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Fiscone C, Sighinolfi G, Manners DN, Motta L, Venturi G, Panzera I, Zaccagna F, Rundo L, Lugaresi A, Lodi R, Tonon C, Castelli M. Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis. Sci Data 2024; 11:575. [PMID: 38834674 DOI: 10.1038/s41597-024-03418-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/24/2024] [Indexed: 06/06/2024] Open
Abstract
Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.
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Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giovanni Sighinolfi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy.
| | - Lorenzo Motta
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Greta Venturi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Ivan Panzera
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
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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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Ding J, Duan Y, Wang M, Yuan Y, Zhuo Z, Gan L, Song Q, Gao B, Yang L, Liu H, Hou Y, Zheng F, Chen R, Wang J, Lin L, Zhang B, Zhang G, Liu Y. Acceleration of Brain Susceptibility-Weighted Imaging with Compressed Sensitivity Encoding: A Prospective Multicenter Study. AJNR Am J Neuroradiol 2022; 43:402-409. [PMID: 35241421 PMCID: PMC8910792 DOI: 10.3174/ajnr.a7441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/17/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE While three-dimensional susceptibility-weighted imaging has been widely suggested for intracranial vessel imaging, hemorrhage detection, and other neuro-diseases, its relatively long scan time has necessitated the clinical verification of recent progresses of fast imaging techniques. Our aim was to evaluate the effectiveness of brain SWI accelerated by compressed sensitivity encoding to identify the optimal acceleration factors for clinical practice. MATERIALS AND METHODS Ninety-nine subjects, prospectively enrolled from 5 centers, underwent 8 brain SWI sequences: 5 different folds of compressed sensitivity encoding acceleration (CS2, CS4, CS6, CS8, and CS10), 2 different folds of sensitivity encoding acceleration (SF2 and SF4), and 1 without acceleration. Images were assessed quantitatively on both the SNR of the red nucleus and its contrast ratio to the CSF and, subjectively, with scoring on overall image quality; visibility of the substantia nigra-red nucleus, basilar artery, and internal cerebral vein; and diagnostic confidence of the cerebral microbleeds and other intracranial diseases. RESULTS Compressed sensitivity encoding showed a promising ability to reduce the acquisition time (from 202 to 41 seconds) of SWI while increasing the acceleration factor from 2 to 10, though at the cost of decreasing the SNR, contrast ratio, and the scores of visual assessments. The visibility of the substantia nigra-red nucleus and internal cerebral vein became unacceptable in CS6 to CS10. The basilar artery was well-distinguished, and diseases including cerebral microbleeds, cavernous angiomas, intracranial gliomas, venous malformations, and subacute hemorrhage were well-diagnosed in all compressed sensitivity encoding sequences. CONCLUSIONS Compressed sensitivity encoding factor 4 is recommended in routine practice. Compressed sensitivity encoding factor 10 is potentially a fast surrogate for distinguishing the basilar artery and detecting susceptibility-related abnormalities (eg, cerebral microbleeds, cavernous angiomas, gliomas, and venous malformation) at the sacrifice of visualization of the substantia nigra-red nucleus and internal cerebral vein.
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Affiliation(s)
- J. Ding
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Y. Duan
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - M. Wang
- Department of Radiology (M.W., B.Z.), The Affiliated Drum Tower Hospital of Nanjing UniversityMedical School, Jiangsu, China
| | - Y. Yuan
- Department of Radiology (Y.Y., G.Z.), Beijing Royal Integrative Medicine Hospital, Beijing, China
| | - Z. Zhuo
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - L. Gan
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Q. Song
- Department of Radiology (Q.S., B.G.), First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - B. Gao
- Department of Radiology (Q.S., B.G.), First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - L. Yang
- Department of Radiology (L.Y., H.L., Y.H.), Shengjing Hospital of ChinaMedical University, Shenyang, China
| | - H. Liu
- Department of Radiology (L.Y., H.L., Y.H.), Shengjing Hospital of ChinaMedical University, Shenyang, China
| | - Y. Hou
- Department of Radiology (L.Y., H.L., Y.H.), Shengjing Hospital of ChinaMedical University, Shenyang, China
| | - F. Zheng
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - R. Chen
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - J. Wang
- Philips Healthcare (J.W., L.L.), Beijing, China
| | - L. Lin
- Philips Healthcare (J.W., L.L.), Beijing, China
| | - B. Zhang
- Department of Radiology (M.W., B.Z.), The Affiliated Drum Tower Hospital of Nanjing UniversityMedical School, Jiangsu, China
| | - G. Zhang
- Department of Radiology (Y.Y., G.Z.), Beijing Royal Integrative Medicine Hospital, Beijing, China
| | - Y. Liu
- From the Department of Radiology (J.D., Y.D., Z.Z., L.G., F.Z., R.C., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Soman S, Liu Z, Kim G, Nemec U, Holdsworth SJ, Main K, Lee B, Kolakowsky-Hayner S, Selim M, Furst AJ, Massaband P, Yesavage J, Adamson MM, Spincemaille P, Moseley M, Wang Y. Brain Injury Lesion Imaging Using Preconditioned Quantitative Susceptibility Mapping without Skull Stripping. AJNR Am J Neuroradiol 2018; 39:648-653. [PMID: 29472296 DOI: 10.3174/ajnr.a5550] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 12/04/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Identifying cerebral microhemorrhage burden can aid in the diagnosis and management of traumatic brain injury, stroke, hypertension, and cerebral amyloid angiopathy. MR imaging susceptibility-based methods are more sensitive than CT for detecting cerebral microhemorrhage, but methods other than quantitative susceptibility mapping provide results that vary with field strength and TE, require additional phase maps to distinguish blood from calcification, and depict cerebral microhemorrhages as bloom artifacts. Quantitative susceptibility mapping provides universal quantification of tissue magnetic property without these constraints but traditionally requires a mask generated by skull-stripping, which can pose challenges at tissue interphases. We evaluated the preconditioned quantitative susceptibility mapping MR imaging method, which does not require skull-stripping, for improved depiction of brain parenchyma and pathology. MATERIALS AND METHODS Fifty-six subjects underwent brain MR imaging with a 3D multiecho gradient recalled echo acquisition. Mask-based quantitative susceptibility mapping images were created using a commonly used mask-based quantitative susceptibility mapping method, and preconditioned quantitative susceptibility images were made using precondition-based total field inversion. All images were reviewed by a neuroradiologist and a radiology resident. RESULTS Ten subjects (18%), all with traumatic brain injury, demonstrated blood products on 3D gradient recalled echo imaging. All lesions were visible on preconditioned quantitative susceptibility mapping, while 6 were not visible on mask-based quantitative susceptibility mapping. Thirty-one subjects (55%) demonstrated brain parenchyma and/or lesions that were visible on preconditioned quantitative susceptibility mapping but not on mask-based quantitative susceptibility mapping. Six subjects (11%) demonstrated pons artifacts on preconditioned quantitative susceptibility mapping and mask-based quantitative susceptibility mapping; they were worse on preconditioned quantitative susceptibility mapping. CONCLUSIONS Preconditioned quantitative susceptibility mapping MR imaging can bring the benefits of quantitative susceptibility mapping imaging to clinical practice without the limitations of mask-based quantitative susceptibility mapping, especially for evaluating cerebral microhemorrhage-associated pathologies, such as traumatic brain injury.
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Affiliation(s)
- S Soman
- From the Departments of Radiology (S.S., G.K., B.L.)
| | - Z Liu
- Department of Biomedical Engineering (Z.L., Y.W.), Cornell University, New York, New York
| | - G Kim
- From the Departments of Radiology (S.S., G.K., B.L.)
| | - U Nemec
- Department of Biomedical Imaging and Image-Guided Therapy (U.N.), Medical University of Vienna, Vienna, Austria
| | | | - K Main
- Research Division, Defense and Veterans Brain Injury Center (K.M.), General Dynamics Health Solutions, Silver Spring, Maryland
| | - B Lee
- From the Departments of Radiology (S.S., G.K., B.L.)
| | - S Kolakowsky-Hayner
- Department of Rehabilitation Medicine (S.K.-H.), Icahn School of Medicine at Mount Sinai, New York, New York
| | - M Selim
- Neurology (M.S.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - A J Furst
- Psychiatry and Behavioral Sciences (A.J.F., J.Y., M.M.A.)
- Departments of Psychiatry (A.J.F., J.Y.)
| | - P Massaband
- Departments of Radiology (S.J.H., P.M., M.M.)
- Radiology (P.M.)
| | - J Yesavage
- Psychiatry and Behavioral Sciences (A.J.F., J.Y., M.M.A.)
- Departments of Psychiatry (A.J.F., J.Y.)
| | - M M Adamson
- Psychiatry and Behavioral Sciences (A.J.F., J.Y., M.M.A.)
- Neurosurgery (M.M.A.), Stanford University, Stanford, California
- Defense and Veterans Brain Injury Center (M.M.A.), VA Palo Alto Health Care System, Palo Alto, California
| | - P Spincemaille
- Department of Radiology (P.S., Y.W.), Weil Cornell Medical College, New York, New York
| | - M Moseley
- Departments of Radiology (S.J.H., P.M., M.M.)
| | - Y Wang
- Department of Biomedical Engineering (Z.L., Y.W.), Cornell University, New York, New York
- Department of Radiology (P.S., Y.W.), Weil Cornell Medical College, New York, New York
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Liu Z, Spincemaille P, Yao Y, Zhang Y, Wang Y. MEDI+0: Morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping. Magn Reson Med 2017; 79:2795-2803. [PMID: 29023982 DOI: 10.1002/mrm.26946] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 09/02/2017] [Accepted: 09/03/2017] [Indexed: 02/01/2023]
Abstract
PURPOSE To develop a quantitative susceptibility mapping (QSM) method with a consistent zero reference using minimal variation in cerebrospinal fluid (CSF) susceptibility. THEORY AND METHODS The ventricular CSF was automatically segmented on the R2* map. An L2 -regularization was used to enforce CSF susceptibility homogeneity within the segmented region, with the averaged CSF susceptibility as the zero reference. This regularization for CSF homogeneity was added to the model used in a prior QSM method (morphology enabled dipole inversion [MEDI]). Therefore, the proposed method was referred to as MEDI+0 and compared with MEDI in a numerical simulation, in multiple sclerosis (MS) lesions, and in a reproducibility study in healthy subjects. RESULTS In both the numerical simulations and in vivo experiments, MEDI+0 not only decreased the susceptibility variation within the ventricular CSF, but also suppressed the artifact near the lateral ventricles. In the simulation, MEDI+0 also provided more accurate quantification compared to MEDI in the globus pallidus, substantia nigra, corpus callosum, and internal capsule. MEDI+0 measurements of MS lesion susceptibility were in good agreement with those obtained by MEDI. Finally, both MEDI+0 and MEDI showed good and similar intrasubject reproducibility. CONCLUSION QSM with a minimal variation in ventricular CSF is viable to provide a consistent zero reference while improving image quality. Magn Reson Med 79:2795-2803, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Zhe Liu
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yihao Yao
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yan Zhang
- Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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