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Li S, Wang L, Priest AN, Horvat-Menih I, Mendichovszky IA, Gallagher FA, Wang H, Li H. Highly accelerated parameter mapping using model-based alternating reconstruction coupling fitting. Phys Med Biol 2024; 69:145014. [PMID: 38917824 DOI: 10.1088/1361-6560/ad5bb8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/25/2024] [Indexed: 06/27/2024]
Abstract
Objective.A model-based alternating reconstruction coupling fitting, termed Model-based Alternating Reconstruction COupling fitting (MARCO), is proposed for accurate and fast magnetic resonance parameter mapping.Approach.MARCO utilizes the signal model as a regularization by minimizing the bias between the image series and the signal produced by the suitable signal model based on iteratively updated parameter maps when reconstructing. The technique can incorporate prior knowledge of both image series and parameters by adding sparsity constraints. The optimization problem is decomposed into three subproblems and solved through three alternating steps involving reconstruction and nonlinear least-square fitting, which can produce both contrast-weighted images and parameter maps simultaneously.Main results.The algorithm is applied toT2mapping with extended phase graph algorithm integrated and validated on undersampled multi-echo spin-echo data from both phantom and in vivo sources. Compared with traditional compressed sensing and model-based methods, the proposed approach yields more accurateT2maps with more details at high acceleration factors.Significance.The proposed method provides a basic framework for quantitative MR relaxometry, theoretically applicable to all quantitative MR relaxometry. It has the potential to improve the diagnostic utility of quantitative imaging techniques.
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Affiliation(s)
- Shaohang Li
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
| | - Lili Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
| | - Andrew N Priest
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Ines Horvat-Menih
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Iosif A Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
| | - Hao Li
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
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Tian Y, Nayak KS. Real-time water/fat imaging at 0.55T with spiral out-in-out-in sampling. Magn Reson Med 2024; 91:649-659. [PMID: 37815020 PMCID: PMC10841523 DOI: 10.1002/mrm.29885] [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: 04/27/2023] [Revised: 08/23/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To develop an efficient and flexible water/fat separated real-time MRI (RT-MRI) method using spiral out-in-out-in (OIOI) sampling and balanced SSFP (bSSFP) at 0.55T. METHODS A bSSFP sequence with golden-angle spiral OIOI readout was developed, capturing three echoes to allow water/fat separation. A low-latency reconstruction that combines all echoes was available for online visualization. An offline reconstruction provided water and fat RT-MRI in two steps: (1) image reconstruction with spatiotemporally constrained reconstruction (STCR) and (2) water/fat separation with hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (HIDEAL). In healthy volunteers, spiral OIOI was acquired in the wrist during a radial-to-ulnar deviation maneuver, in the heart without breath-hold and cardiac gating, and in the lower abdomen during free-breathing for visualizing small bowel motility. RESULTS We demonstrate successful water/fat separated RT-MRI for all tested applications. In the wrist, resulting images provided clear depiction of ligament gaps and their interactions during the radial-to-ulnar deviation maneuver. In the heart, water/fat RT-MRI depicted epicardial fat, provided improved delineation of epicardial coronary arteries, and provided high blood-myocardial contrast for ventricular function assessment. In the abdomen, water-only RT-MRI captured small bowel mobility clearly with improved water-fat contrast. CONCLUSIONS We have demonstrated a novel and flexible bSSFP spiral OIOI sequence at 0.55T that can provide water/fat separated RT-MRI with a variety of application-specific temporal resolution and spatial resolution requirements.
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Affiliation(s)
- Ye Tian
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Krishna S. Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
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Lee NG, Ramasawmy R, Lim Y, Campbell-Washburn AE, Nayak KS. MaxGIRF: Image reconstruction incorporating concomitant field and gradient impulse response function effects. Magn Reson Med 2022; 88:691-710. [PMID: 35445768 PMCID: PMC9232904 DOI: 10.1002/mrm.29232] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/04/2022] [Accepted: 02/23/2022] [Indexed: 02/03/2023]
Abstract
Purpose To develop and evaluate an improved strategy for compensating concomitant field effects in non‐Cartesian MRI at the time of image reconstruction. Theory We present a higher‐order reconstruction method, denoted as MaxGIRF, for non‐Cartesian imaging that simultaneously corrects off‐resonance, concomitant fields, and trajectory errors without requiring specialized hardware. Gradient impulse response functions are used to predict actual gradient waveforms, which are in turn used to estimate the spatiotemporally varying concomitant fields based on analytic expressions. The result, in combination with a reference field map, is an encoding matrix that incorporates a correction for all three effects. Methods The MaxGIRF reconstruction is applied to noiseless phantom simulations, spiral gradient‐echo imaging of an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom, and axial and sagittal multislice spiral spin‐echo imaging of a healthy volunteer at 0.55 T. The MaxGIRF reconstruction was compared against previously established concomitant field‐compensation and image‐correction methods. Reconstructed images are evaluated qualitatively and quantitatively using normalized RMS error. Finally, a low‐rank approximation of MaxGIRF is used to reduce computational burden. The accuracy of the low‐rank approximation is studied as a function of minimum rank. Results The MaxGIRF reconstruction successfully mitigated blurring artifacts both in phantoms and in vivo and was effective in regions where concomitant fields counteract static off‐resonance, superior to the comparator method. A minimum rank of 8 and 30 for axial and sagittal scans, respectively, gave less than 2% error compared with the full‐rank reconstruction. Conclusions The MaxGIRF reconstruction simultaneously corrects off‐resonance, trajectory errors, and concomitant field effects. The impact of this method is greatest when imaging with longer readouts and/or at lower field strength. Click here for author‐reader discussions
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Affiliation(s)
- Nam G Lee
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Rajiv Ramasawmy
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Adrienne E Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Krishna S Nayak
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.,Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
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Dietrich S, Aigner CS, Mayer J, Kolbitsch C, Schulz-Menger J, Schaeffter T, Schmitter S. Motion-compensated fat-water imaging for 3D cardiac MRI at ultra-high fields. Magn Reson Med 2022; 87:2621-2636. [PMID: 35092090 DOI: 10.1002/mrm.29144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 11/23/2021] [Accepted: 12/14/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Respiratory motion-compensated (MC) 3D cardiac fat-water imaging at 7T. METHODS Free-breathing bipolar 3D triple-echo gradient-recalled-echo (GRE) data with radial phase-encoding (RPE) trajectory were acquired in 11 healthy volunteers (7M\4F, 21-35 years, mean: 30 years) with a wide range of body mass index (BMI; 19.9-34.0 kg/m2 ) and volunteer tailored B 1 + shimming. The bipolar-corrected triple-echo GRE-RPE data were binned into different respiratory phases (self-navigation) and were used for the estimation of non-rigid motion vector fields (MF) and respiratory resolved (RR) maps of the main magnetic field deviations (ΔB0 ). RR ΔB0 maps and MC ΔB0 maps were compared to a reference respiratory phase to assess respiration-induced changes. Subsequently, cardiac binned fat-water images were obtained using a model-based, respiratory motion-corrected image reconstruction. RESULTS The 3D cardiac fat-water imaging at 7T was successfully demonstrated. Local respiration-induced frequency shifts in MC ΔB0 maps are small compared to the chemical shifts used in the multi-peak model. Compared to the reference exhale ΔB0 map these changes are in the order of 10 Hz on average. Cardiac binned MC fat-water reconstruction reduced respiration induced blurring in the fat-water images, and flow artifacts are reduced in the end-diastolic fat-water separated images. CONCLUSION This work demonstrates the feasibility of 3D fat-water imaging at UHF for the entire human heart despite spatial and temporal B 1 + and B0 variations, as well as respiratory and cardiac motion.
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Affiliation(s)
- Sebastian Dietrich
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | - Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Experimental and Clinical Research Center, A Joint Cooperation between the Charité Medical Faculty and the Max-Delbrueck Center for Molecular Medicine and HELIOS Hospital Berlin Buch, Berlin, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.,Helios Clinics Berlin-Buch Department of Cardiology and Nephrology, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Department of Medical Engineering, Technische Universität Berlin, Germany
| | - Sebastian Schmitter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
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Nayak KS, Lim Y, Campbell-Washburn AE, Steeden J. Real-Time Magnetic Resonance Imaging. J Magn Reson Imaging 2022; 55:81-99. [PMID: 33295674 PMCID: PMC8435094 DOI: 10.1002/jmri.27411] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 01/03/2023] Open
Abstract
Real-time magnetic resonance imaging (RT-MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast-switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady-state free precession, and single-shot rapid acquisition with relaxation enhancement. RT-MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft-tissue contrast, as well as flow information. In this review, we discuss the history of RT-MRI, fundamental tradeoffs, enabling technology, established applications, and current trends. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Krishna S. Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA,Address reprint requests to: K.S.N., 3740 McClintock Ave, EEB 400C, Los Angeles, CA 90089-2564, USA.
| | - Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Adrienne E. Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer Steeden
- Institute of Cardiovascular Science, Centre for Cardiovascular Imaging, University College London, London, UK
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Wang X, Tan Z, Scholand N, Roeloffs V, Uecker M. Physics-based reconstruction methods for magnetic resonance imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200196. [PMID: 33966457 PMCID: PMC8107652 DOI: 10.1098/rsta.2020.0196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 05/03/2023]
Abstract
Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction-addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Xiaoqing Wang
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
- Partner Site Göttingen, German Centre for Cardiovascular Research (DZHK), Göttingen, Germany
| | - Zhengguo Tan
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
- Partner Site Göttingen, German Centre for Cardiovascular Research (DZHK), Göttingen, Germany
| | - Nick Scholand
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
- Partner Site Göttingen, German Centre for Cardiovascular Research (DZHK), Göttingen, Germany
| | - Volkert Roeloffs
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
| | - Martin Uecker
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
- Partner Site Göttingen, German Centre for Cardiovascular Research (DZHK), Göttingen, Germany
- Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), University of Göttingen, Göttingen, Germany
- Campus Institute Data Science (CIDAS), University of Göttingen, Göttingen, Germany
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Liu S, Wu P, Liu H, Hu Z, Guo H. Referenceless multi‐channel signal combination: A demonstration in chemical‐shift‐encoded water‐fat imaging. Magn Reson Med 2019; 83:1810-1824. [DOI: 10.1002/mrm.28028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 09/09/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Simin Liu
- Center for Biomedical Imaging Research Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China
| | - Peng Wu
- Center for Biomedical Imaging Research Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China
- Philips Healthcare Suzhou Co Ltd Suzhou
| | - Haining Liu
- Department of Radiology University of Washington Seattle Washington
| | - Zhangxuan Hu
- Center for Biomedical Imaging Research Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China
| | - Hua Guo
- Center for Biomedical Imaging Research Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China
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