by Anmol Monga, Dilbag Singh, Hector L. de Moura, Xiaoxia Zhang, Marcelo V. W. Zibetti and Ravinder R. Regatte
Bioengineering 2024, 11(3), 236; https://doi.org/10.3390/bioengineering11030236 - 28 Feb 2024
Viewed by 741
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including [...] Read more.
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing)
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Other

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10 pages, 2196 KiB  
Brief Report
Bi-Exponential 3D UTE-T1ρ Relaxation Mapping of Ex Vivo Human Knee Patellar Tendon at 3T
by Bhavsimran Singh Malhi, Dina Moazamian, Soo Hyun Shin, Jiyo S. Athertya, Livia Silva, Saeed Jerban, Hyungseok Jang, Eric Chang, Yajun Ma, Michael Carl and Jiang Du
Bioengineering 2024, 11(1), 66; https://doi.org/10.3390/bioengineering11010066 - 09 Jan 2024
Cited by 1 | Viewed by 768
Abstract
Introduction: The objective of this study was to assess the bi-exponential relaxation times and fractions of the short and long components of the human patellar tendon ex vivo using three-dimensional ultrashort echo time T1ρ (3D UTE-T1ρ) imaging. Materials and Methods: Five [...] Read more.
Introduction: The objective of this study was to assess the bi-exponential relaxation times and fractions of the short and long components of the human patellar tendon ex vivo using three-dimensional ultrashort echo time T1ρ (3D UTE-T1ρ) imaging. Materials and Methods: Five cadaveric human knee specimens were scanned using a 3D UTE-T1ρ imaging sequence on a 3T MR scanner. A series of 3D UTE-T1ρ images were acquired and fitted using single-component and bi-component models. Single-component exponential fitting was performed to measure the UTE-T1ρ value of the patellar tendon. Bi-component analysis was performed to measure the short and long UTE-T1ρ values and fractions. Results: The single-component analysis showed a mean single-component UTE-T1ρ value of 8.4 ± 1.7 ms for the five knee patellar tendon samples. Improved fitting was achieved with bi-component analysis, which showed a mean short UTE-T1ρ value of 5.5 ± 0.8 ms with a fraction of 77.6 ± 4.8%, and a mean long UTE-T1ρ value of 27.4 ± 3.8 ms with a fraction of 22.4 ± 4.8%. Conclusion: The 3D UTE-T1ρ sequence can detect the single- and bi-exponential decay in the patellar tendon. Bi-component fitting was superior to single-component fitting. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing)
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