Advanced Applications of Magnetic Resonance in Biomedical Imaging

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 10047

Special Issue Editors

Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy
Interests: Electromagnetic field simulations and measurements in Magnetic Resonance environment; radiofrequency coil design and simulation for Magnetic Resonance Imaging and Spectroscopy
Fondazione Toscana Gabriele Monasterio, Bioengineering and clinical engineering unit, 56124 Pisa, Italy
Interests: magnetic resonance imaging; magnetic resonance spectroscopy; hyperpolarization; dynamic nuclear polarization; molecular imaging
1. National Institute for Nuclear Physics (INFN), Gran Sasso National Laboratory (LNGS), 67100 L’Aquila, Italy
2. Department of Physical and Chemical Sciences, CNR-SPIN, 67100 L’Aquila, Italy
3. Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
Interests: nuclear magnetic resonance; imaging; RF coils; low field MRI; metamaterials; biomedical applications

Special Issue Information

Dear Colleagues,

Magnetic resonance imaging (MRI) offers a sensitive and non-invasive approach for imaging of the human body as well as animal models. MRI has become one of the most heavily used medical imaging techniques for the diagnosis and follow up of diseases affecting different organs and tissues. Moreover, the application of MRI beyond anatomical imaging has been proven, providing information about metabolic functions and chemical processes via magnetic resonance spectroscopy (MRS) of 1H and other nuclei such as 13C, 19F, 23Na, and 31P.

The recent MR technical developments, especially the increased field strength, improved gradient performance, and advances in radiofrequency technology, including parallel imaging modalities, have allowed an increase of sensitivity and spatial resolution. Moreover, advanced post-processing methods, including deep learning approaches and artificial intelligence algorithms, are booming, providing further opportunities in the acquisition, reconstruction, and interpretation of MRI/MRS data. There is also an increasing interest in the low and ultra-low field MRI regime, which demonstrates performances considered not attainable until a few years ago, opening up new possibilities in multimodal imaging or integration with other therapeutic devices.

We welcome authors to contribute with original or review manuscripts on advanced applications of MR in biomedical imaging and spectroscopy.

Topics of interest include, but are not limited to the following areas:

  • Electromagnetic simulations in MR
  • Design and manufacturing components for MR (magnets, gradient coils, radiofrequency coils)
  • Metamaterials in MR
  • Novel MRI pulse sequences
  • Low and ultra-low field MRI
  • Advanced MR image acquisition and reconstruction techniques
  • Novel magnetic resonance spectroscopy methods and applications
  • Design of MR contrast agents (including hyperpolarization/DNP)
  • Machine learning and deep learning application in MR
  • Ultra-high field MRI

Technical Program Committee Members:

  1. Prof. Marcello Alecci   University of L’Aquila
  2. Dr. Francesca Frijia   Fondazione CNR/Regione Toscana G. Monasterio, Pisa, Italy

Dr. Giulio Giovannetti
Dr. Alessandra Flori
Prof. Dr. Angelo Galante
Guest Editors

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Published Papers (6 papers)

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Research

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12 pages, 3108 KiB  
Article
mICA-Based fMRI Analysis of Specific CO2-Level-Dependent BOLD Signal Changes in the Human Brainstem
by Miriam Basile, Simone Cauzzo, Alejandro Luis Callara, Domenico Montanaro, Valentina Hartwig, Maria Sole Morelli, Francesca Frijia, Alberto Giannoni, Claudio Passino, Michele Emdin and Nicola Vanello
Electronics 2023, 12(2), 290; https://doi.org/10.3390/electronics12020290 - 06 Jan 2023
Viewed by 1134
Abstract
Noninvasive studies of the central respiratory control are of key importance to understanding the physiopathology of central apneas and periodic breathing. The study of the brainstem and cortical-subcortical centers may be achieved by using functional magnetic resonance imaging (fMRI) during gas challenges (hypercapnia). [...] Read more.
Noninvasive studies of the central respiratory control are of key importance to understanding the physiopathology of central apneas and periodic breathing. The study of the brainstem and cortical-subcortical centers may be achieved by using functional magnetic resonance imaging (fMRI) during gas challenges (hypercapnia). Nonetheless, disentangling specific from non-specific effects of hypercapnia in fMRI is a major methodological challenge, as CO2 vasodilatory effects and physiological noise do strongly impact the BOLD signal. This is particularly true in deep brainstem regions where chemoreceptors and rhythm pattern generators are located. One possibility to detect the true neural-related activation is given by the presence of a supralinear relation between CO2 changes and BOLD signal related to neurovascular coupling in overactive neural areas. Here, we test this hypothesis of a supralinear relationship between CO2 and BOLD signal, as a marker of specificity. We employed a group-masked Independent Component Analysis (mICA) approach and we compared activation levels across different mixtures of inspired CO2 using polynomial regression. Our results highlight key nodes of the central breathing control network, also including dorsal pontine and medullary regions. The suggested methodology allows a voxel-wise parametrization of the response, targeting an issue that affects many fMRI studies employing hypercapnic challenges. Full article
(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)
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12 pages, 2915 KiB  
Article
Fat-Corrected Pancreatic R2* Relaxometry from Multi-Echo Gradient-Recalled Echo Sequence Using Convolutional Neural Network
by Maria Filomena Santarelli, Sara Joubbi, Antonella Meloni, Laura Pistoia, Tommaso Casini, Francesco Massei, Pier Paolo Bitti, Massimo Allò, Filippo Cademartiri and Vincenzo Positano
Electronics 2022, 11(18), 2829; https://doi.org/10.3390/electronics11182829 - 07 Sep 2022
Viewed by 1102
Abstract
Fat-corrected R2* relaxometry from multi-echo gradient-recalled echo sequences (mGRE) could represent an efficient approach for iron overload evaluation, but its use is limited by computational constraints. A new method for the fast generation of R2* and fat fractions (FF) maps from [...] Read more.
Fat-corrected R2* relaxometry from multi-echo gradient-recalled echo sequences (mGRE) could represent an efficient approach for iron overload evaluation, but its use is limited by computational constraints. A new method for the fast generation of R2* and fat fractions (FF) maps from mGRE using a convolutional neural network (U-Net) and deep learning (DL) is presented. A U-Net for the calculation of pancreatic R2* and FF maps was trained with 576 mGRE abdominal images and compared to conventional fat-corrected relaxometry. The U-Net was effectively trained and provided R2* and FF maps visually comparable to conventional methods. Predicted pancreatic R2* and FF values were well correlated with the conventional model. Estimated and ground truth mean R2* values were not significantly different (43.65 ± 21.89 vs. 43.77 ± 19.81 ms, p = 0.692, intraclass correlation coefficient-ICC = 0.9938, coefficient of variation-CoV = 5.3%), while estimated FF values were slightly higher in respect to ground truth values (27.8 ± 16.87 vs. 25.67 ± 15.43 %, p < 0.0001, ICC = 0.986, CoV = 10.1%). Deep learning utilizing the U-Net is a feasible method for pancreatic MR fat-corrected relaxometry. A trained U-Net can be efficiently used for MR fat-corrected relaxometry, providing results comparable to conventional model-based methods. Full article
(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)
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13 pages, 6923 KiB  
Article
Fully Automated Regional Analysis of Myocardial T2* Values for Iron Quantification Using Deep Learning
by Nicola Martini, Antonella Meloni, Vincenzo Positano, Daniele Della Latta, Petra Keilberg, Laura Pistoia, Anna Spasiano, Tommaso Casini, Angelica Barone, Antonella Massa, Andrea Ripoli and Filippo Cademartiri
Electronics 2022, 11(17), 2749; https://doi.org/10.3390/electronics11172749 - 01 Sep 2022
Cited by 3 | Viewed by 1344
Abstract
Cardiovascular magnetic resonance (CMR) T2* mapping is the gold standard technique for the assessment of iron overload in the heart. The quantitative analysis of T2* values requires the manual segmentation of T2* images, which is a time-consuming and operator-dependent procedure. This study describes [...] Read more.
Cardiovascular magnetic resonance (CMR) T2* mapping is the gold standard technique for the assessment of iron overload in the heart. The quantitative analysis of T2* values requires the manual segmentation of T2* images, which is a time-consuming and operator-dependent procedure. This study describes a fully-automated method for the regional analysis of myocardial T2* distribution using a deep convolutional neural network (CNN). A CNN with U-Net architecture was trained to segment multi-echo T2*-weighted images in 16 sectors in accordance with the American Heart Association (AHA) model. We used images from 210 patients (three slices, 10 multi-echo images) with iron overload diseases to train and test the CNN. The performance of the proposed method was quantitatively evaluated on an independent holdout test set by comparing the segmentation accuracy of the CNN and the T2* values obtained by the automated method against ground-truth labels provided by two experts. Segmentation metrics and global and regional T2* values assessed by the proposed DL method closely matched those obtained by experts with excellent intraclass correlation in all myocardial sectors of the AHA model (ICC range [0.944, 0.996]). This method could be effectively adopted in the clinical setting for fast and accurate analysis of myocardial T2*. Full article
(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)
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13 pages, 1938 KiB  
Article
A Practical Guide to Estimating Coil Inductance for Magnetic Resonance Applications
by Giulio Giovannetti, Francesca Frijia, Alessandra Flori, Angelo Galante, Carlo Rizza and Marcello Alecci
Electronics 2022, 11(13), 1974; https://doi.org/10.3390/electronics11131974 - 24 Jun 2022
Viewed by 1989
Abstract
Radiofrequency (RF) coils are employed to transmit and/or receive signals in Magnetic Resonance (MR) systems. The design of home-made, organ-specific RF coils with optimized homogeneity and/or Signal-to-Noise Ratio (SNR) can be a plus in many research projects. The first step requires accurate inductance [...] Read more.
Radiofrequency (RF) coils are employed to transmit and/or receive signals in Magnetic Resonance (MR) systems. The design of home-made, organ-specific RF coils with optimized homogeneity and/or Signal-to-Noise Ratio (SNR) can be a plus in many research projects. The first step requires accurate inductance calculation, this depending on the conductor’s geometry, to later define the tuning capacitor necessary to obtain the desired resonance frequency. To fulfil such a need it is very useful to perform a priori inductance estimation rather than relying on the time-consuming trial-and-error approach. This paper describes and compares two different procedures for coil inductance estimation to allow for a fast coil-prototyping process. The first method, based on calculations in the quasi-static approximation, permits an investigation on how the cross-sectional geometry of the RF coil conductors affects the total inductance and can be easily computed for a wide variety of coil geometries. The second approach uses a numerical full-wave method based on the Finite-Difference Time-Domain (FDTD) algorithm, and permits the simulation of RF coils with any complex geometry, including the case of multi-element phased array. Comparison with workbench measurements validates both the analytical and numerical results for RF coils operating within a wide field range (0.18–7 T). Full article
(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)
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Review

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22 pages, 6128 KiB  
Review
How to Use Nested Probes Coupling to Increase the Local NMR/MRI Resolution and Sensitivity for Specific Experiments
by Mihaela Lupu and Joel Mispelter
Electronics 2023, 12(3), 594; https://doi.org/10.3390/electronics12030594 - 25 Jan 2023
Viewed by 1138
Abstract
In this paper, we address resonant systems intended to be used with the commercial main resonator present on all NMR or MRI instruments. The purpose of this approach is to get an improvement regarding the spatial localization and signal to noise ratio provided [...] Read more.
In this paper, we address resonant systems intended to be used with the commercial main resonator present on all NMR or MRI instruments. The purpose of this approach is to get an improvement regarding the spatial localization and signal to noise ratio provided by an additional smaller coil. Both coils are coupled to the same sample region, and thus, are inductively coupled through their common magnetic flux. The coupling strength is characterized by the so-called mutual inductance M. Two practical devices are presented. Firstly, a geometrical passive decoupled resonant system (M = 0) allows getting a sensitive received signal from the maximized nuclear macroscopic magnetization, excited by the main resonator and detected by the smaller sniffer coil. Secondly, a strongly coupled resonant system allows us to considerably locally improve the magnetic component of the RF near field to provide an efficient nuclear spin magnetization excitation and a high received signal. For both configurations, the behavior of the coils system regarding the amplitude of B1 is addressed. Finally, specific technical hints to achieve optimum energy transfer (impedance matching) are discussed, taking into account the non-ideal RF characteristics of the involved components. Examples of MRI experiments, as well as workbench evaluations and simulations support the principles exposed here. Full article
(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)
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Other

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8 pages, 888 KiB  
Technical Note
Assessment of Exposure to Time-Varying Magnetic Fields in Magnetic Resonance Environments Using Pocket Dosimeters
by Giuseppe Acri, Carmelo Anfuso, Giuseppe Vermiglio and Valentina Hartwig
Electronics 2022, 11(17), 2796; https://doi.org/10.3390/electronics11172796 - 05 Sep 2022
Cited by 2 | Viewed by 1365
Abstract
Staff working in Magnetic Resonance environments are mainly exposed to the static and spatially heterogeneous magnetic field. Moreover, workers movements in such environments give slowly time-varying magnetic field that reflects in an induced electric field in conductive bodies, such as human bodies. It [...] Read more.
Staff working in Magnetic Resonance environments are mainly exposed to the static and spatially heterogeneous magnetic field. Moreover, workers movements in such environments give slowly time-varying magnetic field that reflects in an induced electric field in conductive bodies, such as human bodies. It is very important to have a practice method to personal exposure assessment, also to create a list of procedures and job descriptions at highest risk of exposure, to provide complete information for the workers. This is important especially for the “workers at particular risk”, such as pregnant workers or medical devices wearers. The purpose of this work is to measure the exposure of the staff to time-varying magnetic field in Magnetic Resonance clinical environments, using pocket dosimeter. We present here the assessment of exposure in two different working conditions relative to routine procedures for different kinds of workers. The obtained results show compliance with the safety limits imposed by regulation for controlled exposure conditions. However, during the activity of replacement of the oxygen sensor performed by a maintenance technician, some exposure parameters exceeded the limits, suggesting to pay attention with specific conditions to prevent vertigo or other sensory effects. Full article
(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)
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