1
|
Huaroc Moquillaza E, Weiss K, Stelter J, Steinhelfer L, Lee YJ, Amthor T, Koken P, Makowski MR, Braren R, Doneva M, Karampinos DC. Accelerated liver water T 1 mapping using single-shot continuous inversion-recovery spiral imaging. NMR Biomed 2024; 37:e5097. [PMID: 38269568 DOI: 10.1002/nbm.5097] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/21/2023] [Accepted: 12/06/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE Liver T1 mapping techniques typically require long breath holds or long scan time in free-breathing, need correction for B 1 + inhomogeneities and process composite (water and fat) signals. The purpose of this work is to accelerate the multi-slice acquisition of liver water selective T1 (wT1) mapping in a single breath hold, improving the k-space sampling efficiency. METHODS The proposed continuous inversion-recovery (IR) Look-Locker methodology combines a single-shot gradient echo spiral readout, Dixon processing and a dictionary-based analysis for liver wT1 mapping at 3 T. The sequence parameters were adapted to obtain short scan times. The influence of fat, B 1 + inhomogeneities and TE on the estimation of T1 was first assessed using simulations. The proposed method was then validated in a phantom and in 10 volunteers, comparing it with MRS and the modified Look-Locker inversion-recovery (MOLLI) method. Finally, the clinical feasibility was investigated by comparing wT1 maps with clinical scans in nine patients. RESULTS The phantom results are in good agreement with MRS. The proposed method encodes the IR-curve for the liver wT1 estimation, is minimally sensitive to B 1 + inhomogeneities and acquires one slice in 1.2 s. The volunteer results confirmed the multi-slice capability of the proposed method, acquiring nine slices in a breath hold of 11 s. The present work shows robustness to B 1 + inhomogeneities (wT 1 , No B 1 + = 1.07 wT 1 , B 1 + - 45.63 , R 2 = 0.99 ) , good repeatability (wT 1 , 2 ° = 1 . 0 wT 1 , 1 ° - 2.14 , R 2 = 0.96 ) and is in better agreement with MRS (wT 1 = 0.92 wT 1 MRS + 103.28 , R 2 = 0.38 ) than is MOLLI (wT 1 MOLLI = 0.76 wT 1 MRS + 254.43 , R 2 = 0.44 ) . The wT1 maps in patients captured diverse lesions, thus showing their clinical feasibility. CONCLUSION A single-shot spiral acquisition can be combined with a continuous IR Look-Locker method to perform rapid repeatable multi-slice liver water T1 mapping at a rate of 1.2 s per slice without a B 1 + map. The proposed method is suitable for nine-slice liver clinical applications acquired in a single breath hold of 11 s.
Collapse
Affiliation(s)
- Elizabeth Huaroc Moquillaza
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Jonathan Stelter
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lisa Steinhelfer
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | | | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rickmer Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| |
Collapse
|
2
|
Beljaards L, Pezzotti N, Rao C, Doneva M, van Osch MJP, Staring M. AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models. Med Phys 2024. [PMID: 38167996 DOI: 10.1002/mp.16918] [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: 08/17/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scan motion. PURPOSE To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in the scanner. METHODS We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. RESULTS Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values. CONCLUSIONS The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-based reconstructions.
Collapse
Affiliation(s)
- Laurens Beljaards
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicola Pezzotti
- Cardiologs, Philips, Paris, France
- Faculty of Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Chinmay Rao
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Marius Staring
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| |
Collapse
|
3
|
Wülker C, Gessert NT, Doneva M, Kastryulin S, Ercan E, Nielsen T. Digital reference objects for evaluating algorithm performance in MR image formation. Magn Reson Imaging 2024; 105:67-74. [PMID: 37925111 DOI: 10.1016/j.mri.2023.10.008] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/21/2023] [Accepted: 10/22/2023] [Indexed: 11/06/2023]
Abstract
PURPOSE Digital Reference Objects (DROs) are mathematical phantoms that can serve as a basis for evaluating MR image quality (IQ) in an objective way. Their main purpose is to facilitate the establishment of fully automated and perfectly reproducible IQ metrics to objectively compare different algorithms in MR image formation in a standardized manner. They also allow to re-build parts of standard phantoms. METHODS We sample DROs directly in k-space, using analytical formulas for the continuous Fourier transform of primitive shapes. We demonstrate this DRO approach by applying a state-of-the-art CNN-based denoising algorithm that is robust to varying noise levels to noisy images of the resolution section of the well-known ACR phantom for IQ assessment, reconstructed from both measured and simulated k-space data. RESULTS Applying the CNN-based denoising algorithm to the measured and simulated version of the ACR phantom resolution section produced virtually identical results, as confirmed by visual and quantitative comparison. CONCLUSIONS DROs can help guide technology selection during the development of new algorithms in MR image formation, e.g., via deep learning. This could be an important step towards reproducible MR image formation.
Collapse
Affiliation(s)
| | | | | | | | - Ece Ercan
- Philips Clinical Science, Best, Netherlands
| | | |
Collapse
|
4
|
Wang K, Doneva M, Meineke J, Amthor T, Karasan E, Tan F, Tamir JI, Yu SX, Lustig M. High-fidelity direct contrast synthesis from magnetic resonance fingerprinting. Magn Reson Med 2023; 90:2116-2129. [PMID: 37332200 DOI: 10.1002/mrm.29766] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 02/14/2023] [Revised: 05/03/2023] [Accepted: 05/31/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations. METHODS To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. The performance of our proposed method is demonstrated on in vivo MRF scans from healthy volunteers. Quantitative metrics, including normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID), were used to evaluate the performance of the proposed method and compare it with others. RESULTS In-vivo experiments demonstrated excellent image quality with respect to that of simulation-based contrast synthesis and previous DCS methods, both visually and according to quantitative metrics. We also demonstrate cases in which our trained model is able to mitigate the in-flow and spiral off-resonance artifacts typically seen in MRF reconstructions, and thus more faithfully represent conventional spin echo-based contrast-weighted images. CONCLUSION We present N-DCSNet to directly synthesize high-fidelity multicontrast MR images from a single MRF acquisition. This method can significantly decrease examination time. By directly training a network to generate contrast-weighted images, our method does not require any model-based simulation and therefore can avoid reconstruction errors due to dictionary matching and contrast simulation (code available at:https://github.com/mikgroup/DCSNet).
Collapse
Affiliation(s)
- Ke Wang
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
- International Computer Science Institute, University of California at Berkeley, Berkeley, California, USA
| | | | | | | | - Ekin Karasan
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
| | - Fei Tan
- Bioengineering, UC Berkeley-UCSF, San Francisco, California, USA
| | - Jonathan I Tamir
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA
| | - Stella X Yu
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
- International Computer Science Institute, University of California at Berkeley, Berkeley, California, USA
- Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | | |
Collapse
|
5
|
Yu VY, Otazo R, Wu C, Subashi E, Baumann M, Koken P, Doneva M, Mazurkewitz P, Shasha D, Zelefsky M, Cervino L, Cohen O. Quantitative longitudinal mapping of radiation-treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction. Magn Reson Imaging 2023; 101:25-34. [PMID: 37015305 PMCID: PMC10623548 DOI: 10.1016/j.mri.2023.03.019] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 03/29/2023] [Indexed: 04/06/2023]
Abstract
MR fingerprinting (MRF) enables fast multiparametric quantitative imaging with a single acquisition and has been shown to improve diagnosis of prostate cancer. However, most prostate MRF studies were performed with spiral acquisitions that are sensitive to B0 inhomogeneities and consequent blurring. In this work, a radial MRF acquisition with a novel subspace reconstruction technique was developed to enable fast T1/T2 mapping in the prostate in under 4 min. The subspace reconstruction exploits the extensive temporal correlations in the MRF dictionary to pre-compute a low dimensional space for the solution and thus reduce the number of radial spokes to accelerate the acquisition. Iterative reconstruction with the subspace model and additional regularization of the signal representation in the subspace is performed to minimize the number of spokes and maintain matching quality and SNR. Reconstruction accuracy was assessed using the ISMRM NIST phantom. In-vivo validation was performed on two healthy subjects and two prostate cancer patients undergoing radiation therapy. The longitudinal repeatability was quantified using the concordance correlation coefficient (CCC) in one of the healthy subjects by repeated scans over 1 year. One prostate cancer patient was scanned at three time points, before initiating therapy and following brachytherapy and external beam radiation. Changes in the T1/T2 maps obtained with the proposed method were quantified. The prostate, peripheral and transitional zones, and visible dominant lesion were delineated for each study, and the statistics and distribution of the quantitative mapping values were analyzed. Significant image quality improvements compared with standard reconstruction methods were obtained with the proposed subspace reconstruction method. A notable decrease in the spread of the T1/T2 values without biasing the estimated mean values was observed with the subspace reconstruction and agreed with reported literature values. The subspace reconstruction enabled visualization of small differences in T1/T2 values in the tumor region within the peripheral zone. Longitudinal imaging of a volunteer subject yielded CCC of 0.89 for MRF T1, and 0.81 for MRF T2 in the prostate gland. Longitudinal imaging of the prostate patient confirmed the feasibility of capturing radiation treatment related changes. This work is a proof-of-concept for a high resolution and fast quantitative mapping using golden-angle radial MRF combined with a subspace reconstruction technique for longitudinal treatment response assessment in subjects undergoing radiation treatment.
Collapse
Affiliation(s)
- Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ergys Subashi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Peter Koken
- Philips Research, MR Research, Hamburg, Germany
| | | | | | - Daniel Shasha
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael Zelefsky
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| |
Collapse
|
6
|
Berg RC, Menegaux A, Amthor T, Gilbert G, Mora M, Schlaeger S, Pongratz V, Lauerer M, Sorg C, Doneva M, Vavasour I, Mühlau M, Preibisch C. Comparing myelin-sensitive magnetic resonance imaging measures and resulting g-ratios in healthy and multiple sclerosis brains. Neuroimage 2022; 264:119750. [PMID: 36379421 PMCID: PMC9931395 DOI: 10.1016/j.neuroimage.2022.119750] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
The myelin concentration and the degree of myelination of nerve fibers can provide valuable information on the integrity of human brain tissue. Magnetic resonance imaging (MRI) of myelin-sensitive parameters can help to non-invasively evaluate demyelinating diseases such as multiple sclerosis (MS). Several different myelin-sensitive MRI methods have been proposed to determine measures of the degree of myelination, in particular the g-ratio. However, variability in underlying physical principles and different biological models influence measured myelin concentrations, and consequently g-ratio values. We therefore investigated similarities and differences between five different myelin-sensitive MRI measures and their effects on g-ratio mapping in the brains of both MS patients and healthy volunteers. We compared two different estimates of the myelin water fraction (MWF) as well as the inhomogeneous magnetization transfer ratio (ihMTR), magnetization transfer saturation (MTsat), and macromolecular tissue volume (MTV) in 13 patients with MS and 14 healthy controls. In combination with diffusion-weighted imaging, we derived g-ratio parameter maps for each of the five different myelin measures. The g-ratio values calculated from different myelin measures varied strongly, especially in MS lesions. While, compared to normal-appearing white matter, MTsat and one estimate of the MWF resulted in higher g-ratio values within lesions, ihMTR, MTV, and the second MWF estimate resulted in lower lesion g-ratio values. As myelin-sensitive measures provide rough estimates of myelin content rather than absolute myelin concentrations, resulting g-ratio values strongly depend on the utilized myelin measure and model used for g-ratio mapping. When comparing g-ratio values, it is, thus, important to utilize the same MRI methods and models or to consider methodological differences. Particular caution is necessary in pathological tissue such as MS lesions.
Collapse
Affiliation(s)
- Ronja C. Berg
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Corresponding author at: Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaninger Str. 22, 81675, München, Germany. (R.C. Berg)
| | - Aurore Menegaux
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | | | | | - Maria Mora
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany
| | - Sarah Schlaeger
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany
| | - Viola Pongratz
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Markus Lauerer
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Christian Sorg
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany,Technical University of Munich, School of Medicine, Department of Psychiatry, Munich, Germany
| | | | - Irene Vavasour
- University of British Columbia, Department of Radiology, Vancouver, BC, Canada
| | - Mark Mühlau
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| |
Collapse
|
7
|
Tourais J, Scannell CM, Schneider T, Alskaf E, Crawley R, Bosio F, Sanchez-Gonzalez J, Doneva M, Schülke C, Meineke J, Keupp J, Smink J, Breeuwer M, Chiribiri A, Henningsson M, Correia T. High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration. Front Cardiovasc Med 2022; 9:884221. [PMID: 35571164 PMCID: PMC9099052 DOI: 10.3389/fcvm.2022.884221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/04/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion). Materials and Methods FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm2) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm2) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic). Results FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS. Discussion FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.
Collapse
Affiliation(s)
- Joao Tourais
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
- Department of Imaging Physics, Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Cian M. Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Ebraham Alskaf
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard Crawley
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Filippo Bosio
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | | | | | | | | | - Jouke Smink
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linkoping University, Linkoping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linkoping University, Linkoping, Sweden
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre for Marine Sciences (CCMAR), Faro, Portugal
- *Correspondence: Teresa Correia
| |
Collapse
|
8
|
Guenthner C, Amthor T, Doneva M, Kozerke S. A unifying view on extended phase graphs and Bloch simulations for quantitative MRI. Sci Rep 2021; 11:21289. [PMID: 34711847 PMCID: PMC8553818 DOI: 10.1038/s41598-021-00233-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/05/2021] [Indexed: 12/16/2022] Open
Abstract
Quantitative MRI methods and learning-based algorithms require exact forward simulations. One critical factor to correctly describe magnetization dynamics is the effect of slice-selective RF pulses. While contemporary simulation techniques correctly capture their influence, they only provide final magnetization distributions, require to be run for each parameter set separately, and make it hard to derive general theoretical conclusions and to generate a fundamental understanding of echo formation in the presence of slice-profile effects. This work aims to provide a mathematically exact framework, which is equally intuitive as extended phase graphs (EPGs), but also considers slice-profiles through their natural spatial representation. We show, through an analytical, hybrid Bloch-EPG formalism, that the spatially-resolved EPG approach allows to exactly predict the signal dependency on off-resonance, spoiling moment, microscopic dephasing, and echo time. We also demonstrate that our formalism allows to use the same phase graph to simulate both gradient-spoiled and balanced SSFP-based MR sequences. We present a derivation of the formalism and identify the connection to existing methods, i.e. slice-selective Bloch, slice-selective EPG, and the partitioned EPG. As a use case, the proposed hybrid Bloch-EPG framework is applied to MR Fingerprinting.
Collapse
Affiliation(s)
- Christian Guenthner
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
- Philips Research, Hamburg, Germany.
| | | | | | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
9
|
Keil VC, Bakoeva SP, Jurcoane A, Doneva M, Amthor T, Koken P, Mädler B, Lüchters G, Block W, Wüllner U, Hattingen E. A pilot study of magnetic resonance fingerprinting in Parkinson's disease. NMR Biomed 2020; 33:e4389. [PMID: 32783321 DOI: 10.1002/nbm.4389] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 07/16/2020] [Accepted: 07/18/2020] [Indexed: 06/11/2023]
Abstract
Parkinson's disease (PD) affects more than six million people, but reliable MRI biomarkers with which to diagnose patients have not been established. Magnetic resonance fingerprinting (MRF) is a recent quantitative technique that can provide relaxometric maps from a single sequence. The purpose of this study is to assess the potential of MRF to identify PD in patients and their disease severity, as well as to evaluate comfort during MRF. Twenty-five PD patients and 25 matching controls underwent 3 T MRI, including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 maps were generated by voxel-wise matching the measured MRF signal to a precomputed dictionary. All participants also received standard inversion recovery T1 and multi-echo T2 mapping. An ROI-based analysis of relaxation times was performed. Differences between patients and controls as well as techniques were determined by logistic regression, Spearman correlation and t-test. Patients were asked to estimate the subjective comfort of the MRF sequence. Both MRF-based T1 and T2 mapping discriminated patients from controls: T1 relaxation times differed most in cortical grey matter (PD 1337 ± 38 vs. control 1386 ± 37 ms; mean ± SD; P = .0001) and, in combination with normal-appearing white matter, enabled correct discrimination in 85.7% of cases (sensitivity 83.3%; specificity 88.0%; receiver-operating characteristic [ROC]) area under the curve [AUC] 0.87), while for T2 mapping the left putamen was the strongest classifier (40.54 ± 6.28 vs. 34.17 ± 4.96 ms; P = .0001), enabling differentiation of groups in 84.0% of all cases (sensitivity 80.0%; specificity 88.0%; ROC AUC 0.87). Relaxation time differences were not associated with disease severity. Standard mapping techniques generated significantly different relaxation time values and identified other structures as different between groups other than MRF. Twenty-three out of 25 PD patients preferred the MRF examination instead of a standard MRI. MRF-based mapping can identify PD patients with good comfort but needs further assessment regarding disease severity identification and its potential for comparability with standard mapping technique results.
Collapse
Affiliation(s)
- Vera Catharina Keil
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Radiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Stilyana Peteva Bakoeva
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Neurology, University Hospital Duesseldorf, Düsseldorf, Germany
| | - Alina Jurcoane
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Institute for Neuroradiology, University Hospital, Goethe University Frankfurt am Main, Frankfurt, Germany
| | | | | | | | | | - Guido Lüchters
- Zentrum für Entwicklungsforschung, University of Bonn, Bonn, Germany
| | - Wolfgang Block
- Department of Radiology, University Hospital Bonn, Bonn, Germany
| | - Ullrich Wüllner
- Department of Neurology, University Hospital Bonn, Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Elke Hattingen
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Institute for Neuroradiology, University Hospital, Goethe University Frankfurt am Main, Frankfurt, Germany
| |
Collapse
|
10
|
Nolte T, Scholten H, Gross-Weege N, Amthor T, Koken P, Doneva M, Schulz V. Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects. Magn Reson Med 2020; 85:1865-1880. [PMID: 33118649 DOI: 10.1002/mrm.28545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 03/16/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T. METHODS Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE. RESULTS Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T2 left-right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions. CONCLUSION Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.
Collapse
Affiliation(s)
- Teresa Nolte
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Hannah Scholten
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
| | - Nicolas Gross-Weege
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Thomas Amthor
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Peter Koken
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Mariya Doneva
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Volkmar Schulz
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.,Physics Institute III B, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
11
|
Jaubert O, Cruz G, Bustin A, Hajhosseiny R, Nazir S, Schneider T, Koken P, Doneva M, Rueckert D, Masci PG, Botnar RM, Prieto C. T1, T2, and Fat Fraction Cardiac MR Fingerprinting: Preliminary Clinical Evaluation. J Magn Reson Imaging 2020; 53:1253-1265. [PMID: 33124081 DOI: 10.1002/jmri.27415] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [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: 07/30/2020] [Revised: 10/13/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Dixon cardiac magnetic resonance fingerprinting (MRF) has been recently introduced to simultaneously provide water T1 , water T2 , and fat fraction (FF) maps. PURPOSE To assess Dixon cardiac MRF repeatability in healthy subjects and its clinical feasibility in a cohort of patients with cardiovascular disease. POPULATION T1MES phantom, water-fat phantom, 11 healthy subjects and 19 patients with suspected cardiovascular disease. STUDY TYPE Prospective. FIELD STRENGTH/SEQUENCE 1.5T, inversion recovery spin echo (IRSE), multiecho spin echo (MESE), modified Look-Locker inversion recovery (MOLLI), T2 gradient spin echo (T2 -GRASE), 6-echo gradient rewound echo (GRE), and Dixon cardiac MRF. ASSESSMENT Dixon cardiac MRF precision was assessed through repeated scans against conventional MOLLI, T2 -GRASE, and PDFF in phantom and 11 healthy subjects. Dixon cardiac MRF native T1 , T2 , FF, postcontrast T1 and synthetic extracellular volume (ECV) maps were assessed in 19 patients in comparison to conventional sequences. Measurements in patients were performed in the septum and in late gadolinium enhanced (LGE) areas and assessed using mean value distributions, correlation, and Bland-Altman plots. Image quality and diagnostic confidence were assessed by three experts using 5-point scoring scales. STATISTICAL TESTS Paired Wilcoxon rank signed test and paired t-tests were applied. Statistical significance was indicated by *(P < 0.05). RESULTS Dixon cardiac MRF showed good overall precision in phantom and in vivo. Septal average repeatability was ~23 msec for T1 , ~2.2 msec for T2 , and ~1% for FF. Biases in healthy subjects/patients were measured at +37 msec*/+60 msec* and -8.8 msec*/-8 msec* when compared to MOLLI and T2 -GRASE, respectively. No statistically significant differences in postcontrast T1 (P = 0.17) and synthetic ECV (P = 0.19) measurements were observed in patients. DATA CONCLUSION Dixon cardiac MRF attained good overall precision in phantom and healthy subjects, while providing coregistered T1 , T2 , and fat fraction maps in a single breath-hold scan with similar or better image quality than conventional methods in patients. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 2.
Collapse
Affiliation(s)
- Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Pier-Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Rene M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| |
Collapse
|
12
|
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] [What about the content of this article? (0)] [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
|
13
|
Cruz G, Jaubert O, Qi H, Bustin A, Milotta G, Schneider T, Koken P, Doneva M, Botnar RM, Prieto C. 3D free-breathing cardiac magnetic resonance fingerprinting. NMR Biomed 2020; 33:e4370. [PMID: 32696590 DOI: 10.1002/nbm.4370] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [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: 02/12/2020] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 05/15/2023]
Abstract
PURPOSE To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T1 and T2 mapping from a free-breathing scan. METHODS Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T1 and T2 mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T1 and T2 tissue characterization from a free-breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1 /T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2 -GraSE mapping techniques at 1.5 T. RESULTS 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1 , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T2 . CONCLUSION The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.
Collapse
Affiliation(s)
- Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
14
|
Iyer S, Ong F, Setsompop K, Doneva M, Lustig M. SURE-based automatic parameter selection for ESPIRiT calibration. Magn Reson Med 2020; 84:3423-3437. [PMID: 32686178 DOI: 10.1002/mrm.28386] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 12/09/2019] [Revised: 04/21/2020] [Accepted: 05/29/2020] [Indexed: 11/10/2022]
Abstract
PURPOSE ESPIRiT is a parallel imaging method that estimates coil sensitivity maps from the auto-calibration region (ACS). This requires choosing several parameters for the optimal map estimation. While fairly robust to these parameter choices, occasionally, poor selection can result in reduced performance. The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams. METHODS By viewing ESPIRiT as a denoiser, Stein's unbiased risk estimate (SURE) is leveraged to automatically optimize parameter selection in a data-driven manner. The optimum parameters corresponding to the minimum true squared error, minimum SURE as derived from densely sampled, high-resolution, and non-accelerated data and minimum SURE as derived from ACS are compared using simulation experiments. To avoid optimizing the rank of ESPIRiT's auto-calibrating matrix (one of the parameters), a heuristic derived from SURE-based singular value thresholding is also proposed. RESULTS Simulations show SURE derived from the densely sampled, high-resolution, and non-accelerated data to be an accurate estimator of the true mean squared error, enabling automatic parameter selection. The parameters that minimize SURE as derived from ACS correspond well to the optimal parameters. The soft-threshold heuristic improves computational efficiency while providing similar results to an exhaustive search. In-vivo experiments verify the reliability of this method. CONCLUSIONS Using SURE to determine ESPIRiT parameters allows for automatic parameter selections. In-vivo results are consistent with simulation and theoretical results.
Collapse
Affiliation(s)
- Siddharth Iyer
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Frank Ong
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Michael Lustig
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| |
Collapse
|
15
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
16
|
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: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
Affiliation(s)
- Martijn Nagtegaal
- Department of Quantitative ImagingTechnical University DelftDelftthe Netherlands
- Institut für MathematikTechnische Universität BerlinBerlinGermany
| | | | | | | |
Collapse
|
17
|
Affiliation(s)
| | - Jong Chul Ye
- Department of Mathematical Sciences at the Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Leslie Ying
- University at Buffalo, State University of New York
| | | |
Collapse
|
18
|
Nam JG, Lee JM, Lee SM, Kang HJ, Lee ES, Hur BY, Yoon JH, Kim E, Doneva M. High Acceleration Three-Dimensional T1-Weighted Dual Echo Dixon Hepatobiliary Phase Imaging Using Compressed Sensing-Sensitivity Encoding: Comparison of Image Quality and Solid Lesion Detectability with the Standard T1-Weighted Sequence. Korean J Radiol 2019; 20:438-448. [PMID: 30799575 PMCID: PMC6389821 DOI: 10.3348/kjr.2018.0310] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 09/03/2018] [Indexed: 12/19/2022] Open
Abstract
Objective To compare a high acceleration three-dimensional (3D) T1-weighted gradient-recalled-echo (GRE) sequence using the combined compressed sensing (CS)-sensitivity encoding (SENSE) method with a conventional 3D GRE sequence using SENSE, with respect to image quality and detectability of solid focal liver lesions (FLLs) in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced liver MRI. Materials and Methods A total of 217 patients with gadoxetic acid-enhanced liver MRI at 3T (54 in the preliminary study and 163 in the main study) were retrospectively included. In the main study, HBP imaging was done twice using the standard mDixon-3D-GRE technique with SENSE (acceleration factor [AF]: 2.8, standard mDixon-GRE) and the high acceleration mDixon-3D GRE technique using the combined CS-SENSE technique (CS-SENSE mDixon-GRE). Two abdominal radiologists assessed the two MRI data sets for image quality in consensus. Three other abdominal radiologists independently assessed the diagnostic performance of each data set and its ability to detect solid FLLs in 117 patients with 193 solid nodules and compared them using jackknife alternative free-response receiver operating characteristics (JAFROC). Results There was no significant difference in the overall image quality. CS-SENSE mDixon-GRE showed higher image noise, but lesser motion artifact levels compared with the standard mDixon-GRE (all p < 0.05). In terms of lesion detection, reader-averaged figures-of-merit estimated with JAFROC was 0.918 for standard mDixon-GRE, and 0.953 for CS-SENSE mDixon-GRE (p = 0.142). The non-inferiority of CS-SENSE mDixon-GRE over standard mDixon-GRE was confirmed (difference: 0.064 [−0.012, 0.081]). Conclusion The CS-SENSE mDixon-GRE HBP sequence provided comparable overall image quality and non-inferior solid FFL detectability compared with the standard mDixon-GRE sequence, with reduced acquisition time.
Collapse
Affiliation(s)
- Ju Gang Nam
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Sang Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Hyo Jin Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Eun Sun Lee
- Department of Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Bo Yun Hur
- Department of Radiology, National Cancer Center, Goyang, Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - EunJu Kim
- Department of Clinical Science, MR, Philips Healthcare Korea, Seoul, Korea
| | | |
Collapse
|
19
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
20
|
Keil VC, Bakoeva SP, Jurcoane A, Doneva M, Amthor T, Koken P, Mädler B, Block W, Fimmers R, Fliessbach K, Hattingen E. MR fingerprinting as a diagnostic tool in patients with frontotemporal lobe degeneration: A pilot study. NMR Biomed 2019; 32:e4157. [PMID: 31393654 DOI: 10.1002/nbm.4157] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/27/2019] [Accepted: 07/03/2019] [Indexed: 06/10/2023]
Abstract
Several very rare forms of dementia are associated with characteristic focal atrophy predominantly of the frontal and/or temporal lobes and currently lack imaging solutions to monitor disease. Magnetic resonance fingerprinting (MRF) is a recently developed technique providing quantitative relaxivity maps and images with various tissue contrasts out of a single sequence acquisition. This pilot study explores the utility of MRF-based T1 and T2 mapping to discover focal differences in relaxation times between patients with frontotemporal lobe degenerative dementia and healthy controls. 8 patients and 30 healthy controls underwent a 3 T MRI including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 relaxation maps were generated based on an extended phase graphs algorithm-founded dictionary involving inner product pattern matching. A region of interest (ROI)-based analysis of T1 and T2 relaxation times was performed with FSL and ITK-SNAP. Depending on the brain region analyzed, T1 relaxation times were up to 10.28% longer in patients than in controls reaching significant differences in cortical gray matter (P = .047) and global white matter (P = .023) as well as in both hippocampi (P = .001 left; P = .027 right). T2 relaxation times were similarly longer in the hippocampus by up to 19.18% in patients compared with controls. The clinically most affected patient had the most control-deviant relaxation times. There was a strong correlation of T1 relaxation time in the amygdala with duration of the clinically manifest disease (Spearman Rho = .94; P = .001) and of T1 relaxation times in the left hippocampus with disease severity (Rho = .90, P = .002). In conclusion, MRF-based relaxometry is a promising and time-saving new MRI tool to study focal cerebral alterations and identify patients with frontotemporal lobe degeneration. To validate the results of this pilot study, MRF is worth further exploration as a diagnostic tool in neurodegenerative diseases.
Collapse
Affiliation(s)
- Vera Catharina Keil
- Department of Radiology, University Hospital Bonn, Venusberg Campus 1, Bonn, Germany
| | | | - Alina Jurcoane
- Department of Radiology, University Hospital Bonn, Venusberg Campus 1, Bonn, Germany
- Institute for Neuroradiology, University Hospital Frankfurt/Main, Schleusenweg 2-16, Haus 95, Frankfurt, Germany
| | - Mariya Doneva
- Philips Research, Röntgenstrasse 24-26, Hamburg, Germany
| | - Thomas Amthor
- Philips Research, Röntgenstrasse 24-26, Hamburg, Germany
| | - Peter Koken
- Philips Research, Röntgenstrasse 24-26, Hamburg, Germany
| | - Burkhard Mädler
- Philips Healthcare, Philips GmbH, Röntgenstrasse 22, 22335 Hamburg, Germany
| | - Wolfgang Block
- Department of Radiology, University Hospital Bonn, Venusberg Campus 1, Bonn, Germany
| | - Rolf Fimmers
- IMBIE, University Hospital Bonn, Venusberg Campus 1, Bonn, Germany
| | - Klaus Fliessbach
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Venusberg Campus 1, Bonn, Germany
| | - Elke Hattingen
- Department of Radiology, University Hospital Bonn, Venusberg Campus 1, Bonn, Germany
- Institute for Neuroradiology, University Hospital Frankfurt/Main, Schleusenweg 2-16, Haus 95, Frankfurt, Germany
| |
Collapse
|
21
|
Nolte T, Gross‐Weege N, Doneva M, Koken P, Elevelt A, Truhn D, Kuhl C, Schulz V. Spiral blurring correction with water–fat separation for magnetic resonance fingerprinting in the breast. Magn Reson Med 2019; 83:1192-1207. [DOI: 10.1002/mrm.27994] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 08/23/2019] [Accepted: 08/23/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Teresa Nolte
- Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany
| | - Nicolas Gross‐Weege
- Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany
| | - Mariya Doneva
- Tomographic Imaging Systems Philips Research Europe Hamburg Germany
| | - Peter Koken
- Tomographic Imaging Systems Philips Research Europe Hamburg Germany
| | - Aaldert Elevelt
- Oncology Solutions Philips Research Europe Eindhoven The Netherlands
| | - Daniel Truhn
- Clinic for Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany
| | - Christiane Kuhl
- Clinic for Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany
| | - Volkmar Schulz
- Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany
| |
Collapse
|
22
|
Hampe N, Herrmann M, Amthor T, Findeklee C, Doneva M, Katscher U. Dictionary-based electric properties tomography. Magn Reson Med 2018; 81:342-349. [PMID: 30246342 DOI: 10.1002/mrm.27401] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [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: 12/13/2017] [Revised: 05/18/2018] [Accepted: 05/21/2018] [Indexed: 12/24/2022]
Abstract
PURPOSE To develop and validate a new algorithm called "dictionary-based electric properties tomography" (dbEPT) for deriving tissue electric properties from measured B1 maps. METHODS Inspired by Magnetic Resonance fingerprinting, dbEPT uses a dictionary of local patterns ("atoms") of B1 maps and corresponding electric properties distributions, derived from electromagnetic field simulations. For reconstruction, a pattern from a measured B1 map is compared with the B1 atoms of the dictionary. The B1 atom showing the best match with the measured B1 pattern yields the optimum electric properties pattern that is chosen for reconstruction. Matching was performed through machine learning algorithms. Two dictionaries, using transmit and transceive phases, were evaluated. The spatial distribution of local matching distance between optimal atom and measured pattern yielded a reconstruction reliability map. The method was applied to reconstruct conductivity of 4 volunteers' brains. A conventional, Helmholtz-based Electric properties tomography (EPT) reconstruction was performed for reference. Noise performance was studied through phantom simulations. RESULTS Quantitative values of conductivity agree with literature values. Results of the 2 dictionaries exhibit only minor differences. Somewhat larger differences are visible between dbEPT and Helmholtz-based EPT. Quantified by the correlation between conductivity and anatomic images, dbEPT depicts brain details more clearly than Helmholtz-based EPT. Matching distance is minimal in homogeneous brain ventricles and increases with tissue heterogeneity. Central processing unit time was approximately 2 minutes per dictionary training and 3 minutes per brain conductivity reconstruction using standard hardware equipment. CONCLUSION A new, dictionary-based approach for reconstructing electric properties is presented. Its conductivity reconstruction is able to overcome the EPT transceive-phase problem.
Collapse
Affiliation(s)
| | - Max Herrmann
- University of Applied Sciences, Hamburg, Germany
| | | | | | | | | |
Collapse
|
23
|
Doneva M, Amthor T, Koken P, Sommer K, Börnert P. Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data. Magn Reson Imaging 2017; 41:41-52. [PMID: 28223063 DOI: 10.1016/j.mri.2017.02.007] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 02/14/2017] [Accepted: 02/15/2017] [Indexed: 10/20/2022]
|
24
|
Sommer K, Amthor T, Doneva M, Koken P, Meineke J, Börnert P. Towards predicting the encoding capability of MR fingerprinting sequences. Magn Reson Imaging 2017; 41:7-14. [PMID: 28684268 DOI: 10.1016/j.mri.2017.06.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [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: 05/10/2017] [Revised: 06/29/2017] [Accepted: 06/30/2017] [Indexed: 11/19/2022]
Abstract
Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization.
Collapse
Affiliation(s)
- K Sommer
- Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany.
| | - T Amthor
- Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany
| | - M Doneva
- Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany
| | - P Koken
- Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany
| | - J Meineke
- Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany
| | - P Börnert
- Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany
| |
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Prieto C, Doneva M, Usman M, Henningsson M, Greil G, Schaeffter T, Botnar RM. Highly efficient respiratory motion compensated free-breathing coronary MRA using golden-step Cartesian acquisition. J Magn Reson Imaging 2014; 41:738-46. [PMID: 24573992 DOI: 10.1002/jmri.24602] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 01/31/2014] [Indexed: 11/05/2022] Open
Abstract
PURPOSE To develop an efficient 3D affine respiratory motion compensation framework for Cartesian whole-heart coronary magnetic resonance angiography (MRA). MATERIALS AND METHODS The proposed method achieves 100% scan efficiency by estimating the affine respiratory motion from the data itself and correcting the acquired data in the reconstruction process. For this, a golden-step Cartesian sampling with spiral profile ordering was performed to enable reconstruction of respiratory resolved images at any breathing position and with different respiratory window size. Affine motion parameters were estimated from image-based registration of 3D undersampled respiratory resolved images reconstructed with iterative SENSE and motion correction was performed directly in the reconstruction using a multiple-coils generalized matrix formulation method. This approach was tested on healthy volunteers and compared against a conventional diaphragmatic navigator-gated acquisition using quantitative and qualitative image quality assessment. RESULTS The proposed approach achieved 47 ± 12% and 59 ± 6% vessel sharpness for the right (RCA) and left (LAD) coronary arteries, respectively. Also, good quality visual scores of 2.4 ± 0.74 and 2.44 ± 0.86 were observed for the RCA and LAD (scores from 0, no to 4, excellent coronary vessel delineation). A not statically significant difference (P = 0.05) was found between the proposed method and an 8-mm navigator-gated and tracked scan, although scan efficiency increased from 61 ± 10% to 100%. CONCLUSION We demonstrate the feasibility of a new 3D affine respiratory motion correction technique for Cartesian whole-heart CMRA that achieves 100% scan efficiency and therefore a predictable acquisition time. This approach yields image quality comparable to that of an 8-mm navigator-gated acquisition with lower scan efficiency. Further evaluation of this technique in patients is now warranted to determine its clinical use.
Collapse
Affiliation(s)
- Claudia Prieto
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, UK; Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
| | | | | | | | | | | | | |
Collapse
|
27
|
Valov V, Doneva M, Borisova AM, Tankova T, Czech M, Manova M, Savova A, Peikova L, Petrova G. Regional differences in diabetic patients' pharmacotherapy in Bulgaria. Eur Rev Med Pharmacol Sci 2014; 18:1499-1506. [PMID: 24899609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
BACKGROUND The regional analyses play an important role in understanding a state of diabetes mellitus management and to support informed policy options. They need to be explored in more details in order to ensure an equal patients' access to health care services of the same value and quality. AIM The aim of this study is to analyze regional differences in a cost of diabetes therapy for insulin users in Bulgaria. MATERIALS AND METHODS It is a combined prospective and retrospective observational study with duration of 6 months. Diabetic patients on insulin therapy were recruited by 35 endocrinologists. Information about the health care resources used was collected within 3-prospective and 3 retrospective months in 2010 and 2011. The regional cost of illness analysis was performed by calculating the average cost attributable to each individual patient despite the fact that some might not use a particular health care resource. Subgroup analysis was performed for hospitalized patients. RESULTS A detailed analysis revealed cost differences in the regions, especially with more vulnerable population like Burgas and Pleven regions. Another reason for the cost differences is the type of insulin or type of therapy. Our study confirms the fact that the hospitalizations are the major cost driver. Rising diabetes prevalence and associated costs, including hospitalizations and management of diabetes complications, are a growing concern. The last possible reason for regional differences is the patients' characteristics and therapy differences. We add evidence demonstrating that the patients on insulin and OAD consume more resources including hospitalizations and suffer from more complications of diabetes. CONCLUSIONS Reasons for regional differences might have different origin as there are various population characteristics, type of therapy, socio economic status and others.
Collapse
Affiliation(s)
- V Valov
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria.
| | | | | | | | | | | | | | | | | |
Collapse
|
28
|
Gdaniec N, Eggers H, Börnert P, Doneva M, Mertins A. Robust abdominal imaging with incomplete breath-holds. Magn Reson Med 2013; 71:1733-42. [DOI: 10.1002/mrm.24829] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 05/08/2013] [Accepted: 05/08/2013] [Indexed: 01/19/2023]
Affiliation(s)
- Nadine Gdaniec
- Institute for Signal Processing; University of Lübeck; Lübeck Germany
| | | | - Peter Börnert
- Philips Research Europe; Hamburg Germany
- Department of Radiology; Leiden University Medical Center; Leiden The Netherlands
| | | | - Alfred Mertins
- Institute for Signal Processing; University of Lübeck; Lübeck Germany
| |
Collapse
|
29
|
Doneva M, Börnert P, Eggers H, Stehning C, Sénégas J, Mertins A. Compressed sensing reconstruction for magnetic resonance parameter mapping. Magn Reson Med 2011; 64:1114-20. [PMID: 20564599 DOI: 10.1002/mrm.22483] [Citation(s) in RCA: 225] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging.
Collapse
Affiliation(s)
- Mariya Doneva
- Institute for Signal Processing, University of Luebeck, Luebeck, Germany.
| | | | | | | | | | | |
Collapse
|
30
|
Doneva M, Börnert P, Eggers H, Mertins A, Pauly J, Lustig M. Compressed sensing for chemical shift-based water-fat separation. Magn Reson Med 2010; 64:1749-59. [PMID: 20859998 DOI: 10.1002/mrm.22563] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2010] [Revised: 06/11/2010] [Accepted: 06/15/2010] [Indexed: 12/21/2022]
Abstract
Multi echo chemical shift-based water-fat separation methods allow for uniform fat suppression in the presence of main field inhomogeneities. However, these methods require additional scan time for chemical shift encoding. This work presents a method for water-fat separation from undersampled data (CS-WF), which combines compressed sensing and chemical shift-based water-fat separation. Undersampling was applied in the k-space and in the chemical shift encoding dimension to reduce the total scanning time. The method can reconstruct high quality water and fat images in 2D and 3D applications from undersampled data. As an extension, multipeak fat spectral models were incorporated into the CS-WF reconstruction to improve the water-fat separation quality. In 3D MRI, reduction factors of above three can be achieved, thus fully compensating the additional time needed in three-echo water-fat imaging. The method is demonstrated on knee and abdominal in vivo data.
Collapse
Affiliation(s)
- Mariya Doneva
- Institute for Signal Processing, University of Lübeck, Lübeck, Germany.
| | | | | | | | | | | |
Collapse
|
31
|
Doneva M, Börnert P. Automatic coil selection for channel reduction in SENSE-based parallel imaging. MAGMA 2008; 21:187-96. [PMID: 18386087 DOI: 10.1007/s10334-008-0110-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2007] [Accepted: 03/04/2008] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Coil arrays with large number of receive elements allow improved imaging performance and higher signal-to-noise-ratio. The MR systems supporting these arrays have to handle an increased amount of data and higher reconstruction burden. To overcome these problems, data reduction techniques need to be applied, realized either by linear combination of the original coil data prior to reconstruction or by discarding particular data from unimportant coil elements. MATERIALS AND METHODS This work focuses on the latter approach and presents an efficient algorithm for automatic coil selection applicable to SENSE imaging. A singular value decomposition (SVD)-based coil selection is proposed that performs a coil element ranking quantifying the contribution of each coil element to the image reconstruction allowing appropriate coil selection. This approach makes use of the coil sensitivity information and takes reduction factor and phase encoding direction into account. RESULTS Simulations, phantom and in vivo experiments were performed to validate the SVD-based coil selection algorithm. The proposed approach proved to be computationally efficient without remarkable image quality degradation. CONCLUSION The SVD-based approach offers the opportunity for fast automatic coil selection. This could simplify clinical workflow and may, furthermore, pave the way for various 2D real-time and interventional applications.
Collapse
|