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Tian R, Uecker M, Davids M, Thielscher A, Buckenmaier K, Holder O, Steffen T, Scheffler K. Accelerated 2D Cartesian MRI with an 8-channel local B 0 coil array combined with parallel imaging. Magn Reson Med 2024; 91:443-465. [PMID: 37867407 DOI: 10.1002/mrm.29799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/26/2023] [Accepted: 06/30/2023] [Indexed: 10/24/2023]
Abstract
PURPOSE In MRI, the magnetization of nuclear spins is spatially encoded with linear gradients and radiofrequency receivers sensitivity profiles to produce images, which inherently leads to a long scan time. Cartesian MRI, as widely adopted for clinical scans, can be accelerated with parallel imaging and rapid magnetic field modulation during signal readout. Here, by using an 8-channel localB 0 $$ {\mathrm{B}}_0 $$ coil array, the modulation scheme optimized for sampling efficiency is investigated to speed up 2D Cartesian scans. THEORY AND METHODS An 8-channel localB 0 $$ {\mathrm{B}}_0 $$ coil array is made to carry sinusoidal currents during signal readout to accelerate 2D Cartesian scans. An MRI sampling theory based on reproducing kernel Hilbert space is exploited to visualize the efficiency of nonlinear encoding in arbitrary sampling duration. A field calibration method using current monitors for localB 0 $$ {\mathrm{B}}_0 $$ coils and the ESPIRiT algorithm is proposed to facilitate image reconstruction. Image acceleration with various modulation field shapes, aliasing control, and distinct modulation frequencies are scrutinized to find an optimized modulation scheme. A safety evaluation is conducted. In vivo 2D Cartesian scans are accelerated by the localB 0 $$ {\mathrm{B}}_0 $$ coils. RESULTS For 2D Cartesian MRI, the optimal modulation field by this localB 0 $$ {\mathrm{B}}_0 $$ array converges to a nearly linear gradient field. With the field calibration technique, it accelerates the in vivo scans (i.e., proved safe) by threefold and eightfold free of visible artifacts, without and with SENSE, respectively. CONCLUSION The nonlinear encoding analysis tool, the field calibration method, the safety evaluation procedures, and the in vivo reconstructed scans make significant steps to push MRI speed further with the localB 0 $$ {\mathrm{B}}_0 $$ coil array.
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Affiliation(s)
- Rui Tian
- High-Field MR center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Martin Uecker
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
- BioTechMed-Graz, Graz, Austria
| | - Mathias Davids
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Axel Thielscher
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Kai Buckenmaier
- High-Field MR center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Oliver Holder
- High-Field MR center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Theodor Steffen
- High-Field MR center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Klaus Scheffler
- High-Field MR center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
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Glang F, Nikulin AV, Bause J, Heule R, Steffen T, Avdievich N, Scheffler K. Accelerated MRI at 9.4 T with electronically modulated time-varying receive sensitivities. Magn Reson Med 2022; 88:742-756. [PMID: 35452153 DOI: 10.1002/mrm.29245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/19/2022] [Accepted: 03/04/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE To investigate how electronically modulated time-varying receive sensitivities can improve parallel imaging reconstruction at ultra-high field. METHODS Receive sensitivity modulation was achieved by introducing PIN diodes in the receive loops, which allow rapid switching of capacitances in both arms of each loop coil and by that alter B1 - profiles, resulting in two distinct receive sensitivity configurations. A prototype 8-channel reconfigurable receive coil for human head imaging at 9.4T was built, and MR measurements were performed in both phantom and human subject. A modified SENSE reconstruction for time-varying sensitivities was formulated, and g-factor calculations were performed to investigate how modulation of receive sensitivity profiles during image encoding can improve parallel imaging reconstruction. The optimized modulation pattern was realized experimentally, and reconstructions with the time-varying sensitivities were compared with conventional static SENSE reconstructions. RESULTS The g-factor calculations showed that fast modulation of receive sensitivities in the order of the ADC dwell time during k-space acquisition can improve parallel imaging performance, as this effectively makes spatial information of both configurations simultaneously available for image encoding. This was confirmed by in vivo measurements, for which lower reconstruction errors (SSIM = 0.81 for acceleration R = 4) and g-factors (max g = 2.4; R = 4) were observed for the case of rapidly switched sensitivities compared to conventional reconstruction with static sensitivities (SSIM = 0.74 and max g = 3.2; R = 4). As the method relies on the short RF wavelength at ultra-high field, it does not yield significant benefits at 3T and below. CONCLUSIONS Time-varying receive sensitivities can be achieved by inserting PIN diodes in the receive loop coils, which allow modulation of B1 - patterns. This offers an additional degree of freedom for image encoding, with the potential for improved parallel imaging performance at ultra-high field.
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Affiliation(s)
- Felix Glang
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anton V Nikulin
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Jonas Bause
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Rahel Heule
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Theodor Steffen
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Nikolai Avdievich
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Klaus Scheffler
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany
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Li M, Li Y, Jin J, Yang Z, Zhang B, Liu Y, Song M, Freakly C, Weber E, Liu F, Jiang T, Crozier S. A dedicated eight-channel receive RF coil array for monkey brain MRI at 9.4 T. NMR IN BIOMEDICINE 2020; 33:e4369. [PMID: 32729642 DOI: 10.1002/nbm.4369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 06/09/2020] [Accepted: 06/15/2020] [Indexed: 06/11/2023]
Abstract
The neuroimaging of nonhuman primates (NHPs) realised with magnetic resonance imaging (MRI) plays an important role in understanding brain structures and functions, as well as neurodegenerative diseases and pathological disorders. Theoretically, an ultrahigh field MRI (≥7 T) is capable of providing a higher signal-to-noise ratio (SNR) for better resolution; however, the lack of appropriate radiofrequency (RF) coils for 9.4 T monkey MRI undermines the benefits provided by a higher field strength. In particular, the standard volume birdcage coil at 9.4 T generates typical destructive interferences in the periphery of the brain, which reduces the SNR in the neuroscience-focused cortex region. Also, the standard birdcage coil is not capable of performing parallel imaging. Consequently, extended scan durations may cause unnecessary damage due to overlong anaesthesia. In this work, assisted by numerical simulations, an eight-channel receive RF coil array was specially designed and manufactured for imaging NHPs at 9.4 T. The structure and geometry of the proposed receive array was optimised with numerical simulations, so that the SNR enhancement region was particularly focused on monkey brain. Validated with rhesus monkey and cynomolgus monkey brain images acquired from a 9.4 T MRI scanner, the proposed receive array outperformed standard birdcage coil with higher SNR, mean diffusivity and fractional anisotropy values, as well as providing better capability for parallel imaging.
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Affiliation(s)
- Mingyan Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Yu Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Jin Jin
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthcare Pty. Ltd., Bowen Hills QLD, 4006, Australia
| | - Zhengyi Yang
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Craig Freakly
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Ewald Weber
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
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Li M, Weber E, Jin J, Hugger T, Tesiram Y, Ullmann P, Stark S, Fuentes M, Junge S, Liu F, Crozier S. Radial magnetic resonance imaging (MRI) using a rotating radiofrequency (RF) coil at 9.4 T. NMR IN BIOMEDICINE 2018; 31:e3860. [PMID: 29280211 DOI: 10.1002/nbm.3860] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 08/20/2017] [Accepted: 10/13/2017] [Indexed: 06/07/2023]
Abstract
The rotating radiofrequency coil (RRFC) has been developed recently as an alternative approach to multi-channel phased-array coils. The single-element RRFC avoids inter-channel coupling and allows a larger coil element with better B1 field penetration when compared with an array counterpart. However, dedicated image reconstruction algorithms require accurate estimation of temporally varying coil sensitivities to remove artefacts caused by coil rotation. Various methods have been developed to estimate unknown sensitivity profiles from a few experimentally measured sensitivity maps, but these methods become problematic when the RRFC is used as a transceiver coil. In this work, a novel and practical radial encoding method is introduced for the RRFC to facilitate image reconstruction without the measurement or estimation of rotation-dependent sensitivity profiles. Theoretical analyses suggest that the rotation-dependent sensitivities of the RRFC can be used to create a uniform profile with careful choice of sampling positions and imaging parameters. To test this new imaging method, dedicated electronics were designed and built to control the RRFC speed and hence positions in synchrony with imaging parameters. High-quality phantom and animal images acquired on a 9.4 T pre-clinical scanner demonstrate the feasibility and potential of this new RRFC method.
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Affiliation(s)
- Mingyan Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia
| | - Ewald Weber
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia
| | - Jin Jin
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia
| | - Thimo Hugger
- Bruker BioSpin MRI GmbH, Ettlingen, BadenWürttemberg, Germany
| | - Yasvir Tesiram
- Center for Advanced Imaging, The University of Queensland, Brisbane, Qld, Australia
- Bruker Pty Ltd., Preston, Victoria, Australia
| | - Peter Ullmann
- Bruker BioSpin MRI GmbH, Ettlingen, BadenWürttemberg, Germany
| | - Simon Stark
- Bruker BioSpin MRI GmbH, Ettlingen, BadenWürttemberg, Germany
| | - Miguel Fuentes
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia
| | - Sven Junge
- Bruker BioSpin MRI GmbH, Ettlingen, BadenWürttemberg, Germany
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia
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Jin J, Weber E, Tesiram Y, Hugger T, Li M, Fuentes M, Ullmann P, Stark S, Junge S, Liu F, Crozier S. Image Reconstruction for a Rotating Radiofrequency Coil (RRFC) Using Self-Calibrated Sensitivity From Radial Sampling. IEEE Trans Biomed Eng 2016; 64:274-283. [PMID: 27101591 DOI: 10.1109/tbme.2016.2552489] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study was to develop a practical magnetic resonance imaging (MRI) scheme for the latest rotating radiofrequency coil (RRFC) design at 9.4 T. The new prototype RRFC was integrated with an optical sensor to facilitate recording of its angular positions relative to the sequence timing. In imaging, the RRFC was used together with radial k-space trajectories. To recover the image, the radial spokes were grouped according to the coil locations. Using an Eigen-decomposition approach, an array of location-dependent sensitivity maps was extracted from the central regions of the segmented k-space, enabling parallel-imaging techniques for image recovery in a straightforward manner. When the RRFC angular velocity is carefully designed and accurately controlled according to the sequence timing, the encoding by means of varying RRFC sensitivity maps can be accurately calibrated for a faithful image recovery. Approximations were made to counteract the variations of the RRFC angular velocity, providing successful image reconstruction at 9.4 T. The current study demonstrated a new and practical imaging scheme for RRFC-MRI. It is able to extract the temporally varying sensitivity maps retrospectively from the k-space acquisition itself, without resorting to electromagnetic simulation or numerical interpolation. The proposed imaging scheme and the supporting engineering solutions of the RRFC prototype enable accurate image reconstructions. These new developments pave the way for routine applications of the RRFC, and bode well for its further development in providing simultaneous multinuclear imaging by incorporating, for example, independent X-nuclear coil elements into the rotating structure.
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Li M, Jin J, Zuo Z, Liu F, Trakic A, Weber E, Zhuo Y, Xue R, Crozier S. In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2015; 252:29-40. [PMID: 25635352 DOI: 10.1016/j.jmr.2014.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 12/11/2014] [Accepted: 12/13/2014] [Indexed: 06/04/2023]
Abstract
Using a new rotating SENSitivity Encoding (rotating-SENSE) algorithm, we have successfully demonstrated that the rotating radiofrequency coil array (RRFCA) was capable of achieving a significant reduction in scan time and a uniform image reconstruction for a homogeneous phantom at 7 Tesla. However, at 7 Tesla the in vivo sensitivity profiles (B1(-)) become distinct at various angular positions. Therefore, sensitivity maps at other angular positions cannot be obtained by numerically rotating the acquired ones. In this work, a novel sensitivity estimation method for the RRFCA was developed and validated with human brain imaging. This method employed a library database and registration techniques to estimate coil sensitivity at an arbitrary angular position. The estimated sensitivity maps were then compared to the acquired sensitivity maps. The results indicate that the proposed method is capable of accurately estimating both magnitude and phase of sensitivity at an arbitrary angular position, which enables us to employ the rotating-SENSE algorithm to accelerate acquisition and reconstruct image. Compared to a stationary coil array with the same number of coil elements, the RRFCA was able to reconstruct images with better quality at a high reduction factor. It is hoped that the proposed rotation-dependent sensitivity estimation algorithm and the acceleration ability of the RRFCA will be particularly useful for ultra high field MRI.
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Affiliation(s)
- Mingyan Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
| | - Jin Jin
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Centre for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Adnan Trakic
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ewald Weber
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Yan Zhuo
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Centre for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Rong Xue
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Centre for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
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