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Elsaid NMH, Dispenza NL, Hu C, Peters DC, Constable RT, Tagare HD, Galiana G. Constrained alternating minimization for parameter mapping (CAMP). Magn Reson Imaging 2024; 110:176-183. [PMID: 38657714 PMCID: PMC11193090 DOI: 10.1016/j.mri.2024.04.029] [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: 02/07/2024] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVE To improve image quality in highly accelerated parameter mapping by incorporating a linear constraint that relates consecutive images. APPROACH In multi-echo T1 or T2 mapping, scan time is often shortened by acquiring undersampled but complementary measures of k-space at each TE or TI. However, residual undersampling artifacts from the individual images can then degrade the quality of the final parameter maps. In this work, a new reconstruction method, dubbed Constrained Alternating Minimization for Parameter mapping (CAMP), is introduced. This method simultaneously extracts T2 or T1* maps in addition to an image for each TE or TI from accelerated datasets, leveraging the constraints of the decay to improve the reconstructed image quality. The model enforces exponential decay through a linear constraint, resulting in a biconvex objective function that lends itself to alternating minimization. The method was tested in four in vivo volunteer experiments and validated in phantom studies and healthy subjects, using T2 and T1 mapping, with accelerations of up to 12. MAIN RESULTS CAMP is demonstrated for accelerated radial and Cartesian acquisitions in T2 and T1 mapping. The method is even applied to generate an entire T2 weighted image series from a single TSE dataset, despite the blockwise k-space sampling at each echo time. Experimental undersampled phantom and in vivo results processed with CAMP exhibit reduced artifacts without introducing bias. SIGNIFICANCE For a wide array of applications, CAMP linearizes the model cost function without sacrificing model accuracy so that the well-conditioned and highly efficient reconstruction algorithm improves the image quality of accelerated parameter maps.
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Affiliation(s)
- Nahla M H Elsaid
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - Nadine L Dispenza
- Siemens Healthcare GmbH Allee am Röthelheimpark, 91052 Erlangen, Deutschland
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chenxi Hu
- The Institute of Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
- Department of Neurosurgery, Yale University, New Haven, CT, 06520, USA
| | - Hemant D Tagare
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Gigi Galiana
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
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Kofler A, Kerkering KM, Goschel L, Fillmer A, Kolbitsch C. Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice. IEEE Trans Biomed Eng 2024; 71:388-399. [PMID: 37540614 DOI: 10.1109/tbme.2023.3300090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
OBJECTIVE We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). METHODS Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a T1-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). RESULTS Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. CONCLUSION The proposed method outperforms the reported methods of comparison and yields accurate T1-maps. Although presented for T1-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. SIGNIFICANCE From a clinical perspective, the obtained T1-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.
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Bolan PJ, Saunders SL, Kay K, Gross M, Akcakaya M, Metzger GJ. Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.11.23284194. [PMID: 36711813 PMCID: PMC9882442 DOI: 10.1101/2023.01.11.23284194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
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Affiliation(s)
- Patrick J Bolan
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
| | - Sara L Saunders
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
| | - Mitchell Gross
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis MN
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
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Neumann T, Ludwig J, Kerkering KM, Speier P, Seifert F, Schaeffter T, Kolbitsch C. Ultra-wide band radar for prospective respiratory motion correction in the liver. Phys Med Biol 2023; 68. [PMID: 36763999 DOI: 10.1088/1361-6560/acbb51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/10/2023] [Indexed: 02/12/2023]
Abstract
Objective.T1 mapping of the liver is time consuming and can be challenging due to respiratory motion. Here we present a prospective slice tracking approach, which utilizes an external ultra-wide band radar signal and allows for efficient T1 mapping during free-breathing.Approach.The fast radar signal is calibrated to an MR-based motion signal to create a motion model. This motion model provides motion estimates, which are used to carry out slice tracking for any subsequent clinical scan. This approach was evaluated in simulations, phantom experiments andin vivoscans.Main results.Radar-based slice tracking was implemented on an MR system with a total latency of 77 ms. Moving phantom experiments showed accurate motion prediction with an error of 0.12 mm in anterior-posterior and 0.81 mm in head-feet direction. The model error remained stable for up to two hours.In vivoexperiments showed visible image improvement with a motion model error three times smaller than with a respiratory bellow. For T1 mapping during free-breathing the proposed approach provided similar results compared to reference T1 mapping during a breathhold.Significance.The proposed radar-based approach achieves accurate slice tracking and enables efficient T1 mapping of the liver during free-breathing. This motion correction approach is independent from scanning parameters and could also be used for applications like MR guided radiotherapy or MR Elastography.
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Affiliation(s)
- Tom Neumann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Juliane Ludwig
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Kirsten M Kerkering
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | - Frank Seifert
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Technische Universität Berlin, Biomedical Engineering, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Munoz C, Fotaki A, Botnar RM, Prieto C. Latest Advances in Image Acceleration: All Dimensions are Fair Game. J Magn Reson Imaging 2023; 57:387-402. [PMID: 36205716 PMCID: PMC10092100 DOI: 10.1002/jmri.28462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 01/20/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
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Guo R, Qi H, Amyar A, Cai X, Kucukseymen S, Haji-Valizadeh H, Rodriguez J, Paskavitz A, Pierce P, Goddu B, Thompson RB, Nezafat R. Quantification of changes in myocardial T 1 * values with exercise cardiac MRI using a free-breathing non-electrocardiograph radial imaging. Magn Reson Med 2022; 88:1720-1733. [PMID: 35691942 DOI: 10.1002/mrm.29346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To develop and evaluate a free breathing non-electrocardiograph (ECG) myocardial T1 * mapping sequence using radial imaging to quantify the changes in myocardial T1 * between rest and exercise (T1 *reactivity ) in exercise cardiac MRI (Ex-CMR). METHODS A free-running T1 * sequence was developed using a saturation pulse followed by three Look-Locker inversion-recovery experiments. Each Look-Locker continuously acquired data as radial trajectory using a low flip-angle spoiled gradient-echo readout. Self-navigation was performed with a temporal resolution of ∼100 ms for retrospectively extracting respiratory motion. The mid-diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free-breathing acquisition. Finally, data acquired at both mid-diastole and end-expiration are picked and reconstructed by a low-rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise. RESULTS Numerical simulation demonstrated that changes in T1 * are related to the changes in T1 ; however, other factors such as breathing motion could influence T1 * measurements. Phantom T1 * values measured using free-running T1 * mapping sequence had good correlation with spin-echo T1 values and was insensitive to heart rate. In the Ex-CMR study, the measured T1 * reactivity was 10% immediately after exercise and declined over time. CONCLUSION The free-running T1 * mapping sequence allows free-breathing non-ECG quantification of changes in myocardial T1 * with physiological exercise. Although, absolute myocardial T1 * value is sensitive to various confounders such as B1 and B0 inhomogeneity, quantification of its change may be useful in revealing myocardial tissue properties with exercise.
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Affiliation(s)
- Rui Guo
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Amine Amyar
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Xiaoying Cai
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.,Siemens Medical Solutions USA, Inc., Boston, MA, USA
| | - Selcuk Kucukseymen
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Hassan Haji-Valizadeh
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Jennifer Rodriguez
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Amanda Paskavitz
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Patrick Pierce
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Beth Goddu
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Richard B Thompson
- Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Reza Nezafat
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
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7
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Zhou R, Weller DS, Yang Y, Wang J, Jeelani H, Mugler JP, Salerno M. Dual-excitation flip-angle simultaneous cine and T 1 mapping using spiral acquisition with respiratory and cardiac self-gating. Magn Reson Med 2021; 86:82-96. [PMID: 33590591 PMCID: PMC8849625 DOI: 10.1002/mrm.28675] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/18/2020] [Accepted: 12/19/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To develop a free-breathing cardiac self-gated technique that provides cine images and B1+ slice profile-corrected T1 maps from a single acquisition. METHODS Without breath-holding or electrocardiogram gating, data were acquired continuously on a 3T scanner using a golden-angle gradient-echo spiral pulse sequence, with an inversion RF pulse applied every 4 seconds. Flip angles of 3° and 15° were used for readouts after the first four and second four inversions. Self-gating cardiac triggers were extracted from heart image navigators, and respiratory motion was corrected by rigid registration on each heartbeat. Cine images were reconstructed from the steady-state portion of 15° readouts using a low-rank plus sparse reconstruction. The T1 maps were fit using a projection onto convex sets approach from images reconstructed using slice profile-corrected dictionary learning. This strategy was evaluated in a phantom and 14 human subjects. RESULTS The self-gated signal demonstrated close agreement with the acquired electrocardiogram signal. The image quality for the proposed cine images and standard clinical balanced SSFP images were 4.31 (±0.50) and 4.65 (±0.30), respectively. The slice profile-corrected T1 values were similar to those of the inversion-recovery spin-echo technique in phantom, and had a higher global T1 value than that of MOLLI in human subjects. CONCLUSIONS Cine and T1 mapping using spiral acquisition with respiratory and cardiac self-gating successfully acquired T1 maps and cine images in a single acquisition without the need for electrocardiogram gating or breath-holding. This dual-excitation flip-angle approach provides a novel approach for measuring T1 while accounting for B1+ and slice profile effect on the apparent T1∗ .
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Affiliation(s)
- Ruixi Zhou
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Daniel S. Weller
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Junyu Wang
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Haris Jeelani
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - John P. Mugler
- Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Michael Salerno
- Cardiology, Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
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8
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Qi H, Cruz G, Botnar R, Prieto C. Synergistic multi-contrast cardiac magnetic resonance image reconstruction. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200197. [PMID: 33966456 DOI: 10.1098/rsta.2020.0197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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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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10
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Wang X, Rosenzweig S, Scholand N, Holme HCM, Uecker M. Model-based reconstruction for simultaneous multi-slice T1 mapping using single-shot inversion-recovery radial FLASH. Magn Reson Med 2021; 85:1258-1271. [PMID: 32936487 PMCID: PMC10409492 DOI: 10.1002/mrm.28497] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 01/17/2023]
Abstract
PURPOSE To develop a single-shot multi-slice T 1 mapping method by combing simultaneous multi-slice (SMS) excitations, single-shot inversion-recovery (IR) radial fast low-angle shot (FLASH), and a nonlinear model-based reconstruction method. METHODS SMS excitations are combined with a single-shot IR radial FLASH sequence for data acquisition. A previously developed single-slice calibrationless model-based reconstruction is extended to SMS, formulating the estimation of parameter maps and coil sensitivities from all slices as a single nonlinear inverse problem. Joint-sparsity constraints are further applied to the parameter maps to improve T 1 precision. Validations of the proposed method are performed for a phantom and for the human brain and liver in 6 healthy adult subjects. RESULTS Phantom results confirm good T 1 accuracy and precision of the simultaneously acquired multi-slice T 1 maps in comparison to single-slice references. In vivo human brain studies demonstrate the better performance of SMS acquisitions compared to the conventional spoke-interleaved multi-slice acquisition using model-based reconstruction. Aside from good accuracy and precision, the results of 6 healthy subjects in both brain and abdominal studies confirm good repeatability between scan and re-scans. The proposed method can simultaneously acquire T 1 maps for 5 slices of a human brain ( 0.75 × 0.75 × 5 mm 3 ) or 3 slices of the abdomen ( 1.25 × 1.25 × 6 mm 3 ) within 4 seconds. CONCLUSIONS The IR SMS radial FLASH acquisition together with a nonlinear model-based reconstruction enable rapid high-resolution multi-slice T 1 mapping with good accuracy, precision, and repeatability.
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Affiliation(s)
- Xiaoqing Wang
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Sebastian Rosenzweig
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Nick Scholand
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - H. Christian M. Holme
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Martin Uecker
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
- Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Germany
- Campus Institute Data Science (CIDAS), University of Göttingen, Germany
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11
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Staniszewski M, Klose U. Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T 1 Mapping. SENSORS (BASEL, SWITZERLAND) 2019; 19:s19245371. [PMID: 31817483 PMCID: PMC6960582 DOI: 10.3390/s19245371] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/02/2019] [Accepted: 12/04/2019] [Indexed: 06/10/2023]
Abstract
Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T1 mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can take from several minutes up to few hours. In this study we proposed improvement of model-based approach for T1-mapping by introduction of two steps fitting procedure. We provided analysis of further reduction of k-space data, which lead us to decrease of computational time and perform simulation of multi-slice development. The region of interest (ROI) analysis of human brain measurements with two different initial models shows that the differences between mean values with respect to a reference approach are in white matter-0.3% and 1.1%, grey matter-0.4% and 1.78% and cerebrospinal fluid-2.8% and 11.1% respectively. With further improvements we were able to decrease the time of computational of single slice to 6.5 min and 23.5 min for different initial models, which has been already not achieved by any other algorithm. In result we obtained an accelerated novel method of model-based image reconstruction in which single iteration can be performed within few seconds on home computer.
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Affiliation(s)
- Michał Staniszewski
- Institute of Informatics, Silesian University of Technology, Gliwice 44-100, Poland
| | - Uwe Klose
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University, Tübingen 72076, Germany;
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Hu C, Peters DC. SUPER: A blockwise curve-fitting method for accelerating MR parametric mapping with fast reconstruction. Magn Reson Med 2019; 81:3515-3529. [PMID: 30656730 PMCID: PMC6435434 DOI: 10.1002/mrm.27662] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 12/17/2018] [Accepted: 12/26/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE To investigate Shift Undersampling improves Parametric mapping Efficiency and Resolution (SUPER), a novel blockwise curve-fitting method for accelerating parametric mapping with very fast reconstruction. METHODS SUPER uses interleaved k-space undersampling, which enables a blockwise decomposition of the otherwise large-scale cost function to improve the reconstruction efficiency. SUPER can be readily combined with SENSE to achieve at least 4-fold acceleration. D-factor, a parametric-mapping counterpart of g-factor, was proposed and formulated to compare spatially heterogeneous noise amplification because of different acceleration methods. As a proof-of-concept, SUPER/SUPER-SENSE was validated using T1 mapping, by comparing them to alternative model-based methods, including MARTINI and GRAPPATINI, via simulations, phantom imaging, and in vivo brain imaging (N = 5), over criteria of normalized root-mean-squares error (NRMSE), average d-factor, and computational time per voxel (TPV). A novel SUPER-SENSE MOLLI cardiac T1 -mapping sequence with improved resolution (1.4 mm × 1.4 mm) was compared to standard MOLLI (1.9 mm × 2.5 mm) in 8 healthy subjects. RESULTS In brain imaging, 2-fold SUPER achieved lower NRMSE (0.04 ± 0.02 vs. 0.11 ± 0.02, P < 0.01), lower average d-factor (1.01 ± 0.002 vs. 1.12 ± 0.004, P < 0.001), and lower TPV (4.6 ms ± 0.2 ms vs. 79 ms ± 3 ms, P < 0.001) than 2-fold MARTINI. Similarly, 4-fold SUPER-SENSE achieved lower NRMSE (0.07 ± 0.01 vs. 0.13 ± 0.03, P = 0.02), lower average d-factor (1.15 ± 0.01 vs. 1.20 ± 0.01, P < 0.001), and lower TPV (4.0 ms ± 0.1 ms vs. 72 ms ± 3 ms, P < 0.001) than 4-fold GRAPPATINI. In cardiac T1 mapping, SUPER-SENSE MOLLI yielded similar myocardial T1 (1151 ms ± 63 ms vs. 1159 ms ± 32 ms, P = 0.6), slightly lower blood T1 (1643 ms ± 86 ms vs. 1680 ms ± 79 ms, P = 0.004), but improved spatial resolution compared with standard MOLLI in the same imaging time. CONCLUSION SUPER and SUPER-SENSE provide fast model-based reconstruction methods for accelerating parametric mapping and improving its clinical appeal.
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Affiliation(s)
- Chenxi Hu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
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13
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Shi X, Levine E, Weber H, Hargreaves BA. Accelerated imaging of metallic implants using model-based nonlinear reconstruction. Magn Reson Med 2018; 81:2247-2263. [PMID: 30515853 DOI: 10.1002/mrm.27536] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 08/23/2018] [Accepted: 08/24/2018] [Indexed: 12/26/2022]
Abstract
PURPOSE To accelerate imaging near metallic implants with multi-spectral imaging (MSI) techniques by exploiting a signal model in the spectral dimension. METHODS MSI techniques resolve metal-induced field perturbations by acquiring separate 3D spatial encodings at multiple excitation frequencies, which are referred to as spectral bins. The proposed model-based reconstruction exploits the correlation between spectral bins in image reconstruction by enforcing a signal model to describe the signal profile across bins. This work evaluates the accuracy of the MSI signal model in simulations and in vivo experiments. The proposed model-based reconstruction was evaluated in 6 subjects at an overall undersampling factor of 17.4 and compared with model-free parallel imaging and compressed sensing (PI & CS). The quality of reconstructed images was evaluated using normalized RMS error (nRMSE) and structural similarity index (SSIM) comparisons, with paired Wilcoxon tests in 6 subjects used to determine whether there was a significant difference in the metrics. RESULTS Both simulations and in vivo experiments show that the proposed signal model can represent the MSI signal profiles in the spectral dimension compactly and accurately. In the in vivo experiments, the model-based reconstruction significantly improved image quality over model-free PI & CS, with P < 0.05 for both nRMSE and SSIM at 17.4× acceleration. CONCLUSION This work presents the feasibility of using a model-based reconstruction to accelerate MSI techniques for faster MR imaging near metal.
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Affiliation(s)
- Xinwei Shi
- Department of Radiology, Stanford University, Stanford, California.,Department of Electrical Engineering, Stanford University, Stanford, California
| | - Evan Levine
- Department of Radiology, Stanford University, Stanford, California.,Department of Electrical Engineering, Stanford University, Stanford, California
| | - Hans Weber
- Department of Radiology, Stanford University, Stanford, California
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California.,Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Bioengineering, Stanford University, Stanford, California
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14
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Maier O, Schoormans J, Schloegl M, Strijkers GJ, Lesch A, Benkert T, Block T, Coolen BF, Bredies K, Stollberger R. Rapid T 1 quantification from high resolution 3D data with model-based reconstruction. Magn Reson Med 2018; 81:2072-2089. [PMID: 30346053 PMCID: PMC6588000 DOI: 10.1002/mrm.27502] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 08/01/2018] [Accepted: 08/02/2018] [Indexed: 12/25/2022]
Abstract
Purpose Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model‐based optimization framework in conjunction with undersampling 3D radial stack‐of‐stars data. Theory and Methods High resolution 3D T1 maps are generated from subsampled data by employing model‐based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non‐linear, non‐differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss‐Newton method. The importance of 3D‐spectral regularization is demonstrated by a comparison to 2D‐spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look‐Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data. Results Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions. Conclusions The proposed algorithm is able to recover T1 maps with an isotropic resolution of 1 mm3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.
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Affiliation(s)
- Oliver Maier
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Jasper Schoormans
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam Zuidoost, The Netherlands
| | - Matthias Schloegl
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Gustav J Strijkers
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam Zuidoost, The Netherlands
| | - Andreas Lesch
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York
| | - Tobias Block
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York
| | - Bram F Coolen
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam Zuidoost, The Netherlands
| | - Kristian Bredies
- BioTechMed-Graz, Graz, Austria.,Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Rudolf Stollberger
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
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15
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Zhu Y, Liu Y, Ying L, Peng X, Wang YXJ, Yuan J, Liu X, Liang D. SCOPE: signal compensation for low-rank plus sparse matrix decomposition for fast parameter mapping. Phys Med Biol 2018; 63:185009. [PMID: 30117434 DOI: 10.1088/1361-6560/aadb09] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Magnetic resonance (MR) parameter mapping is useful for many clinical applications. However, its practical utility is limited by the long scan time. To address this problem, this paper developed a novel image reconstruction method for fast MR parameter mapping. The proposed method (SCOPE) used a low-rank plus sparse model to reconstruct the parameter-weighted images from highly undersampled acquisitions. A signal compensation strategy was introduced to promote low rankness along the parametric direction and thus improve the reconstruction accuracy. Specifically, compensation was performed by multiplying the original signal by the inversion of the mono-exponential decay at each voxel. The performance of SCOPE was evaluated via quantitative T 1ρ mapping. The results of the simulation and in vivo experiments with acceleration factors from 3 to 5 are shown. The performance of SCOPE was verified via comparisons with several low-rank and sparsity-based methods. The experimental results showed that the T 1ρ maps obtained using SCOPE were more accurate than those obtained using competing methods and were comparable to the reference, even when the acceleration factor reached 5. SCOPE can greatly reduce the scan time of parameter mapping while still achieving high accuracy. This technique might therefore help facilitate fast MR parameter mapping in clinical use.
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Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China. Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America. These authors contributed equally to this work
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Becker KM, Schulz‐Menger J, Schaeffter T, Kolbitsch C. Simultaneous high‐resolution cardiac T
1
mapping and cine imaging using model‐based iterative image reconstruction. Magn Reson Med 2018; 81:1080-1091. [DOI: 10.1002/mrm.27474] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 06/08/2018] [Accepted: 07/09/2018] [Indexed: 12/26/2022]
Affiliation(s)
- Kirsten M. Becker
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
| | - Jeanette Schulz‐Menger
- Charité‐Universitätsmedizin Berlin Freie Universität Berlin, Humboldt‐Universität zu Berlin Berlin Institute of Health, DZHK Berlin Germany
- Working Group Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center Charité Medical Faculty Max‐Delbrueck Center for Molecular Medicine HELIOS Klinikum Berlin Buch Department of Cardiology and Nephrology Berlin Germany
| | - Tobias Schaeffter
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- Division of Imaging Sciences and Biomedical Engineering King's College London London United Kingdom
| | - Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- Division of Imaging Sciences and Biomedical Engineering King's College London London United Kingdom
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17
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Single-shot late Gd enhancement imaging of myocardial infarction with retrospectively adjustable contrast and heart-phase. Magn Reson Imaging 2018; 47:48-53. [DOI: 10.1016/j.mri.2017.10.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/23/2017] [Accepted: 10/31/2017] [Indexed: 12/12/2022]
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Hilbert T, Sumpf TJ, Weiland E, Frahm J, Thiran JP, Meuli R, Kober T, Krueger G. Accelerated T 2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI. J Magn Reson Imaging 2018; 48:359-368. [PMID: 29446508 DOI: 10.1002/jmri.25972] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 01/24/2018] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Quantitative T2 measurements are sensitive to intra- and extracellular water accumulation and myelin loss. Therefore, quantitative T2 promises to be a good biomarker of disease. However, T2 measurements require long acquisition times. PURPOSE To accelerate T2 quantification and subsequent generation of synthetic T2 -weighted (T2 -w) image contrast for clinical research and routine. To that end, a recently developed model-based approach for rapid T2 and M0 quantification (MARTINI) based on undersampling k-space, was extended by parallel imaging (GRAPPA) to enable high-resolution T2 mapping with access to T2 -w images in less than 2 minutes acquisition time for the entire brain. STUDY TYPE Prospective cross-sectional study. SUBJECTS/PHANTOM Fourteen healthy subjects and a multipurpose phantom. FIELD STRENGTH/SEQUENCE Carr-Purcell-Meiboom-Gill sequence at a 3T scanner. ASSESSMENT The accuracy and reproducibility of the accelerated T2 quantification was assessed. Validations comprised MRI studies on a phantom as well as the brain, knee, prostate, and liver from healthy volunteers. Synthetic T2 -w images were generated from computed T2 and M0 maps and compared to conventional fast spin-echo (SE) images. STATISTICAL TESTS Root mean square distance (RMSD) to the reference method and region of interest analysis. RESULTS The combination of MARTINI and GRAPPA (GRAPPATINI) lead to a 10-fold accelerated T2 mapping protocol with 1:44 minutes acquisition time and full brain coverage. The RMSD of GRAPPATINI increases less (4.3%) than a 10-fold MARTINI reconstruction (37.6%) in comparison to the reference. Reproducibility tests showed low standard deviation (SD) of T2 values in regions of interest between scan and rescan (<0.4 msec) and across subjects (<4 msec). DATA CONCLUSION GRAPPATINI provides highly reproducible and fast whole-brain T2 maps and arbitrary synthetic T2 -w images in clinically compatible acquisition times of less than 2 minutes. These abilities are expected to support more widespread clinical applications of quantitative T2 mapping. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:359-368.
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Affiliation(s)
- Tom Hilbert
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tilman J Sumpf
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | | | - Jens Frahm
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gunnar Krueger
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Siemens Medical Solutions USA, Boston, Massachusetts, USA
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Benkert T, Tian Y, Huang C, DiBella EVR, Chandarana H, Feng L. Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding. Magn Reson Med 2017; 80:286-293. [PMID: 29193380 DOI: 10.1002/mrm.27030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 11/02/2017] [Accepted: 11/10/2017] [Indexed: 12/24/2022]
Abstract
PURPOSE Golden-angle radial sparse parallel (GRASP) MRI reconstruction requires gridding and regridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging. METHODS GROG is an alternative gridding approach based on parallel imaging, in which k-space data acquired on a non-Cartesian grid are shifted onto a Cartesian k-space grid using information from multicoil arrays. For iterative non-Cartesian image reconstruction, GROG is performed only once as a preprocessing step. Therefore, the subsequent iterative reconstruction can be performed directly in Cartesian space, which significantly reduces computational burden. Here, a framework combining GROG with GRASP (GROG-GRASP) is first optimized and then compared with standard GRASP reconstruction in 22 prostate patients. RESULTS GROG-GRASP achieved approximately 4.2-fold reduction in reconstruction time compared with GRASP (∼333 min versus ∼78 min) while maintaining image quality (structural similarity index ≈ 0.97 and root mean square error ≈ 0.007). Visual image quality assessment by two experienced radiologists did not show significant differences between the two reconstruction schemes. With a graphics processing unit implementation, image reconstruction time can be further reduced to approximately 14 min. CONCLUSION The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Thomas Benkert
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ye Tian
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA.,Department of Physics and Astronomy, University of Utah, Salt Lake City, Utah, USA
| | - Chenchan Huang
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Edward V R DiBella
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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20
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Wang X, Roeloffs V, Klosowski J, Tan Z, Voit D, Uecker M, Frahm J. Model-based T 1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH. Magn Reson Med 2017; 79:730-740. [PMID: 28603934 DOI: 10.1002/mrm.26726] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 03/16/2017] [Accepted: 03/28/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop a model-based reconstruction technique for single-shot T1 mapping with high spatial resolution, accuracy, and precision using an inversion-recovery (IR) fast low-angle shot (FLASH) acquisition with radial encoding. METHODS The proposed model-based reconstruction jointly estimates all model parameters, that is, the equilibrium magnetization, steady-state magnetization, 1/ T1*, and all coil sensitivities from the data of a single-shot IR FLASH acquisition with a small golden-angle radial trajectory. Joint sparsity constraints on the parameter maps are exploited to improve the performance of the iteratively regularized Gauss-Newton method chosen for solving the nonlinear inverse problem. Validations include both a numerical and experimental T1 phantom, as well as in vivo studies of the human brain and liver at 3 T. RESULTS In comparison to previous reconstruction methods for single-shot T1 mapping, which are based on real-time MRI with pixel-wise fitting and a model-based approach with a predetermination of coil sensitivities, the proposed method presents with improved robustness against phase errors and numerical precision in both phantom and in vivo studies. CONCLUSION The comprehensive model-based reconstruction with L1 regularization offers rapid and robust T1 mapping with high accuracy and precision. The method warrants accelerated computing and online implementation for extended clinical trials. Magn Reson Med 79:730-740, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Xiaoqing Wang
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Volkert Roeloffs
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Jakob Klosowski
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Zhengguo Tan
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Dirk Voit
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Martin Uecker
- Department of Diagnostic and Interventional Radiology, University Medical Center, Göttingen, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Germany
| | - Jens Frahm
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Germany
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Zhao B, Setsompop K, Ye H, Cauley SF, Wald LL. Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1812-23. [PMID: 26915119 PMCID: PMC5271418 DOI: 10.1109/tmi.2016.2531640] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.
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22
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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Tran-Gia J, Bisdas S, Köstler H, Klose U. A model-based reconstruction technique for fast dynamic T1 mapping. Magn Reson Imaging 2015; 34:298-307. [PMID: 26597832 DOI: 10.1016/j.mri.2015.10.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 09/15/2015] [Accepted: 10/17/2015] [Indexed: 11/27/2022]
Abstract
PURPOSE To present a technique for dynamic T1 mapping. MATERIALS AND METHODS A recently proposed model-based reconstruction entitled IR-MAP allows T1 mapping of a single slice from a single radial inversion recovery Look-Locker FLASH acquisition. To enable dynamic T1 mapping, multiple of these acquisitions are consecutively performed, each followed by a waiting period of 3s for relaxation. Next, IR-MAP is used to reconstruct an individual T1 map for each of these acquisitions. Finally, T1 errors caused by insufficient relaxation between subsequent IR pulses are iteratively corrected. RESULTS The functionality of the proposed setup was validated in a phantom and in seven healthy volunteers. Systematic deviations between subsequent T1 maps originating from insufficient relaxation periods were effectively corrected. Additionally, the approach was successfully applied to monitor the T1 dynamic in a patient with primary lymphoma after the intravenous injection of contrast agent. CONCLUSION The proposed setup enables dynamic T1 mapping of a single slice with a spatial resolution of 1.6 mm × 1.6 mm × 3 mm and a temporal resolution of one parameter map every 9 s. It therefore represents a new opportunity to track changes in T1 over time, as it is desirable in many applications such as dynamic contrast-enhanced MRI.
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Affiliation(s)
- Johannes Tran-Gia
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany; Department of Nuclear Medicine, University of Würzburg, Würzburg, Germany.
| | - Sotirios Bisdas
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University, Tübingen, Germany; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, United Kingdom
| | - Herbert Köstler
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
| | - Uwe Klose
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University, Tübingen, Germany
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Tran-Gia J, Lohr D, Weng AM, Ritter CO, Stäb D, Bley TA, Köstler H. A model-based reconstruction technique for quantitative myocardial perfusion imaging. Magn Reson Med 2015; 76:880-7. [PMID: 26414857 DOI: 10.1002/mrm.25921] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 07/19/2015] [Accepted: 08/14/2015] [Indexed: 12/20/2022]
Abstract
PURPOSE To reduce saturation effects in the arterial input function (AIF) estimation of quantitative myocardial first-pass saturation recovery perfusion imaging by employing a model-based reconstruction. THEORY AND METHODS Imaging was performed with a saturation recovery prepared radial FLASH sequence. A model-based reconstruction was applied for reconstruction. By exploiting prior knowledge about the relaxation process, an image series with different saturation recovery times was reconstructed. By evaluating images with an effective saturation time of approximately 3 ms, saturation effects in the AIF determination were reduced. In a volunteer study, this approach was compared with a standard prebolus technique. RESULTS In comparison to the low-dose injection of a prebolus acquisition, saturation effects were further reduced in the AIFs determined using the model-based approach. These effects, which were clearly visible for all six volunteers, were reflected in a statistically significant difference of up to 20% in the absolute perfusion values. CONCLUSION The application of model-based reconstruction algorithms in quantitative myocardial perfusion imaging promises a significant improvement of the AIF determination. In addition to greatly reducing saturation effects that occur even for the prebolus methods, only a single bolus has to be applied. Magn Reson Med 76:880-887, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Johannes Tran-Gia
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Germany.,Department of Nuclear Medicine, University of Würzburg, Germany
| | - David Lohr
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Germany.,Comprehensive Heart Failure Center Würzburg, University of Würzburg, Germany
| | - Andreas Max Weng
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Germany
| | - Christian Oliver Ritter
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Germany.,Department of Diagnostic and Interventional Radiology, University Medical Center Göttingen, Germany
| | - Daniel Stäb
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Germany.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | | | - Herbert Köstler
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Germany.,Comprehensive Heart Failure Center Würzburg, University of Würzburg, Germany
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Tran-Gia J, Wech T, Bley T, Köstler H. Model-based acceleration of look-locker T1 mapping. PLoS One 2015; 10:e0122611. [PMID: 25860381 PMCID: PMC4393277 DOI: 10.1371/journal.pone.0122611] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 02/23/2015] [Indexed: 11/19/2022] Open
Abstract
Mapping the longitudinal relaxation time T1 has widespread applications in clinical MRI as it promises a quantitative comparison of tissue properties across subjects and scanners. Due to the long scan times of conventional methods, however, the use of quantitative MRI in clinical routine is still very limited. In this work, an acceleration of Inversion-Recovery Look-Locker (IR-LL) T1 mapping is presented. A model-based algorithm is used to iteratively enforce an exponential relaxation model to a highly undersampled radially acquired IR-LL dataset obtained after the application of a single global inversion pulse. Using the proposed technique, a T1 map of a single slice with 1.6mm in-plane resolution and 4mm slice thickness can be reconstructed from data acquired in only 6s. A time-consuming segmented IR experiment was used as gold standard for T1 mapping in this work. In the subsequent validation study, the model-based reconstruction of a single-inversion IR-LL dataset exhibited a T1 difference of less than 2.6% compared to the segmented IR-LL reference in a phantom consisting of vials with T1 values between 200ms and 3000ms. In vivo, the T1 difference was smaller than 5.5% in WM and GM of seven healthy volunteers. Additionally, the T1 values are comparable to standard literature values. Despite the high acceleration, all model-based reconstructions were of a visual quality comparable to fully sampled references. Finally, the reproducibility of the T1 mapping method was demonstrated in repeated acquisitions. In conclusion, the presented approach represents a promising way for fast and accurate T1 mapping using radial IR-LL acquisitions without the need of any segmentation.
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Affiliation(s)
- Johannes Tran-Gia
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
- * E-mail:
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
- Comprehensive Heart Failure Center (CHFC) Würzburg, University of Würzburg, Würzburg, Germany
| | - Thorsten Bley
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
- Comprehensive Heart Failure Center (CHFC) Würzburg, University of Würzburg, Würzburg, Germany
| | - Herbert Köstler
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
- Comprehensive Heart Failure Center (CHFC) Würzburg, University of Würzburg, Würzburg, Germany
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Tran-Gia J, Wech T, Hahn D, Bley TA, Köstler H. Consideration of slice profiles in inversion recovery Look-Locker relaxation parameter mapping. Magn Reson Imaging 2014; 32:1021-30. [PMID: 24960366 DOI: 10.1016/j.mri.2014.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 05/08/2014] [Accepted: 05/26/2014] [Indexed: 10/25/2022]
Abstract
PURPOSE To include the flip angle distribution caused by the slice profile into the model used for describing the relaxation curves observed in inversion recovery Look-Locker FLASH T1 mapping for a more accurate determination of the relaxation parameters. MATERIALS AND METHODS For each inversion time, the flip angle dependent signal of the mono-exponential relaxation model is integrated across the slice profile. The resulting Consideration of Slice Profiles (CSP) relaxation curves are compared to the mono-exponential signal model in numerical simulations as well as in phantom and in-vivo experiments. RESULTS All measured relaxation curves showed systematic deviations from a mono-exponential curve increasing with flip angle and T1 but decreasing with repetition time. Additionally, the accuracy of T1 was found to be largely dependent on the temporal coverage of the relaxation curve. All these systematic errors were largely reduced by the CSP model. CONCLUSION The proposed CSP model represents a useful extension of the conventionally used mono-exponential relaxation model. Despite inherent model inaccuracies, the mono-exponential model was found to be sufficient for many T1 mapping situations. However, if only a poor temporal coverage of the relaxation process is achievable or a very precise modeling of the relaxation course is needed as in model-based techniques, the mono-exponential model leads to systematic errors and the CSP model should be used instead.
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Affiliation(s)
- Johannes Tran-Gia
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany.
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany; Comprehensive Heart Failure Center Würzburg, University of Würzburg, Würzburg, Germany
| | - Dietbert Hahn
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
| | - Thorsten A Bley
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany; Comprehensive Heart Failure Center Würzburg, University of Würzburg, Würzburg, Germany
| | - Herbert Köstler
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany; Comprehensive Heart Failure Center Würzburg, University of Würzburg, Würzburg, Germany
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