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Christodoulou AG, Cruz G, Arami A, Weingärtner S, Artico J, Peters D, Seiberlich N. The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1). J Cardiovasc Magn Reson 2024; 26:100997. [PMID: 38237900 DOI: 10.1016/j.jocmr.2024.100997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/26/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
- Anthony G Christodoulou
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Gastao Cruz
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Ayda Arami
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | | | - Dana Peters
- Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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Contijoch F, Rasche V, Seiberlich N, Peters DC. The future of CMR: All-in-one vs. real-time CMR (Part 2). J Cardiovasc Magn Reson 2024; 26:100998. [PMID: 38237901 DOI: 10.1016/j.jocmr.2024.100998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/20/2024] Open
Abstract
Cardiac Magnetic Resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR protocols. To this end, the vision of all-in-one and real-time imaging are described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 tackles the "All-in-One" approaches, and Part 2 focuses on the "Real-Time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
| | - Volker Rasche
- Ulm University Medical Center, Department of Internal Medicine II, Ulm, Germany
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
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Nykänen O, Nevalainen M, Casula V, Isosalo A, Inkinen SI, Nikki M, Lattanzi R, Cloos MA, Nissi MJ, Nieminen MT. Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint. J Magn Reson Imaging 2022. [PMID: 36562500 DOI: 10.1002/jmri.28573] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. PURPOSE To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. STUDY TYPE Retrospective. POPULATION Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available). FIELD STRENGTH AND SEQUENCE A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). ASSESSMENT Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. STATISTICAL TESTS Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. RESULTS The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. DATA CONCLUSION Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Olli Nykänen
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland.,Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Mika Nevalainen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Victor Casula
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Antti Isosalo
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Satu I Inkinen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Helsinki University Hospital, Helsinki, Finland
| | - Marko Nikki
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Riccardo Lattanzi
- Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Martijn A Cloos
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Mikko J Nissi
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland
| | - Miika T Nieminen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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Arberet S, Chen X, Mailhé B, Speier P, Körzdörfer G, Nittka M, Meyer H, Nadar MS. A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting. Magn Reson Imaging 2021; 82:74-90. [PMID: 34157408 DOI: 10.1016/j.mri.2021.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 05/28/2021] [Accepted: 06/16/2021] [Indexed: 11/18/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.
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Affiliation(s)
- Simon Arberet
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA.
| | - Xiao Chen
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Boris Mailhé
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Peter Speier
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | | | - Mathias Nittka
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | - Heiko Meyer
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | - Mariappan S Nadar
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
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Eck BL, Flamm SD, Kwon DH, Tang WHW, Vasquez CP, Seiberlich N. Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications. Prog Nucl Magn Reson Spectrosc 2021; 122:11-22. [PMID: 33632415 PMCID: PMC8366914 DOI: 10.1016/j.pnmrs.2020.10.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/23/2020] [Accepted: 10/29/2020] [Indexed: 05/02/2023]
Abstract
Quantitative cardiac magnetic resonance has emerged in recent years as an approach for evaluating a range of cardiovascular conditions, with T1 and T2 mapping at the forefront of these developments. Cardiac Magnetic Resonance Fingerprinting (cMRF) provides a rapid and robust framework for simultaneous quantification of myocardial T1 and T2 in addition to other tissue properties. Since the advent of cMRF, a number of technical developments and clinical validation studies have been reported. This review provides an overview of cMRF, recent technical developments, healthy subject and patient studies, anticipated technical improvements, and potential clinical applications. Recent technical developments include slice profile and pulse efficiency corrections, improvements in image reconstruction, simultaneous multislice imaging, 3D whole-ventricle imaging, motion-resolved imaging, fat-water separation, and machine learning for rapid dictionary generation. Future technical developments in cMRF, such as B0 and B1 field mapping, acceleration of acquisition and reconstruction, imaging of patients with implanted devices, and quantification of additional tissue properties are also described. Potential clinical applications include characterization of infiltrative, inflammatory, and ischemic cardiomyopathies, tissue characterization in the left atrium and right ventricle, post-cardiac transplantation assessment, reduction of contrast material, pre-procedural planning for electrophysiology interventions, and imaging of patients with implanted devices.
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Affiliation(s)
- Brendan L Eck
- Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Scott D Flamm
- Heart and Vascular Institute and Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Deborah H Kwon
- Heart and Vascular Institute and Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - W H Wilson Tang
- Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Claudia Prieto Vasquez
- School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, UK.
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI 48109, USA.
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Chen Y, Fang Z, Hung SC, Chang WT, Shen D, Lin W. High-resolution 3D MR Fingerprinting using parallel imaging and deep learning. Neuroimage 2019; 206:116329. [PMID: 31689536 DOI: 10.1016/j.neuroimage.2019.116329] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/10/2019] [Accepted: 10/30/2019] [Indexed: 12/16/2022] Open
Abstract
MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely available, extending the method to 3D MRF has been an actively pursued area of research as a 3D approach can provide a higher spatial resolution and better tissue characterization with an inherently higher signal-to-noise ratio. However, 3D MRF with a high spatial resolution requires lengthy acquisition times, especially for a large volume, making it impractical for most clinical applications. In this study, a high-resolution 3D MR Fingerprinting technique, combining parallel imaging and deep learning, was developed for rapid and simultaneous quantification of T1 and T2 relaxation times. Parallel imaging was first applied along the partition-encoding direction to reduce the amount of acquired data. An advanced convolutional neural network was then integrated with the MRF framework to extract features from the MRF signal evolution for improved tissue characterization and accelerated mapping. A modified 3D-MRF sequence was also developed in the study to acquire data to train the deep learning model that can be directly applied to prospectively accelerate 3D MRF scans. Our results of quantitative T1 and T2 maps demonstrate that improved tissue characterization can be achieved using the proposed method as compared to prior methods. With the integration of parallel imaging and deep learning techniques, whole-brain (26 × 26 × 18 cm3) quantitative T1 and T2 mapping with 1-mm isotropic resolution were achieved in ~7 min. In addition, a ~7-fold improvement in processing time to extract tissue properties was also accomplished with the deep learning approach as compared to the standard template matching method. All of these improvements make high-resolution whole-brain quantitative MR imaging feasible for clinical applications.
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Affiliation(s)
- Yong Chen
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Zhenghan Fang
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Sheng-Che Hung
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Wei-Tang Chang
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Dinggang Shen
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Weili Lin
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA.
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Hoppe E, Thamm F, Körzdörfer G, Syben C, Schirrmacher F, Nittka M, Pfeuffer J, Meyer H, Maier A. Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks. Stud Health Technol Inform 2019; 267:126-133. [PMID: 31483264 DOI: 10.3233/shti190816] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previous methods based on CNNs, RNN models yield significantly improved results using in-vivo data.
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Affiliation(s)
- Elisabeth Hoppe
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Thamm
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Christopher Syben
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Franziska Schirrmacher
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mathias Nittka
- Siemens Healthcare, Application Development, Erlangen, Germany
| | - Josef Pfeuffer
- Siemens Healthcare, Application Development, Erlangen, Germany
| | - Heiko Meyer
- Siemens Healthcare, Application Development, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Körzdörfer G, Pfeuffer J, Kluge T, Gebhardt M, Hensel B, Meyer CH, Nittka M. Effect of spiral undersampling patterns on FISP MRF parameter maps. Magn Reson Imaging 2019; 62:174-80. [PMID: 30654162 DOI: 10.1016/j.mri.2019.01.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 01/11/2019] [Accepted: 01/12/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Artifacts arising from undersampling are not always treatable as incoherent noise for the pattern matching process in Magnetic Resonance Fingerprinting (MRF). To estimate the effect of undersampling artifacts on MRF quantitative results, spiral sampling trajectories and their temporal variation is examined. METHODS The effect of sampling trajectories and their variation during the MRF experiment was assessed by characterizing aliasing artifacts. Temporal rearrangements of sampling trajectories were tested and evaluated in simulations and scans of phantoms and in a volunteer brain. RESULTS Results show that some temporal variations of sampling patterns can lead to spatial biases in MRF parameter maps. Observed effects are consistent with derived performance indicators for different interleaving schemes, leading to substantially improved MRF sampling patterns. CONCLUSION With the help of the presented simulation framework, MRF implementations can be investigated and improved. This was demonstrated for a spiral FISP (Fast imaging with steady-state free precession) MRF implementation, where a significantly improved interleaving scheme was identified, and confirmed by experiment.
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Amthor T, Doneva M, Koken P, Sommer K, Meineke J, Börnert P. Magnetic Resonance Fingerprinting with short relaxation intervals. Magn Reson Imaging 2017; 41:22-28. [PMID: 28666939 DOI: 10.1016/j.mri.2017.06.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/08/2017] [Accepted: 06/22/2017] [Indexed: 11/30/2022]
Abstract
PURPOSE The aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions. MATERIAL AND METHODS A calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T1 and T2 values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated. RESULTS The proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T1 values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T2 values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps. CONCLUSIONS Reducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially resolved MRF.
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Affiliation(s)
- Thomas Amthor
- Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany.
| | - Mariya Doneva
- Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany
| | - Peter Koken
- Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany
| | - Karsten Sommer
- Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany
| | - Jakob Meineke
- Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany
| | - Peter Börnert
- Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany
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Hoppe E, Körzdörfer G, Würfl T, Wetzl J, Lugauer F, Pfeuffer J, Maier A. Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series. Stud Health Technol Inform 2017. [PMID: 28883201 DOI: 10.3233/978-1-61499-808-2-202] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals. To accelerate the computation of quantitative maps we train a Convolutional Neural Network (CNN) on simulated dictionary data. As a proof of principle we show that the neural network implicitly encodes the dictionary and can replace the matching process.
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Affiliation(s)
- Elisabeth Hoppe
- MR Application Development, Siemens Healthcare, Erlangen, Germany
| | | | - Tobias Würfl
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jens Wetzl
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Felix Lugauer
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Josef Pfeuffer
- MR Application Development, Siemens Healthcare, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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