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Hervouin A, Bézy-Wendling J, Noury F. How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T. NMR IN BIOMEDICINE 2024; 37:e5182. [PMID: 38993048 DOI: 10.1002/nbm.5182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/06/2024] [Accepted: 05/06/2024] [Indexed: 07/13/2024]
Abstract
Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.
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Affiliation(s)
| | | | - Fanny Noury
- Univ Rennes, Inserm, LTSI-UMR 1099, Rennes, France
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Tang X, He Z, Yang Q, Yang T, Yu Y, Chen J. Combining Quantitative Susceptibility Mapping With the Gray Matter Volume to Predict Neurological Deficits in Patients With Small Artery Occlusion. Brain Behav 2024; 14:e70080. [PMID: 39363797 PMCID: PMC11450255 DOI: 10.1002/brb3.70080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 09/03/2024] [Accepted: 09/08/2024] [Indexed: 10/05/2024] Open
Abstract
BACKGROUND Currently, there is still a lack of valuable neuroimaging markers to assess the clinical severity of stroke patients with small artery occlusion (SAO). Quantitative susceptibility mapping (QSM) is a quantitative processing method for neuroradiological diagnostics. Gray matter (GM) volume changes in stroke patients are also proved to be associated with neurological deficits. This study aims to explore the predictive value of QSM and GM volume in neurological deficits of patients with SAO. METHODS As neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. Sixty-six SAO participants within 24 h of first onset were enrolled and divided into mild and moderate groups based on NIHSS. QSM values of infarct area and GM volume were calculated from magnetic resonance imaging (MRI) data. Two-sample t-tests were used to compare differences in QSM value and GM volume between the two groups, and the diagnostic efficacy of the combination of QSM value and GM volume was evaluated. RESULTS The results revealed both the QSM value and GM volume within the infarct area of the moderate group were lower compared to the mild group. Moderate group exhibited lower GM volume in some specific gyrus compared with mild group in the case of voxel-wise GM volume on whole-brain voxel level. The support vector machine (SVM) classifier's analysis showed a high power for the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume. CONCLUSION Our research first reported the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume could be used to predict neurological impairment of patients with SAO, which provides new insights for further understanding the SAO stroke.
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Affiliation(s)
- Xuelian Tang
- Department of NeurologyThe Affiliated Jiangning Hospital of Nanjing Medical UniversityNanjingJiangsuChina
| | - Zhenzhen He
- Department of RadiologyThe Affiliated Jiangning Hospital of Nanjing Medical UniversityNanjingJiangsuChina
| | - Qian Yang
- Department of NeurologyThe Affiliated Jiangning Hospital of Nanjing Medical UniversityNanjingJiangsuChina
| | - Tao Yang
- Department of NeurologyThe Affiliated Jiangning Hospital of Nanjing Medical UniversityNanjingJiangsuChina
| | - Yusheng Yu
- Department of RadiologyThe Affiliated Jiangning Hospital of Nanjing Medical UniversityNanjingJiangsuChina
| | - Jinan Chen
- Department of NeurologyThe Affiliated Jiangning Hospital of Nanjing Medical UniversityNanjingJiangsuChina
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Ruan G, Wang Z, Liu C, Xia L, Wang H, Qi L, Chen W. Magnetic Resonance Electrical Properties Tomography Based on Modified Physics- Informed Neural Network and Multiconstraints. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3263-3278. [PMID: 38640054 DOI: 10.1109/tmi.2024.3391651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
This paper presents a novel method based on leveraging physics-informed neural networks for magnetic resonance electrical property tomography (MREPT). MREPT is a noninvasive technique that can retrieve the spatial distribution of electrical properties (EPs) of scanned tissues from measured transmit radiofrequency (RF) in magnetic resonance imaging (MRI) systems. The reconstruction of EP values in MREPT is achieved by solving a partial differential equation derived from Maxwell's equations that lacks a direct solution. Most conventional MREPT methods suffer from artifacts caused by the invalidation of the assumption applied for simplification of the problem and numerical errors caused by numerical differentiation. Existing deep learning-based (DL-based) MREPT methods comprise data-driven methods that need to collect massive datasets for training or model-driven methods that are only validated in trivial cases. Hence we proposed a model-driven method that learns mapping from a measured RF, its spatial gradient and Laplacian to EPs using fully connected networks (FCNNs). The spatial gradient of EP can be computed through the automatic differentiation of FCNNs and the chain rule. FCNNs are optimized using the residual of the central physical equation of convection-reaction MREPT as the loss function ( L) . To alleviate the ill condition of the problem, we added multiconstraints, including the similarity constraint between permittivity and conductivity and the l1 norm of spatial gradients of permittivity and conductivity, to the L . We demonstrate the proposed method with a three-dimensional realistic head model, a digital phantom simulation, and a practical phantom experiment at a 9.4T animal MRI system.
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Venkatesh V, Mathew RS, Yalavarthy PK. Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping. MAGMA (NEW YORK, N.Y.) 2024; 37:411-427. [PMID: 38598165 DOI: 10.1007/s10334-024-01158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
OBJECTIVE Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM. MATERIALS AND METHODS This work proposes a Schatten p -norm-driven model-based deep learning framework for QSM with a learnable norm parameter p to adapt to the data. In contrast to other model-based architectures that enforce the l2 -norm or l1 -norm for the denoiser, the proposed approach can enforce any p -norm ( 0 < p ≤ 2 ) on a trainable regulariser. RESULTS The proposed method was compared with deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits. CONCLUSION The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.
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Affiliation(s)
- Vaddadi Venkatesh
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India
| | - Raji Susan Mathew
- School of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram, Kerala, 695551, India
| | - Phaneendra K Yalavarthy
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India.
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Oltmer J, Mattern H, Beck J, Yakupov R, Greenberg SM, Zwanenburg JJ, Arts T, Düzel E, van Veluw SJ, Schreiber S, Perosa V. Enlarged perivascular spaces in the basal ganglia are associated with arteries not veins. J Cereb Blood Flow Metab 2024:271678X241260629. [PMID: 38863151 DOI: 10.1177/0271678x241260629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
Enlarged perivascular spaces (EPVS) are common in cerebral small vessel disease (CSVD) and have been identified as a marker of dysfunctional brain clearance. However, it remains unknown if the enlargement occurs predominantly around arteries or veins. We combined in vivo ultra-high-resolution MRI and histopathology to investigate the spatial relationship of veins and arteries with EPVS within the basal ganglia (BG). Furthermore, we assessed the relationship between the EPVS and measures of blood-flow (blood-flow velocity, pulsatility index) in the small arteries of the BG. Twenty-four healthy controls, twelve non-CAA CSVD patients, and five probable CAA patients underwent a 3 tesla [T] and 7T MRI-scan, and EPVS, arteries, and veins within the BG were manually segmented. Furthermore, the scans were co-registered. Six autopsy-cases were also assessed. In the BG, EPVS were significantly closer to and overlapped more frequently with arteries than with veins. Histological analysis showed a higher proportion of BG EPVS surrounding arteries than veins. Finally, the pulsatility index of BG arteries correlated with EPVS volume. Our results are in line with previous works and establish a pathophysiological relationship between arteries and EPVS, contributing to elucidating perivascular clearance routes in the human brain.
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Affiliation(s)
- Jan Oltmer
- Athinoula A. Martinos Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Digital Health & Innovation, Vivantes Netzwerk für Gesundheit GmbH, Berlin, Germany
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Biomedical Magnetic Resonance (BMMR), Institute for Physics, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Julia Beck
- Department of Neurology, Otto-Von-Guericke University, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Steven M Greenberg
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaco Jm Zwanenburg
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tine Arts
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Susanne J van Veluw
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital, MassGeneral Institute for Neurodegenerative Disease, Charlestown, MA, USA
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
- Department of Neurology, Otto-Von-Guericke University, Magdeburg, Germany
- Department of Neurology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Valentina Perosa
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Huang Y, Chen L, Li X, Liu J. Improved test-retest reliability of R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and susceptibility quantification using multishot multi-echo 3D EPI. Magn Reson Med 2024; 91:2310-2319. [PMID: 38156825 PMCID: PMC10997481 DOI: 10.1002/mrm.29992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE This study aimed to evaluate the potential of 3D EPI for improving the reliability ofT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted data and quantification ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ decay rate and susceptibility (χ) compared with conventional gradient-echo (GRE)-based acquisition. METHODS Eight healthy subjects in a wide age range were recruited. Each subject received repeated scans for both GRE and EPI acquisitions with an isotropic 1 mm resolution at 3 T. Maps ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and χ were quantified, and their interscan differences were used to evaluate the test-retest reliability. Interprotocol differences ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and χ between GRE and EPI were also measured voxel by voxel and in selected regions of interest to test the consistency between the two acquisition methods. RESULTS The quantifications ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and χ using EPI protocols showed increased test-retest reliability with higher EPI factors up to 5 as performed in the experiment and were consistent with those based on GRE. CONCLUSION The result suggests that multishot multi-echo 3D EPI can be a useful alternative acquisition method forT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted MRI and quantification ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and χ with reduced scan time, improved test-retest reliability, and similar accuracy compared with commonly used 3D GRE.
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Affiliation(s)
- Yujia Huang
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Jiaen Liu
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
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Gao Y, Xiong Z, Shan S, Liu Y, Rong P, Li M, Wilman AH, Pike GB, Liu F, Sun H. Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks. Med Image Anal 2024; 94:103160. [PMID: 38552528 DOI: 10.1016/j.media.2024.103160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 03/09/2024] [Accepted: 03/23/2024] [Indexed: 04/16/2024]
Abstract
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a simulated-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms. The PnP OA-LFE module's versatility was further demonstrated by its successful application to xQSM, a distinct DL-QSM network for dipole inversion. In conclusion, this work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.
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Affiliation(s)
- Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Zhuang Xiong
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia
| | - Shanshan Shan
- State Key Laboratory of Radiation, Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, China
| | - Yin Liu
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Alan H Wilman
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Feng Liu
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia; School of Engineering, University of Newcastle, Newcastle, Australia
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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] [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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Roberts AG, Romano DJ, Şişman M, Dimov AV, Spincemaille P, Nguyen TD, Kovanlikaya I, Gauthier SA, Wang Y. Maximum spherical mean value filtering for whole-brain QSM. Magn Reson Med 2024; 91:1586-1597. [PMID: 38169132 PMCID: PMC11416845 DOI: 10.1002/mrm.29963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 10/30/2023] [Accepted: 11/19/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion. THEORY AND METHODS Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. RESULTS Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis. CONCLUSION The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.
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Affiliation(s)
- Alexandra G. Roberts
- Department of Electrical and Computer Engineering, Cornell University, Ithaca NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Dominick J. Romano
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca NY, USA
| | - Mert Şişman
- Department of Electrical and Computer Engineering, Cornell University, Ithaca NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | | | - Yi Wang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca NY, USA
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10
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Chen L, Shin HG, van Zijl PC, Li X. Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mapping. Magn Reson Med 2024; 91:1676-1693. [PMID: 38102838 PMCID: PMC10880384 DOI: 10.1002/mrm.29958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 10/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE This work is to investigate the microstructure-induced frequency shift in white matter (WM) with crossing fibers and to separate the microstructure-related frequency shift from the bulk susceptibility-induced frequency shift by model fitting the gradient-echo (GRE) frequency evolution for potentially more accurate quantitative susceptibility mapping (QSM). METHODS A hollow-cylinder fiber model (HCFM) with two fiber populations was developed to investigate GRE frequency evolutions in WM voxels with microstructural orientation dispersion. The simulated and experimentally measured TE-dependent local frequency shift was then fitted to a simplified frequency evolution model to obtain a microstructure-related frequency difference parameter (∆ f $$ \Delta f $$ ) and a TE-independent bulk susceptibility-induced frequency shift (C f $$ {C}_f $$ ). The obtainedC f $$ {C}_f $$ was then used for QSM reconstruction. Reconstruction performances were evaluated using a numerical head phantom and in vivo data and then compared to other multi-echo combination methods. RESULTS GRE frequency evolutions and∆ f $$ \Delta f $$ -based tissue parameters in both parallel and crossing fibers determined from our simulations were comparable to those observed in vivo. The TE-dependent frequency fitting method outperformed other multi-echo combination methods in estimatingC f $$ {C}_f $$ in simulations. The fitted∆ f $$ \Delta f $$ ,C f $$ {C}_f $$ , and QSM could be improved further by navigator-based B0 fluctuation correction. CONCLUSION A HCFM with two fiber populations can be used to characterize microstructure-induced frequency shifts in WM regions with crossing fibers. HCFM-based TE-dependent frequency fitting provides tissue contrast related to microstructure (∆ f $$ \Delta f $$ ) and in addition may help improve the quantification accuracy ofC f $$ {C}_f $$ and the corresponding QSM.
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Affiliation(s)
- Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Hyeong-Geol Shin
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
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11
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Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. Neuroimage 2024; 289:120547. [PMID: 38373677 DOI: 10.1016/j.neuroimage.2024.120547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Scott Ayton
- Florey Institute, The University of Melbourne, Australia
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Auenbruggerplatz 22, Prague 8036, Czechia
| | | | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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12
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Thomas GE, Hannaway N, Zarkali A, Shmueli K, Weil RS. Longitudinal Associations of Magnetic Susceptibility with Clinical Severity in Parkinson's Disease. Mov Disord 2024; 39:546-559. [PMID: 38173297 PMCID: PMC11141787 DOI: 10.1002/mds.29702] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/29/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Dementia is common in Parkinson's disease (PD), but there is wide variation in its timing. A critical gap in PD research is the lack of quantifiable markers of progression, and methods to identify early stages of dementia. Atrophy-based magnetic resonance imaging (MRI) has limited sensitivity in detecting or tracking changes relating to PD dementia, but quantitative susceptibility mapping (QSM), sensitive to brain tissue iron, shows potential for these purposes. OBJECTIVE The objective of the paper is to study, for the first time, the longitudinal relationship between cognition and QSM in PD in detail. METHODS We present a longitudinal study of clinical severity in PD using QSM, including 59 PD patients (without dementia at study onset), and 22 controls over 3 years. RESULTS In PD, increased baseline susceptibility in the right temporal cortex, nucleus basalis of Meynert, and putamen was associated with greater cognitive severity after 3 years; and increased baseline susceptibility in basal ganglia, substantia nigra, red nucleus, insular cortex, and dentate nucleus was associated with greater motor severity after 3 years. Increased follow-up susceptibility in these regions was associated with increased follow-up cognitive and motor severity, with further involvement of hippocampus relating to cognitive severity. However, there were no consistent increases in susceptibility over 3 years. CONCLUSIONS Our study suggests that QSM may predict changes in cognitive severity many months prior to overt cognitive involvement in PD. However, we did not find robust longitudinal changes in QSM over the course of the study. Additional tissue metrics may be required together with QSM for it to monitor progression in clinical practice and therapeutic trials. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
| | - Naomi Hannaway
- Dementia Research CentreUCL Institute of NeurologyLondonUK
| | | | - Karin Shmueli
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Rimona S. Weil
- Dementia Research CentreUCL Institute of NeurologyLondonUK
- Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Movement Disorders ConsortiumUniversity College LondonLondonUK
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13
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Jung S, Jeon S, Gho SM, Lee HJ, Jung KJ, Kim DH. Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network. Neuroimage 2024; 288:120528. [PMID: 38311125 DOI: 10.1016/j.neuroimage.2024.120528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/14/2023] [Accepted: 01/27/2024] [Indexed: 02/06/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.
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Affiliation(s)
- Siyun Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | - Soohyun Jeon
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | | | - Ho-Joon Lee
- Department of Radiology, Inje University Haeundae Paik Hospital, South Korea
| | - Kyu-Jin Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea.
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14
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Sandgaard AD, Kiselev VG, Henriques RN, Shemesh N, Jespersen SN. Incorporating the effect of white matter microstructure in the estimation of magnetic susceptibility in ex vivo mouse brain. Magn Reson Med 2024; 91:699-715. [PMID: 37772624 DOI: 10.1002/mrm.29867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/07/2023] [Accepted: 08/25/2023] [Indexed: 09/30/2023]
Abstract
PURPOSE To extend quantitative susceptibility mapping to account for microstructure of white matter (WM) and demonstrate its effect on ex vivo mouse brain at 16.4T. THEORY AND METHODS Previous studies have shown that the MRI measured Larmor frequency also depends on local magnetic microstructure at the mesoscopic scale. Here, we include effects from WM microstructure using our previous results for the mesoscopic Larmor frequencyΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ of cylinders with arbitrary orientations. We scrutinize the validity of our model and QSM in a digital brain phantom includingΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ from a WM susceptibility tensor and biologically stored iron with scalar susceptibility. We also apply susceptibility tensor imaging to the phantom and investigate how the fitted tensors are biased fromΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ . Last, we demonstrate how to combine multi-gradient echo and diffusion MRI images of ex vivo mouse brains acquired at 16.4T to estimate an apparent scalar susceptibility without sample rotations. RESULTS Our new model improves susceptibility estimation compared to QSM for the brain phantom. Applying susceptibility tensor imaging to the phantom withΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ from WM axons with scalar susceptibility produces a highly anisotropic susceptibility tensor that mimics results from previous susceptibility tensor imaging studies. For the ex vivo mouse brain we find theΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ due to WM microstructure to be substantial, changing susceptibility in WM up to 25% root-mean-squared-difference. CONCLUSION Ω ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ impacts susceptibility estimates and biases susceptibility tensor imaging fitting substantially. Hence, it should not be neglected when imaging structurally anisotropic tissue such as brain WM.
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Affiliation(s)
- Anders Dyhr Sandgaard
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Valerij G Kiselev
- Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune Nørhøj Jespersen
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
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15
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Naji N, Wilman A. Thin slab quantitative susceptibility mapping. Magn Reson Med 2023; 90:2290-2305. [PMID: 37526029 DOI: 10.1002/mrm.29800] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/27/2023] [Accepted: 06/30/2023] [Indexed: 08/02/2023]
Abstract
PURPOSE Susceptibility maps reconstructed from thin slabs may suffer underestimation due to background-field removal imperfections near slab boundaries and the increased difficulty of solving a 3D-inversion problem with reduced support, particularly in the direction of the main magnetic field. Reliable QSM reconstruction from thin slabs would enable focal acquisitions in a much-reduced scan time. METHODS This work proposes using additional rapid low-resolution data of extended spatial coverage to improve background-field estimation and regularize the inversion-to-susceptibility process for high resolution, thin slab data. The new method was tested using simulated and in-vivo brain data of high resolution (0.33 × 0.33 × 0.33 mm3 and 0.54 × 0.54 × 0.65 mm3 , respectively) at 3T, and compared to the standard large volume approach. RESULTS Using the proposed method, in-vivo high-resolution QSM at 3T was obtained from slabs of width as small as 10.4 mm, aided by a lower-resolution dataset of 24 times coarser voxels. Simulations showed that the proposed method produced more consistent measurements from slabs of at least eight slices. Reducing the mean ROI error to 5% required the low-resolution data to cover ˜60 mm in the direction of the main field, have at least 2-mm isotropic resolution that is not coarser than the high-resolution data by more than four-fold in any direction. CONCLUSION Applying the proposed method enabled focal QSM acquisitions at sub-millimeter resolution within reasonable acquisition time.
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Affiliation(s)
- Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Alan Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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16
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Yang A, Zhuang H, Du L, Liu B, Lv K, Luan J, Hu P, Chen F, Wu K, Shu N, Shmuel A, Ma G, Wang Y. Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD. Neuroimage 2023; 282:120381. [PMID: 37734476 DOI: 10.1016/j.neuroimage.2023.120381] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 08/14/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023] Open
Abstract
OBJECTIVE The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.
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Affiliation(s)
- Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Hangwei Zhuang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA; Department of Radiology, Weill Cornell Medical College, New York, New York 10065, USA
| | - Lei Du
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing 100142, PR China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, PR China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, PR China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, Hainan, PR China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangdong 510006, Guangzhou, PR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Neurology and Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China.
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA; Department of Radiology, Weill Cornell Medical College, New York, New York 10065, USA
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17
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Zhu X, Gao Y, Liu F, Crozier S, Sun H. BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources. Z Med Phys 2023; 33:578-590. [PMID: 36064695 PMCID: PMC10751722 DOI: 10.1016/j.zemedi.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial hemorrhages, is challenging due to the relatively large scale of the field induced by these pathological susceptibility sources. METHOD This study proposes a new deep learning-based method, BFRnet, to remove the background field in healthy and hemorrhagic subjects. The network is built with the dual-frequency octave convolutions on the U-net architecture, trained with synthetic field maps containing significant susceptibility sources. The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects. Robustness against acquisition field-of-view (FOV) orientation and brain masking are also investigated. RESULTS For both simulation and in vivo experiments, BFRnet led to the best visually appealing results in the local field and QSM results with the minimum contrast loss and the most accurate hemorrhage susceptibility measurements among all five methods. In addition, BFRnet produced the most consistent local field and susceptibility maps between different sizes of brain masks, while conventional methods depend drastically on precise brain extraction and further brain edge erosions. It is also observed that BFRnet performed the best among all BFR methods for acquisition FOVs oblique to the main magnetic field. CONCLUSION The proposed BFRnet improved the accuracy of local field reconstruction in the hemorrhagic subjects compared with conventional BFR algorithms. The BFRnet method was effective for acquisitions of tilted orientations and retained whole brains without edge erosion as often required by traditional BFR methods.
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Affiliation(s)
- Xuanyu Zhu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Yang Gao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
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18
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Kang M, Behr GG, Jafari R, Gambarin M, Otazo R, Kee Y. Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. Magn Reson Med 2023; 90:1844-1858. [PMID: 37392413 PMCID: PMC10529485 DOI: 10.1002/mrm.29779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/15/2023] [Accepted: 06/06/2023] [Indexed: 07/03/2023]
Abstract
PURPOSE To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. METHODS Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF),R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM in in vivo study. RESULTS Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.31 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.05,R 2 $$ {R}^2 $$ = 0.999) and Cartesian without motion (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.32× gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.04,R 2 $$ {R}^2 $$ = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.00261 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.524,R 2 $$ {R}^2 $$ = 0.977) compared to motion-averaged reconstruction (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.0021 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.572,R 2 $$ {R}^2 $$ = 0.723) in ROI-based linear regression analysis betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM. CONCLUSION Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.
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Affiliation(s)
- MungSoo Kang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Gerald G. Behr
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maya Gambarin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Youngwook Kee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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19
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Reeves JA, Mohebbi M, Zivadinov R, Bergsland N, Dwyer MG, Salman F, Schweser F, Jakimovski D. Reliability of paramagnetic rim lesion classification on quantitative susceptibility mapping (QSM) in people with multiple sclerosis: Single-site experience and systematic review. Mult Scler Relat Disord 2023; 79:104968. [PMID: 37716210 PMCID: PMC11092095 DOI: 10.1016/j.msard.2023.104968] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 07/15/2023] [Accepted: 08/28/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND Recent developments in iron-sensitive MRI techniques have enabled visualization of chronic active lesions as paramagnetic rim lesions (PRLs) in vivo. Although PRLs have potential as a diagnostic and prognostic tool for multiple sclerosis (MS), limited studies have reported the reliability of PRL assessment. Further evaluation of PRL reliability, through original investigations and review of PRL literature, are warranted. METHODS A single-center cohort study was conducted to evaluate the inter-rater reliability of PRL identification on quantitative susceptibiltiy mapping (QSM) in 10 people with MS, 5 people with clinically isolated syndrome, and 5 healthy controls. An additional systematic literature search was then conducted of published PRL reliability data, and these results were synthesized. RESULTS In the single-center study, both inter-rater and intra-rater reliability of per-subject PRL number were at an "Excellent" (intraclass correlation coefficient (ICC) of 0.901 for both) level with only 2-years lesion classification experience. Across the reported literature values, reliability of per-lesion rim presence was on average "Near perfect" (for intra-rater; Cohen's κ = 0.833) and "Substantial" (for inter-rater; Cohens κ = 0.687), whereas inter-rater reliability of per-subject PRL number was "Good" (ICC = 0.874). Only 4/22 studies reported complete information on rater experience, rater level of training, detailed PRL classification criteria, and reliability cohort size and disease subtypes. CONCLUSION PRLs can be reliably detected both at per-lesion and per-subject level. We recommend that future PRL studies report detailed reliability results, including rater experience level, and use a standardized set of reliability metrics (Cohen's κ or ICC) for improved comparability between studies.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Maryam Mohebbi
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA; Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA; Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA.
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20
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Shirai T, Sato R, Kawata Y, Bito Y, Ochi H. Region Expansion of Background Field Removal with Local Spherical Harmonics Approximation for Whole-brain Quantitative Susceptibility Mapping. Magn Reson Med Sci 2023; 22:497-514. [PMID: 36372397 PMCID: PMC10552664 DOI: 10.2463/mrms.mp.2021-0043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/07/2022] [Indexed: 10/03/2023] Open
Abstract
PURPOSE Quantitative susceptibility mapping (QSM) is useful for obtaining biological information. To calculate susceptibility distribution, it is necessary to calculate the local field caused by the differences of susceptibility between the tissues. The local field can be obtained by removing a background field from a total field acquired by MR phase image. Conventional approaches based on spherical mean value (SMV) filtering, which are widely used for background field calculations, fail to calculate the background field of the brain surface region corresponding to the radius of the SMV kernel, and consequently cannot calculate the QSM of the brain surface region. Accordingly, a new method calculating the local field by expansively removing the background field is proposed for whole brain QSM. METHODS The proposed method consists of two steps. First, the background field of the brain surface is calculated from the total field using a locally polynomial approximation of spherical harmonics. Second, the whole brain local field is calculated by SMV filtering with a constraint term of the background field of the brain surface. The parameters of the approximation were optimized to reduce calculation errors through simulations using both a numerical phantom and a measured human brain. Performance of the proposed method with the optimized parameters was quantitatively and visually compared with conventional methods in an experiment of five healthy volunteers. RESULTS The proposed method showed the accurate local field over the expanded brain region in the simulation studies. It also showed consistent QSM with conventional methods inside of the brain surface and showed clear vein structures on the brain surface. CONCLUSION The proposed method enables accurate calculation of whole brain QSM without eroding the brain surface region while maintaining same values inside of the brain surface as the conventional methods.
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Affiliation(s)
- Toru Shirai
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Kokubunji, Tokyo, Japan
| | - Ryota Sato
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Kokubunji, Tokyo, Japan
| | - Yasuo Kawata
- Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Kashiwa, Chiba, Japan
| | - Yoshitaka Bito
- Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Kashiwa, Chiba, Japan
| | - Hisaaki Ochi
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Kokubunji, Tokyo, Japan
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21
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García Saborit M, Jara A, Muñoz N, Milovic C, Tepper A, Alliende LM, Mena C, Iruretagoyena B, Ramirez-Mahaluf JP, Diaz C, Nachar R, Castañeda CP, González A, Undurraga J, Crossley N, Tejos C. Quantitative Susceptibility Mapping MRI in Deep-Brain Nuclei in First-Episode Psychosis. Schizophr Bull 2023; 49:1355-1363. [PMID: 37030007 PMCID: PMC10483330 DOI: 10.1093/schbul/sbad041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
BACKGROUND Psychosis is related to neurochemical changes in deep-brain nuclei, particularly suggesting dopamine dysfunctions. We used an magnetic resonance imaging-based technique called quantitative susceptibility mapping (QSM) to study these regions in psychosis. QSM quantifies magnetic susceptibility in the brain, which is associated with iron concentrations. Since iron is a cofactor in dopamine pathways and co-localizes with inhibitory neurons, differences in QSM could reflect changes in these processes. METHODS We scanned 83 patients with first-episode psychosis and 64 healthy subjects. We reassessed 22 patients and 21 control subjects after 3 months. Mean susceptibility was measured in 6 deep-brain nuclei. Using linear mixed models, we analyzed the effect of case-control differences, region, age, gender, volume, framewise displacement (FD), treatment duration, dose, laterality, session, and psychotic symptoms on QSM. RESULTS Patients showed a significant susceptibility reduction in the putamen and globus pallidus externa (GPe). Patients also showed a significant R2* reduction in GPe. Age, gender, FD, session, group, and region are significant predictor variables for QSM. Dose, treatment duration, and volume were not predictor variables of QSM. CONCLUSIONS Reduction in QSM and R2* suggests a decreased iron concentration in the GPe of patients. Susceptibility reduction in putamen cannot be associated with iron changes. Since changes observed in putamen and GPe were not associated with symptoms, dose, and treatment duration, we hypothesize that susceptibility may be a trait marker rather than a state marker, but this must be verified with long-term studies.
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Affiliation(s)
- Marisleydis García Saborit
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Alejandro Jara
- Department of Statistics, Mathematics Faculty, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Néstor Muñoz
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Carlos Milovic
- School of Electrical Engineering, Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Angeles Tepper
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Luz María Alliende
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Carlos Mena
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Bárbara Iruretagoyena
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | | | - Camila Diaz
- Pharmacovigilance, Instituto Psiquiátrico Dr. J. Horwitz Barak, Santiago, Chile
| | - Ruben Nachar
- Pharmacovigilance, Instituto Psiquiátrico Dr. J. Horwitz Barak, Santiago, Chile
| | | | - Alfonso González
- Early Intervention Program, Instituto Psiquiátrico Dr J. Horwitz Barak, Santiago, Chile
- School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Early Intervention Program, Instituto Psiquiátrico Dr J. Horwitz Barak, Santiago, Chile
- Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Nicolas Crossley
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
- Department of Psychiatry, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Cristian Tejos
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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22
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Velikina JV, Zhao R, Buelo CJ, Samsonov AA, Reeder SB, Hernando D. Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping. Magn Reson Med 2023; 90:385-399. [PMID: 36929781 PMCID: PMC11057046 DOI: 10.1002/mrm.29644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 02/24/2023] [Accepted: 03/05/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE To improve repeatability and reproducibility across acquisition parameters and reduce bias in quantitative susceptibility mapping (QSM) of the liver, through development of an optimized regularized reconstruction algorithm for abdominal QSM. METHODS An optimized approach to estimation of magnetic susceptibility distribution is formulated as a constrained reconstruction problem that incorporates estimates of the input data reliability and anatomical priors available from chemical shift-encoded imaging. The proposed data-adaptive method was evaluated with respect to bias, repeatability, and reproducibility in a patient population with a wide range of liver iron concentration (LIC). The proposed method was compared to the previously proposed and validated approach in liver QSM for two multi-echo spoiled gradient-recalled echo protocols with different acquisition parameters at 3T. Linear regression was used for evaluation of QSM methods against a reference FDA-approvedR 2 $$ {R}_2 $$ -based LIC measure andR 2 ∗ $$ {R}_2^{\ast } $$ measurements; repeatability/reproducibility were assessed by Bland-Altman analysis. RESULTS The data-adaptive method produced susceptibility maps with higher subjective quality due to reduced shading artifacts. For both acquisition protocols, higher linear correlation with bothR 2 $$ {R}_2 $$ - andR 2 ∗ $$ {R}_2^{\ast } $$ -based measurements were observed for the data-adaptive method (r 2 = 0 . 74 / 0 . 69 $$ {r}^2=0.74/0.69 $$ forR 2 $$ {R}_2 $$ ,0 . 97 / 0 . 95 $$ 0.97/0.95 $$ forR 2 ∗ $$ {R}_2^{\ast } $$ ) than the standard method (r 2 = 0 . 60 / 0 . 66 $$ {r}^2=0.60/0.66 $$ and0 . 79 / 0 . 88 $$ 0.79/0.88 $$ ). For both protocols, the data-adaptive method enabled better test-retest repeatability (repeatability coefficients 0.19/0.30 ppm for the data-adaptive method, 0.38/0.47 ppm for the standard method) and reproducibility across protocols (reproducibility coefficient 0.28 vs. 0.53ppm) than the standard method. CONCLUSIONS The proposed data-adaptive QSM algorithm may enable quantification of LIC with improved repeatability/reproducibility across different acquisition parameters as 3T.
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Affiliation(s)
- Julia V Velikina
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ruiyang Zhao
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Collin J Buelo
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Alexey A Samsonov
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
- Department of Emergency Medicine, University of Wisconsin, Madison, Wisconsin, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
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23
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Fang Z, Lai KW, van Zijl P, Li X, Sulam J. DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging. Med Image Anal 2023; 87:102829. [PMID: 37146440 PMCID: PMC10288385 DOI: 10.1016/j.media.2023.102829] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 03/11/2023] [Accepted: 04/18/2023] [Indexed: 05/07/2023]
Abstract
Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.
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Affiliation(s)
- Zhenghan Fang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD 21218, USA
| | - Kuo-Wei Lai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Peter van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD 21205, USA.
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD 21218, USA.
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24
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Dimov AV, Li J, Nguyen TD, Roberts AG, Spincemaille P, Straub S, Zun Z, Prince MR, Wang Y. QSM Throughout the Body. J Magn Reson Imaging 2023; 57:1621-1640. [PMID: 36748806 PMCID: PMC10192074 DOI: 10.1002/jmri.28624] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/08/2023] Open
Abstract
Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Jiahao Li
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | | | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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25
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Kiersnowski OC, Karsa A, Wastling SJ, Thornton JS, Shmueli K. Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method. Magn Reson Med 2023; 89:1791-1808. [PMID: 36480002 PMCID: PMC10953050 DOI: 10.1002/mrm.29550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/28/2022] [Accepted: 11/16/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Quantitative susceptibility mapping (QSM) is used increasingly for clinical research where oblique image acquisition is commonplace, but its effects on QSM accuracy are not well understood. THEORY AND METHODS The QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic fieldB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ with respect to the acquired image volume axes. The direction ofB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ is dependent on the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in vivo acquisitions in 5 healthy volunteers, we analyzed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt-correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the RMS error and QSM-tuned structural similarity index. RESULTS Rotation of wrapped phase images gave severe artifacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ . Laplacian boundary value and variable-kernel sophisticated harmonic artifact reduction for phase data background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularization, and weighted linear total variation regularization, all performed most accurately when local field maps were rotated into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ before susceptibility calculation. CONCLUSION For accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ should be carried out after phase unwrapping and before background-field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.
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Affiliation(s)
- Oliver C. Kiersnowski
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Anita Karsa
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Stephen J. Wastling
- Neuroradiological Academic UnitUCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Lysholm Department of NeuroradiologyNational Hospital for Neurology and NeurosurgeryLondonUnited Kingdom
| | - John S. Thornton
- Neuroradiological Academic UnitUCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Lysholm Department of NeuroradiologyNational Hospital for Neurology and NeurosurgeryLondonUnited Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
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26
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Chen Z, Zhai X, Chen Z. Computed cancer magnetic susceptibility imaging (canχ): Computational inverse mappings of cancer MRI. Magn Reson Imaging 2023; 102:86-95. [PMID: 37075866 DOI: 10.1016/j.mri.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/31/2023] [Accepted: 04/16/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE We report a new cancer imaging modality in the contrast of tissue intrinsic susceptibility property by computed inverse magnetic resonance imaging (CIMRI). METHODS In MRI physics, an MRI signal is formed from tissue magnetism source (primarily magnetic susceptibility χ) through a cascade of MRI-introduced transformations (e.g. dipole-convolved magnetization) involving MRI setting parameters (e.g. echo time). In two-step computational inverse mappings (from phase image to internal fieldmap to susceptibility source), we could remove the MRI transformations and imaging parameters, thereby obtaining χ-depicted cancer images (canχ) from MRI phase images. Canχ is computationally implemented from clinical cancer MRI phase image by CIMRI. RESULTS As a result of MRI effect removal through computational inverse mappings, the reconstructed χ map (canχ) could provide a new cancerous tissue depiction in contrast of tissue intrinsic magnetism property (i.e. diamagnetism vs paramagnetism) as in an off-scanner state (e.g. in absence of main field B0). CONCLUSION Through retrospective clinical cancer MRI data analysis, we reported on the canχ method in technical details and demonstrated its feasibility of innovating cancer imaging in the contrast of tissue intrinsic paramagnetism/diamagnetism property (in a cancer tissue state free from MRI effect).
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Affiliation(s)
- Zikuan Chen
- Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA 91010, United States of America; Zinv LLC, Albuquerque, NM 87108, United States of America.
| | - Xiulan Zhai
- Zinv LLC, Albuquerque, NM 87108, United States of America
| | - Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA 95616, United States of America; Microsoft Corporation, Seattle, WA 98052, United States of America.
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27
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van Gelderen P, Li X, de Zwart JA, Beck ES, Okar SV, Huang Y, Lai K, Sulam J, van Zijl PCM, Reich DS, Duyn JH, Liu J. Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R 2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T. Neuroimage 2023; 270:119992. [PMID: 36858332 PMCID: PMC10278242 DOI: 10.1016/j.neuroimage.2023.119992] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 02/17/2023] [Accepted: 02/25/2023] [Indexed: 03/03/2023] Open
Abstract
MR images of the effective relaxation rate R2* and magnetic susceptibility χ derived from multi-echo T2*-weighted (T2*w) MRI can provide insight into iron and myelin distributions in the brain, with the potential of providing biomarkers for neurological disorders. Quantification of R2* and χ at submillimeter resolution in the cortex in vivo has been difficult because of challenges such as head motion, limited signal to noise ratio, long scan time, and motion related magnetic field fluctuations. This work aimed to improve the robustness for quantifying intracortical R2* and χ and analyze the effects from motion, spatial resolution, and cortical orientation. T2*w data was acquired with a spatial resolution of 0.3 × 0.3 × 0.4 mm3 at 7 T and downsampled to various lower resolutions. A combined correction for motion and B0 changes was deployed using volumetric navigators. Such correction improved the T2*w image quality rated by experienced image readers and test-retest reliability of R2* and χ quantification with reduced median inter-scan differences up to 10 s-1 and 5 ppb, respectively. R2* and χ near the line of Gennari, a cortical layer high in iron and myelin, were as much as 10 s-1 and 10 ppb higher than the region at adjacent cortical depth. In addition, a significant effect due to the cortical orientation relative to the static field (B0) was observed in χ with a peak-to-peak amplitude of about 17 ppb. In retrospectively downsampled data, the capability to distinguish different cortical depth regions based on R2* or χ contrast remained up to isotropic 0.5 mm resolution. This study highlights the unique characteristics of R2* and χ along the cortical depth at submillimeter resolution and the need for motion and B0 corrections for their robust quantification in vivo.
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Affiliation(s)
- Peter van Gelderen
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, NIH, Bethesda, MD, United States of America
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jacco A de Zwart
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, NIH, Bethesda, MD, United States of America
| | - Erin S Beck
- Translational Neurology Section, NINDS, NIH, Bethesda, MD, United States of America; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, NY, United States of America
| | - Serhat V Okar
- Translational Neurology Section, NINDS, NIH, Bethesda, MD, United States of America
| | - Yujia Huang
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - KuoWei Lai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America; Department of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Peter C M van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - Daniel S Reich
- Translational Neurology Section, NINDS, NIH, Bethesda, MD, United States of America
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, NIH, Bethesda, MD, United States of America
| | - Jiaen Liu
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States of America.
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28
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Chen Z, Zhai X, Chen Z. Tilted quantitative susceptibility mapping at oblique MRI (tiltQSM). Comput Biol Med 2023; 157:106802. [PMID: 36965324 DOI: 10.1016/j.compbiomed.2023.106802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/05/2023] [Accepted: 03/20/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVE If the phase image matrix was acquired from oblique MRI, it is needed to deal with the oblique effect for quantitative susceptibility mapping (QSM), as addressed in this paper. METHODS We proposed two methods for QSM reconstruction from slice-tilted MRI phase image (tiltQSM): 1) rotData per anti-tilting phase image rotation back into the B0-upright system, and 2) rotKernel per pro-tilting dipole kernel rotation into the same oblique setting as defined by the tilted phase image. Both matrix methods were implemented in an additional preprocessing subroutine to ensure that the phase image and the dipole kernel were represented in the same coordinate system (either in B0-upright system or in B0-tilted system); thereafter tiltQSM could be completed through a regular QSM procedure. Besides the oblique effect, tiltQSM also suffers from MRI anisotropy. We provided numeric simulations, phantom tests and in vivo brain experiments on tiltQSM with oblique MRI (axial slice tilting at 3T). RESULTS The tiltQSM reconstruction could attain a performance corr > 0.90 (spatial correlation conformance) for small tilting angles <10°. The tiltQSM performance could be further degraded by voxel anisotropy due to image matrix rotation (digital geometry error). CONCLUSIONS To seek inverse solutions of MRI phase images acquired at oblique MRI (e.g. in axial slice tilting), we proposed tiltQSM to deal with the oblique effect per matrix rotation (either rotData or rotKernel) in a preprocessing subroutine prior to a regular QSM procedure. In practice, it is always recommended to acquire MRI phase images in isotropic matrix at zero obliqueness (or limited to small tilting angles <10°) for maximal (optimal) QSM reconstruction.
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Affiliation(s)
- Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA, USA; Microsoft Corporation, Seattle, WA, USA
| | | | - Zikuan Chen
- Zinv LLC, Albuquerque, NM, USA; Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.
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29
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Perosa V, Rotta J, Yakupov R, Kuijf HJ, Schreiber F, Oltmer JT, Mattern H, Heinze HJ, Düzel E, Schreiber S. Implications of quantitative susceptibility mapping at 7 Tesla MRI for microbleeds detection in cerebral small vessel disease. Front Neurol 2023; 14:1112312. [PMID: 37006483 PMCID: PMC10050564 DOI: 10.3389/fneur.2023.1112312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/20/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundCerebral microbleeds (MBs) are a hallmark of cerebral small vessel disease (CSVD) and can be found on T2*-weighted sequences on MRI. Quantitative susceptibility mapping (QSM) is a postprocessing method that also enables MBs identification and furthermore allows to differentiate them from calcifications.AimsWe explored the implications of using QSM at submillimeter resolution for MBs detection in CSVD.MethodsBoth 3 and 7 Tesla (T) MRI were performed in elderly participants without MBs and patients with CSVD. MBs were quantified on T2*-weighted imaging and QSM. Differences in the number of MBs were assessed, and subjects were classified in CSVD subgroups or controls both on 3T T2*-weighted imaging and 7T QSM.Results48 participants [mean age (SD) 70.9 (8.8) years, 48% females] were included: 31 were healthy controls, 6 probable cerebral amyloid angiopathy (CAA), 9 mixed CSVD, and 2 were hypertensive arteriopathy [HA] patients. After accounting for the higher number of MBs detected at 7T QSM (Median = Mdn; Mdn7T−QSM = 2.5; Mdn3T−T2 = 0; z = 4.90; p < 0.001) and false positive MBs (6.1% calcifications), most healthy controls (80.6%) demonstrated at least one MB and more MBs were discovered in the CSVD group.ConclusionsOur observations suggest that QSM at submillimeter resolution improves the detection of MBs in the elderly human brain. A higher prevalence of MBs than so far known in healthy elderly was revealed.
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Affiliation(s)
- Valentina Perosa
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- *Correspondence: Valentina Perosa
| | - Johanna Rotta
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Renat Yakupov
- Institute of Cognitive Neurology and Dementia Research (IKND), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo J. Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Frank Schreiber
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jan T. Oltmer
- Athinoula A. Martinos Center, Massachusetts General Hospital, Department of Radiology, Boston, MA, United States
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Physics, Otto-von-Guericke University, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Emrah Düzel
- Institute of Cognitive Neurology and Dementia Research (IKND), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Stefanie Schreiber
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
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Sandgaard AD, Shemesh N, Kiselev VG, Jespersen SN. Larmor frequency shift from magnetized cylinders with arbitrary orientation distribution. NMR IN BIOMEDICINE 2023; 36:e4859. [PMID: 36285793 PMCID: PMC10078263 DOI: 10.1002/nbm.4859] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 06/01/2023]
Abstract
The magnetic susceptibility of tissue can provide valuable information about its chemical composition and microstructural organization. However, the relation between the magnetic microstructure and the measurable Larmor frequency shift is understood only for a few idealized cases. Here we analyze the microstructure formed by magnetized, NMR-invisible infinite cylinders suspended in an NMR-reporting fluid. Through simulations, we scrutinize various geometries of mesoscopic Lorentz cavities and inclusions, and show that the cavity size should be approximately one order of magnitude larger than the width of the inclusions. We also analytically derive the Larmor frequency shift for a population of cylinders with arbitrary orientation dispersion and show that it is determined by the l = 2 Laplace expansion coefficients p 2 m of the cylinders' orientation distribution function. Our work underscores the need to account for microstructural organization when estimating magnetic tissue properties.
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Affiliation(s)
- Anders Dyhr Sandgaard
- Center for Functionally Integrative Neuroscience, Department of Clinical MedicineAarhus UniversityDenmark
| | - Noam Shemesh
- Champalimaud ResearchChampalimaud Centre for the UnknownLisbonPortugal
| | - Valerij G. Kiselev
- Division of Medical Physics, Department of RadiologyUniversity Medical Center FreiburgFreiburgGermany
| | - Sune Nørhøj Jespersen
- Center for Functionally Integrative Neuroscience, Department of Clinical MedicineAarhus UniversityDenmark
- Department of Physics and AstronomyAarhus UniversityDenmark
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31
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Cognolato F, O'Brien K, Jin J, Robinson S, Laun FB, Barth M, Bollmann S. NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data. Med Image Anal 2023; 84:102700. [PMID: 36529002 DOI: 10.1016/j.media.2022.102700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/16/2022] [Accepted: 11/21/2022] [Indexed: 11/24/2022]
Abstract
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.
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Affiliation(s)
- Francesco Cognolato
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Simon Robinson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurology, Medical University of Graz, Graz, Austria; Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Markus Barth
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Steffen Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
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32
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Kames C, Doucette J, Rauscher A. Multi-echo dipole inversion for magnetic susceptibility mapping. Magn Reson Med 2023; 89:2391-2401. [PMID: 36695283 DOI: 10.1002/mrm.29588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/08/2022] [Accepted: 12/31/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE Reconstructing tissue magnetic susceptibility (QSM) from MRI phase data involves solving multiple consecutive ill-posed inverse problems such as phase unwrapping, background field removal, and field-to-source inversion. Multi-echo acquisitions present an additional challenge, as the magnetization field is typically computed from the multiple phase data prior to reconstructing the susceptibility map. Processing the multiple phase data introduces errors during the field estimation, violating assumptions of the subsequent inverse problems, manifesting as streaking artifacts in the susceptibility map. To address this challenge, we propose a multi-echo field-to-source forward model that forgoes the field estimation step. Moreover, we propose a fully general underestimation correction step to recover susceptibility sources that were regularized away during the field-to-source inversion. METHODS The multi-echo forward model and correction step were validated on the QSM Challenge 2.0 datasets and compared to the standard single field-to-source model in in vivo human brains using different types of deconvolution algorithms. RESULTS On the QSM Challenge 2.0 datasets the multi-echo forward model and correction step attain state-of-the-art results on all metrics by a wide margin. Experiments in in vivo brains show that the multi-echo model is in agreement with the single field-to-source model and that the proposed forward model and correction step can be used with any available dipole inversion method. CONCLUSION A multi-echo field-to-source forward model forgoes the need to fit multi-echo phase data and achieves state-of-the-art results on the QSM Challenge 2.0 data. Underestimated low-frequency susceptibility distributions can be partially recovered using a correction step.
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Affiliation(s)
- Christian Kames
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jonathan Doucette
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Rauscher
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pediatrics, The University of British Columbia, Vancouver, British Columbia, Canada
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33
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Tan H, Hubertus S, Thomas S, Lee AM, Gerhardt S, Gerchen MF, Sommer WH, Kiefer F, Schad L, Vollstädt-Klein S. Association between iron accumulation in the dorsal striatum and compulsive drinking in alcohol use disorder. Psychopharmacology (Berl) 2023; 240:249-257. [PMID: 36577866 PMCID: PMC9879829 DOI: 10.1007/s00213-022-06301-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/16/2022] [Indexed: 12/30/2022]
Abstract
RATIONALE Brain iron accumulation has been observed in neuropsychiatric disorders and shown to be related to neurodegeneration. OBJECTIVES In this study, we used quantitative susceptibility mapping (QSM), an emerging MRI technique developed for quantifying tissue magnetic susceptibility, to examine brain iron accumulation in individuals with alcohol use disorder (AUD) and its relation to compulsive drinking. METHODS Based on our previous projects, QSM was performed as a secondary analysis with gradient echo sequence images, in 186 individuals with AUD and 274 healthy participants. Whole-brain susceptibility values were calculated with morphology-enabled dipole inversion and referenced to the cerebrospinal fluid. Then, the susceptibility maps were compared between AUD individuals and healthy participants. The relationship between drinking patterns and susceptibility was explored. RESULTS Whole-brain analyses showed that the susceptibility in the dorsal striatum (putamen and caudate) among AUD individuals was higher than healthy participants and was positively related to the Obsessive Compulsive Drinking Scale (OCDS) scores and the amount of drinking in the past three months. CONCLUSIONS Increased susceptibility suggests higher iron accumulation in the dorsal striatum in AUD. This surrogate for the brain iron level was linearly associated with the compulsive drinking pattern and the recent amount of drinking, which provides us a new clinical perspective in relation to brain iron accumulation, and also might indicate an association of AUD with neuroinflammation as a consequence of brain iron accumulation. The iron accumulation in the striatum is further relevant for functional imaging studies in AUD by potentially producing signal dropout and artefacts in fMRI images.
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Affiliation(s)
- Haoye Tan
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Simon Hubertus
- grid.7700.00000 0001 2190 4373Computer Assisted Clinical Medicine, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Sebastian Thomas
- grid.7700.00000 0001 2190 4373Computer Assisted Clinical Medicine, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Alycia M. Lee
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Sarah Gerhardt
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Martin Fungisai Gerchen
- grid.7700.00000 0001 2190 4373Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany ,grid.455092.fBernstein Center for Computational Neuroscience Heidelberg/Mannheim, 68159 Mannheim, Germany ,grid.7700.00000 0001 2190 4373Department of Psychology, Heidelberg University, 69117 Heidelberg, Germany
| | - Wolfgang H. Sommer
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany ,grid.7700.00000 0001 2190 4373Institute of Psychopharmacology, Central Institute of Mental Health, Heidelberg University, 68159 Mannheim, Germany ,Bethania Hospital for Psychiatry, Psychosomatics, and Psychotherapy, Greifswald, Germany
| | - Falk Kiefer
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany ,grid.7700.00000 0001 2190 4373Mannheim Center for Translational Neurosciences (MCTN), Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany ,grid.7700.00000 0001 2190 4373Feuerlein Center on Translational Addiction Medicine, Heidelberg University, 69117 Heidelberg, Germany
| | - Lothar Schad
- grid.7700.00000 0001 2190 4373Computer Assisted Clinical Medicine, Medical Faculty of Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Sabine Vollstädt-Klein
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, 68159, Mannheim, Germany. .,Mannheim Center for Translational Neurosciences (MCTN), Medical Faculty of Mannheim, Heidelberg University, 68159, Mannheim, Germany.
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Dadarwal R, Ortiz-Rios M, Boretius S. Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates. Neuroimage 2022; 264:119730. [PMID: 36332851 DOI: 10.1016/j.neuroimage.2022.119730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/12/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter-scale subcortical brain structures in humans. However, the simultaneous visualization of cortical, subcortical, and white matter structure remains challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortex and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first applied QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analysis methods allowed a similar accurate delineation of subcortical structures in humans. However, the QSM contrast of white and cortical gray matter was not sufficient for appropriate segmentation. Applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of subcortical brain structures as compared to the single T1 contrast by maintaining an excellent white to cortical gray matter contrast. Furthermore, we validated our dual-contrast fusion approach in humans and similarly demonstrated improvements in automated segmentation of the cortex and subcortical structures. We believe the proposed contrast will facilitate translational studies in nonhuman primates to investigate the pathophysiology of neurodegenerative diseases that affect subcortical structures such as the basal ganglia in humans.
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Affiliation(s)
- Rakshit Dadarwal
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany; Georg-August University of Göttingen, Göttingen, Germany.
| | - Michael Ortiz-Rios
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany; Georg-August University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
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35
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Naji N, Lauzon ML, Seres P, Stolz E, Frayne R, Lebel C, Beaulieu C, Wilman AH. Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads. NMR IN BIOMEDICINE 2022; 35:e4788. [PMID: 35704837 DOI: 10.1002/nbm.4788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/28/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R2 *) mapping from multiecho T2 *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R2 * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R2 * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R2 * were highly correlated (R2 ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R2 *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s-1 for R2 *. For within-site measurements of QSM and R2 *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.
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Affiliation(s)
- Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - M Louis Lauzon
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Emily Stolz
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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Hagberg GE, Eckstein K, Tuzzi E, Zhou J, Robinson S, Scheffler K. Phase-based masking for quantitative susceptibility mapping of the human brain at 9.4T. Magn Reson Med 2022; 88:2267-2276. [PMID: 35754142 PMCID: PMC7613679 DOI: 10.1002/mrm.29368] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 05/05/2022] [Accepted: 05/31/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop improved tissue masks for QSM. METHODS Masks including voxels at the brain surface were automatically generated from the magnitude alone (MM) or combined with test functions from the first (PG) or second (PB) derivative of the sign of the wrapped phase. Phase images at 3T and 9.4T were simulated at different TEs and used to generate a mask, PItoh , with between-voxel phase differences less than π. MM, PG, and PB were compared with PItoh . QSM were generated from 3D multi-echo gradient-echo data acquired at 9.4T (21 subjects aged: 20-56y), and from the QSM2016 challenge 3T data using different masks, unwrapping, background removal, and dipole inversion algorithms. QSM contrast was quantified using age-based iron concentrations. RESULTS Close to air cavities, phase wraps became denser with increasing field and echo time, yielding increased values of the test functions. Compared with PItoh , PB had the highest Dice coefficient, while PG had the lowest and MM the highest percentage of voxels outside PItoh. Artifacts observed in QSM at 9.4T with MM were mitigated by stronger background filters but yielded a reduced QSM contrast. With PB, QSM contrast was greater and artifacts diminished. Similar results were obtained with challenge data, evidencing larger effects of mask close to air cavities. CONCLUSION Automatic, phase-based masking founded on the second derivative of the sign of the wrapped phase, including cortical voxels at the brain surface, was able to mitigate artifacts and restore QSM contrast across cortical and subcortical brain regions.
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Affiliation(s)
- Gisela E. Hagberg
- Department for Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
- High Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Korbinian Eckstein
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- High Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Elisa Tuzzi
- Department for Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
- High Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Jiazheng Zhou
- Department for Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
- High Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Simon Robinson
- Department of Neurology, Medical University of Graz, Graz, Austria
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
- High Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
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Chen Z, Zhai X, Chen Z. Proof of linear MRI phase imaging from an internal fieldmap. NMR IN BIOMEDICINE 2022; 35:e4741. [PMID: 35411962 DOI: 10.1002/nbm.4741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/20/2022] [Accepted: 04/09/2022] [Indexed: 06/14/2023]
Abstract
PURPOSE Brain MRI phase imaging assumes a linear spatial mapping of the internal fieldmap that continues to lack theoretical proof. We herein present one proof by replacing the arithmetic mean (in MRI signal formation from the intravoxel spin precession dephasing mechanism) with the geometric mean. METHODS By replacing the complex arithmetic mean of intravoxel dephasing isochromats with a complex geometric mean, we readily derive a linear spatial mapping of MRI phase imaging from an internal fieldmap without any restriction in phase angles. To justify the replacement of the complex arithmetic mean with the complex geometric mean for realistic brain MRI, we provide numerical T2*MRI simulations to observe the similarity and difference between arithmetic- and geometric-mean phase images in diverse settings with respect to spatial resolution and echo time, with or without proton density weighting. RESULTS Theoretically, the complex geometric mean model offers a theoretical proof of linear spatial mapping for MRI phase imaging. Numerical simulations of T2*MRI phase imaging show that the geometric mean conforms to the arithmetic mean at a high similarity in the small phase condition (e.g., corr > 0.90 in phase pre-wrapping status at TE < 10 ms) and the similarity falls at large phase angles (e.g., corr ≈ 0.80 in phase-wrapped status at TE = 30 ms). CONCLUSION By replacing the arithmetic mean of intravoxel spin precession dephasing with the geometric mean, we find a theoretical proof for linear MRI phase imaging beyond the small phase condition on spin precession angles.
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Affiliation(s)
- Zikuan Chen
- Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
- Zinv LLC, Albuquerque, NM, USA
| | | | - Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA, USA
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Aimo A, Huang L, Tyler A, Barison A, Martini N, Saccaro LF, Roujol S, Masci PG. Quantitative susceptibility mapping (QSM) of the cardiovascular system: challenges and perspectives. J Cardiovasc Magn Reson 2022; 24:48. [PMID: 35978351 PMCID: PMC9387036 DOI: 10.1186/s12968-022-00883-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 08/05/2022] [Indexed: 11/10/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a powerful, non-invasive, magnetic resonance imaging (MRI) technique that relies on measurement of magnetic susceptibility. So far, QSM has been employed mostly to study neurological disorders characterized by iron accumulation, such as Parkinson's and Alzheimer's diseases. Nonetheless, QSM allows mapping key indicators of cardiac disease such as blood oxygenation and myocardial iron content. For this reason, the application of QSM offers an unprecedented opportunity to gain a better understanding of the pathophysiological changes associated with cardiovascular disease and to monitor their evolution and response to treatment. Recent studies on cardiovascular QSM have shown the feasibility of a non-invasive assessment of blood oxygenation, myocardial iron content and myocardial fibre orientation, as well as carotid plaque composition. Significant technical challenges remain, the most evident of which are related to cardiac and respiratory motion, blood flow, chemical shift effects and susceptibility artefacts. Significant work is ongoing to overcome these challenges and integrate the QSM technique into clinical practice in the cardiovascular field.
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Affiliation(s)
- Alberto Aimo
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Li Huang
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Tyler
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrea Barison
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | | | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical Engineering, King's College London, St Thomas' Hospital, 4th Floor Lambeth Wing, London, SE1 7EH, UK.
| | - Pier-Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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39
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Uchida Y, Kan H, Sakurai K, Oishi K, Matsukawa N. Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations. Front Neurosci 2022; 16:938092. [PMID: 35992906 PMCID: PMC9389285 DOI: 10.3389/fnins.2022.938092] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/14/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia and a distressing diagnosis for individuals and caregivers. Researchers and clinical trials have mainly focused on β-amyloid plaques, which are hypothesized to be one of the most important factors for neurodegeneration in AD. Meanwhile, recent clinicopathological and radiological studies have shown closer associations of tau pathology rather than β-amyloid pathology with the onset and progression of Alzheimer’s symptoms. Toward a biological definition of biomarker-based research framework for AD, the 2018 National Institute on Aging–Alzheimer’s Association working group has updated the ATN classification system for stratifying disease status in accordance with relevant pathological biomarker profiles, such as cerebral β-amyloid deposition, hyperphosphorylated tau, and neurodegeneration. In addition, altered iron metabolism has been considered to interact with abnormal proteins related to AD pathology thorough generating oxidative stress, as some prior histochemical and histopathological studies supported this iron-mediated pathomechanism. Quantitative susceptibility mapping (QSM) has recently become more popular as a non-invasive magnetic resonance technique to quantify local tissue susceptibility with high spatial resolution, which is sensitive to the presence of iron. The association of cerebral susceptibility values with other pathological biomarkers for AD has been investigated using various QSM techniques; however, direct evidence of these associations remains elusive. In this review, we first briefly describe the principles of QSM. Second, we focus on a large variety of QSM applications, ranging from common applications, such as cerebral iron deposition, to more recent applications, such as the assessment of impaired myelination, quantification of venous oxygen saturation, and measurement of blood– brain barrier function in clinical settings for AD. Third, we mention the relationships among QSM, established biomarkers, and cognitive performance in AD. Finally, we discuss the role of QSM as an imaging biomarker as well as the expectations and limitations of clinically useful diagnostic and therapeutic implications for AD.
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Affiliation(s)
- Yuto Uchida
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Yuto Uchida,
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, Ōbu, Japan
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Noriyuki Matsukawa
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Noriyuki Matsukawa,
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40
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Nikparast F, Ganji Z, Zare H. Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder? BMC Neurosci 2022; 23:48. [PMID: 35902793 PMCID: PMC9336059 DOI: 10.1186/s12868-022-00725-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
During neurodegenerative diseases, the brain undergoes morphological and pathological changes; Iron deposits are one of the causes of pathological changes in the brain. The Quantitative susceptibility mapping (QSM) technique, a type of magnetic resonance (MR) image reconstruction, is one of the newest diagnostic methods for iron deposits to detect changes in magnetic susceptibility. Numerous research projects have been conducted in this field. The purpose of writing this review article is to identify the first deep brain nuclei that undergo magnetic susceptibility changes during neurodegenerative diseases such as Alzheimer's or Parkinson's disease. The purpose of this article is to identify the brain nuclei that are prone to iron deposition in any specific disorder. In addition to the mentioned purpose, this paper proposes the optimal scan parameters and appropriate algorithms of each QSM reconstruction step by reviewing the results of different articles. As a result, The QSM technique can identify nuclei exposed to iron deposition in various neurodegenerative diseases. Also, the selection of scan parameters is different based on the sequence and purpose; an example of the parameters is placed in the tables. The BET toolbox in FSL, Laplacian-based phase-unwrapping process, the V_SHARP algorithm, and morphology-enabled dipole inversion (MEDI) method are the most widely used algorithms in various stages of QSM reconstruction.
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Affiliation(s)
- Farzaneh Nikparast
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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41
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Nouwens SAN, Paulides MM, Fölker J, VilasBoas-Ribeiro I, de Jager B, Heemels WPMH. Integrated thermal and magnetic susceptibility modeling for air-motion artifact correction in proton resonance frequency shift thermometry. Int J Hyperthermia 2022; 39:967-976. [PMID: 35853735 DOI: 10.1080/02656736.2022.2094475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
Abstract
PURPOSE Hyperthermia treatments are successful adjuvants to conventional cancer therapies in which the tumor is sensitized by heating. To monitor and guide the hyperthermia treatment, measuring the tumor and healthy tissue temperature is important. The typical clinical practice heavily relies on intraluminal probe measurements that are uncomfortable for the patient and only provide spatially sparse temperature information. A solution may be offered through recent advances in magnetic resonance thermometry, which allows for three-dimensional internal temperature measurements. However, these measurements are not widely used in the pelvic region due to a low signal-to-noise ratio and presence of image artifacts. METHODS To advance the clinical integration of magnetic resonance-guided cancer treatments, we consider the problem of removing air-motion-induced image artifacts. Thereto, we propose a new combined thermal and magnetic susceptibility model-based temperature estimation scheme that uses temperature estimates to improve the removal of air-motion-induced image artifacts. The method is experimentally validated using a dedicated phantom that enables the controlled injection of air-motion artifacts and with in vivo thermometry from a clinical hyperthermia treatment. RESULTS We showed, using probe measurements in a heated phantom, that our method reduced the mean absolute error (MAE) by 58% compared to the state-of-the-art near a moving air volume. Moreover, with in vivo thermometry our method obtained a MAE reduction between 17% and 95% compared to the state-of-the-art. CONCLUSION We expect that the combined thermal and magnetic susceptibility modeling used in model-based temperature estimation can significantly improve the monitoring in hyperthermia treatments and enable feedback strategies to further improve MR-guided hyperthermia cancer treatments.
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Affiliation(s)
- S A N Nouwens
- Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - M M Paulides
- Department of Radiotherapy, Erasmus University Medical Center Cancer Institute, Rotterdam, The Netherlands.,Electromagnetics for Care & Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - J Fölker
- Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - I VilasBoas-Ribeiro
- Department of Radiotherapy, Erasmus University Medical Center Cancer Institute, Rotterdam, The Netherlands
| | - B de Jager
- Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - W P M H Heemels
- Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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42
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De A, Sun H, Emery DJ, Butcher KS, Wilman AH. Quantitative susceptibility-weighted imaging in presence of strong susceptibility sources: Application to hemorrhage. Magn Reson Imaging 2022; 92:224-231. [PMID: 35772582 DOI: 10.1016/j.mri.2022.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/13/2022] [Accepted: 06/23/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE To optimize quantitative susceptibility-weighted imaging also known as true susceptibility-weighted imaging (tSWI) for strong susceptibility sources like hemorrhage and compare to standard susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM). METHODS Ten patients with known intracerebral hemorrhage were scanned using a 3D SWI sequence. The magnitude and phase images were utilized to compute QSM, tSWI and SWI images. tSWI parameters including the upper threshold for creating susceptibility-weighted masks and the multiplication factor were optimized for hemorrhage depiction. Combined tSWI was also computed with independent optimized parameters for both veins and hemorrhagic regions. tSWI results were compared to SWI and QSM utilizing region-of-interest measurements, Pearson's correlation and Kruskal-Wallis test. RESULTS Fifteen hemorrhages were found, with mean susceptibility 0.81 ± 0.37 ppm. Unlike SWI which utilizes a phase mask, tSWI uses a mask computed from QSM. In tSWI, the weighted mask required an extended upper threshold far beyond the standard level for more effective visualization of hemorrhage texture. The upper threshold was set to the mean maximum susceptibility in the hemorrhagic region (3.24 ppm) with a multiplication factor of 2. The blooming effect, seen in SWI, was observed to be larger in hemorrhages with higher susceptibility values (r = 0.78, p < 0.001) with reduced blooming on tSWI. On SWI, 4 out of 15 hemorrhages showed phase wrap artifacts in the hemorrhagic region and all patients showed some phase wraps in the air-tissue interface near the auditory and frontal sinuses. These phase wrap artifacts were absent on tSWI. In hemorrhagic region, a higher correlation was observed between the actual susceptibility values and mean gray value for tSWI (r = -0.93, p < 0.001) than SWI (r = -0.87, p < 0.001). CONCLUSION In hemorrhage, tSWI minimizes both blooming effects and phase wrap artifacts observed in SWI. However, unlike SWI, tSWI requires an altered upper threshold for best hemorrhage depiction that greatly differs from the standard value. tSWI can be used as a complementary technique for visualizing hemorrhage along with SWI.
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Affiliation(s)
- Ashmita De
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada.
| | - Hongfu Sun
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Derek J Emery
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
| | - Kenneth S Butcher
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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Gao Y, Xiong Z, Fazlollahi A, Nestor PJ, Vegh V, Nasrallah F, Winter C, Pike GB, Crozier S, Liu F, Sun H. Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks. Neuroimage 2022; 259:119410. [PMID: 35753595 DOI: 10.1016/j.neuroimage.2022.119410] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/12/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 seconds using the trained iQFM and iQSM neural networks.
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Affiliation(s)
- Yang Gao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Zhuang Xiong
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Amir Fazlollahi
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Peter J Nestor
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, Brisbane, Australia
| | - Fatima Nasrallah
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Craig Winter
- Kenneth G Jamieson Department of Neurosurgery, Royal Brisbane and Women's Hospital, Brisbane, Australia; Centre for Clinical Research, University of Queensland, Brisbane, Australia; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Australia
| | - G Bruce Pike
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Hongfu Sun
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
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44
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Jung W, Bollmann S, Lee J. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR IN BIOMEDICINE 2022; 35:e4292. [PMID: 32207195 DOI: 10.1002/nbm.4292] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/04/2020] [Accepted: 02/25/2020] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.
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Affiliation(s)
- Woojin Jung
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
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45
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Chan KS, Hédouin R, Mollink J, Schulz J, van Cappellen van Walsum AM, Marques JP. Imaging white matter microstructure with gradient-echo phase imaging: Is ex vivo imaging with formalin-fixed tissue a good approximation of the in vivo brain? Magn Reson Med 2022; 88:380-390. [PMID: 35344591 PMCID: PMC9314807 DOI: 10.1002/mrm.29213] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/17/2022] [Accepted: 02/10/2022] [Indexed: 11/20/2022]
Abstract
Purpose Ex vivo imaging is a commonly used approach to investigate the biophysical mechanism of orientation‐dependent signal phase evolution in white matter. Yet, how phase measurements are influenced by the structural alteration in the tissue after formalin fixation is not fully understood. Here, we study the effects on magnetic susceptibility, microstructural compartmentalization, and chemical exchange measurement with a postmortem formalin‐fixed whole‐brain human tissue. Methods A formalin‐fixed, postmortem human brain specimen was scanned with multiple orientations to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. White matter samples were subsequently excised from the whole‐brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure‐related contributions in the signal phase and to validate the findings of the whole‐brain data. Results The bulk isotropic magnetic susceptibility of ex vivo whole‐brain imaging is comparable to in vivo imaging, with noticeable enhanced nonsusceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalization phase effect were considerably reduced in the formalin‐fixed white matter specimens. Conclusions Formalin‐fixed postmortem white matter exhibits comparable isotropic magnetic susceptibility to previous in vivo imaging findings. However, the measured phase and magnitude data of the fixed white matter tissue shows a significantly weaker orientation dependency and compartmentalization effect. Alternatives to formalin fixation are needed to better reproduce the in vivo microstructural effects in postmortem samples.
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Affiliation(s)
- Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Renaud Hédouin
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.,Empenn, INRIA, INSERM, CNRS, Université de Rennes 1, Rennes, France
| | - Jeroen Mollink
- Department of Medical Imaging, Anatomy, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jenni Schulz
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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46
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Boehm C, Goeger-Neff M, Mulder HT, Zilles B, Lindner LH, van Rhoon GC, Karampinos DC, Wu M. Susceptibility artifact correction in MR thermometry for monitoring of mild radiofrequency hyperthermia using total field inversion. Magn Reson Med 2022; 88:120-132. [PMID: 35313384 DOI: 10.1002/mrm.29191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/28/2022]
Abstract
PURPOSE MR temperature monitoring of mild radiofrequency hyperthermia (RF-HT) of cancer exploits the linear resonance frequency shift of water with temperature. Motion-induced susceptibility distribution changes cause artifacts that we correct here using the total field inversion (TFI) approach. METHODS The performance of TFI was compared to two background field removal (BFR) methods: Laplacian boundary value (LBV) and projection onto dipole fields (PDF). Data sets with spatial susceptibility change and B 0 -drift were simulated, phantom heating experiments were performed, four volunteer data sets at thermoneutral conditions as well as data from one cervical cancer, two sarcoma, and one seroma patients undergoing mild RF-HT were corrected using the proposed methods. RESULTS Simulations and phantom heating experiments revealed that using BFR or TFI preserves temperature-induced phase change, while removing susceptibility artifacts and B 0 -drift. TFI resulted in the least cumulative error for all four volunteers. Temperature probe information from four patient data sets were best depicted by TFI-corrected data in terms of accuracy and precision. TFI also performed best in case of the sarcoma treatment without temperature probe. CONCLUSION TFI outperforms previously suggested BFR methods in terms of accuracy and robustness. While PDF consistently overestimates susceptibility contribution, and LBV removes valuable pixel information, TFI is more robust and leads to more accurate temperature estimations.
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Affiliation(s)
- Christof Boehm
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany
| | | | | | - Benjamin Zilles
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Lars H Lindner
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | | | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Mingming Wu
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany
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47
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Cho H, Lee H, Gong Y, Kim YR, Cho J, Cho HJ. Quantitative susceptibility mapping and R1 measurement: Determination of the myelin volume fraction in the aging ex vivo rat corpus callosum. NMR IN BIOMEDICINE 2022; 35:e4645. [PMID: 34739153 DOI: 10.1002/nbm.4645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 10/03/2021] [Accepted: 10/16/2021] [Indexed: 06/13/2023]
Abstract
In studies of the white matter (WM) in aging brains, both quantitative susceptibility mapping (QSM) and direct R1 measurement offer potentially useful ex vivo MRI tools that allow volumetric characterization of myelin content changes. Despite the technical importance of such MRI methods in numerous age-related diseases, the supposed linear relationship between the estimates of either the QSM or R1 method and age-affected myelin contents has not been validated. In this study, the absolute myelin volume fraction (MVF) was determined by transmission electron microscopy (TEM) as a gold standard measure for comparison with the values obtained by the aforementioned MR methods. To theoretically evaluate and understand the MR signal characteristics, QSM simulations were performed using the finite perturber method (FPM). Specifically, the simulation geometry modeling was based on TEM-derived structures aligned orthogonally to the main magnetic field, the construct of which was used to estimate the magnetic field shift (ΔB) changes arising from the conjectured myelin structures. Experimentally, ex vivo corpus callosum (CC) samples from rat brains obtained at 6 weeks (n = 3), 4 months (n = 3), and 20 months (n = 3) after birth were used to establish the relationship between changes quantified by either QSM or R1 with the absolute MVF by TEM. From the ex vivo brain samples, the scatterplot of mean MVF versus R1 was fitted to a linear equation, where R1mean = 0.7948 × MVFmean + 0.8118 (Pearson's correlation coefficient r = 0.9138; p < 0.01), while the scatterplot of mean MVF versus MRI-derived magnetic susceptibility (χ) was also fitted to a line where χmeasured,mean = -0.1218 × MVFmean - 0.006345 (r = -0.8435; p < 0.01). As a result of the FPM-based QSM simulations, a linearly proportional relationship between the simulated magnetic susceptibility, χsimulated,mean , and MVF (r = -0.9648; p < 0.01) was established. Such a statistically significant linear correlation between MRI-derived values by the QSM (or R1 ) method and MVF demonstrated that variable myelin contents in the WM (i.e., CC) can be quantified across multiple stages of aging. These findings further support that both techniques based on QSM and R1 provide an efficient means of studying the brain-aging process with accurate volumetric quantification of the myelin content in WM.
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Affiliation(s)
- Hwapyeong Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Hansol Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Yelim Gong
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Young Ro Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Hyung Joon Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
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48
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He J, Wang L, Cao Y, Wang R, Zhu Y. Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping. Front Neurosci 2022; 16:837721. [PMID: 35250469 PMCID: PMC8888664 DOI: 10.3389/fnins.2022.837721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/17/2022] [Indexed: 12/02/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) aims to evaluate the distribution of magnetic susceptibility from magnetic resonance phase measurements by solving the ill-conditioned dipole inversion problem. Removing the artifacts and preserving the anisotropy of tissue susceptibility simultaneously is still a challenge in QSM. To deal with this issue, a novel k-QSM network is proposed to resolve dipole inversion issues in QSM reconstruction. The k-QSM network converts the results obtained by truncated k-space division (TKD) into the Fourier domain as inputs. After passing through several convolutional and residual blocks, the ill-posed signals of TKD are corrected by making the network output close to the calculation of susceptibility through multiple orientation sampling (COSMOS)-labeled QSM. To evaluate the superiority of k-QSM, comparisons with several state-of-the-art methods are performed in terms of QSM artifacts removing, anisotropy preserving, generalization ability, and clinical applications. Compared to existing methods, the k-QSM achieves a 22.31% lower normalized root mean square error, 10.30% higher peak signal-to-noise ratio (PSNR), 33.10% lower high-frequency error norm, and 1.06% higher structural similarity. In addition, the orientation-dependent susceptibility variation obtained by k-QSM is significant, verifying that k-QSM has the ability to preserve susceptibility anisotropy. When the trained models are tested on the dataset from different centers, our k-QSM shows a strong generalization ability with the highest PSNR. Moreover, by comparing the susceptibility maps between healthy controls and drug addicts with different methods, we found the proposed k-QSM is more sensitive to the susceptibility abnormality in the patients. The proposed k-QSM method learns less—only to fix the ill-posed signals of TKD, but infers more—both COSMOS-like and anisotropy-preserving QSM results. Its generalization ability and great sensitivity to susceptibility changes can make it a potential method for distinguishing some diseases.
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Affiliation(s)
- Junjie He
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China
- International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China
- *Correspondence: Lihui Wang
| | - Ying Cao
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Rongpin Wang
- International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
- Rongpin Wang
| | - Yuemin Zhu
- CREATIS, IRP Metislab, University of Lyon, INSA Lyon, CNRS UMR 5220, Inserm U1294, Lyon, France
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Uchida Y, Kan H, Inoue H, Oomura M, Shibata H, Kano Y, Kuno T, Usami T, Takada K, Yamada K, Kudo K, Matsukawa N. Penumbra Detection With Oxygen Extraction Fraction Using Magnetic Susceptibility in Patients With Acute Ischemic Stroke. Front Neurol 2022; 13:752450. [PMID: 35222239 PMCID: PMC8873150 DOI: 10.3389/fneur.2022.752450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/25/2022] [Indexed: 12/14/2022] Open
Abstract
Background The oxygen extraction fraction (OEF) has been applied to identify ischemic penumbral tissue, but is difficult to use in an urgent care setting. This study aimed to investigate whether an OEF map generated via magnetic resonance quantitative susceptibility mapping (QSM) could help identify the ischemic penumbra in patients with acute ischemic stroke. Materials and Methods This prospective imaging study included 21 patients with large anterior circulation vessel occlusion who were admitted <24 h after stroke onset and 21 age-matched healthy controls. We identified the ischemic penumbra as the region with a Tmax of >6 s during dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) and calculated the perfusion-core mismatch ratio between the ischemic penumbra and infarct core volumes. The OEF values were measured based on magnetic susceptibility differences between the venous structures and brain tissues using rapid QSM acquisition. Volumes with increased OEF values were compared to the ischemic penumbra volumes using an anatomical template. Results Eleven patients had a perfusion-core mismatch ratio of ≥1.8, and reperfusion therapy was recommended. In these patients, the volumes with increased OEF values of >51.5%, which was defined using the anterior circulation territory OEF values from the 21 healthy controls, were positively correlated with the ischemic penumbra volumes (r = 0.636, 95% CI: 0.059 to 0.895, P = 0.035) and inversely correlated with the 30-day change in the National Institutes of Health Stroke Scale scores (r = −0.624, 95% CI: −0.891 to −0.039, P = 0.041). Conclusion Tissue volumes with increased OEF values could predict ischemic penumbra volumes based on DSC-MRI, highlighting the potential of the QSM-derived OEF map as a penumbra biomarker to guide treatment selection in patients with acute ischemic stroke.
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Affiliation(s)
- Yuto Uchida
- Department of Neurology, Nagoya City University, Nagoya, Japan
- Department of Neurology, Toyokawa City Hospital, Aichi, Japan
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University, Nagoya, Japan
| | - Hiroyasu Inoue
- Department of Neurology, Nagoya City University, Nagoya, Japan
| | - Masahiro Oomura
- Department of Neurology, Nagoya City University, Nagoya, Japan
| | - Haruto Shibata
- Department of Neurology, Nagoya City East Medical Center, Nagoya, Japan
| | - Yuya Kano
- Department of Neurology, Nagoya City East Medical Center, Nagoya, Japan
| | - Tomoyuki Kuno
- Department of Neurology, Toyokawa City Hospital, Aichi, Japan
| | - Toshihiko Usami
- Department of Neurology, Toyokawa City Hospital, Aichi, Japan
| | - Koji Takada
- Department of Neurology, Toyokawa City Hospital, Aichi, Japan
| | - Kentaro Yamada
- Department of Neurology, Nagoya City East Medical Center, Nagoya, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University, Hokkaido, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Hokkaido, Japan
| | - Noriyuki Matsukawa
- Department of Neurology, Nagoya City University, Nagoya, Japan
- *Correspondence: Noriyuki Matsukawa
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Du J, Ji Y, Zhu J, Mai X, Zou J, Chen Y, Gu N. Edge prior guided dictionary learning for quantitative susceptibility mapping reconstruction. Quant Imaging Med Surg 2022; 12:510-525. [PMID: 34993097 DOI: 10.21037/qims-21-243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/07/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Compared with conventional magnetic resonance imaging methods, the quantitative magnetic susceptibility mapping (QSM) technique can quantitatively measure the magnetic susceptibility distribution of tissues, which has an important clinical application value in the investigations of brain micro-bleeds, Parkinson's, and liver iron deposition, etc. However, the quantitative susceptibility mapping algorithm is an ill-posed inverse problem due to the near-zero value in the dipole kernel, and high-quality QSM reconstruction with effective streaking artifact suppression remains a challenge. In recent years, the performance of sparse representation has been well validated in improving magnetic resonance image (MRI) reconstruction. METHODS In this study, by incorporating feature learning into sparse representation, we propose an edge prior guided dictionary learning-based reconstruction method for the dipole inversion in quantitative susceptibility mapping reconstruction. The structure feature dictionary relies on magnitude images for susceptibility maps have similar structures with magnitude images, and this structure feature dictionary and edge prior information are used in the dipole inversion step. RESULTS The performance of the proposed algorithm is assessed through in vivo human brain clinical data, leading to high-quality susceptibility maps with improved streaking artifact suppression, structural recovery, and quantitative metrics. CONCLUSIONS The proposed edge prior guided dictionary learning method for dipole inversion in QSM achieves improved performance in streaking artifacts suppression, structural recovery and deep gray matter reconstruction.
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Affiliation(s)
- Jiacheng Du
- The State Key Laboratory of Bioelectronics and Jiangsu Key Laboratory of Biomaterials and Devices, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Yuxin Ji
- The Laboratory of Image Science and Technology, Key Laboratory of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Jiali Zhu
- The Laboratory of Image Science and Technology, Key Laboratory of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Xiaoli Mai
- The Radiology, Nanjing Drum Tower Hospital, Nanjing, China
| | - Junting Zou
- The Radiology, Nanjing Drum Tower Hospital, Nanjing, China
| | - Yang Chen
- The Laboratory of Image Science and Technology, Key Laboratory of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Ning Gu
- The State Key Laboratory of Bioelectronics and Jiangsu Key Laboratory of Biomaterials and Devices, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
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