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Advanced Techniques in Body Magnetic Resonance Imaging

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 44370

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Special Issue Editors

Dr. Kyung Sung

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Guest Editor

Radiological Sciences, Bioengineering, and Physics & Biology in Medicine, Magnetic Resonance Research Labs, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
Interests: magnetic resonance imaging (MRI); deep learning algorithms; image analysis techniques; early diagnosis; treatment guidance; therapeutic response assessment; oncology; cardiology; biologic markers
Special Issues, Collections and Topics in MDPI journals

Dr. Harrison Kim

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Guest Editor

Radiology, Biomedical Engineering, O’Neal Comprehensive Cancer Center, Cystic Fibrosis Center, Hepatorenal Fibrocystic Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA
Interests: diffusion and perfusion magnetic resonance imaging; abdominal cancers; data standardization and quantification
Special Issues, Collections and Topics in MDPI journals

Dr. Fatemeh Zabihollahy

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Guest Editor

Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
Interests: development of machine kearning and deep learning-based algorithms for medical image analysis; characterization of myocardial fibrosis tissue; automated detection and evaluation of abdominal cancers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Recent years have seen rapid growth in the interest in advanced MRI techniques of the body. Body MRI is promising in this respect due to its non-invasive nature and potential for the early detection of abnormal tissue changes. Since morphological changes in body disease tend to be subtle, the ultimate utility of body MRI will depend on the ability to derive reliable quantitative MRI biomarkers. In addition, the mining of MRI images to derive quantitative signatures based on large feature sets (radiomics) as a distinct approach has been a primary research endeavor in recent years. Our aim with this Special Issue is to recognize the scientific underpinnings behind methods utilized in deep learning, radiomics, perfusion MRI, diffusion MRI, and image reconstruction as applied to body imaging applications, including breast, liver, prostate, pelvic, and interventional MRI. This Special Issue is also dedicated to describing applications of the advanced MRI techniques to fundamental anatomic, physiologic, and pathophysiologic studies involving animals and humans. We invite submissions of original research in any area of renal MRI biomarker research, including:

Machine learning and deep learning;
Radiomics;
Perfusion and diffusion imaging;
Fast MRI technique.

Dr. Kyung Sung
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

Body magnetic resonance imaging
Artificial intelligence
Deep learning and machine learning
Imaging reconstruction
Image analysis and modeling

Published Papers (12 papers)

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Research

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13 pages, 1197 KiB

Open AccessArticle

Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer

by Benedetta Gui, Rosa Autorino, Maura Miccò, Alessia Nardangeli, Adele Pesce, Jacopo Lenkowicz, Davide Cusumano, Luca Russo, Salvatore Persiani, Luca Boldrini, Nicola Dinapoli, Gabriella Macchia, Giuseppina Sallustio, Maria Antonietta Gambacorta, Gabriella Ferrandina, Riccardo Manfredi, Vincenzo Valentini and Giovanni Scambia

Diagnostics 2021, 11(4), 631; https://doi.org/10.3390/diagnostics11040631 - 31 Mar 2021

Cited by 15 | Viewed by 2312

Abstract

The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR―assessed on surgical specimen―was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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15 pages, 1935 KiB

Open AccessArticle

Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping

by Yinzhe Wu, Suzan Hatipoglu, Diego Alonso-Álvarez, Peter Gatehouse, Binghuan Li, Yikai Gao, David Firmin, Jennifer Keegan and Guang Yang

Diagnostics 2021, 11(2), 346; https://doi.org/10.3390/diagnostics11020346 - 19 Feb 2021

Cited by 26 | Viewed by 3521

Abstract

Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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15 pages, 16218 KiB

Open AccessArticle

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

by Jun Lv, Chengyan Wang and Guang Yang

Diagnostics 2021, 11(1), 61; https://doi.org/10.3390/diagnostics11010061 - 02 Jan 2021

Cited by 34 | Viewed by 4438

Abstract

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in

\times 10^{- 5}

) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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13 pages, 2987 KiB

Open AccessArticle

Hydrophilic Biocompatible Poly(Acrylic Acid-co-Maleic Acid) Polymer as a Surface-Coating Ligand of Ultrasmall Gd₂O₃ Nanoparticles to Obtain a High r₁ Value and T₁ MR Images

by Yeong-Ji Jang, Shuwen Liu, Huan Yue, Ji Ae Park, Hyunsil Cha, Son Long Ho, Shanti Marasini, Adibehalsadat Ghazanfari, Mohammad Yaseen Ahmad, Xu Miao, Tirusew Tegafaw, Kwon-Seok Chae, Yongmin Chang and Gang Ho Lee

Diagnostics 2021, 11(1), 2; https://doi.org/10.3390/diagnostics11010002 - 22 Dec 2020

Cited by 19 | Viewed by 3250

Abstract

The water proton spin relaxivity, colloidal stability, and biocompatibility of nanoparticle-based magnetic resonance imaging (MRI) contrast agents depend on the surface-coating ligands. Here, poly(acrylic acid-co-maleic acid) (PAAMA) (M_w = ~3000 amu) is explored as a surface-coating ligand of ultrasmall gadolinium oxide (Gd₂O₃) nanoparticles. Owing to the numerous carboxylic groups in PAAMA, which allow its strong conjugation with the nanoparticle surfaces and the attraction of abundant water molecules to the nanoparticles, the synthesized PAAMA-coated ultrasmall Gd₂O₃ nanoparticles (d_avg = 1.8 nm and a_avg = 9.0 nm) exhibit excellent colloidal stability, extremely low cellular toxicity, and a high longitudinal water proton spin relaxivity (r₁) of 40.6 s⁻¹mM⁻¹ (r₂/r₁ = 1.56, where r₂ = transverse water proton spin relaxivity), which is approximately 10 times higher than those of commercial molecular contrast agents. The effectiveness of PAAMA-coated ultrasmall Gd₂O₃ nanoparticles as a T₁ MRI contrast agent is confirmed by the high positive contrast enhancements of the in vivo T₁ MR images at the 3.0 T MR field. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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24 pages, 9704 KiB

Open AccessArticle

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

by Marek Piorecky, Vlastimil Koudelka, Eva Miletinova, Jitka Buskova, Jan Strobl, Jiri Horacek, Martin Brunovsky, Stanislav Jiricek, Jaroslav Hlinka, David Tomecek and Vaclava Piorecka

Diagnostics 2020, 10(12), 1087; https://doi.org/10.3390/diagnostics10121087 - 14 Dec 2020

Cited by 6 | Viewed by 3329

Abstract

Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2–4% in adults, DOAs are significant disorders that are currently gaining attention among [...] Read more.

n = 10

). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG–fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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10 pages, 2201 KiB

Open AccessArticle

Characterization of Uterine Motion in Early Gestation Using MRI-Based Motion Tracking

by Thomas Martin, Carla Janzen, Xinzhou Li, Irish Del Rosario, Teresa Chanlaw, Sarah Choi, Tess Armstrong, Rinat Masamed, Holden H. Wu, Sherin U. Devaskar and Kyunghyun Sung

Diagnostics 2020, 10(10), 840; https://doi.org/10.3390/diagnostics10100840 - 19 Oct 2020

Cited by 6 | Viewed by 1996

Abstract

Magnetic resonance imaging (MRI) is a promising non-invasive imaging technique that can be safely used to study placental development and function. However, studies of the human placenta performed by MRI are limited by uterine motion and motion in the uterus during MRI remains one of the major limiting factors. Here, we aimed to investigate the characterization of uterine activity during MRI in the second trimester of pregnancy using MRI-based motion tracking. In total, 46 pregnant women were scanned twice (first scan between 14 and 18 weeks and second scan between 19 and 24 weeks), and 20 pregnant subjects underwent a single MRI between 14 and 18 weeks GA, resulting in 112 MRI scans. An MRI-based algorithm was used to track uterine motion in the superior-inferior and left-right directions. Uterine contraction and maternal motion cases were separated by the experts, and unpaired Wilcoxon tests were performed within the groups of gestational age (GA), fetal sex, and placental location in terms of the overall intensity measures of the uterine activity. In total, 22.3% of cases had uterine contraction during MRI, which increased from 18.6% at 14–18 weeks to 26.4% at 19–24 weeks GA. The dominant direction of the uterine contraction and maternal motion was the superior to the inferior direction during early gestation. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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12 pages, 1422 KiB

Open AccessArticle

Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

by Luu-Ngoc Do, Byung Hyun Baek, Seul Kee Kim, Hyung-Jeong Yang, Ilwoo Park and Woong Yoon

Diagnostics 2020, 10(10), 803; https://doi.org/10.3390/diagnostics10100803 - 09 Oct 2020

Cited by 28 | Viewed by 4552

Abstract

The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) using diffusion-weighted imaging (DWI) in acute stroke patients. Three hundred and ninety DWI datasets with acute anterior circulation stroke were included. A classifier algorithm utilizing a recurrent residual convolutional neural network (RRCNN) was developed for classification between low (1–6) and high (7–10) DWI-ASPECTS groups. The model performance was compared with a pre-trained VGG16, Inception V3, and a 3D convolutional neural network (3DCNN). The proposed RRCNN model demonstrated higher performance than the pre-trained models and 3DCNN with an accuracy of 87.3%, AUC of 0.941, and F1-score of 0.888 for classification between the low and high DWI-ASPECTS groups. These results suggest that the deep learning algorithm developed in this study can provide a rapid assessment of DWI-ASPECTS and may serve as an ancillary tool that can assist physicians in making urgent clinical decisions. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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9 pages, 620 KiB

Open AccessArticle

Real-World Experience Measurement of Liver Iron Concentration by R2 vs. R2 Star MRI in Hemoglobinopathies

by Riad Abou Zahr, Barbara E. U. Burkhardt, Lubaina Ehsan, Amanda Potersnak, Gerald Greil, Jeanne Dillenbeck, Zora Rogers and Tarique Hussain

Diagnostics 2020, 10(10), 768; https://doi.org/10.3390/diagnostics10100768 - 29 Sep 2020

Cited by 4 | Viewed by 2418

Abstract

Background: Non-invasive determination of liver iron concentration (LIC) is a valuable tool that guides iron chelation therapy in transfusion-dependent patients. Multiple methods have been utilized to measure LIC by MRI. The purpose of this study was to compare free breathing R2* (1/T2*) to whole-liver Ferriscan R2 method for estimation of LIC in a pediatric and young adult population who predominantly have hemoglobinopathies. Methods: Clinical liver and cardiac MRI scans from April 2016 to May 2018 on a Phillips 1.5 T scanner were reviewed. Free breathing T2 and T2* weighted images were acquired on each patient. For T2, multi-slice spin echo sequences were obtained. For T2*, a single mid-liver slice fast gradient echo was performed starting at 0.6 ms with 1.2 ms increments with signal averaging. R2 measurements were performed by Ferriscan analysis. R2* measurements were performed by quantitative T2* map analysis. Results: 107 patients underwent liver scans with the following diagnoses: 76 sickle cell anemia, 20 Thalassemia, 9 malignancies and 2 Blackfan Diamond anemia. Mean age was 12.5 ± 4.5 years. Average scan time for R2 sequences was 10 min, while R2* sequence time was 20 s. R2* estimation of LIC correlated closely with R2 with a correlation coefficient of 0.94. Agreement was strongest for LIC < 15 mg Fe/g dry weight. Overall bias from Bland–Altman plot was 0.66 with a standard deviation of 2.8 and 95% limits of agreement −4.8 to 6.1. Conclusion: LIC estimation by R2* correlates well with R2-Ferriscan in the pediatric age group. Due to the very short scan time of R2*, it allows imaging without sedation or anesthesia. Cardiac involvement was uncommon in this cohort. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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14 pages, 4788 KiB

Open AccessArticle

Optimized Breath-Hold Compressed-Sensing 3D MR Cholangiopancreatography at 3T: Image Quality Analysis and Clinical Feasibility Assessment

by Ji Soo Song, Seung Hun Kim, Bernd Kuehn and Mun Young Paek

Diagnostics 2020, 10(6), 376; https://doi.org/10.3390/diagnostics10060376 - 05 Jun 2020

Cited by 12 | Viewed by 2824

Abstract

Magnetic resonance cholangiopancreatography (MRCP) has been widely used in clinical practice, and recently developed compressed-sensing accelerated MRCP (CS-MRCP) has shown great potential in shortening the acquisition time. The purpose of this prospective study was to evaluate the clinical feasibility and image quality of optimized breath-hold CS-MRCP (BH-CS-MRCP) and conventional navigator-triggered MRCP. Data from 124 consecutive patients with suspected pancreaticobiliary diseases were analyzed by two radiologists using a five-point Likert-type scale. Communication between a cyst and the pancreatic duct (PD) was analyzed. Signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast ratio between the CBD and periductal tissue, and contrast-to-noise ratio (CNR) of the CBD and liver were measured. Optimized BH-CS-MRCP showed significantly fewer artifacts with better background suppression and overall image quality. Optimized BH-CS-MRCP demonstrated communication between a cyst and the PD better than conventional MRCP (96.7% vs. 76.7%, p = 0.048). SNR, contrast ratio, and CNR were significantly higher with optimized BH-CS-MRCP (p < 0.001). Optimized BH-CS-MRCP showed comparable or even better image quality than conventional MRCP, with improved visualization of communication between a cyst and the PD. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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13 pages, 1562 KiB

Open AccessArticle

Novel Diagnostic Options without Contrast Media or Radiation: Triggered Angiography Non-Contrast-Enhanced Sequence Magnetic Resonance Imaging in Treating Different Leg Venous Diseases

by Chien-Wei Chen, Yuan-Hsi Tseng, Chien-Chiao Lin, Chih-Chen Kao, Min Yi Wong, Bor-Shyh Lin and Yao-Kuang Huang

Diagnostics 2020, 10(6), 355; https://doi.org/10.3390/diagnostics10060355 - 29 May 2020

Cited by 10 | Viewed by 4045

Abstract

Objectives: Venous diseases in the lower extremities long lacked an objective diagnostic tool prior to the advent of the triggered angiography non-contrast-enhanced (TRANCE) technique. Methods: An observational study with retrospective data analysis. Materials: Between April 2017 and June 2019, 66 patients were evaluated for venous diseases through TRANCE-magnetic resonance imaging (MRI) and were grouped according to whether they had occlusive venous (OV) disease, a static venous ulcer (SU), or symptomatic varicose veins (VV). The clinical appliance of TRANCE-MRI was analysed by groups. Results: In total, 63 patients completed the study. TRANCE-MRI could identify venous thrombosis, including that of the abdominal and pelvic vessels, and it enabled the timely treatment of underlying diseases in patients with OV disease. TRANCE-MRI was statistically compared with the duplex scan, the gold standard to exclude deep vein thrombosis (DVT) in the legs, with regard to their abilities to detect venous thrombosis by using Cohen’s kappa coefficient at a compatible value of 0.711. It could provide the occlusion degree of the peripheral artery for treating an SU. Finally, TRANCE-MRI can be used to outline all collateral veins and occult thrombi before treating symptomatic or recurrent VV to ensure a perfect surgical plan and to avoid complications. Conclusions: TRANCE-MRI is an innovative tool in the treatment of versatile venous pathology in the lower extremities and is widely used for vascular diseases in our institution. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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Review

Jump to: Research

37 pages, 1232 KiB

Open AccessReview

Electrical Properties Tomography: A Methodological Review

by Reijer Leijsen, Wyger Brink, Cornelis van den Berg, Andrew Webb and Rob Remis

Diagnostics 2021, 11(2), 176; https://doi.org/10.3390/diagnostics11020176 - 26 Jan 2021

Cited by 24 | Viewed by 3976

Abstract

Electrical properties tomography (EPT) is an imaging method that uses a magnetic resonance (MR) system to non-invasively determine the spatial distribution of the conductivity and permittivity of the imaged object. This manuscript starts by providing clear definitions about the data required for, and acquired in, EPT, followed by comprehensively formulating the physical equations underlying a large number of analytical EPT techniques. This thorough mathematical overview of EPT harmonizes several EPT techniques in a single type of formulation and gives insight into how they act on the data and what their data requirements are. Furthermore, the review describes machine learning-based algorithms. Matlab code of several differential and iterative integral methods is available upon request. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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19 pages, 1258 KiB

Open AccessReview

Recent Progress in Birdcage RF Coil Technology for MRI System

by Sheikh Faisal Ahmad, Young Cheol Kim, Ick Chang Choi and Hyun Deok Kim

Diagnostics 2020, 10(12), 1017; https://doi.org/10.3390/diagnostics10121017 - 27 Nov 2020

Cited by 20 | Viewed by 6628

Abstract

The radio frequency (RF) coil is one of the key components of the magnetic resonance imaging (MRI) system. It has a significant impact on the performance of the nuclear magnetic resonance (NMR) detection. Among numerous practical designs of RF coils for NMR imaging, the birdcage RF coil is the most popular choice from low field to ultra-high field MRI systems. In the transmission mode, it can establish a strong and homogeneous transverse magnetic field B1 for any element at its Larmor frequency. Similarly, in the reception mode, it exhibits extremely high sensitivity for the detection of even faint NMR signals from the volume of interest. Despite the sophisticated 3D structure of the birdcage coil, the developments in the design, analysis, and implementation technologies during the past decade have rendered the development of the birdcage coils quite reasonable. This article provides a detailed review of the recent progress in the birdcage RF coil technology for the MRI system. Full article

(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)

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