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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - 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
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter 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
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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Spincemaille P, Prince MR. Editorial for "An Empirical Approach to Derive Water T 1 From Multiparametric MR Images Using an Automated Pipeline and Comparison With Liver Stiffness". J Magn Reson Imaging 2024; 59:1204-1205. [PMID: 37522374 DOI: 10.1002/jmri.28905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 08/01/2023] Open
Affiliation(s)
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Roberts AG, Romano DJ, Şişman M, Dimov AV, Nguyen TD, Kovanlikaya I, Gauthier SA, Wang Y, Spincemaille P. Maximum spherical mean value filtering for whole-brain QSM. Magn Reson Med 2024; 91:1586-1597. [PMID: 38169132 DOI: 10.1002/mrm.29963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Dominick J Romano
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Mert Şişman
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Alexey V Dimov
- 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
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan A Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Cho J, Zhang J, Spincemaille P, Zhang H, Nguyen TD, Zhang S, Gupta A, Wang Y. Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping. Bioengineering (Basel) 2024; 11:131. [PMID: 38391617 PMCID: PMC10886243 DOI: 10.3390/bioengineering11020131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra-tissue with low perfusion yet intact cellular integrity-making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.
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Affiliation(s)
- Junghun Cho
- Department of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY 14228, USA
| | - Jinwei Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Hang Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
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Ikram A, Sharma R, Selim M, Kim-Sun G, Shahraki T, Thomas AJ, Filippidis A, Wen Y, Spincemaille P, Wang Y, Soman S. mcTFI QSM MRI ABC/2 intracranial hemorrhage to noncontrast head CT volume measurement equivalence. J Neurol Sci 2024; 456:122859. [PMID: 38171071 PMCID: PMC10796171 DOI: 10.1016/j.jns.2023.122859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND/OBJECTIVES Intracranial hemorrhage (ICH) volume assessment is an important part of patient management and is routinely obtained by non-contrast head CT (NCHCT) using the validated ABC/2 measurement method. Because conventional MRI imaging sequences demonstrate variability in ICH appearance, volumetric analyses for MRI bleed volume in a standardized manner using ABC/2 is not possible. The recently introduced multiecho-complex total field inversion quantitative susceptibility mapping (mcTFI QSM) MRI technique, which maps brain tissue susceptibility to both depict brain tissue structures and quantify tissue susceptibility, may provide a viable alternative. In this study we evaluated mcTFI QSM ABC/2 ICH volume assessment relative to NCHCT. METHODS Patients with ICH who had undergone NCHCT and MRI brain scans within 48 h were recruited for this retrospective study. The ABC/2 method was applied to estimate the bleed volume for both NCHCT and MRI by a CAQ-certified neuroradiologist with 10 years of experience and a trained laboratory assistant. Results were analyzed via Bland-Altman (B-A) and linear regression. RESULTS 54 patients (27 females) who had undergone NCHCT and MRI within 48 h (<24 h., n = 31, 24-48 h, n = 10) were enrolled. mcTFI QSM ICH volume measurement method showed a positive correlation (99.5%) compared to NCHCT. B-A plot comparing ABC/2 ICH volume on NCHCT and mcTFI MRI done for patients within 24 h demonstrates a bias of -0.09%. CONCLUSIONS ICH volume calculation using ABC/2 on mcTFI QSM showed a high correlation with NCHCT measurement. These results suggest mcTFI QSM is a promising MRI method for ABC/2 for bleed volume measurement.
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Affiliation(s)
- Asad Ikram
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Ria Sharma
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Magdy Selim
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | | | - Tamkin Shahraki
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ajith J Thomas
- Cooper University Healthcare/Cooper Medical School of Rowan University, Camden, NJ, United States.
| | - Aristotelis Filippidis
- Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Yan Wen
- GE Healthcare, Lincoln Medical Center, New York, NY, USA
| | | | - Yi Wang
- Weill Cornell Medicine, New York, NY, USA.
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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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 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] [What about the content of this article? (0)] [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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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson S, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. ArXiv 2023:arXiv:2307.02306v1. [PMID: 37461418 PMCID: PMC10350101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Zhang J, Spincemaille P, Zhang H, Nguyen TD, Li C, Li J, Kovanlikaya I, Sabuncu MR, Wang Y. LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping. Neuroimage 2023; 268:119886. [PMID: 36669747 PMCID: PMC10021353 DOI: 10.1016/j.neuroimage.2023.119886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/12/2022] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO-QSM.git.
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Affiliation(s)
- Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Hang Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Chao Li
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Applied Physics, Cornell University, Ithaca, NY, USA
| | - Jiahao Li
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Mert R Sabuncu
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
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11
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Lee K, Ellison B, Selim M, Long NH, Filippidis A, Thomas AJ, Spincemaille P, Wang Y, Soman S. Quantitative susceptibility mapping improves cerebral microbleed detection relative to susceptibility-weighted images. J Neuroimaging 2023; 33:138-146. [PMID: 36168880 DOI: 10.1111/jon.13054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND AND PURPOSE Cerebral microbleed (CMB) detection impacts disease diagnosis and management. Susceptibility-weighted imaging (SWI) MRI depictions of CMBs are used with phase images (SWIP) to distinguish blood from calcification, via qualitative intensity evaluation (bright/dark). However, the intensities depicted for a single lesion can vary within and across consecutive SWIP image planes, impairing the classification of findings as a CMB. We hypothesize that quantitative susceptibility mapping (QSM) MRI, which maps tissue susceptibility, demonstrates less in- and through-plane intensity variation, improving the clinician's ability to categorize a finding as a CMB. METHODS Forty-eight patients with acute intracranial hemorrhage who received multi-echo gradient echo MRI used to generate both SWI/SWIP and morphology-enabled dipole inversion QSM images were enrolled. Five hundred and sixty lesions were visually classified as having homogeneous or heterogeneous in-plane and through-plane intensity by a neuroradiologist and two diagnostic radiology residents using published rating criteria. When available, brain CT scans were analyzed for calcification or acute hemorrhage. Relative risk (RR) ratios and confidence intervals (CIs) were calculated using a generalized linear model with log link and binary error. RESULTS QSM showed unambiguous lesion signal intensity three times more frequently than SWIP (RR = 0.3235, 95% CI 0.2386-0.4386, p<.0001). The probability of QSM depicting homogeneous lesion intensity was three times greater than SWIP for small (RR = 0.3172, 95% CI 0.2382-0.4225, p<.0001), large (RR = 0.3431, 95% CI 0.2045-0.5758, p<.0001), lobar (RR = 0.3215, 95% CI 0.2151-0.4805, p<.0001), cerebellar (RR = 0.3215, 95% CI 0.2151-0.4805, p<.0001), brainstem (RR = 0.3100, 95% CI 0.1192-0.8061, p = .0163), and basal ganglia (RR = 0.3380, 95% CI 0.1980-0.5769, p<.0001) lesions. CONCLUSIONS QSM more consistently demonstrates interpretable lesion intensity compared to SWIP as used for distinguishing CMBs from calcification.
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Affiliation(s)
- Kyuwon Lee
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian Ellison
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Magdy Selim
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ngo H Long
- Department of General Medicine/Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Aristotelis Filippidis
- Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ajith J Thomas
- Department of Neurological Surgery, Cooper University Health Care, Cooper Medical School of Rowan University, Camden, New Jersey, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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12
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Zhang Q, Luo X, Zhou L, Nguyen TD, Prince MR, Spincemaille P, Wang Y. Fluid mechanics approach to perfusion quantification: vasculature computational fluid dynamics simulation, quantitative transport mapping (QTM) analysis of dynamics contrast enhanced MRI, and application in nonalcoholic fatty liver disease classification. IEEE Trans Biomed Eng 2022; 70:980-990. [PMID: 36107908 DOI: 10.1109/tbme.2022.3207057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. METHODS QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude [Formula: see text] derived from QTM, liver arterial blood flow [Formula: see text], liver venous blood flow [Formula: see text], permeability [Formula: see text], blood volume [Formula: see text] and extravascular space volume [Formula: see text] averaged in liver ROI. RESULTS In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. CONCLUSION QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. SIGNIFICANCE We propose to apply QTM method in liver DCE MRI perfusion quantification.
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13
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Parikh NS, Kamel H, Zhang C, Kumar S, Rosenblatt R, Spincemaille P, Gupta A, Cohen DE, de Leon MJ, Gottesman RF, Iadecola C. Association between liver fibrosis and incident dementia in the UK Biobank study. Eur J Neurol 2022; 29:2622-2630. [PMID: 35666174 PMCID: PMC9986963 DOI: 10.1111/ene.15437] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/26/2022] [Accepted: 06/03/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND PURPOSE There is growing recognition that chronic liver conditions influence brain health. The impact of liver fibrosis on dementia risk was unclear. We evaluated the association between liver fibrosis and incident dementia in a cohort study. METHODS We performed a cohort analysis using data from the UK Biobank study, which prospectively enrolled adults starting in 2007, and continues to follow them. People with a Fibrosis-4 (FIB-4) liver fibrosis score >2.67 were categorized as at high risk of advanced fibrosis. The primary outcome was incident dementia, ascertained using a validated approach. We excluded participants with prevalent dementia at baseline. We used Cox proportional hazards models to evaluate the association between liver fibrosis and dementia while adjusting for potential confounders. RESULTS Among 455,226 participants included in this analysis, the mean age was 56.5 years and 54% were women. Approximately 2.17% (95% confidence interval [CI] 2.13%-2.22%) had liver fibrosis. The rate of dementia per 1000 person-years was 1.76 (95% CI 1.50-2.07) in participants with liver fibrosis and 0.52 (95% CI 0.50-0.54) in those without. After adjusting for demographics, socioeconomic deprivation, educational attainment, metabolic syndrome, hypertension, diabetes, dyslipidemia, and tobacco and alcohol use, liver fibrosis was associated with an increased risk of dementia (hazard ratio 1.52, 95% CI 1.22-1.90). Results were robust to sensitivity analyses. Effect modification by sex, metabolic syndrome, and apolipoprotein E4 carrier status was not observed. CONCLUSION Liver fibrosis in middle age was associated with an increased risk of incident dementia, independent of shared risk factors. Liver fibrosis may be an underrecognized risk factor for dementia.
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Affiliation(s)
- Neal S Parikh
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Hooman Kamel
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Cenai Zhang
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Sonal Kumar
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Russell Rosenblatt
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - David E Cohen
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mony J de Leon
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, Maryland, USA
| | - Costantino Iadecola
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, New York, USA
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14
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Kuchcinski G, Patin L, Lopes R, Leroy M, Delbeuck X, Rollin-Sillaire A, Lebouvier T, Wang Y, Spincemaille P, Tourdias T, Hacein-Bey L, Devos D, Pasquier F, Leclerc X, Pruvo JP, Verclytte S. Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer's disease. Eur Radiol 2022; 33:184-195. [PMID: 35881183 DOI: 10.1007/s00330-022-09014-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES We aimed to define brain iron distribution patterns in subtypes of early-onset Alzheimer's disease (EOAD) by the use of quantitative susceptibility mapping (QSM). METHODS EOAD patients prospectively underwent MRI on a 3-T scanner and concomitant clinical and neuropsychological evaluation, between 2016 and 2019. An age-matched control group was constituted of cognitively healthy participants at risk of developing AD. Volumetry of the hippocampus and cerebral cortex was performed on 3DT1 images. EOAD subtypes were defined according to the hippocampal to cortical volume ratio (HV:CTV). Limbic-predominant atrophy (LPMRI) is referred to HV:CTV ratios below the 25th percentile, hippocampal-sparing (HpSpMRI) above the 75th percentile, and typical-AD between the 25th and 75th percentile. Brain iron was estimated using QSM. QSM analyses were made voxel-wise and in 7 regions of interest within deep gray nuclei and limbic structures. Iron distribution in EOAD subtypes and controls was compared using an ANOVA. RESULTS Sixty-eight EOAD patients and 43 controls were evaluated. QSM values were significantly higher in deep gray nuclei (p < 0.001) and limbic structures (p = 0.04) of EOAD patients compared to controls. Among EOAD subtypes, HpSpMRI had the highest QSM values in deep gray nuclei (p < 0.001) whereas the highest QSM values in limbic structures were observed in LPMRI (p = 0.005). QSM in deep gray nuclei had an AUC = 0.92 in discriminating HpSpMRI and controls. CONCLUSIONS In early-onset Alzheimer's disease patients, we observed significant variations of iron distribution reflecting the pattern of brain atrophy. Iron overload in deep gray nuclei could help to identify patients with atypical presentation of Alzheimer's disease. KEY POINTS • In early-onset AD patients, QSM indicated a significant brain iron overload in comparison with age-matched controls. • Iron load in limbic structures was higher in participants with limbic-predominant subtype. • Iron load in deep nuclei was more important in participants with hippocampal-sparing subtype.
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Affiliation(s)
- Grégory Kuchcinski
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France. .,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France. .,Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France.
| | - Lucas Patin
- Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Renaud Lopes
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France
| | - Mélanie Leroy
- Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France
| | - Xavier Delbeuck
- Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France
| | - Adeline Rollin-Sillaire
- Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.,Department of Neurology, CHU Lille, F-59000, Lille, France
| | - Thibaud Lebouvier
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.,Department of Neurology, CHU Lille, F-59000, Lille, France
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Thomas Tourdias
- Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, F-33000, Bordeaux, France.,Neurocentre Magendie, Inserm, U1215, Université de Bordeaux, F-33000, Bordeaux, France
| | - Lotfi Hacein-Bey
- Radiology Department, University of California Davis School of Medicine, Sacramento, CA, USA
| | - David Devos
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,Department of Pharmacology, CHU Lille, F-59000, Lille, France
| | - Florence Pasquier
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.,Department of Neurology, CHU Lille, F-59000, Lille, France
| | - Xavier Leclerc
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Jean-Pierre Pruvo
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Sébastien Verclytte
- Department of Imaging, Lille Catholic Hospitals, Lille Catholic University, F-59000, Lille, France
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15
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Rahmanzadeh R, Galbusera R, Lu PJ, Bahn E, Weigel M, Barakovic M, Franz J, Nguyen TD, Spincemaille P, Schiavi S, Daducci A, La Rosa F, Absinta M, Sati P, Cuadra MB, Radue EW, Leppert D, Kuhle J, Kappos L, Brück W, Reich DS, Stadelmann C, Wang Y, Granziera C. A new advanced MRI biomarker for remyelinated lesions in Multiple Sclerosis. Ann Neurol 2022; 92:486-502. [PMID: 35713309 PMCID: PMC9527017 DOI: 10.1002/ana.26441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/28/2022]
Abstract
Objectives Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS. Methods We performed 3 studies: (1) a cross‐sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso‐intense, hypo‐intense, hyperintense, lesions with hypo‐intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology‐QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions. Results At baseline, hypo‐ and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p < 0.0001). Further, at 2‐year follow‐up, hypo‐/iso‐intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo‐/iso‐intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%). Interpretation These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486–502
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Affiliation(s)
- Reza Rahmanzadeh
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Erik Bahn
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | - Matthias Weigel
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jonas Franz
- Institute of Neuropathology, University Medical Center, Göttingen, Germany.,Max Planck Institute for Experimental Medicine, Göttingen, Germany.,Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
| | - Thanh D Nguyen
- 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
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Martina Absinta
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and Hospital, Milan, Italy
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, Maryland, USA.,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - David Leppert
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Wolfgang Brück
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, Maryland, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
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16
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Dimov AV, Nguyen TD, Gillen KM, Marcille M, Spincemaille P, Pitt D, Gauthier SA, Wang Y. Susceptibility source separation from gradient echo data using magnitude decay modeling. J Neuroimaging 2022; 32:852-859. [PMID: 35668022 DOI: 10.1111/jon.13014] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE The objective is to demonstrate feasibility of separating magnetic sources in quantitative susceptibility mapping (QSM) by incorporating magnitude decay rates R 2 ∗ $R_2^{\rm{*}}$ in gradient echo (GRE) MRI. METHODS Magnetic susceptibility source separation was developed using R 2 ∗ $R_2^{\rm{*}}$ and compared with a prior method using R 2 ' = R 2 ∗ - R 2 ${R^{\prime}_2} = R_2^* - {R_2}$ that required an additional sequence to measure the transverse relaxation rate R2 . Both susceptibility separation methods were compared in multiple sclerosis (MS) patients (n = 17). Susceptibility values of negative sources estimated with R 2 ∗ $R_2^{\rm{*}}$ -based source separation in a set of enhancing MS lesions (n = 44) were correlated against longitudinal myelin water fraction (MWF) changes. RESULTS In in vivo data, linear regression of the estimated χ + ${\chi}^{+}$ and χ - ${\chi}^{-}$ susceptibility values between the R 2 ∗ $R_2^*$ - and the R 2 ' ${R^{\prime}_2}$ -based separation methods performed across 182 segmented lesions revealed correlation coefficient r = .96 and slope close .99. Correlation analysis in enhancing lesions revealed a significant positive association between the χ - ${\chi}^{-}$ increase at 1-year post-onset relative to 0 year and the MWF increase at 1 year relative to 0 year (β = -0.144, 95% confidence interval: [-0.199, -0.1], p = .0008) and good agreement between R 2 ' ${R^{\prime}_2}$ and R 2 ∗ $R_2^*$ methods (r = .79, slope = .95). CONCLUSIONS Separation of magnetic sources based solely on GRE complex data is feasible by combining magnitude decay rate modeling and phase-based QSM and χ - ${\chi}^{-}$ change may serve as a biomarker for myelin recovery or damage in acute MS lesions.
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Affiliation(s)
- Alexey V Dimov
- 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
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | | | - David Pitt
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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17
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Cho J, Nguyen TD, Huang W, Sweeney EM, Luo X, Kovanlikaya I, Zhang S, Gillen KM, Spincemaille P, Gupta A, Gauthier SA, Wang Y. Brain oxygen extraction fraction mapping in patients with multiple sclerosis. J Cereb Blood Flow Metab 2022; 42:338-348. [PMID: 34558996 PMCID: PMC9122515 DOI: 10.1177/0271678x211048031] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We aimed to demonstrate the feasibility of whole brain oxygen extraction fraction (OEF) mapping for measuring lesion specific and regional OEF abnormalities in multiple sclerosis (MS) patients. In 22 MS patients and 11 healthy controls (HC), OEF and neural tissue susceptibility (χn) maps were computed from MRI multi-echo gradient echo data. In MS patients, 80 chronic active lesions with hyperintense rim on quantitative susceptibility mapping were identified, and the mean OEF and χn within the rim and core were compared using linear mixed-effect model analysis. The rim showed higher OEF and χn than the core: relative to their adjacent normal appearing white matter, OEF contrast = -6.6 ± 7.0% vs. -9.8 ± 7.8% (p < 0.001) and χn contrast = 33.9 ± 20.3 ppb vs. 25.7 ± 20.5 ppb (p = 0.017). Between MS and HC, OEF and χn were compared using a linear regression model in subject-based regions of interest. In the whole brain, compared to HC, MS had lower OEF, 30.4 ± 3.3% vs. 21.4 ± 4.4% (p < 0.001), and higher χn, -23.7 ± 7.0 ppb vs. -11.3 ± 7.7 ppb (p = 0.018). Our feasibility study suggests that OEF may serve as a useful quantitative marker of tissue oxygen utilization in MS.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Weiyuan Huang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth M Sweeney
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Xianfu Luo
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Shun Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan A Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA.,Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
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18
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Zhang Q, Spincemaille P, Drotman M, Chen C, Eskreis-Winkler S, Huang W, Zhou L, Morgan J, Nguyen TD, Prince MR, Wang Y. Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics. Magn Reson Imaging 2022; 86:86-93. [PMID: 34748928 PMCID: PMC8726426 DOI: 10.1016/j.mri.2021.10.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 10/29/2021] [Accepted: 10/30/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE To test the feasibility of using quantitative transport mapping (QTM) method, which is based on the inversion of transport equation using spatial deconvolution without any arterial input function, for automatically postprocessing dynamic contrast enhanced MRI (DCE-MRI) to differentiate malignant and benign breast tumors. MATERIALS AND METHODS Breast DCE-MRI data with biopsy confirmed malignant (n = 13) and benign tumors (n = 13) was used to assess QTM velocity (|u|) and diffusion coefficient (D), volume transfer constant (Ktrans), volume fraction of extravascular extracellular space (Ve) from kinetics method, and traditional enhancement curve characteristics (ECC: amplitude A, wash-in rate α, wash-out rate β). A Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis were performed to assess the diagnostic performance of these parameters for distinguishing between benign and malignant tumors. RESULTS Between malignant and benign tumors, there was a significant difference in |u| and Ktrans, (p = 0.0066, 0.0274, respectively), but not in D, Ve, A, α and β (p = 0.1119, 0.2382, 0.4418,0.2592 and 0.9591, respectively). ROC area-under-the-curve was 0.82, 0.75 (95% confidence level 0.60-0.95, 0.51-0.90) for |u| and Ktrans, respectively. CONCLUSION QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with traditional kinetics method and ECC, QTM method showed better diagnostic accuracy in differentiating benign from malignant breast tumors in this study.
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Affiliation(s)
- Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Michele Drotman
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Christine Chen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | | | - Weiyuan Huang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Liangdong Zhou
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - John Morgan
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Thanh D. Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Martin R. Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
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19
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Kim J, Nguyen TD, Zhang J, Gauthier SA, Marcille M, Zhang H, Cho J, Spincemaille P, Wang Y. Subsecond accurate myelin water fraction reconstruction from FAST-T 2 data with 3D UNET. Magn Reson Med 2022; 87:2979-2988. [PMID: 35092094 DOI: 10.1002/mrm.29176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 12/31/2021] [Accepted: 01/08/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop a 3D UNET convolutional neural network for rapid extraction of myelin water fraction (MWF) maps from six-echo fast acquisition with spiral trajectory and T2 -prep data and to evaluate its accuracy in comparison with multilayer perceptron (MLP) network. METHODS The MWF maps were extracted from 138 patients with multiple sclerosis using an iterative three-pool nonlinear least-squares algorithm (NLLS) without and with spatial regularization (srNLLS), which were used as ground-truth labels to train, validate, and test UNET and MLP networks as a means to accelerate data fitting. Network testing was performed in 63 patients with multiple sclerosis and a numerically simulated brain phantom at SNR of 200, 100 and 50. RESULTS Simulations showed that UNET reduced the MWF mean absolute error by 30.1% to 56.4% and 16.8% to 53.6% over the whole brain and by 41.2% to 54.4% and 21.4% to 49.4% over the lesions for predicting srNLLS and NLLS MWF, respectively, compared to MLP, with better performance at lower SNRs. UNET also outperformed MLP for predicting srNLLS MWF in the in vivo multiple-sclerosis brain data, reducing mean absolute error over the whole brain by 61.9% and over the lesions by 67.5%. However, MLP yielded 41.1% and 51.7% lower mean absolute error for predicting in vivo NLLS MWF over the whole brain and the lesions, respectively, compared with UNET. The whole-brain MWF processing time using a GPU was 0.64 seconds for UNET and 0.74 seconds for MLP. CONCLUSION Subsecond whole-brain MWF extraction from fast acquisition with spiral trajectory and T2 -prep data using UNET is feasible and provides better accuracy than MLP for predicting MWF output of srNLLS algorithm.
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Affiliation(s)
- Jeremy Kim
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jinwei Zhang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Hang Zhang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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20
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Huang W, Zhang Q, Wu G, Chen P, Li J, McCabe Gillen K, Spincemaille P, Chiang GC, Gupta A, Wang Y, Chen F. Corrigendum to "DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1α, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients" [Radiother Oncol (2021) 164:146-154]. Radiother Oncol 2022; 169:168. [PMID: 35012820 DOI: 10.1016/j.radonc.2021.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 11/16/2022]
Affiliation(s)
- Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), China; Department of Radiology, Weill Medical College of Cornell University, New York, USA
| | - Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, USA; Meinig School of Biomedical Engineering, Cornell University, Ithaca, USA
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), China
| | - Pianpian Chen
- Department of Pathology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), China
| | - Jiao Li
- Department of Pathology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), China
| | - Kelly McCabe Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Medical College of Cornell University/New York-Presbyterian Hospital, New York, USA
| | - Ajay Gupta
- Department of Radiology, Weill Medical College of Cornell University/New York-Presbyterian Hospital, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, USA; Meinig School of Biomedical Engineering, Cornell University, Ithaca, USA
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), China
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21
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Dimov AV, Nguyen TD, Spincemaille P, Sweeney EM, Zinger N, Kovanlikaya I, Kopell BH, Gauthier SA, Wang Y. Global cerebrospinal fluid as a zero-reference regularization for brain quantitative susceptibility mapping. J Neuroimaging 2022; 32:141-147. [PMID: 34480496 PMCID: PMC8752493 DOI: 10.1111/jon.12923] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 07/11/2021] [Accepted: 08/09/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND AND PURPOSE The objective ofthis study was to demonstrate a global cerebrospinal fluid (CSF) method for a consistent and automated zero referencing of brain quantitative susceptibility mapping (QSM). METHODS Whole brain CSF mask was automatically segmented by thresholding the gradient echo transverse relaxation ( R2∗) map, and regularization was employed to enforce uniform susceptibility distribution within the CSF volume in the field-to-susceptibility inversion. This global CSF regularization method was compared with a prior ventricular CSF regularization. Both reconstruction methods were compared in a repeatability study of 12 healthy subjects using t-test on susceptibility measurements, and in patient studies of 17 multiple sclerosis (MS) and 10 Parkinson's disease (PD) patients using Wilcoxon rank-sum test on radiological scores. RESULTS In scan-rescan experiments, global CSF regularization provided more consistent CSF volume as well as higher repeatability of QSM measurements than ventricular CSF regularization with a smaller bias: -2.7 parts per billion (ppb) versus -0.13 ppb (t-test p<0.05) and a narrower 95% limits of agreement: [-7.25, 6.99] ppb versus [-16.60, 11.19 ppb] (f-test p<0.05). In PD and MS patients, global CSF regularization reduced smoothly varying shadow artifacts and significantly improved the QSM quality score (p<0.001). CONCLUSIONS The proposed whole brain CSF method for QSM zero referencing improves repeatability and image quality of brain QSM compared to the ventricular CSF method.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | | | | | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, USA
| | | | - Brian H. Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, USA
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22
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Champagne AA, Wen Y, Selim M, Filippidis A, Thomas AJ, Spincemaille P, Wang Y, Soman S. Quantitative Susceptibility Mapping for Staging Acute Cerebral Hemorrhages: Comparing the Conventional and Multiecho Complex Total Field Inversion magnetic resonance imaging MR Methods. J Magn Reson Imaging 2021; 54:1843-1854. [PMID: 34117811 DOI: 10.1002/jmri.27763] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The perceived acuity of intracerebral hemorrhage (ICH) impacts the management of patients, both within emergent and outpatient/urgent settings. Morphology enabled dipole inversion (MEDI) quantitative susceptibility imaging (QSM) has improved characterization of ICH acuity, despite outstanding limitations in distinguishing blood products. PURPOSE/HYPOTHESIS Using improved susceptibility quantification, novel postprocessing QSM method from multiecho complex total field inversion (mcTFI) may better discriminate between acute and subacute ICH, compared to MEDI. STUDY TYPE Retrospective cohort study. SUBJECTS A total of 121 subjects enrolled following positive computerized tomography (CT) findings for ICH. Subjects were grouped based on time between admission and MR imaging: hyperacute (<24 hours), acute (1-3 days), early subacute (3-7 days), and late subacute (7-18 days). FIELD STRENGTH/SEQUENCE A multiecho gradient echo sequence at 3.0 T was paired with clinical noncontrast CT imaging. ASSESSMENT A quantitative index (CTindex ) was derived based on relative intensities of blood on noncontrast CT. All images were co-registered, from which QSM parameters within the ICH area were assessed across groups, as well as the correlation with CTindex . STATISTICAL TESTS Group differences were assessed using ANOVAs. Linear regressions between the CTindex , MEDI, and mcTFI measurements were used to assess their relationships. Statistical significance was set at P < 0.05. RESULTS A total of 21 hyperacute, 72 acute, 21 early subacute, and 7 late-subacute patients were included in this analysis. Significant changes in blood susceptibility were found over time for the MEDI and mcTFI, although mcTFI better differentiated the hyperacute/acute from subacute stages. CTindex values within the ICH were more strongly correlated with mcTFI QSM (r = 0.727) than MEDI (r = 0.412) QSM. DATA CONCLUSION McTFI susceptibility estimation demonstrated better correlation with ICH acuity as suggested by CT, providing an improved method to assess acuity of intracranial blood products in clinical settings to identify cases that may require acute intervention. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Allen A Champagne
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
- School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Yan Wen
- Department of Radiology, Weill Cornell Medicine, Ithaca, New York, USA
| | - Magdy Selim
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Aristotelis Filippidis
- Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Massachusetts, USA
| | - Ajith J Thomas
- Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Massachusetts, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, Ithaca, New York, USA
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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23
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Xie H, Zhuang H, Guo Y, Sharma RD, Zhang Q, Li J, Lu S, Xu L, Chan Q, Yoneda T, Spincemaille P, Zhang H, Guo H, Prince MR, Yu C, Wang Y. The appearance of magnetic susceptibility objects in SWI phase depends on object size: Comparison with QSM and CT. Clin Imaging 2021; 82:67-72. [PMID: 34798560 DOI: 10.1016/j.clinimag.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/14/2021] [Accepted: 11/07/2021] [Indexed: 11/03/2022]
Abstract
PURPOSE Tissue magnetic susceptibility sign can potentially be detected on susceptibility weighted imaging (SWI) phase (SW-P). This study aims to investigate its performance for depicting brain susceptibility structures. METHODS A simulation was performed to depict magnetic susceptibility structures of various geometries on SW-P and quantitative susceptibility mapping (QSM). Brain MRI was performed on 25 subjects using SWI on a 3 T MRI system. QSM was generated from the same data. SW-P and QSM were analyzed according to radiological assessment for depicting globus pallidus nuclei, optic radiation white matter tracts, and lateral ventricular choroid plexus calcifications. In 11 of these subjects, CT was available and correlated with SW-P and QSM to assess their performance in quantifying calcifications in the choroid plexus. RESULTS In simulation, the appearance of a sphere on SW-P ranged from centric nodule to mixed positive and negative values as the diameter increased. Large cylinders also appeared as mixed positive and negative values. In comparison, QSM correctly depicted the susceptibility distribution of all magnetic structures. On human brain images, SW-P depicted the globus pallidus and optic radiation with mixed positive and negative values, consistent with simulation, and small choroid plexus calcifications as either mixed positive and negative values or as centric nodules; QSM depicted all structures as solid structures with the expected signs. For measuring calcification in the choroid plexus, QSM vs CT linear regression had a higher coefficient of determination compared to SW-P vs CT and SW-P vs QSM. CONCLUSION Appearance of susceptibility sources on SW-P changes with object size. This problem can be overcome using QSM.
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Affiliation(s)
- Hong Xie
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | - Hangwei Zhuang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Yihao Guo
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ria D Sharma
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Jiahao Li
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Shimin Lu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Liang Xu
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | | | - Tetsuya Yoneda
- Department of Medical Imaging Sciences, Kumamoto University, Kumamoto, Japan
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Honglei Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Chengxin Yu
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
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24
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Cho J, Zhang J, Spincemaille P, Zhang H, Hubertus S, Wen Y, Jafari R, Zhang S, Nguyen TD, Dimov AV, Gupta A, Wang Y. QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping. Magn Reson Med 2021; 87:1583-1594. [PMID: 34719059 DOI: 10.1002/mrm.29057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 09/01/2021] [Accepted: 10/07/2021] [Indexed: 01/17/2023]
Abstract
PURPOSE To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET). METHODS The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test. RESULTS In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps. CONCLUSION QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Hang Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Simon Hubertus
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yan Wen
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Ramin Jafari
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Shun Zhang
- 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
| | - Alexey V Dimov
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Ajay Gupta
- 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.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Li J, Huang W, Luo X, Wen Y, Cho J, Kovanlikaya I, Gauthier SA, Nguyen TD, Spincemaille P, Wang Y. The central vein sign in multiple sclerosis lesions: Susceptibility relaxation optimization from a routine MRI multiecho gradient echo sequence. J Neuroimaging 2021; 32:48-56. [PMID: 34664747 DOI: 10.1111/jon.12938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND AND PURPOSE The white matter lesion central vein sign (CVS) is an emerging biomarker for multiple sclerosis (MS) differential diagnosis. Currently, CVS is detected on susceptibility weighted imaging (SWI) with suboptimal contrast. We developed an imaging method called susceptibility relaxation optimization (SRO) to improve CVS visualization. METHODS This was a retrospective study of MS patients who had MRI in June 2018 with routine 3D multiecho gradient echo (GRE) and T2-weighted fluid-attenuated inversion recovery (FLAIR) sequences. SRO and SWI images were reconstructed from GRE data. MS lesions were identified on FLAIR image. The CVS detection rate, the image quality score of CVS conspicuity (range 0-3), and central vein-to-lesion contrast were compared between SRO and SWI images. RESULTS In 20 MS patients (mean age 45 ± 9 years; 15 women), SRO significantly increased CVS detection rate compared to SWI (53.3%, 274/514 vs. 32.9%, 169/514; p<.001, McNemar's test). The median image quality score for SRO was 2 compared to 1 for SWI (p<.001, Wilcoxon signed-rank test). The median overall image quality score for SRO was 7 compared to 6 for SWI (p = .003; Wilcoxon signed-rank test). Central vein-to-lesion contrast was 0.12 ± 0.12 in SRO compared to 0.031 ± 0.075 in SWI (p<.001, t-test). CONCLUSIONS SRO yields better central vein contrast and increases CVS detection rate compared to SWI.
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Affiliation(s)
- Jiahao Li
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Weiyuan Huang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Xianfu Luo
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Yan Wen
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Junghun Cho
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Susan A Gauthier
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Brain and Mind Institute, Weill Medical College of Cornell University, New York, New York, USA.,Department of Neurology, Weill Medical College of Cornell University, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
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26
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Zhang H, Zhang J, Li C, Sweeney EM, Spincemaille P, Nguyen TD, Gauthier SA, Wang Y, Marcille M. ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation. Neuroimage Clin 2021; 32:102854. [PMID: 34666289 PMCID: PMC8521204 DOI: 10.1016/j.nicl.2021.102854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/08/2021] [Accepted: 10/10/2021] [Indexed: 11/30/2022]
Abstract
Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.
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Affiliation(s)
- Hang Zhang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Jinwei Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Chao Li
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA
| | - Elizabeth M Sweeney
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan A Gauthier
- 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; Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
| | - Melanie Marcille
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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27
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Huang W, Zhang Q, Wu G, Chen PP, Li J, McCabe Gillen K, Spincemaille P, Chiang GC, Gupta A, Wang Y, Chen F. DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1α, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients. Radiother Oncol 2021; 164:146-154. [PMID: 34592360 DOI: 10.1016/j.radonc.2021.09.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 08/13/2021] [Accepted: 09/20/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to noninvasively detect expression of hypoxia inducible factor-1-alpha (HIF-1α), epidermal growth factor receptor (EGFR), and Ki-67 in nasopharyngeal carcinoma (NPC) by quantitatively measuring tumor blood flow, vascularity, and permeability. PURPOSE We aim to explore the utility of DCE-MRI in detecting HIF-1α, EGFR, and Ki-67 expression levels using traditional Kety's/Tofts' modeling and quantitative transport mapping (QTM). MATERIALS AND METHODS Eighty-nine NPC patients underwent DCE-MRI before treatment were enrolled. DCE-MRI was processed to generate the following kinetic parameters: |u| and D from the QTM model, tumor blood flow (TBF) from Kety's model, and Ktrans, Ve, and Kep from Tofts' model. Pretreatment levels of HIF-1α, EGFR, and Ki-67 were assessed by immunohistochemistry and classified into low and high expression groups. RESULTS |u| (p < 0.001) and TBF (p = 0.015) values were significantly higher in the HIF-1α high-expression group compared to low-expression group. Only Ktrans (p = 0.016) was significantly higher in the EGFR high-expression group. Only |u| (p < 0.001) values were significantly higher in the Ki-67 high-expression group compared to low-expression group. Multiple linear regression analyses showed that |u| independently correlated with HIF-1α and Ki-67 expression, and Ktrans independently correlated with EGFR. The areas under the ROC curves of |u| for HIF-1α and Ki-67, and Ktrans for EGFR were 0.83, 0.74, and 0.70, respectively. CONCLUSION |u| and Ktrans derived from DCE-MRI may be considered as noninvasive imaging markers for detecting hypoxia and proliferation in NPC patients.
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Affiliation(s)
- Weiyuan Huang
- Department of Radiology, Weill Medical College of Cornell University, New York, USA; Department of Radiology, Hainan General Hospital (Affiliated Hainan Hospital of Hainan Medical University), China.
| | - Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, USA; Meinig School of Biomedical Engineering, Cornell University, Ithaca, USA
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Affiliated Hainan Hospital of Hainan Medical University), China
| | - Pian Pian Chen
- Department of Pathology, Hainan General Hospital (Affiliated Hainan Hospital of Hainan Medical University), China
| | - Jiao Li
- Department of Pathology, Hainan General Hospital (Affiliated Hainan Hospital of Hainan Medical University), China
| | - Kelly McCabe Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, USA
| | - Gloria C Chiang
- Departments of Radiology, Weill Medical College of Cornell University/New York-Presbyterian Hospital, New York, USA
| | - Ajay Gupta
- Departments of Radiology, Weill Medical College of Cornell University/New York-Presbyterian Hospital, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, USA; Meinig School of Biomedical Engineering, Cornell University, Ithaca, USA.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Affiliated Hainan Hospital of Hainan Medical University), China.
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28
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Murray KD, Uddin MN, Tivarus ME, Sahin B, Wang HZ, Singh MV, Qiu X, Wang L, Spincemaille P, Wang Y, Maggirwar SB, Zhong J, Schifitto G. Increased risk for cerebral small vessel disease is associated with quantitative susceptibility mapping in HIV infected and uninfected individuals. Neuroimage Clin 2021; 32:102786. [PMID: 34500428 PMCID: PMC8429957 DOI: 10.1016/j.nicl.2021.102786] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/19/2021] [Accepted: 08/06/2021] [Indexed: 11/10/2022]
Abstract
The aim of this study was to assess, in the context of cerebral small vessel disease (CSVD), whether cardiovascular risk factors and white matter hyperintensities (WMHs) were associated with brain tissue susceptibility as measured by quantitative susceptibility mapping (QSM). Given that CSVD is diagnosed by the presence of lacunar strokes, periventricular and deep WMHs, increased perivascular spaces, and microbleeds, we expected that QSM could capture changes in brain tissue due to underlying CSVD pathology. We compared a cohort of 101 HIV-infected individuals (mean age ± SD = 53.2 ± 10.9 years) with mild to moderate cardiovascular risk scores, as measured by the Reynolds risk score, to 102 age-matched controls (mean age (SD) = 50.3 (15.7) years) with similar Reynolds scores. We performed brain MRI to assess CSVD burden by acquiring 3D T1-MPRAGE, 3D FLAIR, 2D T2-TSE, and mGRE for QSM. We found that signs of CSVD are significantly higher in individuals with HIV-infection compared to controls and that WMH volumes are significantly correlated with age and cardiovascular risk scores. Regional QSM was associated with cardiovascular risk factors, age, sex, and WMH volumes but not HIV status. These results suggest that QSM may be an early imaging marker reflective of alterations in brain microcirculation.
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Affiliation(s)
- Kyle D Murray
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States
| | - Md Nasir Uddin
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Madalina E Tivarus
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States; Department of Neuroscience, University of Rochester, Rochester, NY, United States
| | - Bogachan Sahin
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Henry Z Wang
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
| | - Meera V Singh
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States
| | - Lu Wang
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, 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; Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States
| | - Sanjay B Maggirwar
- Department of Microbiology, Immunology, and Tropical Medicine, The George Washington University, Washington, DC, United States
| | - Jianhui Zhong
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States; Department of Imaging Sciences, University of Rochester, Rochester, NY, United States; Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester, Rochester, NY, United States; Department of Imaging Sciences, University of Rochester, Rochester, NY, United States; Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States.
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Rashid T, Abdulkadir A, Nasrallah IM, Ware JB, Liu H, Spincemaille P, Romero JR, Bryan RN, Heckbert SR, Habes M. DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI. Sci Rep 2021; 11:14124. [PMID: 34238951 PMCID: PMC8266884 DOI: 10.1038/s41598-021-93427-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/24/2021] [Indexed: 12/24/2022] Open
Abstract
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.
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Affiliation(s)
- Tanweer Rashid
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA. .,Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey B Ware
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
| | - Hangfan Liu
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | | | - J Rafael Romero
- Department of Neurology, School of Medicine, Boston University, Boston, MA, USA
| | - R Nick Bryan
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA.,Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Susan R Heckbert
- Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA. .,Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
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30
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Cho J, Spincemaille P, Nguyen TD, Gupta A, Wang Y. Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD. Magn Reson Med 2021; 86:2635-2646. [PMID: 34110656 DOI: 10.1002/mrm.28875] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/02/2021] [Accepted: 05/10/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using temporal clustering, tissue composition, and total variation (CCTV). METHODS Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO2 were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test. RESULTS In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001). CONCLUSION The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.
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Affiliation(s)
- Junghun Cho
- 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
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Ajay Gupta
- 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.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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31
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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32
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Jafari R, Spincemaille P, Zhang J, Nguyen TD, Luo X, Cho J, Margolis D, Prince MR, Wang Y. Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training. Magn Reson Med 2021; 85:2263-2277. [PMID: 33107127 PMCID: PMC7809709 DOI: 10.1002/mrm.28546] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 09/12/2020] [Accepted: 09/14/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. METHODS The current T 2 ∗ -IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T 2 ∗ -IDEAL. RESULTS All DNN methods generated consistent water/fat separation results that agreed well with T 2 ∗ -IDEAL under proper initialization. CONCLUSION The water/fat separation problem can be solved using unsupervised deep neural networks.
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Affiliation(s)
- Ramin Jafari
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
- Department of Radiology, Weill Cornell Medicine, New York, NY
| | | | - Jinwei Zhang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
- Department of Radiology, Weill Cornell Medicine, New York, NY
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY
| | - Xianfu Luo
- Department of Radiology, Weill Cornell Medicine, New York, NY
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Junghun Cho
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
- Department of Radiology, Weill Cornell Medicine, New York, NY
| | - Daniel Margolis
- Department of Radiology, Weill Cornell Medicine, New York, NY
| | | | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
- Department of Radiology, Weill Cornell Medicine, New York, NY
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33
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Parker DB, Spincemaille P, Razlighi QR. Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT). Magn Reson Med 2021; 86:1586-1599. [PMID: 33797118 DOI: 10.1002/mrm.28723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/16/2021] [Accepted: 01/19/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems. THEORY AND METHODS Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner. RESULTS The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data. CONCLUSION Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.
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Affiliation(s)
- David B Parker
- Department of Biomedical Engineering, Columbia University, New York City, NY, USA
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Eskreis-Winkler S, Simon K, Reichman M, Spincemaille P, Nguyen TD, Christos PJ, Drotman M, Prince MR, Pinker K, Sutton EJ, Morris EA, Wang Y. Multispectral Imaging for Metallic Biopsy Marker Detection During MRI-Guided Breast Biopsy: A Feasibility Study for Clinical Translation. Front Oncol 2021; 11:605014. [PMID: 33828972 PMCID: PMC8020905 DOI: 10.3389/fonc.2021.605014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/04/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose To assess the feasibility and diagnostic accuracy of multispectral MRI (MSI) in the detection and localization of biopsy markers during MRI-guided breast biopsy. Methods This prospective study included 20 patients undergoing MR-guided breast biopsy. In 10 patients (Group 1), MSI was acquired following tissue sampling and biopsy marker deployment. In the other 10 patients (Group 2), MSI was acquired following tissue sampling but before biopsy marker deployment (to simulate deployment failure). All patients received post-procedure mammograms. Group 1 and Group 2 designations, in combination with the post-procedure mammogram, served as the reference standard. The diagnostic performance of MSI for biopsy marker identification was independently evaluated by two readers using two-spectral-bin MR and one-spectral-bin MR. The κ statistic was used to assess inter-rater agreement for biopsy marker identification. Results The sensitivity, specificity, and accuracy of biopsy marker detection for readers 1 and 2 using 2-bin MSI were 90.0% (9/10) and 90.0% (9/10), 100.0% (10/10) and 100.0% (10/10), 95.0% (19/20) and 95.0% (19/20); and using 1-bin MSI were 70.0% (7/10) and 80.0% (8/10), 100.0% (8/8) and 100.0% (10/10), 85.0% (17/20) and 90.0% (18/20). Positive predictive value was 100% for both readers for all numbers of bins. Inter-rater agreement was excellent: κ was 1.0 for 2-bin MSI and 0.81 for 1-bin MSI. Conclusion MSI is a feasible, diagnostically accurate technique for identifying metallic biopsy markers during MRI-guided breast biopsy and may eliminate the need for a post-procedure mammogram.
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Affiliation(s)
- Sarah Eskreis-Winkler
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Katherine Simon
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Melissa Reichman
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Pascal Spincemaille
- 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
| | - Paul J Christos
- Division of Biostatistics and Epidemiology, Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, NY, United States
| | - Michele Drotman
- 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
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Elizabeth J Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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Jafari R, Hectors SJ, Koehne de González AK, Spincemaille P, Prince MR, Brittenham GM, Wang Y. Integrated quantitative susceptibility and R 2 * mapping for evaluation of liver fibrosis: An ex vivo feasibility study. NMR Biomed 2021; 34:e4412. [PMID: 32959425 PMCID: PMC7768551 DOI: 10.1002/nbm.4412] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/08/2020] [Accepted: 08/31/2020] [Indexed: 05/10/2023]
Abstract
To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R2 * mapping using corrections for the effects of liver iron. Liver fibrosis is characterized by excessive accumulation of collagen and other extracellular matrix proteins. While collagen increases R2 * relaxation, measures of R2 * for fibrosis are confounded by liver iron, which may be present in the liver over a wide range of concentrations. The diamagnetic collagen contribution to susceptibility values measured by QSM is much less than the contribution of highly paramagnetic iron. In 19 ex vivo liver explants with and without fibrosis, QSM (χ), R2 * and proton density fat fraction (PDFF) maps were constructed from multiecho gradient-recalled echo (mGRE) sequence acquisition at 3 T. Median parameter values were recorded and differences between the MRI parameters in nonfibrotic vs. advanced fibrotic/cirrhotic samples were evaluated using Mann-Whitney U tests and receiver operating characteristic analyses. Logistic regression with stepwise feature selection was employed to evaluate the utility of combined MRI measurements for detection of fibrosis. Median R2 * increased in fibrotic vs. nonfibrotic liver samples (P = .041), while differences in χ and PDFF were nonsignificant (P = .545 and P = .395, respectively). Logistic regression identified the combination of χ and R2 * significant for fibrosis detection (logit [prediction] = -8.45 + 0.23 R2 * - 28.8 χ). For this classifier, a highly significant difference between nonfibrotic vs. advanced fibrotic/cirrhotic samples was observed (P = .002). The model exhibited an AUC of 0.909 (P = .003) for detection of advanced fibrosis/cirrhosis, which was substantially higher compared with AUCs of the individual parameters (AUC 0.591-0.784). An integrated QSM and R2 * analysis of mGRE 3 T imaging data is promising for noninvasive diagnostic assessment of liver fibrosis.
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Affiliation(s)
- Ramin Jafari
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, 10021
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, 14853
| | - Stefanie J Hectors
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, 10021
| | | | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, 10021
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, 10021
| | - Gary M Brittenham
- Department of Pediatrics, Columbia University, New York, New York, 10032
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, 10021
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, 14853
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36
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Lu PJ, Yoo Y, Rahmanzadeh R, Galbusera R, Weigel M, Ceccaldi P, Nguyen TD, Spincemaille P, Wang Y, Daducci A, La Rosa F, Bach Cuadra M, Sandkühler R, Nael K, Doshi A, Fayad ZA, Kuhle J, Kappos L, Odry B, Cattin P, Gibson E, Granziera C. GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology. Neuroimage Clin 2020; 29:102522. [PMID: 33360973 PMCID: PMC7773673 DOI: 10.1016/j.nicl.2020.102522] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/11/2020] [Accepted: 11/30/2020] [Indexed: 12/24/2022]
Abstract
INTRODUCTION During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. METHODS GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. RESULTS In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively. CONCLUSIONS This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.
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Affiliation(s)
- Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Youngjin Yoo
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Reza Rahmanzadeh
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Pascal Ceccaldi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Robin Sandkühler
- Center for Medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amish Doshi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zahi A Fayad
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; BioMedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benjamin Odry
- AI for Clinical Analytics, Covera Health, New York, NY, USA
| | - Philippe Cattin
- Center for Medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Eli Gibson
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
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Zhou L, Zhang Q, Spincemaille P, Nguyen TD, Morgan J, Dai W, Li Y, Gupta A, Prince MR, Wang Y. Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network. Magn Reson Med 2020; 85:2247-2262. [PMID: 33210310 PMCID: PMC7839791 DOI: 10.1002/mrm.28584] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/29/2020] [Accepted: 10/12/2020] [Indexed: 12/20/2022]
Abstract
Purpose Proof‐of‐concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer‐labeled MRI data. Theory and Methods To determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time‐resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety’s method that uses a global arterial input function. Multiple post‐label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility. Results Against the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety’s method (45.7%, 2.5‐fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety’s method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region. Conclusions QTM flow velocity mapping is feasible from multi‐delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety’s method with deconvolution in time only.
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Affiliation(s)
- Liangdong Zhou
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - John Morgan
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Weiying Dai
- Department of Computer Science, Binghamton University, Binghamton, New York, USA
| | - Yi Li
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Ajay Gupta
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Zhang S, Cho J, Nguyen TD, Spincemaille P, Gupta A, Zhu W, Wang Y. Initial Experience of Challenge-Free MRI-Based Oxygen Extraction Fraction Mapping of Ischemic Stroke at Various Stages: Comparison With Perfusion and Diffusion Mapping. Front Neurosci 2020; 14:535441. [PMID: 33041755 PMCID: PMC7525031 DOI: 10.3389/fnins.2020.535441] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 08/18/2020] [Indexed: 01/01/2023] Open
Abstract
MRI-based oxygen extraction fraction imaging has a great potential benefit in the selection of clinical strategies for ischemic stroke patients. This study aimed to evaluate the performance of a challenge-free oxygen extraction fraction (OEF) mapping in a cohort of acute and subacute ischemic stroke patients. Consecutive ischemic stroke patients (a total of 30 with 5 in the acute stage, 19 in the early subacute stage, and 6 in the late subacute stage) were recruited. All subjects underwent MRI including multi-echo gradient echo (mGRE), diffusion weighted imaging (DWI), and 3D-arterial spin labeling (ASL). OEF maps were generated from mGRE phase + magnitude data, which were processed using quantitative susceptibility mapping (QSM) + quantitative blood oxygen level-dependent (qBOLD) imaging with cluster analysis of time evolution. Cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) maps were reconstructed from 3D-ASL and DWI, respectively. Further, cerebral metabolic rate of oxygen (CMRO2) was calculated as the product of CBF and OEF. OEF, CMRO2, CBF, and ADC values in the ischemic cores (absolute values) and their contrasts to the contralateral regions (relative values) were evaluated. One-way analysis of variance (ANOVA) was used to compare OEF, CMRO2, CBF, and ADC values and their relative values among different stroke stages. The OEF value of infarct core showed a trend of decrease from acute, to early subacute, and to late subacute stages of ischemic stroke. Significant differences among the three stroke stages were only observed in the absolute OEF (F = 6.046, p = 0.005) and relative OEF (F = 5.699, p = 0.009) values of the ischemic core, but not in other measurements (absolute and relative CMRO2, CBF, ADC values, all values of p > 0.05). In conclusion, the challenge-free QSM + qBOLD-generated OEF mapping can be performed on stroke patients. It can provide more information on tissue viability that was not available with CBF and ADC and, thus, may help to better manage ischemic stroke patients.
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Affiliation(s)
- Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Department of Biomedical Engineering, Cornell University, Ithaca, 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
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States
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Balasubramanian PS, Spincemaille P, Guo L, Huang W, Kovanlikaya I, Wang Y. Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping. iScience 2020; 23:101553. [PMID: 33083722 PMCID: PMC7522736 DOI: 10.1016/j.isci.2020.101553] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 07/18/2020] [Accepted: 09/09/2020] [Indexed: 11/28/2022] Open
Abstract
Adaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction. TFIR's adaptive regularization obtains magnetic susceptibility from magnetic field TFIR has low artifact incidence on both quantitative and clinical scores The error for TFIR is low on various numerical and ground truth tests Clinical applications for TFIR include hemorrhages and whole head mapping
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Affiliation(s)
- Priya S Balasubramanian
- Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.,Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Lingfei Guo
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Weiyuan Huang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA.,Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
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40
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Cho J, Ma Y, Spincemaille P, Pike GB, Wang Y. Cerebral oxygen extraction fraction: Comparison of dual-gas challenge calibrated BOLD with CBF and challenge-free gradient echo QSM+qBOLD. Magn Reson Med 2020; 85:953-961. [PMID: 32783233 DOI: 10.1002/mrm.28447] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/23/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To compare cortical gray matter oxygen extraction fraction (OEF) estimated from 2 MRI methods: (1) the quantitative susceptibility mapping (QSM) plus quantitative blood oxygen level dependent imaging (qBOLD) (QSM+qBOLD or QQ), and (2) the dual-gas calibrated-BOLD (DGCB) in healthy subjects; and to investigate the validity of iso-cerebral metabolic rate of oxygen consumption assumption during hypercapnia using QQ. METHODS In 10 healthy subjects, 3 tesla MRI including a multi-echo gradient echo sequence at baseline and hypercapnia for QQ, as well as an EPI dual-echo pseudo-continuous arterial spin labeling for DGCB, were performed under a hypercapnic and a hyperoxic condition. OEFs from QQ and DGCB were compared using region of interest analysis and paired t test. For QQ, cerebral metabolic rate of oxygen consumption = cerebral blood flow*OEF*arterial oxygen content was generated for both baseline and hypercapnia, which were compared. RESULTS Average OEF in cortical gray matter across 10 subjects from QQ versus DGCB was 35.5 ± 6.7% versus 38.0 ± 9.1% (P = .49) at baseline and 20.7 ± 4.4% versus 28.4 ± 7.6% (P = .02) in hypercapnia: OEF in cortical gray matter was significantly reduced as measured in QQ (P < .01) and in DGCB (P < .01). Cerebral metabolic rate of oxygen consumption (in μmol O2 /min/100 g) was 168.2 ± 54.1 at baseline from DGCB and was 153.1 ± 33.8 at baseline and 126.4 ± 34.2 (P < .01) in hypercapnia from QQ. CONCLUSION The differences in OEF obtained from QQ and DGCB are small and nonsignificant at baseline but are statistically significant during hypercapnia. In addition, QQ shows a cerebral metabolic rate of oxygen consumption decrease (17.4%) during hypercapnia.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yuhan Ma
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Gilbert Bruce Pike
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.,Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Li G, Wu R, Tong R, Bo B, Zhao Y, Gillen KM, Spincemaille P, Ku Y, Du Y, Wang Y, Wang X, Li J. Quantitative Measurement of Metal Accumulation in Brain of Patients With Wilson's Disease. Mov Disord 2020; 35:1787-1795. [PMID: 32681698 DOI: 10.1002/mds.28141] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 05/12/2020] [Accepted: 05/18/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Currently, no study has evaluated metal accumulation in the brains of patients with Wilson's disease by using quantitative susceptibility mapping at 3T MRI. The objectives of this study were to qualitatively and quantitatively evaluate changes in magnetic susceptibility and R2* maps in deep gray matter nuclei to discriminate Wilson's disease patients from healthy controls and to evaluate their sensitivities in diagnosing Wilson's disease. METHODS Magnetic susceptibility and R2* maps and conventional T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery images were obtained from 17 Wilson's disease patients and 14 age-matched healthy controls on a 3T MRI scanner. Differences between Wilson's disease and healthy control groups in susceptibility and R2* values in multiple deep nuclei were evaluated using a Mann-Whitney U test and receiver operating characteristic curves. The correlations of susceptibility and R2* values with Unified Wilson's Disease Rating Scale score were also performed. RESULTS Magnetic susceptibility and R2* can effectively distinguish different types of signal abnormalities. Magnetic susceptibility and R2* values in multiple deep nuclei of Wilson's disease patients were significantly higher than those in healthy controls. Magnetic susceptibility value in the substantia nigra had the highest area under the curve (0.888). There were positive correlations of the Unified Wilson's Disease Rating Scale score with susceptibility values in the caudate nucleus (r = 0.757, P = 0.011), putamen (r = 0.679, P = 0.031), and red nucleus (r = 0.638, P = 0.047), as well as R2* values in the caudate nucleus (r = 0.754, P = 0.012). CONCLUSIONS Quantitative susceptibility mapping at 3T could be a useful tool to evaluate metal accumulation in deep gray matter nuclei of Wilson's disease patients. © 2020 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Rong Wu
- Department of Neurology, Shanghai Tong-Ren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Binshi Bo
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Yu Zhao
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Yixuan Ku
- Department of Psychology, Sun Yat-sen University, Guangzhou Higher Education Mega Center, Guangzhou, China
| | - Yasong Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Xiaoping Wang
- Department of Neurology, Shanghai Tong-Ren Hospital, Shanghai Jiao Tong University School of 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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Zhang J, Liu Z, Zhang S, Zhang H, Spincemaille P, Nguyen TD, Sabuncu MR, Wang Y. Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction. Neuroimage 2020; 211:116579. [PMID: 31981779 PMCID: PMC7093048 DOI: 10.1016/j.neuroimage.2020.116579] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/20/2019] [Accepted: 01/20/2020] [Indexed: 01/19/2023] Open
Abstract
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.
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Affiliation(s)
- Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Shun Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Hang Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Mert R Sabuncu
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
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43
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Nguyen TD, Wen Y, Du J, Liu Z, Gillen K, Spincemaille P, Gupta A, Yang Q, Wang Y. Quantitative susceptibility mapping of carotid plaques using nonlinear total field inversion: Initial experience in patients with significant carotid stenosis. Magn Reson Med 2020; 84:1501-1509. [DOI: 10.1002/mrm.28227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 01/30/2020] [Accepted: 02/03/2020] [Indexed: 12/13/2022]
Affiliation(s)
| | - Yan Wen
- Radiology Weill Cornell Medicine New York NY
- Meinig School of Biomedical Engineering Cornell University Ithaca NY
| | - Jingwen Du
- Xuanwu Hospital Capital Medical University Beijing China
| | - Zhe Liu
- Radiology Weill Cornell Medicine New York NY
- Meinig School of Biomedical Engineering Cornell University Ithaca NY
| | | | | | - Ajay Gupta
- Radiology Weill Cornell Medicine New York NY
| | - Qi Yang
- Xuanwu Hospital Capital Medical University Beijing China
| | - Yi Wang
- Radiology Weill Cornell Medicine New York NY
- Meinig School of Biomedical Engineering Cornell University Ithaca NY
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44
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Deh K, Zaman M, Vedvyas Y, Liu Z, Gillen KM, O' Malley P, Bedretdinova D, Nguyen T, Lee R, Spincemaille P, Kim J, Wang Y, Jin MM. Validation of MRI quantitative susceptibility mapping of superparamagnetic iron oxide nanoparticles for hyperthermia applications in live subjects. Sci Rep 2020; 10:1171. [PMID: 31980695 PMCID: PMC6981186 DOI: 10.1038/s41598-020-58219-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 01/10/2020] [Indexed: 02/06/2023] Open
Abstract
The use of magnetic fluid hyperthermia (MFH) for cancer therapy has shown promise but lacks suitable methods for quantifying exogenous irons such as superparamagnetic iron oxide (SPIO) nanoparticles as a source of heat generation under an alternating magnetic field (AMF). Application of quantitative susceptibility mapping (QSM) technique to prediction of SPIO in preclinical models has been challenging due to a large variation of susceptibility values, chemical shift from tissue fat, and noisier data arising from the higher resolution required to visualize the anatomy of small animals. In this study, we developed a robust QSM for the SPIO ferumoxytol in live mice to examine its potential application in MFH for cancer therapy. We demonstrated that QSM was able to simultaneously detect high level ferumoxytol accumulation in the liver and low level localization near the periphery of tumors. Detection of ferumoxytol distribution in the body by QSM, however, required imaging prior to and post ferumoxytol injection to discriminate exogenous iron susceptibility from other endogenous sources. Intratumoral injection of ferumoxytol combined with AMF produced a ferumoxytol-dose dependent tumor killing. Histology of tumor sections corroborated QSM visualization of ferumoxytol distribution near the tumor periphery, and confirmed the spatial correlation of cell death with ferumoxytol distribution. Due to the dissipation of SPIOs from the injection site, quantitative mapping of SPIO distribution will aid in estimating a change in temperature in tissues, thereby maximizing MFH effects on tumors and minimizing side-effects by avoiding unwanted tissue heating.
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Affiliation(s)
- Kofi Deh
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Marjan Zaman
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Yogindra Vedvyas
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Zhe Liu
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA
| | | | - Padraic O' Malley
- Department of Urology, University of Florida, Gainesville, FL, 32610, USA
| | | | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Richard Lee
- Urology, Weill Cornell Medicine, New York, NY, 10065, USA
| | | | - Juyoung Kim
- Department of Advanced Materials Engineering, Kangwon National University, Samcheok, 245-711, South Korea
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, 14853, USA
| | - Moonsoo M Jin
- Department of Radiology, Weill Cornell Medicine, New York, NY, 10065, USA. .,Department of Biomedical Engineering, Cornell University, Ithaca, NY, 14853, USA.
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Liu Z, Wen Y, Spincemaille P, Zhang S, Yao Y, Nguyen T, Wang Y. Automated adaptive preconditioner for quantitative susceptibility mapping. Magn Reson Med 2020; 83:271-285. [PMID: 31402519 PMCID: PMC6778703 DOI: 10.1002/mrm.27900] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 06/15/2019] [Accepted: 06/17/2019] [Indexed: 12/23/2022]
Abstract
PURPOSE To develop an automated adaptive preconditioner for QSM reconstruction with improved susceptibility quantification accuracy and increased image quality. THEORY AND METHODS The total field was used to rapidly produce an approximate susceptibility map, which was then averaged and trended over R 2 ∗ binning to generate a spatially varying distribution of preconditioning values. This automated adaptive preconditioner was used to reconstruct QSM via total field inversion and was compared with its empirical counterparts in a numerical simulation, a brain experiment with 5 healthy subjects and 5 patients with intracerebral hemorrhage, and a cardiac experiment with 3 healthy subjects. RESULTS Among evaluated preconditioners, the automated adaptive preconditioner achieved the fastest convergence in reducing the RMSE of the QSM in the simulation, suppressed hemorrhage-associated artifacts while preserving surrounding brain tissue contrasts, and provided cardiac chamber oxygenation values consistent with those reported in the literature. CONCLUSION An automated adaptive preconditioner allows high-quality QSM from the total field in imaging various anatomies with dynamic susceptibility ranges.
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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
| | - Yan Wen
- 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
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
- Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yihao Yao
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
- Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Thanh Nguyen
- 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
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Spincemaille P, Anderson J, Wu G, Yang B, Fung M, Li K, Li S, Kovanlikaya I, Gupta A, Kelley D, Benhamo N, Wang Y. Quantitative Susceptibility Mapping: MRI at 7T versus 3T. J Neuroimaging 2020; 30:65-75. [PMID: 31625646 PMCID: PMC6954973 DOI: 10.1111/jon.12669] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/02/2019] [Accepted: 10/02/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND AND PURPOSE Ultrahigh-field 7T promises more than doubling the signal-to-noise ratio (SNR) of 3T for magnetic resonance imaging (MRI), particularly for MRI of magnetic susceptibility effects induced by B0 . Quantitative susceptibility mapping (QSM) is based on deconvolving the induced phase (or field) and would therefore benefit substantially from 7T. The purpose of this work was to compare QSM performance at 7T versus 3T in an intrascanner test-retest experiment with varying echo numbers (5 and 10 echoes). METHODS A prospective study in N = 10 healthy subjects was carried out at both 3T and 7T field strengths. Gradient echo data using 5 and 10 echoes were acquired twice in each subject. Test-retest reproducibility was assessed using Bland-Altman and regression analysis of region of interest measurements. Image quality was scored by an experienced neuroradiologist. RESULTS Intrascanner bias was below 3.6 parts-per-billion (ppb) with correlation R2 > .85. Interscanner bias was below 10.9 ppb with correlation R2 > .8. The image quality score for the 3T 10 echo protocol was not different from the 7T 5 echo protocol (P = .65). CONCLUSION Excellent image quality and good reproducibility was observed. 7T allows equivalent image quality of 3T in half of the scan time.
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Affiliation(s)
- Pascal Spincemaille
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
- Corresponding author: Pascal Spincemaille, Ph.D.,
Department of Radiology, 515 East 71st St, Suite S101, New York, NY, 10021,
, tel: +1 646 962 2630
| | | | - Gaohong Wu
- General Electrical Healthcare, Waukesha, WI
| | | | | | - Ke Li
- General Electrical Healthcare, Waukesha, WI
| | - Shaojun Li
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
| | - Ilhami Kovanlikaya
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
| | - Ajay Gupta
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
| | | | | | - Yi Wang
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
- Department of Biomedical Engineering, Cornell University,
Ithaca, NY
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47
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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48
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Guo Y, Liu Z, Wen Y, Spincemaille P, Zhang H, Jafari R, Zhang S, Eskreis-Winkler S, Gillen KM, Yi P, Feng Q, Feng Y, Wang Y. Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation. NMR Biomed 2019; 32:e4156. [PMID: 31424131 DOI: 10.1002/nbm.4156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi-echo GRE sequence was implemented to acquire out-phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*-IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO-GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi-peak fat model. There was no substantial difference on QSM between the single peak and multi-peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO-GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.
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Affiliation(s)
- Yihao Guo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Yan Wen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Honglei Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Ramin Jafari
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Shun Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Sarah Eskreis-Winkler
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Peiwei Yi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
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Eskreis-Winkler S, Simon K, Reichman M, Spincemaille P, Nguyen T, Kee Y, Cho J, Christos PJ, Drotman M, Prince MR, Morris EA, Wang Y. Dipole modeling of multispectral signal for detecting metallic biopsy markers during MRI-guided breast biopsy: a pilot study. Magn Reson Med 2019; 83:1380-1389. [PMID: 31631408 DOI: 10.1002/mrm.28017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 09/05/2019] [Accepted: 09/05/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE During MRI-guided breast biopsy, a metallic biopsy marker is deployed at the biopsy site to guide future interventions. Conventional MRI during biopsy cannot distinguish such markers from biopsy site air, and a post-biopsy mammogram is therefore performed to localize marker placement. The purpose of this pilot study is to develop dipole modeling of multispectral signal (DIMMS) as an MRI alternative to eliminate the cost, inefficiency, inconvenience, and ionizing radiation of a mammogram for biopsy marker localization. METHODS DIMMS detects and localizes the biopsy marker by fitting the measured multispectral imaging (MSI) signal to the MRI signal model and marker properties. MSI was performed on phantoms containing titanium biopsy markers and air to illustrate the clinical challenge that DIMMS addresses and on 20 patients undergoing MRI-guided breast biopsy to assess DIMMS feasibility for marker detection. DIMMS was compared to conventional MSI field map thresholding, using the post-procedure mammogram as the reference standard. RESULTS Biopsy markers were detected and localized in 20 of 20 cases using MSI with automated DIMMS post-processing (using a threshold of 0.7) and in 18 of 20 cases using MSI field mapping (using a threshold of 0.65 kHz). CONCLUSION MSI with DIMMS post-processing is a feasible technique for biopsy marker detection and localization during MRI-guided breast biopsy. With a 2-min MSI scan, DIMMS is a promising MRI alternative to the standard-of-care post-biopsy mammogram.
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Affiliation(s)
- Sarah Eskreis-Winkler
- Department of Radiology, Weill Cornell Medicine, New York, New York.,Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Katherine Simon
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Melissa Reichman
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | | | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Youngwook Kee
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Paul J Christos
- Division of Biostatistics and Epidemiology, Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, New York
| | - Michele Drotman
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York
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Cho J, Zhang S, Kee Y, Spincemaille P, Nguyen TD, Hubertus S, Gupta A, Wang Y. Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent magnitude (qBOLD)-based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO 2 ) mapping. Magn Reson Med 2019; 83:844-857. [PMID: 31502723 DOI: 10.1002/mrm.27967] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/07/2019] [Accepted: 08/04/2019] [Indexed: 01/01/2023]
Abstract
PURPOSE To improve the accuracy of QSM plus quantitative blood oxygen level-dependent magnitude (QSM + qBOLD or QQ)-based mapping of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using cluster analysis of time evolution (CAT). METHODS 3D multi-echo gradient echo and arterial spin labeling images were acquired in 11 healthy subjects and 5 ischemic stroke patients. DWI was also carried out on patients. CAT was developed for analyzing signal evolution over TE. QQ-based OEF and CMRO2 were reconstructed with and without CAT, and results were compared using region of interest analysis and a paired t-test. RESULTS Simulations demonstrated that CAT substantially reduced noise error in QQ-based OEF. In healthy subjects, QQ-based OEF appeared less noisy and more uniform with CAT than without CAT; average OEF with and without CAT in cortical gray matter was 32.7 ± 4.0% and 37.9 ± 4.5%, with corresponding CMRO2 of 148.4 ± 23.8 and 171.4 ± 22.4 μmol/100 g/min, respectively. In patients, regions of low OEF were confined within the ischemic lesions defined on DWI when using CAT, which was not observed without CAT. CONCLUSION The cluster analysis of time evolution (CAT) significantly improves the robustness of QQ-based OEF against noise.
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Affiliation(s)
- Junghun Cho
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York
- Department of Radiology, Tongji Hospital, Wuhan, China
| | - Youngwook Kee
- Department of Radiology, Weill Cornell Medical College, New York, New York
| | | | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York
| | - Simon Hubertus
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
- Department of Radiology, Weill Cornell Medical College, New York, New York
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