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Roberts AG, Romano DJ, Şişman M, Dimov AV, Spincemaille P, Nguyen TD, Kovanlikaya I, Gauthier SA, Wang Y. Maximum spherical mean value filtering for whole-brain QSM. Magn Reson Med 2024; 91:1586-1597. [PMID: 38169132 PMCID: PMC11416845 DOI: 10.1002/mrm.29963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 10/30/2023] [Accepted: 11/19/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion. THEORY AND METHODS Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. RESULTS Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis. CONCLUSION The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.
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Affiliation(s)
- Alexandra G. Roberts
- Department of Electrical and Computer Engineering, Cornell University, Ithaca NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Dominick J. Romano
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca NY, USA
| | - Mert Şişman
- Department of Electrical and Computer Engineering, Cornell University, Ithaca NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | | | - Yi Wang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca NY, USA
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Zhang J, Nguyen TD, Solomon E, Li C, Zhang Q, Li J, Zhang H, Spincemaille P, Wang Y. mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping. Magn Reson Med 2024; 91:344-356. [PMID: 37655444 DOI: 10.1002/mrm.29854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE To develop a method for rapid sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO). METHODS A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method withR = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard. RESULTS The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal. CONCLUSION mcLARO enabled fast sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.
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Affiliation(s)
- Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Eddy Solomon
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Chao Li
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Department of Applied Physics, Cornell University, Ithaca, New York, USA
| | - Qihao Zhang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jiahao Li
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Hang Zhang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
| | | | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Zhu X, Gao Y, Liu F, Crozier S, Sun H. BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources. Z Med Phys 2023; 33:578-590. [PMID: 36064695 PMCID: PMC10751722 DOI: 10.1016/j.zemedi.2022.08.001] [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: 04/06/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial hemorrhages, is challenging due to the relatively large scale of the field induced by these pathological susceptibility sources. METHOD This study proposes a new deep learning-based method, BFRnet, to remove the background field in healthy and hemorrhagic subjects. The network is built with the dual-frequency octave convolutions on the U-net architecture, trained with synthetic field maps containing significant susceptibility sources. The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects. Robustness against acquisition field-of-view (FOV) orientation and brain masking are also investigated. RESULTS For both simulation and in vivo experiments, BFRnet led to the best visually appealing results in the local field and QSM results with the minimum contrast loss and the most accurate hemorrhage susceptibility measurements among all five methods. In addition, BFRnet produced the most consistent local field and susceptibility maps between different sizes of brain masks, while conventional methods depend drastically on precise brain extraction and further brain edge erosions. It is also observed that BFRnet performed the best among all BFR methods for acquisition FOVs oblique to the main magnetic field. CONCLUSION The proposed BFRnet improved the accuracy of local field reconstruction in the hemorrhagic subjects compared with conventional BFR algorithms. The BFRnet method was effective for acquisitions of tilted orientations and retained whole brains without edge erosion as often required by traditional BFR methods.
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Affiliation(s)
- Xuanyu Zhu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Yang Gao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
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Şişman M, Nguyen TD, Roberts AG, Romano DJ, Dimov AV, Kovanlikaya I, Spincemaille P, Wang Y. Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.22.23295993. [PMID: 37808826 PMCID: PMC10557811 DOI: 10.1101/2023.09.22.23295993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed. Highlights novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.
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Böhm C, Stelter JK, Weiss K, Meineke J, Komenda A, Borde T, Makowski MR, Fallenberg EM, Karampinos DC. Robust breast quantitative susceptibility mapping in the presence of silicone. Magn Reson Med 2023; 90:1209-1218. [PMID: 37125658 DOI: 10.1002/mrm.29694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/17/2023] [Accepted: 04/18/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE To (a) develop a preconditioned water-fat-silicone total field inversion (wfsTFI) algorithm that directly estimates the susceptibility map from complex multi-echo data in the breast in the presence of silicone and to (b) evaluate the performance of wfsTFI for breast quantitative susceptibility mapping (QSM) in silico and in vivo in comparison with formerly proposed methods. METHODS Numerical simulations and in vivo multi-echo gradient echo breast measurements were performed to compare wfsTFI to a previously proposed field map-based linear total field inversion algorithm (lTFI) with and without the consideration of the chemical shift of silicone in the field map estimation step. Specifically, a simulation based on an in vivo scan and data from five patients were included in the analysis. RESULTS In the simulation, wfsTFI is able to significantly decrease the normalized root mean square error from lTFI without (4.46) and with (1.77) the consideration of the chemical shift of silicone to 0.68. Both the in silico and in vivo wfsTFI susceptibility maps show reduced shadowing artifacts in local tissue adjacent to silicone, reduced streaking artifacts and no erroneous single voxels of diamagnetic susceptibility in proximity to silicone. CONCLUSION The proposed wfsTFI method can automatically distinguish between subjects with and without silicone. Furthermore wfsTFI accounts for the presence of silicone in the QSM dipole inversion and allows for the robust estimation of susceptibility in proximity to silicone breast implants and hence allows the visualization of structures that would otherwise be dominated by artifacts on susceptibility maps.
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Affiliation(s)
- Christof Böhm
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jonathan K Stelter
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Alexander Komenda
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tabea Borde
- 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
| | - Eva M Fallenberg
- 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
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Dimov AV, Li J, Nguyen TD, Roberts AG, Spincemaille P, Straub S, Zun Z, Prince MR, Wang Y. QSM Throughout the Body. J Magn Reson Imaging 2023; 57:1621-1640. [PMID: 36748806 PMCID: PMC10192074 DOI: 10.1002/jmri.28624] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/08/2023] Open
Abstract
Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Jiahao Li
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | | | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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Lehmann PM, Seidemo A, Andersen M, Xu X, Li X, Yadav NN, Wirestam R, Liebig P, Testud F, Sundgren P, van Zijl PCM, Knutsson L. A numerical human brain phantom for dynamic glucose-enhanced (DGE) MRI: On the influence of head motion at 3T. Magn Reson Med 2023; 89:1871-1887. [PMID: 36579955 PMCID: PMC9992166 DOI: 10.1002/mrm.29563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 11/09/2022] [Accepted: 12/07/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE Dynamic glucose-enhanced (DGE) MRI relates to a group of exchange-based MRI techniques where the uptake of glucose analogues is studied dynamically. However, motion artifacts can be mistaken for true DGE effects, while motion correction may alter true signal effects. The aim was to design a numerical human brain phantom to simulate a realistic DGE MRI protocol at 3T that can be used to assess the influence of head movement on the signal before and after retrospective motion correction. METHODS MPRAGE data from a tumor patient were used to simulate dynamic Z-spectra under the influence of motion. The DGE responses for different tissue types were simulated, creating a ground truth. Rigid head movement patterns were applied as well as physiological dilatation and pulsation of the lateral ventricles and head-motion-induced B0 -changes in presence of first-order shimming. The effect of retrospective motion correction was evaluated. RESULTS Motion artifacts similar to those previously reported for in vivo DGE data could be reproduced. Head movement of 1 mm translation and 1.5 degrees rotation led to a pseudo-DGE effect on the order of 1% signal change. B0 effects due to head motion altered DGE changes due to a shift in the water saturation spectrum. Pseudo DGE effects were partly reduced or enhanced by rigid motion correction depending on tissue location. CONCLUSION DGE MRI studies can be corrupted by motion artifacts. Designing post-processing methods using retrospective motion correction including B0 correction will be crucial for clinical implementation. The proposed phantom should be useful for evaluation and optimization of such techniques.
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Affiliation(s)
- Patrick M Lehmann
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anina Seidemo
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Mads Andersen
- Philips Healthcare, Copenhagen, Denmark
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
| | - Xiang Xu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Nirbhay N Yadav
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Ronnie Wirestam
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | | | | | - Pia Sundgren
- Lund University Bioimaging Centre, Lund University, Lund, Sweden
- Department of Radiology, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Aimo A, Huang L, Tyler A, Barison A, Martini N, Saccaro LF, Roujol S, Masci PG. Quantitative susceptibility mapping (QSM) of the cardiovascular system: challenges and perspectives. J Cardiovasc Magn Reson 2022; 24:48. [PMID: 35978351 PMCID: PMC9387036 DOI: 10.1186/s12968-022-00883-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 08/05/2022] [Indexed: 11/10/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a powerful, non-invasive, magnetic resonance imaging (MRI) technique that relies on measurement of magnetic susceptibility. So far, QSM has been employed mostly to study neurological disorders characterized by iron accumulation, such as Parkinson's and Alzheimer's diseases. Nonetheless, QSM allows mapping key indicators of cardiac disease such as blood oxygenation and myocardial iron content. For this reason, the application of QSM offers an unprecedented opportunity to gain a better understanding of the pathophysiological changes associated with cardiovascular disease and to monitor their evolution and response to treatment. Recent studies on cardiovascular QSM have shown the feasibility of a non-invasive assessment of blood oxygenation, myocardial iron content and myocardial fibre orientation, as well as carotid plaque composition. Significant technical challenges remain, the most evident of which are related to cardiac and respiratory motion, blood flow, chemical shift effects and susceptibility artefacts. Significant work is ongoing to overcome these challenges and integrate the QSM technique into clinical practice in the cardiovascular field.
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Affiliation(s)
- Alberto Aimo
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Li Huang
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Tyler
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrea Barison
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | | | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical Engineering, King's College London, St Thomas' Hospital, 4th Floor Lambeth Wing, London, SE1 7EH, UK.
| | - Pier-Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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Bachrata B, Trattnig S, Robinson SD. Quantitative susceptibility mapping of the head-and-neck using SMURF fat-water imaging with chemical shift and relaxation rate corrections. Magn Reson Med 2022; 87:1461-1479. [PMID: 34850446 PMCID: PMC7612304 DOI: 10.1002/mrm.29069] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/23/2021] [Accepted: 10/15/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To address the challenges posed by fat-water chemical shift artifacts and relaxation rate discrepancies to quantitative susceptibility mapping (QSM) outside the brain, and to generate accurate susceptibility maps of the head-and-neck at 3 and 7 Tesla. METHODS Simultaneous Multiple Resonance Frequency (SMURF) imaging was extended to 7 Tesla and used to acquire head-and-neck gradient echo images at both 3 and 7 Tesla. Separated fat and water images were corrected for Type 1 (displacement) and Type 2 (phase discrepancy) chemical shift artefacts, and for the bias resulting from differences in T1 and T 2 ∗ relaxation rates, recombined and used as the basis for QSM. A novel phase signal-based masking approach was used to generate head-and-neck masks. RESULTS SMURF generated well-separated fat and water images of the head-and-neck. Corrections for chemical shift artefacts and relaxation rate differences removed overestimation of the susceptibility values, blurring in the susceptibility maps, and the disproportionate influence of fat in mixed voxels. The resulting susceptibility maps showed high correspondence between the paramagnetic areas and the locations of fatty tissues and the susceptibility estimates were similar to literature values. The proposed masking approach was shown to provide a simple means of generating head-and-neck masks. CONCLUSION Corrections for Type 1 and Type 2 chemical shift artefacts and for fat-water relaxation rate differences, mainly in T1 , were shown to be required for accurate susceptibility mapping of fatty-body regions. SMURF made it possible to apply these corrections and generate high-quality susceptibility maps of the entire head-and-neck at both 3 and 7 Tesla.
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Affiliation(s)
- Beata Bachrata
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
- Department of Neurology, Medical University of Graz, Graz, Austria
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Kan H, Tsuchiya T, Yamada M, Kunitomo H, Kasai H, Shibamoto Y. Delineation of prostatic calcification using quantitative susceptibility mapping: Spatial accuracy for magnetic resonance-only radiotherapy planning. J Appl Clin Med Phys 2021; 23:e13469. [PMID: 34726833 PMCID: PMC8833270 DOI: 10.1002/acm2.13469] [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: 07/29/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 11/16/2022] Open
Abstract
To investigate the spatial accuracy of delineating prostatic calcifications by quantitative susceptibility mapping (QSM) in comparison with computed tomography (CT), we conducted phantom and human studies. Five differently‐sized spherical hydroxyapatites mimicking prostatic calcification (pseudo‐calcification) were arranged in the order of their sizes at the center of a plastic container filled with gelatin. This calcification phantom underwent magnetic resonance (MR) imaging, including the multiple spoiled gradient‐echo sequences (SPGR) for the QSM and CT as a reference. The volume of each pseudo‐calcification and center‐to‐center distance between the pseudo‐calcifications delineated by QSM and CT were measured. In the human study, eight patients with prostate cancer who underwent radiation therapy and had some prostatic calcifications were included. The patients underwent CT and SPGR and modified DIXON sequence for MR‐only simulation. The hybrid QSM processing combined with the complex signals in the SPGR and water and fat fraction maps estimated from the modified DIXON sequence were used to reconstruct the pelvic susceptibility map in humans. The threshold of CT numbers was set at 130 HU, while the QSM images were manually segmented in the calcification phantom and human studies. In the phantom study, there was an excellent agreement in the pseudo‐calcification volumes between QSM and CT (y = 1.02x – 7.38, R2 = 0.99). The signal profiles had similar trends in CT and QSM. The center‐to‐center distances between the pseudo‐calcifications in the phantom were also identical in QSM and CT. The calcification volumes were almost identical between the QSM and CT in the human study (y = 0.95x – 9.32, R2 = 1.00). QSM can offer geometric and volumetric accuracies to delineate prostatic calcifications, similar to CT. The prostatic calcification delineated by QSM may facilitate image‐guided radiotherapy in the MR‐only simulation workflow.
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Affiliation(s)
- Hirohito Kan
- Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan.,Department of Radiology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | - Takahiro Tsuchiya
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Masato Yamada
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Hiroshi Kunitomo
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Harumasa Kasai
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Yuta Shibamoto
- Department of Radiology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
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Chen L, Cai S, van Zijl PC, Li X. Single-step calculation of susceptibility through multiple orientation sampling. NMR IN BIOMEDICINE 2021; 34:e4517. [PMID: 33822416 PMCID: PMC8184590 DOI: 10.1002/nbm.4517] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 03/06/2021] [Accepted: 03/14/2021] [Indexed: 06/12/2023]
Abstract
Quantitative susceptibility mapping (QSM) was developed to estimate the spatial distribution of magnetic susceptibility from MR signal phase acquired using a gradient echo (GRE) sequence. The field-to-susceptibility inversion in QSM is known to be ill-posed and needs numerical stabilization through either regularization or data oversampling. The calculation of susceptibility through the multiple orientation sampling (COSMOS) method uses phase data acquired at three or more head orientations to achieve a well-conditioned field-to-susceptibility inversion and is often considered the gold standard for in vivo QSM. However, the conventional COSMOS approach, here named multistep COSMOS (MSCOSMOS), solves the dipole inversion from the local field derived from raw GRE phase through multiple steps of phase preprocessing. Error propagations between these consecutive phase processing steps can thus affect the final susceptibility quantification. On the other hand, recently proposed single-step QSM (SSQSM) methods aim to solve an integrated inversion from unprocessed or total phase to mitigate such error propagations but have been limited to single orientation QSM. This study therefore aimed to test the feasibility of using single-step COSMOS (SSCOSMOS) to jointly perform background field removal and dipole inversion with multiple orientation sampling, which could serve as a better standard for gauging SSQSM methods. We incorporated multiple spherical mean value (SMV) kernels of various radii with the dipole inversion in SSCOSMOS. QSM reconstructions with SSCOSMOS and MSCOSMOS were compared using both simulations with a numerical head phantom and in vivo human brain data. SSCOSMOS permitted integrated background removal and dipole inversion without the need to adjust any regularization parameters. In addition, with sufficiently large SMV kernels, SSCOSMOS performed consistently better than MSCOSMOS in all the tested error metrics in our simulations, giving better susceptibility quantification and smaller reconstruction error. Consistent tissue susceptibility values were obtained between SSCOSMOS and MSCOSMOS.
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Affiliation(s)
- Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
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12
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Wen Y, Spincemaille P, Nguyen T, Cho J, Kovanlikaya I, Anderson J, Wu G, Yang B, Fung M, Li K, Kelley D, Benhamo N, Wang Y. Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping. Magn Reson Med 2021; 86:2165-2178. [PMID: 34028868 DOI: 10.1002/mrm.28814] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 02/20/2021] [Accepted: 03/28/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE Typical quantitative susceptibility mapping (QSM) reconstruction steps consist of first estimating the magnetization field from the gradient-echo images, and then reconstructing the susceptibility map from the estimated field. The errors from the field-estimation steps may propagate into the final QSM map, and the noise in the estimated field map may no longer be zero-mean Gaussian noise, thus, causing streaking artifacts in the resulting QSM. A multiecho complex total field inversion (mcTFI) method was developed to compute the susceptibility map directly from the multiecho gradient echo images using an improved signal model that retains the Gaussian noise property in the complex domain. It showed improvements in QSM reconstruction over the conventional field-to-source inversion. METHODS The proposed mcTFI method was compared with the nonlinear total field inversion (nTFI) method in a numerical brain with hemorrhage and calcification, the numerical brains provided by the QSM Challenge 2.0, 18 brains with intracerebral hemorrhage scanned at 3T, and 6 healthy brains scanned at 7T. RESULTS Compared with nTFI, the proposed mcTFI showed more accurate QSM reconstruction around the lesions in the numerical simulations. The mcTFI reconstructed QSM also showed the best image quality with the least artifacts in the brains with intracerebral hemorrhage scanned at 3T and healthy brains scanned at 7T. CONCLUSION The proposed multiecho complex total field inversion improved QSM reconstruction over traditional field-to-source inversion through better signal modeling.
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Affiliation(s)
- Yan Wen
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Junghun Cho
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Gaohong Wu
- General Electrical Healthcare, Waukesha, Wisconsin, USA
| | - Baolian Yang
- General Electrical Healthcare, Waukesha, Wisconsin, USA
| | - Maggie Fung
- General Electrical Healthcare, Waukesha, Wisconsin, USA
| | - Ke Li
- General Electrical Healthcare, Waukesha, Wisconsin, USA
| | | | | | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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13
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Qu Z, Yang S, Xing F, Tong R, Yang C, Guo R, Huang J, Lu F, Fu C, Yan X, Hectors S, Gillen K, Wang Y, Liu C, Zhan S, Li J. Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study. Quant Imaging Med Surg 2021; 11:1170-1183. [PMID: 33816158 DOI: 10.21037/qims-20-720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Noninvasive methods for the early diagnosis and staging of hepatic fibrosis are needed. The present study aimed to investigate the alteration of magnetic susceptibility in the liver of patients with various fibrosis stages and to evaluate the feasibility of using susceptibility to stage hepatic fibrosis. Methods A total of 30 consecutive patients with chronic liver diseases (CLDs) underwent magnetic resonance imaging (MRI) and liver biopsy evaluation of hepatic fibrosis, necroinflammatory activity, iron load, and steatosis. Quantitative susceptibility mapping (QSM), R2* and proton density fat fraction (PDFF) images were postprocessed from the same gradient-echo data for quantitative tissue characterization using region of interest (ROI) analysis. The differences for MRI measurements between cohorts of non-significant (Ishak-F <3) and significant fibrosis (Ishak-F ≥3) and the correlation of MRI measurements with fibrosis stages and necroinflammatory activity grades were tested. Receiver operating characteristic (ROC) analysis was also performed. Results There was a significant difference in liver susceptibility between the cohorts of significant and non-significant fibrosis (Z=-2.880, P=0.004). A moderate negative correlation between the stages of liver fibrosis and liver susceptibility was observed (r=-0.471, P=0.015). Liver magnetic susceptibility differentiated non-significant from significant hepatic fibrosis with an area under the receiver operating curve (AUC) of 0.836 (P=0.004). A highly sensitive diagnostic performance with an AUC of 0.933 was obtained using magnetic susceptibility and PDFF together (P<0.001). Conclusions A noninvasive liver QSM-based evaluation promises an accurate assessment of significant fibrosis in patients with CLDs.
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Affiliation(s)
- Zheng Qu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Shuohui Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Feng Xing
- Department of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Chenyao Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rongfang Guo
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiling Huang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Caixia Fu
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Stefanie Hectors
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Kelly Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Chenghai Liu
- Department of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Shanghai Key Laboratory of Traditional Chinese Clinical Medicine, Shanghai, China
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
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14
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Karsa A, Punwani S, Shmueli K. An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head-and-neck region. Magn Reson Med 2020; 84:3206-3222. [PMID: 32621302 DOI: 10.1002/mrm.28377] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/06/2020] [Accepted: 05/23/2020] [Indexed: 02/11/2024]
Abstract
PURPOSE Quantitative Susceptibility Mapping (QSM) is an emerging technique sensitive to disease-related changes including oxygenation. It is extensively used in brain studies and has increasing clinical applications outside the brain. Here we present the first MRI acquisition protocol and QSM pipeline optimized for the head-and-neck region together with a repeatability analysis performed in healthy volunteers. METHODS We investigated both the intrasession and the intersession repeatability of the optimized method in 10 subjects. We also implemented two, Tikhonov-regularisation-based susceptibility calculation techniques that were found to have higher contrast-to-noise than existing methods in the head-and-neck region. Repeatability was evaluated by calculating the distributions of susceptibility differences between repeated scans and the corresponding minimum detectable effect sizes (MDEs). RESULTS Deep brain regions had higher QSM repeatability than neck regions. As expected, intrasession repeatability was generally better than intersession repeatability. Susceptibility maps calculated using projection onto dipole fields for background field removal were more repeatable than using the Laplacian boundary value method in the head-and-neck region. Small (short-axis diameter <5 mm) lymph nodes had the lowest repeatability (MDE = 0.27 ppm) as imperfect segmentation included some of the surrounding paramagnetic fatty fascia, highlighting the importance of accurate region delineation. MDEs in the larger lymph nodes (0.16 ppm), submandibular glands (0.10 ppm), and especially the parotid glands (0.06 ppm) were much lower, comparable to those of the brain regions. CONCLUSIONS The high repeatability of the acquisition and pipeline optimized for QSM will facilitate clinical studies in the head-and-neck region.
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Affiliation(s)
- Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Centre for Medical Imaging, University College London, London, United Kingdom
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15
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Effectiveness of fat suppression using a water-selective binomial-pulse excitation in chemical exchange saturation transfer (CEST) magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2020; 33:809-818. [PMID: 32462557 DOI: 10.1007/s10334-020-00851-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 04/24/2020] [Accepted: 05/12/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE The purpose of this study was to characterize the individual contribution of multiple fat peaks to the measured chemical exchange saturation transfer (CEST) signal when using water-selective binomial-pulse excitation and to determine the effects of multiple fat peaks in the presence of B0 inhomogeneity. METHODS The excitation profiles of multiple binomial pulses were simulated. A CEST sequence with binomial-pulse excitation and modified point-resolved spectroscopy localization was then applied to the in vivo lumbar spinal vertebrae to determine the signal contributions of three distinct groups of lipid resonances. These confounding signal contributions were measured as a function of the irradiation frequency offset to determine the effect of the multi-peak nature of the fat signal on CEST imaging of exchange sites (at 1.0, 2.0 and 3.5 ppm) and robustness in the presence of B0 inhomogeneity. RESULTS Numerical simulations and in vivo experiments showed that water excitation (WE) using a 1-3-3-1 (WE-4) pulse provided the broadest signal suppression, which provided partial robustness against B0 inhomogeneity effects. Confounding fat signal contributions to the CEST contrasts at 1.0, 2.0 and 3.5 ppm were unavoidable due to the multi-peak nature of the fat signal. However, these CEST sites only suffer from small lipid artifacts with ∆B0 spanning roughly from - 50 to 50 Hz. Especially for the CEST site at 3.5 ppm, the lipid artifacts are smaller than 1% with ∆B0 in this range. CONCLUSION In WE-4-based CEST magnetic resonance imaging, B0 inhomogeneity is the limiting factor for fat suppression. The CEST sites at 1.0, 2.0 ppm and 3.5 ppm unavoidably suffer from lipid artifacts. However, when the ∆B0 is confined to a limited range, these CEST sites are only affected by small lipid artifacts, which may be ignorable in some cases of clinical applications.
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16
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Boehm C, Diefenbach MN, Makowski MR, Karampinos DC. Improved body quantitative susceptibility mapping by using a variable-layer single-min-cut graph-cut for field-mapping. Magn Reson Med 2020; 85:1697-1712. [PMID: 33151604 DOI: 10.1002/mrm.28515] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/20/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop a robust algorithm for field-mapping in the presence of water-fat components, large B 0 field inhomogeneities and MR signal voids and to apply the developed method in body applications of quantitative susceptibility mapping (QSM). METHODS A framework solving the cost-function of the water-fat separation problem in a single-min-cut graph-cut based on the variable-layer graph construction concept was developed. The developed framework was applied to a numerical phantom enclosing an MR signal void, an air bubble experimental phantom, 14 large field of view (FOV) head/neck region in vivo scans and to 6 lumbar spine in vivo scans. Field-mapping and subsequent QSM results using the proposed algorithm were compared to results using an iterative graph-cut algorithm and a formerly proposed single-min-cut graph-cut. RESULTS The proposed method was shown to yield accurate field-map and susceptibility values in all simulation and in vivo datasets when compared to reference values (simulation) or literature values (in vivo). The proposed method showed improved field-map and susceptibility results compared to iterative graph-cut field-mapping especially in regions with low SNR, strong field-map variations and high R 2 ∗ values. CONCLUSIONS A single-min-cut graph-cut field-mapping method with a variable-layer construction was developed for field-mapping in body water-fat regions, improving quantitative susceptibility mapping particularly in areas close to MR signal voids.
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Affiliation(s)
- Christof Boehm
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Maximilian N Diefenbach
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany.,Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
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17
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Sato R, Shirai T, Soutome Y, Bito Y, Ochi H. Quantitative susceptibility mapping of prostate with separate calculations for water and fat regions for reducing shading artifacts. Magn Reson Imaging 2020; 66:22-29. [DOI: 10.1016/j.mri.2019.11.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 11/03/2019] [Accepted: 11/03/2019] [Indexed: 12/12/2022]
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18
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Ruetten PPR, Cluroe AD, Usman A, Priest AN, Gillard JH, Graves MJ. Simultaneous MRI water‐fat separation and quantitative susceptibility mapping of carotid artery plaque pre‐ and post‐ultrasmall superparamagnetic iron oxide‐uptake. Magn Reson Med 2020; 84:686-697. [DOI: 10.1002/mrm.28151] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 11/28/2019] [Accepted: 12/08/2019] [Indexed: 12/20/2022]
Affiliation(s)
| | - Alison D. Cluroe
- Department of Histopathology Addenbrooke’s Hospital Histopathology, Cambridge United Kingdom
| | - Ammara Usman
- Department of Radiology University of Cambridge Cambridge United Kingdom
| | - Andrew N. Priest
- Department of Medical Physics Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom
| | | | - Martin J. Graves
- Department of Radiology Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom
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19
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Wen Y, Weinsaft JW, Nguyen TD, Liu Z, Horn EM, Singh H, Kochav J, Eskreis-Winkler S, Deh K, Kim J, Prince MR, Wang Y, Spincemaille P. Free breathing three-dimensional cardiac quantitative susceptibility mapping for differential cardiac chamber blood oxygenation - initial validation in patients with cardiovascular disease inclusive of direct comparison to invasive catheterization. J Cardiovasc Magn Reson 2019; 21:70. [PMID: 31735165 PMCID: PMC6859622 DOI: 10.1186/s12968-019-0579-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 10/04/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Differential blood oxygenation between left (LV) and right ventricles (RV; ΔSaO2) is a key index of cardiac performance; LV dysfunction yields increased RV blood pool deoxygenation. Deoxyhemoglobin increases blood magnetic susceptibility, which can be measured using an emerging cardiovascular magnetic resonance (CMR) technique, Quantitative Susceptibility Mapping (QSM) - a concept previously demonstrated in healthy subjects using a breath-hold 2D imaging approach (2DBHQSM). This study tested utility of a novel 3D free-breathing QSM approach (3DNAVQSM) in normative controls, and validated 3DNAVQSM for non-invasive ΔSaO2 quantification in patients undergoing invasive cardiac catheterization (cath). METHODS Initial control (n = 10) testing compared 2DBHQSM (ECG-triggered 2D gradient echo acquired at end-expiration) and 3DNAVQSM (ECG-triggered navigator gated gradient echo acquired in free breathing using a phase-ordered automatic window selection algorithm to partition data based on diaphragm position). Clinical testing was subsequently performed in patients being considered for cath, including 3DNAVQSM comparison to cine-CMR quantified LV function (n = 39), and invasive-cath quantified ΔSaO2 (n = 15). QSM was acquired using 3 T scanners; analysis was blinded to comparator tests (cine-CMR, cath). RESULTS 3DNAVQSM generated interpretable QSM in all controls; 2DBHQSM was successful in 6/10. Among controls in whom both pulse sequences were successful, RV/LV susceptibility difference (and ΔSaO2) were not significantly different between 3DNAVQSM and 2DBHQSM (252 ± 39 ppb [17.5 ± 3.1%] vs. 211 ± 29 ppb [14.7 ± 2.0%]; p = 0.39). Acquisition times were 30% lower with 3DNAVQSM (4.7 ± 0.9 vs. 6.7 ± 0.5 min, p = 0.002), paralleling a trend towards lower LV mis-registration on 3DNAVQSM (p = 0.14). Among cardiac patients (63 ± 10y, 56% CAD) 3DNAVQSM was successful in 87% (34/39) and yielded higher ΔSaO2 (24.9 ± 6.1%) than in controls (p < 0.001). QSM-calculated ΔSaO2 was higher among patients with LV dysfunction as measured on cine-CMR based on left ventricular ejection fraction (29.4 ± 5.9% vs. 20.9 ± 5.7%, p < 0.001) or stroke volume (27.9 ± 7.5% vs. 22.4 ± 5.5%, p = 0.013). Cath measurements (n = 15) obtained within a mean interval of 4 ± 3 days from CMR demonstrated 3DNAVQSM to yield high correlation (r = 0.87, p < 0.001), small bias (- 0.1%), and good limits of agreement (±8.6%) with invasively measured ΔSaO2. CONCLUSION 3DNAVQSM provides a novel means of assessing cardiac performance. Differential susceptibility between the LV and RV is increased in patients with cine-CMR evidence of LV systolic dysfunction; QSM-quantified ΔSaO2 yields high correlation and good agreement with the reference of invasively-quantified ΔSaO2.
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Affiliation(s)
- Yan Wen
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY USA
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
| | | | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
| | - Zhe Liu
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY USA
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
| | - Evelyn M. Horn
- Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Harsimran Singh
- Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Jonathan Kochav
- Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | | | - Kofi Deh
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
| | - Jiwon Kim
- Department of Medicine, Weill Cornell Medicine, New York, NY USA
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
| | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY USA
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY USA
- Weill Cornell Medical College, 515 East 71th Street, S101, New York, NY 10021 USA
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20
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Wáng YXJ, Wang X, Wu P, Wang Y, Chen W, Chen H, Li J. Topics on quantitative liver magnetic resonance imaging. Quant Imaging Med Surg 2019; 9:1840-1890. [PMID: 31867237 DOI: 10.21037/qims.2019.09.18] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.
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Affiliation(s)
- Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR, China
| | | | - Peng Wu
- Philips Healthcare (Suzhou) Co., Ltd., Suzhou 215024, China
| | - Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Weibo Chen
- Philips Healthcare, Shanghai 200072, China.,Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
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21
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Hobson N, Polster SP, Cao Y, Flemming K, Shu Y, Huston J, Gerrard CY, Selwyn R, Mabray M, Zafar A, Girard R, Carrión-Penagos J, Chen YF, Parrish T, Zhou XJ, Koenig JI, Shenkar R, Stadnik A, Koskimäki J, Dimov A, Turley D, Carroll T, Awad IA. Phantom validation of quantitative susceptibility and dynamic contrast-enhanced permeability MR sequences across instruments and sites. J Magn Reson Imaging 2019; 51:1192-1199. [PMID: 31515878 DOI: 10.1002/jmri.26927] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 08/27/2019] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) and dynamic contrast-enhanced quantitative permeability (DCEQP) on magnetic resonance (MR) have been shown to correlate with neurovascular disease progression as markers of vascular leakage and hemosiderin deposition. Applying these techniques as monitoring biomarkers in clinical trials will be necessary; however, their validation across multiple MR platforms and institutions has not been rigorously verified. PURPOSE To validate quantitative measurement of MR biomarkers on multiple instruments at different institutions. STUDY TYPE Phantom validation between platforms and institutions. PHANTOM MODEL T1 /susceptibility phantom, two-compartment dynamic flow phantom. FIELD STRENGTH/SEQUENCE 3T/QSM, T1 mapping, dynamic 2D SPGR. ASSESSMENT Philips Ingenia, Siemens Prisma, and Siemens Skyra at three different institutions were assessed. A QSM phantom with concentrations of gadolinium, corresponding to magnetic susceptibilities of 0, 0.1, 0.2, 0.4, and 0.8 ppm was assayed. DCEQP was assessed by measuring a MultiHance bolus as the consistency of the width ratio of the curves at the input and outputs over a range of flow ratios between outputs. STATISTICAL TESTS Each biomarker was assessed by measures of accuracy (Pearson correlation), precision (paired t-test between repeated measurements), and reproducibility (analysis of covariance [ANCOVA] between instruments). RESULTS QSM accuracy of r2 > 0.997 on all three platforms was measured. Precision (P = 0.66 Achieva, P = 0.76 Prisma, P = 0.69 Skyra) and reproducibility (P = 0.89) were good. T1 mapping of accuracy was r2 > 0.98. No significant difference between width ratio regression slopes at site 2 (P = 0.669) or site 3 (P = 0.305), and no significant difference between width ratio regression slopes between sites was detected by ANCOVA (P = 0.48). DATA CONCLUSION The phantom performed as expected and determined that MR measures of QSM and DCEQP are accurate and consistent across repeated measurements and between platforms. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1192-1199.
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Affiliation(s)
- Nicholas Hobson
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Sean P Polster
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Ying Cao
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Kelly Flemming
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Chandra Y Gerrard
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Reed Selwyn
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Marc Mabray
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Atif Zafar
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Romuald Girard
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Julián Carrión-Penagos
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Yu Fen Chen
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, USA
| | - Todd Parrish
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, USA
| | - Xiaohong Joe Zhou
- Center for MR Research and Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - James I Koenig
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Robert Shenkar
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Agnieszka Stadnik
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Janne Koskimäki
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Alexey Dimov
- Department of Diagnostic Radiology, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Dallas Turley
- Department of Diagnostic Radiology, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Timothy Carroll
- Department of Diagnostic Radiology, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Issam A Awad
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
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Ruetten PPR, Gillard JH, Graves MJ. Introduction to Quantitative Susceptibility Mapping and Susceptibility Weighted Imaging. Br J Radiol 2019; 92:20181016. [PMID: 30933548 DOI: 10.1259/bjr.20181016] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Quantitative Susceptibility Mapping (QSM) and Susceptibility Weighted Imaging (SWI) are MRI techniques that measure and display differences in the magnetization that is induced in tissues, i.e. their magnetic susceptibility, when placed in the strong external magnetic field of an MRI system. SWI produces images in which the contrast is heavily weighted by the intrinsic tissue magnetic susceptibility. It has been applied in a wide range of clinical applications. QSM is a further advancement of this technique that requires sophisticated post-processing in order to provide quantitative maps of tissue susceptibility. This review explains the steps involved in both SWI and QSM as well as describing some of their uses in both clinical and research applications.
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Affiliation(s)
- Pascal P R Ruetten
- 1Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jonathan H Gillard
- 1Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Martin J Graves
- 2Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
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Bechler E, Stabinska J, Wittsack H. Analysis of different phase unwrapping methods to optimize quantitative susceptibility mapping in the abdomen. Magn Reson Med 2019; 82:2077-2089. [DOI: 10.1002/mrm.27891] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/12/2019] [Accepted: 06/12/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Eric Bechler
- Department of Diagnostic and Interventional Radiology, Medical Faculty Heinrich Heine University Düsseldorf Düsseldorf Germany
| | - Julia Stabinska
- Department of Diagnostic and Interventional Radiology, Medical Faculty Heinrich Heine University Düsseldorf Düsseldorf Germany
| | - Hans‐Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty Heinrich Heine University Düsseldorf Düsseldorf Germany
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24
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Karsa A, Shmueli K. SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1347-1357. [PMID: 30561341 DOI: 10.1109/tmi.2018.2884093] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Recent magnetic resonance imaging (MRI) techniques, such as quantitative magnetic susceptibility mapping, employ the signal phase to reveal disease-related changes in tissue composition, including iron or calcium content. The MRI phase is also routinely used in functional and diffusion MRI for distortion correction. However, phase images are wrapped into a range of 2π radians. Phase region expanding labeller for unwrapping discrete estimates (PRELUDE) is the gold standard method for robust, spatial, 3-D, MRI phase unwrapping. Unfortunately, PRELUDE's computation time can reach 15 min for a severely wrapped brain image and nearly 10 h to unwrap a full head-and-neck image on a standard PC. In this paper, we develop a Speedy rEgion-Growing algorithm for Unwrapping Estimated phase (SEGUE) based on similar principles to PRELUDE, implemented with additional methods for acceleration. We compared PRELUDE and SEGUE in numerical phantoms, and using in vivo images of the brain, head and neck, and pelvis acquired in 4-5 healthy volunteers and at 4-6 echo times. To overcome chemical-shift-induced errors within the head and neck, and pelvic images, we also investigated applying both techniques within fat and water masks separately. SEGUE provided almost identical unwrapped phase maps to the gold standard PRELUDE. SEGUE was (1.5 to 70 times) faster than PRELUDE, especially in severely wrapped images at later echoes and in the head and neck, and pelvic images. Applying these techniques within fat and water masks separately removed chemical-shift-induced errors successfully. SEGUE's MATLAB implementation is available for download. SEGUE is a general unwrapping algorithm not specific to MRI, and therefore could be used in images acquired with other modalities.
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25
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Bray TJ, Karsa A, Bainbridge A, Sakai N, Punwani S, Hall‐Craggs MA, Shmueli K. Association of bone mineral density and fat fraction with magnetic susceptibility in inflamed trabecular bone. Magn Reson Med 2019; 81:3094-3107. [PMID: 30615213 PMCID: PMC6492090 DOI: 10.1002/mrm.27634] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 11/19/2018] [Accepted: 11/20/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE To evaluate the relationship between bone mineral density (BMD) and magnetic susceptibility, and between proton density fat fraction and susceptibility, in inflamed trabecular bone. METHODS Two different phantoms modeling the fat fraction (FF) and BMD values of healthy bone marrow and disease states were scanned using a multiecho gradient echo acquisition at 3T. After correction for fat-water chemical shift, susceptibility mapping was performed, and susceptibility measurements were compared with BMD and FF values using linear regression. Patients with spondyloarthritis were scanned using the same protocol, and susceptibility values were calculated in areas of inflamed bone (edema) and fat metaplasia, both before and after accounting for the contribution of fat to the total susceptibility. RESULTS Susceptibility values in the phantoms were accurately described by a 2D linear function, with a negative correlation between BMD and susceptibility and a positive correlation between FF and susceptibility (adjusted R2 = 0.77; P = 3·10-5 ). In patients, significant differences in susceptibility were observed between fat metaplasia and normal marrow, but these differences were eliminated by removing the fat contribution to the total susceptibility. CONCLUSIONS BMD and proton density fat fraction both influence the total susceptibility of bone marrow and failure to account for the fat contribution could lead to errors in BMD quantification. We propose a method for removing the fat contribution from the total susceptibility, based on the observed linear relationship between susceptibility and FF. In inflamed bone, the overall increase in susceptibility in areas of fat metaplasia is at least partly due to increased fat content.
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Affiliation(s)
- Timothy J.P. Bray
- Centre for Medical ImagingUniversity College LondonUnited Kingdom
- Arthritis Research UK Centre for Adolescent RheumatologyUniversity College LondonUnited Kingdom
| | - Anita Karsa
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonUnited Kingdom
| | - Alan Bainbridge
- Department of Medical PhysicsUniversity College London HospitalsUnited Kingdom
| | - Naomi Sakai
- Centre for Medical ImagingUniversity College LondonUnited Kingdom
| | - Shonit Punwani
- Centre for Medical ImagingUniversity College LondonUnited Kingdom
| | | | - Karin Shmueli
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonUnited Kingdom
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26
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Lin F, Prince MR, Spincemaille P, Wang Y. Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism. Recent Pat Biotechnol 2018; 13:90-113. [PMID: 30556508 DOI: 10.2174/1872208313666181217112745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/04/2018] [Accepted: 12/11/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice. OBJECTIVE To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community. METHOD We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility. RESULTS We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase. CONCLUSION Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.
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Affiliation(s)
- Feng Lin
- School of Law, City University of Hong Kong, Hong Kong, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States
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Diefenbach MN, Meineke J, Ruschke S, Baum T, Gersing A, Karampinos DC. On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density. Magn Reson Med 2018; 81:1739-1754. [PMID: 30265769 PMCID: PMC6585956 DOI: 10.1002/mrm.27531] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/09/2018] [Accepted: 08/24/2018] [Indexed: 01/13/2023]
Abstract
Purpose To develop a methodological framework to simultaneously measure R2* and magnetic susceptibility in trabecularized yellow bone marrow and to investigate the sensitivity of Quantitative Susceptibility Mapping (QSM) for measuring trabecular bone density using a non‐UTE multi‐gradient echo sequence. Methods The ankle of 16 healthy volunteers and two patients was scanned using a time‐interleaved multi‐gradient‐echo (TIMGRE) sequence. After field mapping based on water–fat separation methods and background field removal based on the Laplacian boundary value method, three different QSM dipole inversion schemes were implemented. Mean susceptibility values in regions of different trabecular bone density in the calcaneus were compared to the corresponding values in the R2* maps, bone volume to total volume ratios (BV/TV) estimated from high resolution imaging (in 14 subjects), and CT attenuation (in two subjects). In addition, numerical simulations were performed in a simplified trabecular bone model of randomly positioned spherical bone inclusions to verify and compare the scaling of R2* and susceptibility with BV/TV. Results Differences in calcaneus trabecularization were well depicted in susceptibility maps, in good agreement with high‐resolution MR and CT images. Simulations and in vivo scans showed a linear relationship of measured susceptibility with BV/TV and R2*. The ankle in vivo results showed a strong linear correlation between susceptibility and R2* (R2 = 0.88, p < 0.001) with a slope and intercept of −0.004 and 0.2 ppm, respectively. Conclusions A method for multi‐paramteric mapping, including R2*‐mapping and QSM was developed for measuring trabecularized yellow bone marrow, showing good sensitivity of QSM for measuring trabecular bone density.
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Affiliation(s)
- Maximilian N Diefenbach
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | | | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
| | - Alexandra Gersing
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
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28
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Straub S, Emmerich J, Schlemmer HP, Maier-Hein KH, Ladd ME, Röthke MC, Bonekamp D, Laun FB. Mask-Adapted Background Field Removal for Artifact Reduction in Quantitative Susceptibility Mapping of the Prostate. ACTA ACUST UNITED AC 2018; 3:96-100. [PMID: 30042974 PMCID: PMC6024456 DOI: 10.18383/j.tom.2017.00005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
We propose an alternative processing method for quantitative susceptibility mapping of the prostate that reduces artifacts and enables better visibility and quantification of calcifications and other lesions. Three-dimensional gradient-echo magnetic resonance data were obtained from 26 patients at 3 T who previously received a planning computed tomography of the prostate. Phase images were unwrapped using Laplacian-based phase unwrapping. The background field was removed with the V-SHARP method using tissue masks for the entire abdomen (Method 1) and masks that excluded bone and the rectum (Method 2). Susceptibility maps were calculated with the iLSQR method. The quality of susceptibility maps was assessed by one radiologist and two physicists who rated the data for visibility of lesions and data quality on a scale from 1 (poor) to 4 (good). The readers rated susceptibility maps computed with Method 2 to be, on average, better for visibility of lesions with a score of 2.9 ± 1.1 and image quality with a score of 2.8 ± 0.8 compared with maps computed with Method 1 (2.4 ± 1.2/2.3 ± 1.0). Regarding strong artifacts, these could be removed using adapted masks, and the susceptibility values seemed less biased by the artifacts. Thus, using an adapted mask for background field removal when calculating susceptibility maps of the prostate from phase data reduces artifacts and improves visibility of lesions.
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Affiliation(s)
- Sina Straub
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julian Emmerich
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Klaus H Maier-Hein
- Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; and
| | - Mark E Ladd
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias C Röthke
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik B Laun
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Institute of Radiology, University Hospital Erlangen, Erlangen, Germany
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29
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Single multi-echo GRE acquisition with short and long echo spacing for simultaneous quantitative mapping of fat fraction, B0 inhomogeneity, and susceptibility. Neuroimage 2018; 172:703-717. [DOI: 10.1016/j.neuroimage.2018.02.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 02/01/2018] [Accepted: 02/06/2018] [Indexed: 12/23/2022] Open
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30
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Li J, Lin H, Liu T, Zhang Z, Prince MR, Gillen K, Yan X, Song Q, Hua T, Zhao X, Zhang M, Zhao Y, Li G, Tang G, Yang G, Brittenham GM, Wang Y. Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration. J Magn Reson Imaging 2018; 48:1069-1079. [PMID: 29566449 DOI: 10.1002/jmri.26019] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 03/06/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A challenge for R2 and R2* methods in measuring liver iron concentration (LIC) is that fibrosis, fat, and other hepatic cellular pathology contribute to R2 and R2* and interfere with LIC estimation. PURPOSE To examine the interfering effects of fibrosis, fat, and other lesions on R2* LIC estimation and to use quantitative susceptibility mapping (QSM) to reduce these distortions. STUDY TYPE Prospective. PHANTOMS, SUBJECTS Water phantoms with various concentrations of gadolinium (Gd), collagen (Cl, modeling fibrosis), and fat; nine healthy controls with no known hepatic disease, nine patients with known or suspected hepatic iron overload, and nine patients with focal liver lesions. FIELD STRENGTH/SEQUENCE The phantoms and human subjects were imaged using a 3D multiecho gradient-echo on clinical 1.5T and 3T MRI systems. ASSESSMENT QSM and R2* images were postprocessed from the same gradient-echo data. Fat contributions to susceptibility and R2* were corrected in signal models for LIC estimation. STATISTICAL TESTS Polynomial regression analyses were performed to examine relations among susceptibility, R2* and true [Gd] and [Cl] in phantoms, and among susceptibility and R2* in patient livers. RESULTS In phantoms, R2* had a strong nonlinear dependency on [Cl], [fat], and [Gd], while susceptibility was linearly dependent (R2 > 0.98). In patients, R2* was highly sensitive to liver pathological changes, including fat, fibrosis, and tumors, while QSM was relatively insensitive to these abnormalities (P = 0.015). With moderate iron overload, liver susceptibility and R2* were not linearly correlated over a common R2* range [0, 100] sec-1 (P = 0.35). DATA CONCLUSION R2* estimation of LIC is prone to substantial nonlinear interference from fat, fibrosis, and other lesions. QSM processing of the same gradient echo MRI data can effectively minimize the effects of cellular pathology. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1069-1079.
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Affiliation(s)
- Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tian Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Zhuwei Zhang
- Department of Radiology, Shanghai Tenth People's Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Kelly Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Qi Song
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Hua
- Department of Radiology, Shanghai Tenth People's Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Xiance Zhao
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Miao Zhang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Yu Zhao
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Gary M Brittenham
- Department of Pediatrics, Columbia University, New York, New York, USA
| | - Yi Wang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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31
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Kee Y, Liu Z, Zhou L, Dimov A, Cho J, de Rochefort L, Seo JK, Wang Y. Quantitative Susceptibility Mapping (QSM) Algorithms: Mathematical Rationale and Computational Implementations. IEEE Trans Biomed Eng 2018; 64:2531-2545. [PMID: 28885147 DOI: 10.1109/tbme.2017.2749298] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.
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Affiliation(s)
- Youngwook Kee
- Department of Radiology, Weill Cornell Medical College, New York, USA
| | - Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, USA
| | - Liangdong Zhou
- Department of Radiology, Weill Cornell Medical College, New York, USA
| | - Alexey Dimov
- Department of Biomedical Engineering, Cornell University, Ithaca, USA
| | - Junghun Cho
- Department of Biomedical Engineering, Cornell University, Ithaca, USA
| | - Ludovic de Rochefort
- Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, 13284 Marseille, France
| | - Jin Keun Seo
- Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
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32
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Lu X, Ma Y, Chang EY, He Q, Searleman A, von Drygalski A, Du J. Simultaneous quantitative susceptibility mapping (QSM) and R2* for high iron concentration quantification with 3D ultrashort echo time sequences: An echo dependence study. Magn Reson Med 2018; 79:2315-2322. [PMID: 29314215 DOI: 10.1002/mrm.27062] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 12/05/2017] [Accepted: 12/05/2017] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate the echo dependence of 3D ultrashort echo time (TE) quantitative susceptibility mapping (3D UTE-QSM) and effective transverse relaxation rate ( R2*) measurement in the setting of high concentrations of iron oxide nanoparticles. METHODS A phantom study with iron concentrations ranging from 2 to 22 mM was performed using a 3D UTE Cones sequence. Simultaneous QSM processing with morphology-enabled dipole inversion (MEDI) and R2* single exponential fitting was conducted offline with the acquired 3D UTE data. The dependence of UTE-QSM and R2* on echo spacing (ΔTE) and first TE (TE1 ) was investigated. RESULTS A linear relationship was observed between UTE-QSM measurement and iron concentration up to 22 mM only, with the minimal TE1 of 0.032 ms and ΔTE of less than 0.1 ms. A linear relationship was observed between R2* and iron concentration up to 22 mM only when TE1 was less than 0.132 ms and ΔTE was less than 1.2 ms. UTE-QSM with MEDI processing showed strong dependence on ΔTE and TE1 , especially at high iron concentrations. CONCLUSION UTE-QSM is more sensitive than R2* measurement to TE selection. Both an ultrashort TE1 and a small ΔTE are needed to achieve accurate QSM for high iron concentrations. Magn Reson Med 79:2315-2322, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Xing Lu
- Department of Radiology, University of California, San Diego, California, USA.,Institute of Electrical Engineering, Chinese Academy of Science, Beijing, China
| | - Yajun Ma
- Department of Radiology, University of California, San Diego, California, USA
| | - Eric Y Chang
- Department of Radiology, University of California, San Diego, California, USA.,Radiology Service, VA San Diego Healthcare System, San Diego, California, USA
| | - Qun He
- Department of Radiology, University of California, San Diego, California, USA
| | - Adam Searleman
- Department of Radiology, University of California, San Diego, California, USA
| | - Annette von Drygalski
- Department of Medicine, Division of Hematology/Oncology, University of California, San Diego, California, USA
| | - Jiang Du
- Department of Radiology, University of California, San Diego, California, USA
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33
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Peng X, Lam F, Li Y, Clifford B, Liang ZP. Simultaneous QSM and metabolic imaging of the brain using SPICE. Magn Reson Med 2018; 79:13-21. [PMID: 29067730 PMCID: PMC5744903 DOI: 10.1002/mrm.26972] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 08/31/2017] [Accepted: 09/26/2017] [Indexed: 12/28/2022]
Abstract
PURPOSE To map brain metabolites and tissue magnetic susceptibility simultaneously using a single three-dimensional 1 H-MRSI acquisition without water suppression. METHODS The proposed technique builds on a subspace imaging method called spectroscopic imaging by exploiting spatiospectral correlation (SPICE), which enables ultrashort echo time (TE)/short pulse repetition time (TR) acquisitions for 1 H-MRSI without water suppression. This data acquisition scheme simultaneously captures both the spectral information of brain metabolites and the phase information of the water signals that is directly related to tissue magnetic susceptibility variations. In extending this scheme for simultaneous QSM and metabolic imaging, we increase k-space coverage by using dual density sparse sampling and ramp sampling to achieve spatial resolution often required by QSM, while maintaining a reasonable signal-to-noise ratio (SNR) for the spatiospectral data used for metabolite mapping. In data processing, we obtain high-quality QSM from the unsuppressed water signals by taking advantage of the larger number of echoes acquired and any available anatomical priors; metabolite spatiospectral distributions are reconstructed using a union-of-subspaces model. RESULTS In vivo experimental results demonstrate that the proposed method can produce susceptibility maps at a resolution higher than 1.8 × 1.8 × 2.4 mm3 along with metabolite spatiospectral distributions at a nominal spatial resolution of 2.4 × 2.4 × 2.4 mm3 from a single 7-min MRSI scan. The estimated susceptibility values are consistent with those obtained using the conventional QSM method with 3D multi-echo gradient echo acquisitions. CONCLUSION This article reports a new capability for simultaneous susceptibility mapping and metabolic imaging of the brain from a single 1 H-MRSI scan, which has potential for a wide range of applications. Magn Reson Med 79:13-21, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Xi Peng
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China
| | - Fan Lam
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bryan Clifford
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Fortier V, Levesque IR. Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal. Magn Reson Med 2017; 79:3103-3113. [DOI: 10.1002/mrm.26989] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 09/26/2017] [Accepted: 10/11/2017] [Indexed: 02/03/2023]
Affiliation(s)
- Véronique Fortier
- Medical Physics Unit; McGill University; Montréal Quebec Canada
- Biomedical Engineering; McGill University; Montréal Quebec Canada
| | - Ives R. Levesque
- Medical Physics Unit; McGill University; Montréal Quebec Canada
- Biomedical Engineering; McGill University; Montréal Quebec Canada
- Research Institute of the McGill University Health Centre; Montréal Quebec Canada
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Dymerska B, Bohndorf K, Schennach P, Rauscher A, Trattnig S, Robinson SD. In vivo phase imaging of human epiphyseal cartilage at 7 T. Magn Reson Med 2017; 79:2149-2155. [PMID: 28758241 DOI: 10.1002/mrm.26858] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 07/10/2017] [Accepted: 07/11/2017] [Indexed: 12/23/2022]
Abstract
PURPOSE To assess the potential clinical utility of in vivo susceptibility-weighted imaging and quantitative susceptibility mapping of growth cartilage in the juvenile human knee at 7 T. METHODS High-resolution gradient-echo images of the knees of six healthy children and adolescents aged 6 to 15 were acquired with a 28-channel coil at 7 T. Phase images from the coils were combined using a short echo-time reference scan method (COMPOSER). RESULTS Veins oriented perpendicular to the static B0 field appeared doubled in susceptibility-weighted imaging, but not quantitative susceptibility mapping. Veins and layers in the cartilage were visible in all children up to the age of 13. CONCLUSIONS Phase imaging using susceptibility-weighted imaging and quantitative susceptibility mapping allows the in vivo visualization of veins and layers in human growth cartilage. Magn Reson Med 79:2149-2155, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Barbara Dymerska
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Klaus Bohndorf
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Paul Schennach
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Alexander Rauscher
- UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Siegfried Trattnig
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Simon D Robinson
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
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36
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Wen Y, Nguyen TD, Liu Z, Spincemaille P, Zhou D, Dimov A, Kee Y, Deh K, Kim J, Weinsaft JW, Wang Y. Cardiac quantitative susceptibility mapping (QSM) for heart chamber oxygenation. Magn Reson Med 2017; 79:1545-1552. [PMID: 28653375 DOI: 10.1002/mrm.26808] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 05/30/2017] [Accepted: 05/30/2017] [Indexed: 12/18/2022]
Abstract
PURPOSE To demonstrate the feasibility of in vivo quantitative susceptibility mapping (QSM) in cardiac MRI and to show that mixed-venous oxygen saturation (SvO2 ) can be measured non-invasively using QSM. METHODS Electrocardiographic-gated multi-echo 2D gradient echo data were collected at 1.5 T from 14 healthy volunteers during successive breath-holds. Phase wraps and fat chemical shift were removed using a graph-cut-based phase analysis and IDEAL in an iterative approach. The large susceptibility range from air in the lungs to blood in the heart was addressed by using the preconditioning approach in the dipole field inversion. SvO2 was calculated based on the difference in blood susceptibility between the right ventricle (RV) and left ventricle (LV). Cardiac QSM quality was assessed by two independent readers. RESULTS Nine out of fourteen volunteers (64%) yielded interpretable cardiac QSM. QSM maps showed strong differential contrast between RV and LV blood with RV blood having higher susceptibility values (291.5 ± 32.4 ppb), which correspond to 78.3 ± 2.3% SvO2 . CONCLUSION In vivo cardiac QSM is feasible and can be used to measure SvO2 , but improvements in data acquisition are needed. Magn Reson Med 79:1545-1552, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yan Wen
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Zhe Liu
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Dong Zhou
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Alexey Dimov
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Youngwook Kee
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Kofi Deh
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jiwon Kim
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | | | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Wang Y, Spincemaille P, Liu Z, Dimov A, Deh K, Li J, Zhang Y, Yao Y, Gillen KM, Wilman AH, Gupta A, Tsiouris AJ, Kovanlikaya I, Chiang GCY, Weinsaft JW, Tanenbaum L, Chen W, Zhu W, Chang S, Lou M, Kopell BH, Kaplitt MG, Devos D, Hirai T, Huang X, Korogi Y, Shtilbans A, Jahng GH, Pelletier D, Gauthier SA, Pitt D, Bush AI, Brittenham GM, Prince MR. Clinical quantitative susceptibility mapping (QSM): Biometal imaging and its emerging roles in patient care. J Magn Reson Imaging 2017; 46:951-971. [PMID: 28295954 DOI: 10.1002/jmri.25693] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 02/10/2017] [Indexed: 12/13/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) has enabled magnetic resonance imaging (MRI) of tissue magnetic susceptibility to advance from simple qualitative detection of hypointense blooming artifacts to precise quantitative measurement of spatial biodistributions. QSM technology may be regarded to be sufficiently developed and validated to warrant wide dissemination for clinical applications of imaging isotropic susceptibility, which is dominated by metals in tissue, including iron and calcium. These biometals are highly regulated as vital participants in normal cellular biochemistry, and their dysregulations are manifested in a variety of pathologic processes. Therefore, QSM can be used to assess important tissue functions and disease. To facilitate QSM clinical translation, this review aims to organize pertinent information for implementing a robust automated QSM technique in routine MRI practice and to summarize available knowledge on diseases for which QSM can be used to improve patient care. In brief, QSM can be generated with postprocessing whenever gradient echo MRI is performed. QSM can be useful for diseases that involve neurodegeneration, inflammation, hemorrhage, abnormal oxygen consumption, substantial alterations in highly paramagnetic cellular iron, bone mineralization, or pathologic calcification; and for all disorders in which MRI diagnosis or surveillance requires contrast agent injection. Clinicians may consider integrating QSM into their routine imaging practices by including gradient echo sequences in all relevant MRI protocols. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:951-971.
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Affiliation(s)
- Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Zhe Liu
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Ithaca, New York, USA
| | - Alexey Dimov
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Ithaca, New York, USA
| | - Kofi Deh
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Jianqi Li
- Department of Physics, East China Normal University, Shanghai, P.R. China
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Yihao Yao
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | | | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | | | - Jonathan W Weinsaft
- Division of Cardiology, Department of Medicine, Weill Cornell Medical College, New York, New York, USA
| | | | - Weiwei Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Shixin Chang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese & Western Medicine, Shanghai, P.R. China
| | - Min Lou
- Department of Neurology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, P.R. China
| | - Brian H Kopell
- Department of Neurosurgery, Mount Sinai Hospital, New York, New York, USA
| | - Michael G Kaplitt
- Department of Neurological Surgery, Weill Cornell Medical College, New York, New York, USA
| | - David Devos
- Department of Medical Pharmacology, University of Lille, Lille, France.,Department of Neurology and Movement Disorders, University of Lille, Lille, France.,Department of Toxicology, Public Health and Environment, University of Lille, Lille, France.,INSERM U1171, University of Lille, Lille, France
| | - Toshinori Hirai
- Department of Radiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Xuemei Huang
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Pharmacology, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Neurosurgery, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Radiology, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Yukunori Korogi
- Department of Radiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Alexander Shtilbans
- Department of Neurology, Hospital for Special Surgery, New York, New York, USA.,Parkinson's Disease and Movement Disorder Institute, Weill Cornell Medical College, New York, New York, USA
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, South Korea
| | - Daniel Pelletier
- Department of Neurology, Department of Neurology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Susan A Gauthier
- Department of Neurology and Neuroscience, Weill Cornell Medical College, New York, New York, USA
| | - David Pitt
- Department of Neurology, School of Medicine, Yale University, New Haven, Connecticut, USA
| | - Ashley I Bush
- Oxidation Biology Unit, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Gary M Brittenham
- Department of Pediatrics, Columbia University, Children's Hospital of New York, New York, New York, USA
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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Wang S, Chen W, Wang C, Liu T, Wang Y, Pan C, Mu K, Zhu C, Zhang X, Cheng J. Structure Prior Effects in Bayesian Approaches of Quantitative Susceptibility Mapping. BIOMED RESEARCH INTERNATIONAL 2016; 2016:2738231. [PMID: 28097129 PMCID: PMC5206478 DOI: 10.1155/2016/2738231] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 11/02/2016] [Indexed: 01/11/2023]
Abstract
Quantitative susceptibility mapping (QSM) has shown its potential for anatomical and functional MRI, as it can quantify, for in vivo tissues, magnetic biomarkers and contrast agents which have differential susceptibilities to the surroundings substances. For reconstructing the QSM with a single orientation, various methods have been proposed to identify a unique solution for the susceptibility map. Bayesian QSM approach is the major type which uses various regularization terms, such as a piece-wise constant, a smooth, a sparse, or a morphological prior. Six QSM algorithms with or without structure prior are systematically discussed to address the structure prior effects. The methods are evaluated using simulations, phantom experiments with the given susceptibility, and human brain data. The accuracy and image quality of QSM were increased when using structure prior in the simulation and phantom compared to same regularization term without it, respectively. The image quality of QSM method using the structure prior is better comparing, respectively, to the method without it by either sharpening the image or reducing streaking artifacts in vivo. The structure priors improve the performance of the various QSMs using regularized minimization including L1, L2, and TV norm.
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Affiliation(s)
- Shuai Wang
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Weiwei Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chunmei Wang
- School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, Hubei, China
| | - Tian Liu
- Medimagemetric LLC, New York, NY, USA
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Chu Pan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ketao Mu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ce Zhu
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiang Zhang
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Jian Cheng
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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Kim SH, Oh SN, Choi HS, Lee HS, Jun J, Nam Y, Lee SH, Lee JK, Lee HG. USPIO enhanced lymph node MRI using 3D multi-echo GRE in a rabbit model. CONTRAST MEDIA & MOLECULAR IMAGING 2016; 11:544-549. [PMID: 27976506 DOI: 10.1002/cmmi.1716] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 08/18/2016] [Accepted: 10/07/2016] [Indexed: 01/15/2023]
Abstract
Ultrasmallsuperparamagnetic iron oxide (USPIO) has been suggested to be a negative MR contrast agent to detect metastatic lymph nodes. Previously reported studies have evaluated the diagnostic performance of USPIO-enhanced MR lymph node imaging based on signal intensity. In this study, we investigate the specific performance of three different parametric approaches (normalized signal intensity, R2 * and susceptibility) using 3D multi-echo gradient echo to quantify the USPIO particles in lymph nodes. Nine rabbits with VX2 tumor implants were scanned before and after USPIO injection. From 3D multi-echo GRE magnitude and phase data, we generated multi-echo combined T2 *-weighted images, an R2 * map, and a quantitative susceptibility map. Eighteen lymph nodes (nine reactive and nine metastatic) were evaluated and showed remarkable signal drops in the area of USPIO accumulation. On parametric analysis, the R2 * difference before and after USPIO injection was significantly different (p < 0.05) between reactive and metastatic lymph nodes; in contrast, the normalized signal intensity and susceptibility were not significantly different between the nodes. Our study showed the potential utility of USPIO-enhanced MRI using R2* mapping from 3D multi-echo GRE for the detection of lymph node metastasis and parametric analysis of lymph node status in a rabbit model. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Soon Nam Oh
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hyun Seok Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hyun Sil Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jaeseop Jun
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yoonho Nam
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jin-Kwon Lee
- Department of surgery, Gyeongsang National University School of Medicine and Changwon Gyeongsang National University of Hospital, Changwon, South Korea
| | - Hae Giu Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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Colgan TJ, Hernando D, Sharma SD, Reeder SB. The effects of concomitant gradients on chemical shift encoded MRI. Magn Reson Med 2016; 78:730-738. [PMID: 27650137 DOI: 10.1002/mrm.26461] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 08/19/2016] [Accepted: 08/22/2016] [Indexed: 01/07/2023]
Abstract
PURPOSE The purpose of this work was to characterize the effects of concomitant gradients (CGs) on chemical shift encoded (CSE)-based estimation of B0 field map, proton density fat fraction (PDFF), and R2*. THEORY A theoretical framework was used to determine the effects of CG-induced phase errors on CSE-MRI data. METHODS Simulations, phantom experiments, and in vivo experiments were conducted at 3 Tesla to assess the effects of CGs on quantitative CSE-MRI techniques. Correction of phase errors attributable to CGs was also investigated to determine whether these effects could be removed. RESULTS Phase errors attributed to CGs introduce errors in the estimation of B0 field map, PDFF, and R2*. Phantom and in vivo experiments demonstrated that CGs can introduce estimation errors greater than 30 Hz in the B0 field map, 10% in PDFF, and 16 s-1 in R2*, 16 cm off isocenter. However, CG phase correction before parameter estimation was able to reduce estimation errors to less than 10 Hz in the B0 field map, 1% in PDFF, and 2 s-1 in R2*. CONCLUSION CG effects can impact CSE-MRI, leading to inaccurate estimation of B0 field map, PDFF, and R2*. However, correction for phase errors caused by CGs improve the accuracy of quantitative parameters estimated from CSE-MRI acquisitions. Magn Reson Med 78:730-738, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Timothy J Colgan
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Samir D Sharma
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin, USA.,Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA.,Department of Emergency Medicine, University of Wisconsin, Madison, Wisconsin, USA
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41
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Liu Z, Kee Y, Zhou D, Wang Y, Spincemaille P. Preconditioned total field inversion (TFI) method for quantitative susceptibility mapping. Magn Reson Med 2016; 78:303-315. [PMID: 27464893 DOI: 10.1002/mrm.26331] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Revised: 05/26/2016] [Accepted: 06/14/2016] [Indexed: 01/02/2023]
Abstract
PURPOSE To investigate systematic errors in traditional quantitative susceptibility mapping (QSM) where background field removal and local field inversion (LFI) are performed sequentially, to develop a total field inversion (TFI) QSM method to reduce these errors, and to improve QSM quality in the presence of large susceptibility differences. THEORY AND METHODS The proposed TFI is a single optimization problem which simultaneously estimates the background and local fields, preventing error propagation from background field removal to QSM. To increase the computational speed, a new preconditioner is introduced and analyzed. TFI is compared with the traditional combination of background field removal and LFI in a numerical simulation and in phantom, 5 healthy subjects, and 18 patients with intracerebral hemorrhage. RESULTS Compared with the traditional method projection onto dipole fields+LFI, preconditioned TFI substantially reduced error in QSM along the air-tissue boundaries in simulation, generated high-quality in vivo QSM within similar processing time, and suppressed streaking artifacts in intracerebral hemorrhage QSM. Moreover, preconditioned TFI was capable of generating QSM for the entire head including the brain, air-filled sinus, skull, and fat. CONCLUSION Preconditioned total field inversion improves the accuracy of QSM over the traditional method where background and local fields are separately estimated. Magn Reson Med 78:303-315, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Youngwook Kee
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Dong Zhou
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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Straub S, Laun FB, Emmerich J, Jobke B, Hauswald H, Katayama S, Herfarth K, Schlemmer HP, Ladd ME, Ziener CH, Bonekamp D, Röthke MC. Potential of quantitative susceptibility mapping for detection of prostatic calcifications. J Magn Reson Imaging 2016; 45:889-898. [PMID: 27418017 DOI: 10.1002/jmri.25385] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 06/26/2016] [Indexed: 01/31/2023] Open
Abstract
PURPOSE To evaluate whether quantitative susceptibility (QSM) may be used as an alternative to computed tomography (CT) to detect calcification in prostate cancer patients. MATERIALS AND METHODS Susceptibility map calculation was performed using 3D gradient echo magnetic resonance imaging (MRI) data from 26 patients measured at 3T who previously received a planning CT of the prostate. Phase images were unwrapped using Laplacian-based phase unwrapping, the background field was removed with the V-SHARP method, and susceptibility maps were calculated with the iLSQR method. Two blinded readers were asked to identify peri- and intraprostatic calcifications. RESULTS Average mean and minimum susceptibility values (referenced to iliopsoas muscle) of calcifications were -0.249 ± 0.179 ppm and -0.551 ± 0.323 ppm, and average mean and maximum intensities in CT images were 319 ± 164 HU and 679 ± 392 HU. Twenty-one and 17 out of 22 prostatic calcifications were identified using susceptibility maps and magnitude images, respectively, as well as more than half of periprostatic phleboliths depicted by CT. Calcifications in the prostate and its periphery were quantitatively differentiable from noncalcified prostate tissue in CT (mean values for calcifications / for noncalcified tissue: 71 to 649 / -1 to 83 HU) and in QSM (mean values for calcifications / for noncalcified tissue: -0.641 to 0.063 / -0.046 to 0.181 ppm). Moreover, there was a significant correlation between susceptibility values and CT image intensities for calcifications (P < 0.004). CONCLUSION Prostatic calcifications could be well identified with QSM. Susceptibility maps can be easily obtained from clinical prostate MR protocols that include a 3D gradient echo sequence, rendering it a promising technique for detection and quantification of intraprostatic calcifications. LEVEL OF EVIDENCE 1 J. Magn. Reson. Imaging 2017;45:889-898.
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Affiliation(s)
- Sina Straub
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Heidelberg, Germany
| | - Frederik B Laun
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Heidelberg, Germany
| | - Julian Emmerich
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Heidelberg, Germany
| | - Björn Jobke
- German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany
| | - Henrik Hauswald
- German Cancer Research Center (DKFZ), Clinical Cooperation Unit Radiation Oncology, Heidelberg, Germany.,Heidelberg University Hospital, Department of Radiation Oncology, Heidelberg, Germany
| | - Sonja Katayama
- Heidelberg University Hospital, Department of Radiation Oncology, Heidelberg, Germany
| | - Klaus Herfarth
- Heidelberg University Hospital, Department of Radiation Oncology, Heidelberg, Germany
| | | | - Mark E Ladd
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Heidelberg, Germany
| | - Christian H Ziener
- German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany
| | - David Bonekamp
- German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany
| | - Matthias C Röthke
- German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany
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Wang L, Nissi MJ, Toth F, Johnson CP, Garwood M, Carlson CS, Ellermann J. Quantitative susceptibility mapping detects abnormalities in cartilage canals in a goat model of preclinical osteochondritis dissecans. Magn Reson Med 2016; 77:1276-1283. [PMID: 27018370 DOI: 10.1002/mrm.26214] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 02/18/2016] [Accepted: 02/19/2016] [Indexed: 12/31/2022]
Abstract
PURPOSE To use quantitative susceptibility mapping (QSM) to investigate changes in cartilage canals in the distal femur of juvenile goats after their surgical transection. METHODS Chondronecrosis was surgically induced in the right medial femoral condyles of four 4-day-old goats. Both the operated and control knees were harvested at 2, 3, 5, and 10 weeks after the surgeries. Ex vivo MRI scans were conducted at 9.4 Tesla using TRAFF (relaxation time along a fictitious field)-weighted fast spin echo imaging and QSM to detect areas of chondronecrosis and investigate cartilage canal abnormalities. Histological sections from these same areas stained with hematoxylin and eosin and safranin O were evaluated to assess the affected tissues. RESULTS Both the histological sections and the TRAFF -weighted images of the femoral condyles demonstrated focal areas of chondronecrosis, evidenced by pyknotic chondrocyte nuclei, loss of matrix staining, and altered MR image contrast. At increasing time points after surgery, progressive changes and eventual disappearance of abnormal cartilage canals were observed in areas of chondronecrosis by using QSM. CONCLUSION Abnormal cartilage canals were directly visualized in areas of surgically induced chondronecrosis. Quantitative susceptibility mapping enabled investigation of the vascular changes accompanying chondronecrosis in juvenile goats. Magn Reson Med 77:1276-1283, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Luning Wang
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mikko J Nissi
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Ferenc Toth
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA
| | - Casey P Johnson
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael Garwood
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Cathy S Carlson
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA
| | - Jutta Ellermann
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
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Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM). Z Med Phys 2015; 26:6-34. [PMID: 26702760 DOI: 10.1016/j.zemedi.2015.10.002] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Revised: 09/18/2015] [Accepted: 10/27/2015] [Indexed: 01/27/2023]
Abstract
Quantitative Susceptibility Mapping (QSM) is a novel MRI based technique that relies on estimates of the magnetic field distribution in the tissue under examination. Several sophisticated data processing steps are required to extract the magnetic field distribution from raw MRI phase measurements. The objective of this review article is to provide a general overview and to discuss several underlying assumptions and limitations of the pre-processing steps that need to be applied to MRI phase data before the final field-to-source inversion can be performed. Beginning with the fundamental relation between MRI signal and tissue magnetic susceptibility this review covers the reconstruction of magnetic field maps from multi-channel phase images, background field correction, and provides an overview of state of the art QSM solution strategies.
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Deh K, Nguyen TD, Eskreis-Winkler S, Prince MR, Spincemaille P, Gauthier S, Kovanlikaya I, Zhang Y, Wang Y. Reproducibility of quantitative susceptibility mapping in the brain at two field strengths from two vendors. J Magn Reson Imaging 2015; 42:1592-600. [PMID: 25960320 DOI: 10.1002/jmri.24943] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Accepted: 04/24/2015] [Indexed: 12/29/2022] Open
Abstract
PURPOSE To assess the reproducibility of brain quantitative susceptibility mapping (QSM) in healthy subjects and in patients with multiple sclerosis (MS) on 1.5 and 3T scanners from two vendors. MATERIALS AND METHODS Ten healthy volunteers and 10 patients were scanned twice on a 3T scanner from one vendor. The healthy volunteers were also scanned on a 1.5T scanner from the same vendor and on a 3T scanner from a second vendor. Similar imaging parameters were used for all scans. QSM images were reconstructed using a recently developed nonlinear morphology-enabled dipole inversion (MEDI) algorithm with L1 regularization. Region-of-interest (ROI) measurements were obtained for 20 major brain structures. Reproducibility was evaluated with voxel-wise and ROI-based Bland-Altman plots and linear correlation analysis. RESULTS ROI-based QSM measurements showed excellent correlation between all repeated scans (correlation coefficient R ≥ 0.97), with a mean difference of less than 1.24 ppb (healthy subjects) and 4.15 ppb (patients), and 95% limits of agreements of within -25.5 to 25.0 ppb (healthy subjects) and -35.8 to 27.6 ppb (patients). Voxel-based QSM measurements had a good correlation (0.64 ≤ R ≤ 0.88) and limits of agreements of -60 to 60 ppb or less. CONCLUSION Brain QSM measurements have good interscanner and same-scanner reproducibility for healthy and MS subjects, respectively, on the systems evaluated in this study.
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Affiliation(s)
- Kofi Deh
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | | | - Martin R Prince
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yan Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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Dong J, Liu T, Chen F, Zhou D, Dimov A, Raj A, Cheng Q, Spincemaille P, Wang Y. Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:531-540. [PMID: 25312917 DOI: 10.1109/tmi.2014.2361764] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that reveals tissue magnetic susceptibility. It relies on having a high quality field map, typically acquired with a relatively long echo spacing and long final TE. Applications of QSM outside the brain require the removal of fat contributions to the total signal phase. However, current water/fat separation methods applied on typical data acquired for QSM suffer from three issues: inadequacy when using large echo spacing, over-smoothing of the field maps and high computational cost. In this paper, the general phase wrap and chemical shift problem is formulated using a single species fitting and is solved using graph cuts with conditional jump moves. This method is referred as simultaneous phase unwrapping and removal of chemical shift (SPURS). The result from SPURS is then used as the initial guess for a voxel-wise iterative decomposition of water and fat with echo asymmetric and least-squares estimation (IDEAL). The estimated 3-D field maps are used to compute QSM in body regions outside of the brain, such as the liver. Experimental results show substantial improvements in field map estimation, water/fat separation and reconstructed QSM compared to two existing water/fat separation methods on 1.5T and 3T magnetic resonance human data with long echo spacing and rapid field map variation.
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Phase-corrected bipolar gradients in multi-echo gradient-echo sequences for quantitative susceptibility mapping. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2014; 28:347-55. [PMID: 25408108 DOI: 10.1007/s10334-014-0470-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 09/25/2014] [Accepted: 10/22/2014] [Indexed: 10/24/2022]
Abstract
OBJECTIVE Large echo spacing of unipolar readout gradients in current multi-echo gradient-echo (GRE) sequences for mapping fields in quantitative susceptibility mapping (QSM) can be reduced using bipolar readout gradients thereby improving acquisition efficiency. MATERIALS AND METHODS Phase discrepancies between odd and even echoes in the bipolar readout gradients caused by non-ideal gradient behaviors were measured, modeled as polynomials in space and corrected for accordingly in field mapping. The bipolar approach for multi-echo GRE field mapping was compared with the unipolar approach for QSM. RESULTS The odd-even-echo phase discrepancies were approximately constant along the phase encoding direction and linear along the readout and slice-selection directions. A simple linear phase correction in all three spatial directions was shown to enable accurate QSM of the human brain using a bipolar multi-echo GRE sequence. Bipolar multi-echo acquisition provides QSM in good quantitative agreement with unipolar acquisition while also reducing noise. CONCLUSION With a linear phase correction between odd-even echoes, bipolar readout gradients can be used in multi-echo GRE sequences for QSM.
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