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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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Ikram A, Sharma R, Selim M, Kim-Sun G, Shahraki T, Thomas AJ, Filippidis A, Wen Y, Spincemaille P, Wang Y, Soman S. mcTFI QSM MRI ABC/2 intracranial hemorrhage to noncontrast head CT volume measurement equivalence. J Neurol Sci 2024; 456:122859. [PMID: 38171071 PMCID: PMC10796171 DOI: 10.1016/j.jns.2023.122859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND/OBJECTIVES Intracranial hemorrhage (ICH) volume assessment is an important part of patient management and is routinely obtained by non-contrast head CT (NCHCT) using the validated ABC/2 measurement method. Because conventional MRI imaging sequences demonstrate variability in ICH appearance, volumetric analyses for MRI bleed volume in a standardized manner using ABC/2 is not possible. The recently introduced multiecho-complex total field inversion quantitative susceptibility mapping (mcTFI QSM) MRI technique, which maps brain tissue susceptibility to both depict brain tissue structures and quantify tissue susceptibility, may provide a viable alternative. In this study we evaluated mcTFI QSM ABC/2 ICH volume assessment relative to NCHCT. METHODS Patients with ICH who had undergone NCHCT and MRI brain scans within 48 h were recruited for this retrospective study. The ABC/2 method was applied to estimate the bleed volume for both NCHCT and MRI by a CAQ-certified neuroradiologist with 10 years of experience and a trained laboratory assistant. Results were analyzed via Bland-Altman (B-A) and linear regression. RESULTS 54 patients (27 females) who had undergone NCHCT and MRI within 48 h (<24 h., n = 31, 24-48 h, n = 10) were enrolled. mcTFI QSM ICH volume measurement method showed a positive correlation (99.5%) compared to NCHCT. B-A plot comparing ABC/2 ICH volume on NCHCT and mcTFI MRI done for patients within 24 h demonstrates a bias of -0.09%. CONCLUSIONS ICH volume calculation using ABC/2 on mcTFI QSM showed a high correlation with NCHCT measurement. These results suggest mcTFI QSM is a promising MRI method for ABC/2 for bleed volume measurement.
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Affiliation(s)
- Asad Ikram
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Ria Sharma
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Magdy Selim
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | | | - Tamkin Shahraki
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ajith J Thomas
- Cooper University Healthcare/Cooper Medical School of Rowan University, Camden, NJ, United States.
| | - Aristotelis Filippidis
- Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Yan Wen
- GE Healthcare, Lincoln Medical Center, New York, NY, USA
| | | | - Yi Wang
- Weill Cornell Medicine, New York, NY, USA.
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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Zeng S, Ma H, Xie D, Huang Y, Wang M, Zeng W, Zhu N, Ma Z, Yang Z, Chu J, Zhao J. Quantitative susceptibility mapping evaluation of glioma. Eur Radiol 2023; 33:6636-6647. [PMID: 37095360 DOI: 10.1007/s00330-023-09647-4] [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: 04/29/2022] [Revised: 12/28/2022] [Accepted: 02/24/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVES To comprehensively evaluate the glioma using quantitative susceptibility mapping (QSM). MATERIALS AND METHODS Forty-two patients (18 women; mean age, 45 years) with pathologically confirmed gliomas were retrospectively included. All the patients underwent conventional and advanced MRI examinations (QSM, DWI, MRS, etc.). Five patients underwent paired QSM (pre- and post-enhancement). Four Visually Accessible Rembrandt Image (VASARI) features and intratumoural susceptibility signal (ITSS) were observed. Three ROIs each were manually drawn separately in the tumour parenchyma with relatively high and low magnetic susceptibility. The association between the tumour's magnetic susceptibility and other MRI parameters was also analysed. RESULTS Morphologically, gliomas with heterogeneous ITSS were more similar to high-grade gliomas (p = 0.006, AUC: 0.72, sensitivity: 70%, and specificity: 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. Quantitatively, tumour parenchyma magnetic susceptibility had limited value in grading gliomas and identifying IDH mutation status, whereas the relatively low magnetic susceptibility of the tumour parenchyma helped identify oligodendrogliomas in IDH mutated gliomas (AUC = 0.78) with high specificity (100%). The relatively high tumour magnetic susceptibility significantly increased after enhancement (p = 0.039). Additionally, we found that the magnetic susceptibility of the tumour parenchyma was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40). CONCLUSIONS QSM is a promising candidate for the comprehensive evaluation of gliomas, except for IDH mutation status. The magnetic susceptibility of tumour parenchyma may be affected by tumour cell proliferation. KEY POINTS • Morphologically, gliomas with a heterogeneous intratumoural susceptibility signal (ITSS) are more similar to high-grade gliomas (p = 0.006; AUC, 0.72; sensitivity, 70%; and specificity, 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. • Tumour parenchyma's relatively low magnetic susceptibility helped identify oligodendroglioma with high specificity. • Tumour parenchyma magnetic susceptibility was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40).
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Affiliation(s)
- Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Hui Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Dingxiang Xie
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Yingqian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Mengzhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong, People's Republic of China
| | - Wenting Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Nengjin Zhu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Zuliwei Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Jianping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China.
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China.
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Şişman M, Nguyen TD, Roberts AG, Romano DJ, Dimov AV, Kovanlikaya I, Spincemaille P, Wang Y. Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.22.23295993. [PMID: 37808826 PMCID: PMC10557811 DOI: 10.1101/2023.09.22.23295993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed. Highlights novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.
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Böhm C, Stelter JK, Weiss K, Meineke J, Komenda A, Borde T, Makowski MR, Fallenberg EM, Karampinos DC. Robust breast quantitative susceptibility mapping in the presence of silicone. Magn Reson Med 2023; 90:1209-1218. [PMID: 37125658 DOI: 10.1002/mrm.29694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/17/2023] [Accepted: 04/18/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE To (a) develop a preconditioned water-fat-silicone total field inversion (wfsTFI) algorithm that directly estimates the susceptibility map from complex multi-echo data in the breast in the presence of silicone and to (b) evaluate the performance of wfsTFI for breast quantitative susceptibility mapping (QSM) in silico and in vivo in comparison with formerly proposed methods. METHODS Numerical simulations and in vivo multi-echo gradient echo breast measurements were performed to compare wfsTFI to a previously proposed field map-based linear total field inversion algorithm (lTFI) with and without the consideration of the chemical shift of silicone in the field map estimation step. Specifically, a simulation based on an in vivo scan and data from five patients were included in the analysis. RESULTS In the simulation, wfsTFI is able to significantly decrease the normalized root mean square error from lTFI without (4.46) and with (1.77) the consideration of the chemical shift of silicone to 0.68. Both the in silico and in vivo wfsTFI susceptibility maps show reduced shadowing artifacts in local tissue adjacent to silicone, reduced streaking artifacts and no erroneous single voxels of diamagnetic susceptibility in proximity to silicone. CONCLUSION The proposed wfsTFI method can automatically distinguish between subjects with and without silicone. Furthermore wfsTFI accounts for the presence of silicone in the QSM dipole inversion and allows for the robust estimation of susceptibility in proximity to silicone breast implants and hence allows the visualization of structures that would otherwise be dominated by artifacts on susceptibility maps.
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Affiliation(s)
- Christof Böhm
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jonathan K Stelter
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Alexander Komenda
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tabea Borde
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Eva M Fallenberg
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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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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7
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Butler T, Wang XH, Chiang GC, Li Y, Zhou L, Xi K, Wickramasuriya N, Tanzi E, Spector E, Ozsahin I, Mao X, Razlighi QR, Fung EK, Dyke JP, Maloney T, Gupta A, Raj A, Shungu DC, Mozley PD, Rusinek H, Glodzik L. Choroid Plexus Calcification Correlates with Cortical Microglial Activation in Humans: A Multimodal PET, CT, MRI Study. AJNR Am J Neuroradiol 2023; 44:776-782. [PMID: 37321857 PMCID: PMC10337614 DOI: 10.3174/ajnr.a7903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/04/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND PURPOSE The choroid plexus (CP) within the brain ventricles is well-known to produce cerebrospinal fluid (CSF). Recently, the CP has been recognized as critical in modulating inflammation. MRI-measured CP enlargement has been reported in neuroinflammatory disorders like MS as well as with aging and neurodegeneration. The basis of MRI-measured CP enlargement is unknown. On the basis of tissue studies demonstrating CP calcification as a common pathology associated with aging and disease, we hypothesized that previously unmeasured CP calcification contributes to MRI-measured CP volume and may be more specifically associated with neuroinflammation. MATERIALS AND METHODS We analyzed 60 subjects (43 healthy controls and 17 subjects with Parkinson's disease) who underwent PET/CT using 11C-PK11195, a radiotracer sensitive to the translocator protein expressed by activated microglia. Cortical inflammation was quantified as nondisplaceable binding potential. Choroid plexus calcium was measured via manual tracing on low-dose CT acquired with PET and automatically using a new CT/MRI method. Linear regression assessed the contribution of choroid plexus calcium, age, diagnosis, sex, overall volume of the choroid plexus, and ventricle volume to cortical inflammation. RESULTS Fully automated choroid plexus calcium quantification was accurate (intraclass correlation coefficient with manual tracing = .98). Subject age and choroid plexus calcium were the only significant predictors of neuroinflammation. CONCLUSIONS Choroid plexus calcification can be accurately and automatically quantified using low-dose CT and MRI. Choroid plexus calcification-but not choroid plexus volume-predicted cortical inflammation. Previously unmeasured choroid plexus calcium may explain recent reports of choroid plexus enlargement in human inflammatory and other diseases. Choroid plexus calcification may be a specific and relatively easily acquired biomarker for neuroinflammation and choroid plexus pathology in humans.
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Affiliation(s)
- T Butler
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - X H Wang
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - G C Chiang
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - Y Li
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - L Zhou
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - K Xi
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - N Wickramasuriya
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - E Tanzi
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - E Spector
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - I Ozsahin
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - X Mao
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
- Department of Radiology (X.M., E.K.F., J.P.D., D.C.S., P.D.M.), Weill Cornell Medicine, New York, New York
| | - Q R Razlighi
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - E K Fung
- Department of Radiology (X.M., E.K.F., J.P.D., D.C.S., P.D.M.), Weill Cornell Medicine, New York, New York
| | - J P Dyke
- Department of Radiology (X.M., E.K.F., J.P.D., D.C.S., P.D.M.), Weill Cornell Medicine, New York, New York
| | - T Maloney
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - A Gupta
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
| | - A Raj
- Department of Radiology (A.R.), University of California, San Francisco, San Francisco, California
| | - D C Shungu
- Department of Radiology (X.M., E.K.F., J.P.D., D.C.S., P.D.M.), Weill Cornell Medicine, New York, New York
| | - P D Mozley
- Department of Radiology (X.M., E.K.F., J.P.D., D.C.S., P.D.M.), Weill Cornell Medicine, New York, New York
| | - H Rusinek
- Department of Radiology (H.R.), New York University, New York, New York
| | - L Glodzik
- From the Brain Health Imaging Institute (T.B., X.H.W., G.C.C., Y.L., L.Z., K.X., N.W., E.T., E.S., I.O., X.M., Q.R.R., T.M., A.G., L.G.)
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8
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Si W, Guo Y, Zhang Q, Zhang J, Wang Y, Feng Y. Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs. Front Neurosci 2023; 17:1165446. [PMID: 37383103 PMCID: PMC10293650 DOI: 10.3389/fnins.2023.1165446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.
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Affiliation(s)
- Wenbin Si
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Qianqian Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jinwei Zhang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence and Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
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9
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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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10
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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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11
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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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12
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Wang Z, Xia P, Huang F, Wei H, Hui ESK, Mak HKF, Cao P. A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM). Magn Reson Imaging 2022; 88:89-100. [DOI: 10.1016/j.mri.2022.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 01/28/2022] [Accepted: 01/29/2022] [Indexed: 10/19/2022]
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13
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Chen L. Editorial for "Quantitative Susceptibility Mapping for Staging Acute Cerebral Hemorrhages: Comparing the Conventional and Multi-Echo Complex Total Field Inversion MR Methods". J Magn Reson Imaging 2021; 54:1855-1856. [PMID: 34415666 DOI: 10.1002/jmri.27896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 08/12/2021] [Indexed: 11/06/2022] Open
Affiliation(s)
- Luguang Chen
- Department of Radiology, Changhai Hospital of Shanghai, Shanghai, China
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14
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Boehm C, Sollmann N, Meineke J, Ruschke S, Dieckmeyer M, Weiss K, Zimmer C, Makowski MR, Baum T, Karampinos DC. Preconditioned water-fat total field inversion: Application to spine quantitative susceptibility mapping. Magn Reson Med 2021; 87:417-430. [PMID: 34255370 DOI: 10.1002/mrm.28903] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/14/2021] [Accepted: 06/07/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To (a) develop a preconditioned water-fat total field inversion (wfTFI) algorithm that directly estimates the susceptibility map from complex multi-echo gradient echo data for water-fat regions and to (b) evaluate the performance of the proposed wfTFI quantitative susceptibility mapping (QSM) method in comparison with a local field inversion (LFI) method and a linear total field inversion (TFI) method in the spine. METHODS Numerical simulations and in vivo spine multi-echo gradient echo measurements were performed to compare wfTFI to an algorithm based on disjoint background field removal (BFR) and LFI and to a formerly proposed TFI algorithm. The data from 1 healthy volunteer and 10 patients with metastatic bone disease were included in the analysis. Clinical routine computed tomography (CT) images were used as a reference standard to distinguish osteoblastic from osteolytic changes. The ability of the QSM methods to distinguish osteoblastic from osteolytic changes was evaluated. RESULTS The proposed wfTFI method was able to decrease the normalized root mean square error compared to the LFI and TFI methods in the simulation. The in vivo wfTFI susceptibility maps showed reduced BFR artifacts, noise amplification, and streaking artifacts compared to the LFI and TFI maps. wfTFI provided a significantly higher diagnostic confidence in differentiating osteolytic and osteoblastic lesions in the spine compared to the LFI method (p = .012). CONCLUSION The proposed wfTFI method can minimize BFR artifacts, noise amplification, and streaking artifacts in water-fat regions and can thus better differentiate between osteoblastic and osteolytic changes in patients with metastatic disease compared to LFI and the original TFI method.
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Affiliation(s)
- Christof Boehm
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | | | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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