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Monga A, Singh D, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review. Bioengineering (Basel) 2024; 11:236. [PMID: 38534511 DOI: 10.3390/bioengineering11030236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
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Affiliation(s)
- Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector L de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V W Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder R Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Nagtegaal MA, Hermann I, Weingärtner S, Martinez-Heras E, Solana E, Llufriu S, Gass A, Poot DHJ, van Osch MJP, Vos FM, de Bresser J. White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis. Neuroimage Clin 2023; 40:103528. [PMID: 37837891 PMCID: PMC10589890 DOI: 10.1016/j.nicl.2023.103528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 09/11/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006-0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.
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Affiliation(s)
- Martijn A Nagtegaal
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Ingo Hermann
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM). Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM). Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM). Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dirk H J Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Matthias J P van Osch
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frans M Vos
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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Nagtegaal M, Hartsema E, Koolstra K, Vos F. Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints. Magn Reson Med 2023; 89:286-298. [PMID: 36121015 PMCID: PMC9825911 DOI: 10.1002/mrm.29442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. METHODS The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted. The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference. RESULTS The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 5.8% with the conventional, two-step approach to 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.9% and 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN). CONCLUSION The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.
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Affiliation(s)
- Martijn Nagtegaal
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Emiel Hartsema
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Kirsten Koolstra
- Division of Image Processing, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Frans Vos
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands,Department of RadiologyErasmus MCRotterdamThe Netherlands
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Nunez-Gonzalez L, Nagtegaal MA, Poot DHJ, de Bresser J, van Osch MJP, Hernandez-Tamames JA, Vos FM. Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting. Neuroimage 2022; 263:119638. [PMID: 36122685 DOI: 10.1016/j.neuroimage.2022.119638] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/11/2022] [Accepted: 08/31/2022] [Indexed: 11/30/2022] Open
Abstract
MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.
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Affiliation(s)
- L Nunez-Gonzalez
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - M A Nagtegaal
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - D H J Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - J de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - M J P van Osch
- C.J. Gorter Center for MRI, Radiology Department, Leiden University Medical Center, Leiden, the Netherlands
| | - J A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - F M Vos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
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Lo WC, Panda A, Jiang Y, Ahad J, Gulani V, Seiberlich N. MR fingerprinting of the prostate. MAGMA (NEW YORK, N.Y.) 2022; 35:557-571. [PMID: 35419668 PMCID: PMC10288492 DOI: 10.1007/s10334-022-01012-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 06/03/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T1 and T2 values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T1 and T2 along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.
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Affiliation(s)
- Wei-Ching Lo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - Ananya Panda
- Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - James Ahad
- Case Western Reserve University, Cleveland, OH, USA
| | - Vikas Gulani
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA.
- Case Western Reserve University, Cleveland, OH, USA.
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Piredda GF, Hilbert T, Ravano V, Canales-Rodríguez EJ, Pizzolato M, Meuli R, Thiran JP, Richiardi J, Kober T. Data-driven myelin water imaging based on T 1 and T 2 relaxometry. NMR IN BIOMEDICINE 2022; 35:e4668. [PMID: 34936147 DOI: 10.1002/nbm.4668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/16/2021] [Accepted: 11/30/2021] [Indexed: 06/14/2023]
Abstract
Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T1 and T2 relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T1 /T2 data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Marco Pizzolato
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Bnaiahu N, Omer N, Wilczynski E, Levy S, Blumenfeld-Katzir T, Ben-Eliezer N. Correcting for imaging gradients-related bias of T 2 relaxation times at high-resolution MRI. Magn Reson Med 2022; 88:1806-1817. [PMID: 35666831 PMCID: PMC9544944 DOI: 10.1002/mrm.29319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/15/2022] [Accepted: 05/11/2022] [Indexed: 11/16/2022]
Abstract
Purpose High‐resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T2 relaxation times (qT2) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High‐resolution T2 mapping, however, suffers from severe underestimation of T2 values due to molecular diffusion. This affects both single‐echo and multi‐echo spin echo (SSE and MESE), on top of the well‐known contamination of MESE signals by stimulated echoes, and especially on high‐field and preclinical scanners in which high imaging gradients are used in comparison to clinical scanners. Methods Diffusion bias due to imaging gradients was analyzed by quantifying the effective b‐value for each coherence pathway in SSE and MESE protocols, and incorporating this information in a joint T2‐diffusion reconstruction algorithm. Validation was done on phantoms and in vivo mouse brain using a 9.4T and a 7T MRI scanner. Results Underestimation of T2 values due to strong imaging gradients can reach up to 70%, depending on scan parameters and on the sample's diffusion coefficient. The algorithm presented here produced T2 values that agreed with reference spectroscopic measurements, were reproducible across scan settings, and reduced the average bias of T2 values from −33.5 ± 20.5% to −0.1 ± 3.6%. Conclusions A new joint T2‐diffusion reconstruction algorithm is able to negate imaging gradient–related underestimation of T2 values, leading to reliable mapping of T2 values at high resolutions. Click here for author‐reader discussions
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Affiliation(s)
- Natalie Bnaiahu
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Noam Omer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Ella Wilczynski
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Shir Levy
- School of Chemistry, Tel Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
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Ogier AC, Bustin A, Cochet H, Schwitter J, van Heeswijk RB. The Road Toward Reproducibility of Parametric Mapping of the Heart: A Technical Review. Front Cardiovasc Med 2022; 9:876475. [PMID: 35600490 PMCID: PMC9120534 DOI: 10.3389/fcvm.2022.876475] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/11/2022] [Indexed: 01/02/2023] Open
Abstract
Parametric mapping of the heart has become an essential part of many cardiovascular magnetic resonance imaging exams, and is used for tissue characterization and diagnosis in a broad range of cardiovascular diseases. These pulse sequences are used to quantify the myocardial T1, T2, T2*, and T1ρ relaxation times, which are unique surrogate indices of fibrosis, edema and iron deposition that can be used to monitor a disease over time or to compare patients to one another. Parametric mapping is now well-accepted in the clinical setting, but its wider dissemination is hindered by limited inter-center reproducibility and relatively long acquisition times. Recently, several new parametric mapping techniques have appeared that address both of these problems, but substantial hurdles remain for widespread clinical adoption. This review serves both as a primer for newcomers to the field of parametric mapping and as a technical update for those already well at home in it. It aims to establish what is currently needed to improve the reproducibility of parametric mapping of the heart. To this end, we first give an overview of the metrics by which a mapping technique can be assessed, such as bias and variability, as well as the basic physics behind the relaxation times themselves and what their relevance is in the prospect of myocardial tissue characterization. This is followed by a summary of routine mapping techniques and their variations. The problems in reproducibility and the sources of bias and variability of these techniques are reviewed. Subsequently, novel fast, whole-heart, and multi-parametric techniques and their merits are treated in the light of their reproducibility. This includes state of the art segmentation techniques applied to parametric maps, and how artificial intelligence is being harnessed to solve this long-standing conundrum. We finish up by sketching an outlook on the road toward inter-center reproducibility, and what to expect in the future.
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Affiliation(s)
- Augustin C. Ogier
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Aurelien Bustin
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, Pessac, France
| | - Hubert Cochet
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, Pessac, France
| | - Juerg Schwitter
- Cardiac MR Center, Cardiology Service, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Ruud B. van Heeswijk
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
- *Correspondence: Ruud B. van Heeswijk
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Arridge SR, Ehrhardt MJ, Thielemans K. (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200205. [PMID: 33966461 DOI: 10.1098/rsta.2020.0205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Simon R Arridge
- Department of Computer Science, University College London, London, UK
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, Bath, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
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10
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Avram AV, Sarlls JE, Basser PJ. Whole-Brain Imaging of Subvoxel T1-Diffusion Correlation Spectra in Human Subjects. Front Neurosci 2021; 15:671465. [PMID: 34177451 PMCID: PMC8232058 DOI: 10.3389/fnins.2021.671465] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/14/2021] [Indexed: 12/12/2022] Open
Abstract
T1 relaxation and water mobility generate eloquent MRI tissue contrasts with great diagnostic value in many neuroradiological applications. However, conventional methods do not adequately quantify the microscopic heterogeneity of these important biophysical properties within a voxel, and therefore have limited biological specificity. We describe a new correlation spectroscopic (CS) MRI method for measuring how T1 and mean diffusivity (MD) co-vary in microscopic tissue environments. We develop a clinical pulse sequence that combines inversion recovery (IR) with single-shot isotropic diffusion encoding (IDE) to efficiently acquire whole-brain MRIs with a wide range of joint T1-MD weightings. Unlike conventional diffusion encoding, the IDE preparation ensures that all subvoxel water pools are weighted by their MDs regardless of the sizes, shapes, and orientations of their corresponding microscopic diffusion tensors. Accordingly, IR-IDE measurements are well-suited for model-free, quantitative spectroscopic analysis of microscopic water pools. Using numerical simulations, phantom experiments, and data from healthy volunteers we demonstrate how IR-IDE MRIs can be processed to reconstruct maps of two-dimensional joint probability density functions, i.e., correlation spectra, of subvoxel T1-MD values. In vivo T1-MD spectra show distinct cerebrospinal fluid and parenchymal tissue components specific to white matter, cortical gray matter, basal ganglia, and myelinated fiber pathways, suggesting the potential for improved biological specificity. The one-dimensional marginal distributions derived from the T1-MD correlation spectra agree well with results from other relaxation spectroscopic and quantitative MRI studies, validating the T1-MD contrast encoding and the spectral reconstruction. Mapping subvoxel T1-diffusion correlations in patient populations may provide a more nuanced, comprehensive, sensitive, and specific neuroradiological assessment of the non-specific changes seen on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted MRIs (DWIs) in cancer, ischemic stroke, or brain injury.
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Affiliation(s)
- Alexandru V Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States.,Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Joelle E Sarlls
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
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11
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Kratzer FJ, Flassbeck S, Schmitter S, Wilferth T, Magill AW, Knowles BR, Platt T, Bachert P, Ladd ME, Nagel AM. 3D sodium ( 23 Na) magnetic resonance fingerprinting for time-efficient relaxometric mapping. Magn Reson Med 2021; 86:2412-2425. [PMID: 34061397 DOI: 10.1002/mrm.28873] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/16/2021] [Accepted: 05/08/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To develop a framework for 3D sodium (23 Na) MR fingerprinting (MRF), based on irreducible spherical tensor operators with tailored flip angle (FA) pattern and time-efficient data acquisition for simultaneous quantification of T1 , T 2 l ∗ , T 2 s ∗ , and T 2 ∗ in addition to ΔB0 . METHODS 23 Na-MRF was implemented in a 3D sequence and irreducible spherical tensor operators were exploited in the simulations. Furthermore, the Cramér Rao lower bound was used to optimize the flip angle pattern. A combination of single and double echo readouts was implemented to increase the readout efficiency. A study was conducted to compare results in a multicompartment phantom acquired with MRF and reference methods. Finally, the relaxation times in the human brain were measured in four healthy volunteers. RESULTS Phantom experiments revealed a mean difference of 1.0% between relaxation times acquired with MRF and results determined with the reference methods. Simultaneous quantification of the longitudinal and transverse relaxation times in the human brain was possible within 32 min using 3D 23 Na-MRF with a nominal resolution of (5 mm)3 . In vivo measurements in four volunteers yielded average relaxation times of: T1,brain = (35.0 ± 3.2) ms, T 2 l , brain ∗ = (29.3 ± 3.8) ms and T 2 s , brain ∗ = (5.5 ± 1.3) ms in brain tissue, whereas T1,CSF = (61.9 ± 2.8) ms and T 2 , CSF ∗ = (46.3 ± 4.5) ms was found in cerebrospinal fluid. CONCLUSION The feasibility of in vivo 3D relaxometric sodium mapping within roughly ½ h is demonstrated using MRF in the human brain, moving sodium relaxometric mapping toward clinically relevant measurement times.
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Affiliation(s)
- Fabian J Kratzer
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, Ruprecht-Karls University Heidelberg, Heidelberg, Germany
| | - Sebastian Flassbeck
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Center for Biomedical Imaging, Department of Radiology, New York University, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, New York University, New York, New York, USA
| | - Sebastian Schmitter
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
| | - Tobias Wilferth
- Institute of Radiology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Arthur W Magill
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Benjamin R Knowles
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tanja Platt
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Bachert
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, Ruprecht-Karls University Heidelberg, Heidelberg, Germany
| | - Mark E Ladd
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, Ruprecht-Karls University Heidelberg, Heidelberg, Germany
- Faculty of Medicine, Ruprecht-Karls University Heidelberg, Heidelberg, Germany
| | - Armin M Nagel
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Radiology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
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12
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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13
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Hsieh JJL, Svalbe I. Magnetic resonance fingerprinting: from evolution to clinical applications. J Med Radiat Sci 2020; 67:333-344. [PMID: 32596957 PMCID: PMC7754037 DOI: 10.1002/jmrs.413] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 05/19/2020] [Accepted: 05/23/2020] [Indexed: 02/06/2023] Open
Abstract
In 2013, Magnetic Resonance Fingerprinting (MRF) emerged as a method for fast, quantitative Magnetic Resonance Imaging. This paper reviews the current status of MRF up to early 2020 and aims to highlight the advantages MRF can offer medical imaging professionals. By acquiring scan data as pseudorandom samples, MRF elicits a unique signal evolution, or 'fingerprint', from each tissue type. It matches 'randomised' free induction decay acquisitions against pre-computed simulated tissue responses to generate a set of quantitative images of T1 , T2 and proton density (PD) with co-registered voxels, rather than as traditional relative T1 - and T2 -weighted images. MRF numeric pixel values retain accuracy and reproducibility between 2% and 8%. MRF acquisition is robust to strong undersampling of k-space. Scan sequences have been optimised to suppress sub-sampling artefacts, while artificial intelligence and machine learning techniques have been employed to increase matching speed and precision. MRF promises improved patient comfort with reduced scan times and fewer image artefacts. Quantitative MRF data could be used to define population-wide numeric biomarkers that classify normal versus diseased tissue. Certification of clinical centres for MRF scan repeatability would permit numeric comparison of sequential images for any individual patient and the pooling of multiple patient images across large, cross-site imaging studies. MRF has to date shown promising results in early clinical trials, demonstrating reliable differentiation between malignant and benign prostate conditions, and normal and sclerotic hippocampal tissue. MRF is now undergoing small-scale trials at several sites across the world; moving it closer to routine clinical application.
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Affiliation(s)
- Jean J. L. Hsieh
- Department of Diagnostic RadiologyTan Tock Seng HospitalSingaporeSingapore
- Department of Medical Imaging and Radiation SciencesMonash UniversityClaytonVictoriaAustralia
| | - Imants Svalbe
- School of Physics and AstronomyMonash UniversityClaytonVictoriaAustralia
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14
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Kim D, Wisnowski JL, Nguyen CT, Haldar JP. Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications. NMR IN BIOMEDICINE 2020; 33:e4244. [PMID: 31909534 PMCID: PMC7338241 DOI: 10.1002/nbm.4244] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/09/2019] [Accepted: 11/27/2019] [Indexed: 05/02/2023]
Abstract
Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.
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Affiliation(s)
- Daeun Kim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
- Correspondence Daeun Kim,
| | - Jessica L. Wisnowski
- Radiology, Children’s Hospital Los Angeles, CA, USA
- Pediatrics, Children’s Hospital Los Angeles, CA, USA
| | - Christopher T. Nguyen
- Harvard Medical School and Cardiovascular Research Center, Massachusetts General Hospital, MA, USA
- Martinos Center for Biomedical Imaging, Radiology, Massachusetts General Hospital, MA, USA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, CA, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
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15
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Nagtegaal M, Koken P, Amthor T, de Bresser J, Mädler B, Vos F, Doneva M. Myelin water imaging from multi-echo T2 MR relaxometry data using a joint sparsity constraint. Neuroimage 2020; 219:117014. [DOI: 10.1016/j.neuroimage.2020.117014] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 05/29/2020] [Accepted: 05/30/2020] [Indexed: 11/24/2022] Open
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16
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Bernstein B, Liu S, Papadaniil C, Fernandez-Granda C. Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems. IEEE TRANSACTIONS ON INFORMATION THEORY 2020; 66:5904-5926. [PMID: 32921802 PMCID: PMC7480821 DOI: 10.1109/tit.2020.2985015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Extracting information from nonlinear measurements is a fundamental challenge in data analysis. In this work, we consider separable inverse problems, where the data are modeled as a linear combination of functions that depend nonlinearly on certain parameters of interest. These parameters may represent neuronal activity in a human brain, frequencies of electromagnetic waves, fluorescent probes in a cell, or magnetic relaxation times of biological tissues. Separable nonlinear inverse problems can be reformulated as underdetermined sparse-recovery problems, and solved using convex programming. This approach has had empirical success in a variety of domains, from geophysics to medical imaging, but lacks a theoretical justification. In particular, compressed-sensing theory does not apply, because the measurement operators are deterministic and violate incoherence conditions such as the restricted-isometry property. Our main contribution is a theory for sparse recovery adapted to deterministic settings. We show that convex programming succeeds in recovering the parameters of interest, as long as their values are sufficiently distinct with respect to the correlation structure of the measurement operator. The theoretical results are illustrated through numerical experiments for two applications: heat-source localization and estimation of brain activity from electroencephalography data.
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Affiliation(s)
- Brett Bernstein
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10011 USA
| | - Sheng Liu
- Center for Data Science, New York University, New York, NY 10011 USA
| | - Chrysa Papadaniil
- Center for Brain Imaging, New York University, New York, NY 10011 USA
| | - Carlos Fernandez-Granda
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10011 USA; Center for Data Science, New York University, New York, NY 10011 USA
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17
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McGivney DF, Boyacioğlu R, Jiang Y, Poorman ME, Seiberlich N, Gulani V, Keenan KE, Griswold MA, Ma D. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51:993-1007. [PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.
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Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioğlu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Megan E. Poorman
- Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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18
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Jaubert O, Cruz G, Bustin A, Schneider T, Koken P, Doneva M, Rueckert D, Botnar RM, Prieto C. Free-running cardiac magnetic resonance fingerprinting: Joint T1/T2 map and Cine imaging. Magn Reson Imaging 2020; 68:173-182. [PMID: 32061964 PMCID: PMC7677167 DOI: 10.1016/j.mri.2020.02.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/21/2020] [Accepted: 02/09/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop and evaluate a novel non-ECG triggered 2D magnetic resonance fingerprinting (MRF) sequence allowing for simultaneous myocardial T1 and T2 mapping and cardiac Cine imaging. METHODS Cardiac MRF (cMRF) has been recently proposed to provide joint T1/T2 myocardial mapping by triggering the acquisition to mid-diastole and relying on a subject-dependent dictionary of MR signal evolutions to generate the maps. In this work, we propose a novel "free-running" (non-ECG triggered) cMRF framework for simultaneous myocardial T1 and T2 mapping and cardiac Cine imaging in a single scan. Free-running cMRF is based on a transient state bSSFP acquisition with tiny golden angle radial readouts, varying flip angle and multiple adiabatic inversion pulses. The acquired data is retrospectively gated into several cardiac phases, which are reconstructed with an approach that combines parallel imaging, low rank modelling and patch-based high-order tensor regularization. Free-running cMRF was evaluated in a standardized phantom and ten healthy subjects. Comparison with reference spin-echo, MOLLI, SASHA, T2-GRASE and Cine was performed. RESULTS T1 and T2 values obtained with the proposed approach were in good agreement with reference phantom values (ICC(A,1) > 0.99). Reported values for myocardium septum T1 were 1043 ± 48 ms, 1150 ± 100 ms and 1160 ± 79 ms for MOLLI, SASHA and free-running cMRF respectively and for T2 of 51.7 ± 4.1 ms and 44.6 ± 4.1 ms for T2-GRASE and free-running cMRF respectively. Good agreement was observed between free-running cMRF and conventional Cine 2D ejection fraction (bias = -0.83%). CONCLUSION The proposed free-running cardiac MRF approach allows for simultaneous assessment of myocardial T1 and T2 and Cine imaging in a single scan.
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Affiliation(s)
- O Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - G Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - A Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - T Schneider
- Philips Healthcare, Guilford, United Kingdom
| | - P Koken
- Philips Research Europe, Hamburg, Germany
| | - M Doneva
- Philips Research Europe, Hamburg, Germany
| | - D Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - R M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - C Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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19
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Nagtegaal M, Koken P, Amthor T, Doneva M. Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting. Magn Reson Med 2020; 83:521-534. [PMID: 31418918 PMCID: PMC6899479 DOI: 10.1002/mrm.27947] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/22/2019] [Accepted: 07/23/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop an efficient algorithm for multi-component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties. METHODS Different tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non-negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi-component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi-component analysis of MRF data. A new algorithm combining joint sparsity and non-negativity constraints is proposed and compared to state-of-the-art multi-component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers. RESULTS Simulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi-component MRF publications. CONCLUSIONS The proposed algorithm is faster than previously proposed methods for multi-component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel-wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi-component MRF.
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Affiliation(s)
- Martijn Nagtegaal
- Department of Quantitative ImagingTechnical University DelftDelftthe Netherlands
- Institut für MathematikTechnische Universität BerlinBerlinGermany
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20
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Jaubert O, Cruz G, Bustin A, Schneider T, Lavin B, Koken P, Hajhosseiny R, Doneva M, Rueckert D, Botnar RM, Prieto C. Water-fat Dixon cardiac magnetic resonance fingerprinting. Magn Reson Med 2019; 83:2107-2123. [PMID: 31736146 PMCID: PMC7064906 DOI: 10.1002/mrm.28070] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 10/15/2019] [Accepted: 10/17/2019] [Indexed: 12/12/2022]
Abstract
Purpose Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1, T2, and M0 maps. Here, we develop a 3‐point Dixon‐cMRF approach to enable simultaneous water specific T1, T2, and M0 mapping of the heart and fat fraction (FF) estimation in a single breath‐hold scan. Methods Dixon‐cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3‐echo GRE acquisition with golden angle radial readout and a high‐dimensional low‐rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water–fat separation of the Dixon‐cMRF time series is performed to allow for water‐ and fat‐specific T1, T2, and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon‐cMRF was evaluated in a standardized T1–T2 phantom, in a water–fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2‐GRASE, and 6‐point Dixon proton density FF (PDFF) mapping. Results Dixon‐cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon‐cMRF FF and reference PDFF (R2 > 0.99) and between Dixon‐cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water–fat phantom. In vivo Dixon‐cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2‐GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon‐cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2‐GRASE and water Dixon‐cMRF, respectively. Dixon‐cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. Conclusion The proposed Dixon‐cMRF allows to simultaneously quantify myocardial water T1, water T2, and FF in a single breath‐hold scan, enabling multi‐parametric T1, T2, and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water–fat partial volume was demonstrated in phantom experiments.
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Affiliation(s)
- Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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21
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Cruz G, Jaubert O, Botnar RM, Prieto C. Cardiac Magnetic Resonance Fingerprinting: Technical Developments and Initial Clinical Validation. Curr Cardiol Rep 2019; 21:91. [PMID: 31352620 PMCID: PMC6661029 DOI: 10.1007/s11886-019-1181-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW Magnetic resonance imaging (MRI) has enabled non-invasive myocardial tissue characterization in a wide range of cardiovascular diseases by quantifying several tissue specific parameters such as T1, T2, and T2* relaxation times. Simultaneous assessment of these parameters has recently gained interest to potentially improve diagnostic accuracy and enable further understanding of the underlying disease. However, these quantitative maps are usually acquired sequentially and are not necessarily co-registered, making multi-parametric analysis challenging. Magnetic resonance fingerprinting (MRF) has been recently introduced to unify and streamline parametric mapping into a single simultaneous, multi-parametric, fully co-registered, and efficient scan. Feasibility of cardiac MRF has been demonstrated and initial clinical validation studies are ongoing. Provide an overview of the cardiac MRF framework, recent technical developments and initial undergoing clinical validation. RECENT FINDINGS Cardiac MRF has enabled the acquisition of co-registered T1 and T2 maps in a single, efficient scan. Initial results demonstrate feasibility of cardiac MRF in healthy subjects and small patient cohorts. Current in vivo results show a small bias and comparable precision in T1 and T2 with respect to conventional clinical parametric mapping approaches. This bias may be explained by several confounding factors such as magnetization transfer and field inhomogeneities, which are currently not included in the cardiac MRF model. Initial clinical validation for cardiac MRF has demonstrated good reproducibility in healthy subjects and heart transplant patients, reduced artifacts in inflammatory cardiomyopathy patients and good differentiation between hypertrophic cardiomyopathy and healthy controls. Cardiac MRF has emerged as a novel technique for simultaneous, multi-parametric, and co-registered mapping of different tissue parameters. Initial efforts have focused on enabling T1, T2, and fat quantification; however this approach has the potential of enabling quantification of several other parameters (such as T2*, diffusion, perfusion, and flow) from a single scan. Initial results in healthy subjects and patients are promising, thus further clinical validation is now warranted.
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Affiliation(s)
- G. Cruz
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - O. Jaubert
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - R. M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Pontificia Universidad Católica de Chile Escuela de Ingeniería, Santiago, Chile
| | - C. Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Pontificia Universidad Católica de Chile Escuela de Ingeniería, Santiago, Chile
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Lima da Cruz G, Bustin A, Jaubert O, Schneider T, Botnar RM, Prieto C. Sparsity and locally low rank regularization for MR fingerprinting. Magn Reson Med 2019; 81:3530-3543. [PMID: 30720209 PMCID: PMC6492150 DOI: 10.1002/mrm.27665] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 12/03/2018] [Accepted: 12/29/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE Develop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF). METHODS Recent works have introduced low rank reconstructions to MRF, based on temporal compression operators learned from the MRF dictionary. In other MR applications, LLR regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and LLR regularization terms in the MRF reconstruction. This approach, so called SLLR-MRF, further reduces aliasing in the time-point images and enables higher acceleration factors. The proposed approach was evaluated in simulations, T1 /T2 phantom acquisition, and in vivo brain acquisitions in 5 healthy subjects with different undersampling factors. Acceleration was also used in vivo to enable acquisitions with higher in-plane spatial resolution in comparable scan time. RESULTS Simulations, phantom, and in vivo results show that low rank MRF reconstructions with high acceleration factors (<875 time-point images, 1 radial spoke per time-point) have residual aliasing artifacts that propagate into the parametric maps. The artifacts are reduced with the proposed SLLR-MRF resulting in considerable improvements in precision, without changes in accuracy. In vivo results show improved parametric maps for the proposed SLLR-MRF, potentially enabling MRF acquisitions with 1 radial spoke per time-point in approximately 2.6 s (~600 time-point images) for 2 × 2 mm and 9.6 s (1750 time-point images) for 1 × 1 mm in-plane resolution. CONCLUSION The proposed SLLR-MRF reconstruction further improves parametric map quality compared with low rank MRF, enabling shorter scan times and/or increased spatial resolution.
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Affiliation(s)
- Gastão Lima da Cruz
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Aurélien Bustin
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Oliver Jaubert
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | | | - René M. Botnar
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Católica de ChileEscuela de IngenieríaSantiagoChile
| | - Claudia Prieto
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
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