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Bhattacharya S, Price AN, Uus A, Sousa HS, Marenzana M, Colford K, Murkin P, Lee M, Cordero-Grande L, Teixeira RPAG, Malik SJ, Deprez M. In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences. Magn Reson Med 2024; 92:715-729. [PMID: 38623934 DOI: 10.1002/mrm.30094] [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: 10/26/2023] [Revised: 02/18/2024] [Accepted: 03/08/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. METHODS Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. RESULTS The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. CONCLUSION The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.
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Affiliation(s)
- Suryava Bhattacharya
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Helena S Sousa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Kathleen Colford
- Centre for the Developing Brain, King's College London, London, UK
| | - Peter Murkin
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maggie Lee
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, ETSI Telecomunicración, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Rui Pedro A G Teixeira
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Shaihan J Malik
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
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Schmidbauer VU, Houech IVM, Malik J, Watzenboeck ML, Mittermaier R, Kienast P, Haberl C, Pogledic I, Mitter C, Dovjak GO, Krauskopf A, Prayer F, Stuempflen M, Dorittke T, Gantner NA, Binder J, Bettelheim D, Kiss H, Haberler C, Gelpi E, Prayer D, Kasprian G. Synthetic MRI and MR Fingerprinting-Derived Relaxometry of Antenatal Human Brainstem Myelination: A Postmortem-Based Quantitative Imaging Study. AJNR Am J Neuroradiol 2024:ajnr.A8337. [PMID: 38991765 DOI: 10.3174/ajnr.a8337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND AND PURPOSE The radiologic evaluation of ongoing myelination is currently limited prenatally. Novel quantitative MR imaging modalities provide relaxometric properties that are linked to myelinogenesis. In this retrospective postmortem imaging study, the capability of Synthetic MR imaging and MR fingerprinting-derived relaxometry for tracking fetal myelin development was investigated. Moreover, the consistency of results for both MR approaches was analyzed. MATERIALS AND METHODS In 26 cases, quantitative postmortem fetal brain MR data were available (gestational age range, 15 + 1 to 32 + 1; female/male ratio, 14/12). Relaxometric measurements (T1-/T2-relexation times) were determined in the medulla oblongata and the midbrain using Synthetic MR imaging/MR fingerprinting-specific postprocessing procedures (Synthetic MR imaging and MR Robust Quantitative Tool for MR fingerprinting). The Pearson correlations were applied to detect relationships between T1-relaxation times/T2-relaxation times metrics and gestational age at MR imaging. Intraclass correlation coefficients were calculated to assess the consistency of the results provided by both modalities. RESULTS Both modalities provided quantitative data that revealed negative correlations with gestational age at MR imaging: Synthetic MR imaging-derived relaxation times (medulla oblongata [r = -0.459; P = .021]; midbrain [r = -0.413; P = .040]), T2-relaxation times (medulla oblongata [r = -0.625; P < .001]; midbrain [r = -0.571; P = .003]), and MR fingerprinting-derived T1-relaxation times (medulla oblongata [r = -0.433; P = .035]; midbrain [r = -0.386; P = .062]), and T2-relaxation times (medulla oblongata [r =-0.883; P < .001]; midbrain [r = -0.890; P < .001]).The intraclass correlation coefficient analysis for result consistency between both MR approaches ranged between 0.661 (95% CI, 0.351-0.841) (T2-relaxation times: medulla oblongata) and 0.920 (95% CI, 0.82-0.965) (T1-relaxation times: midbrain). CONCLUSIONS There is a good-to-excellent consistency between postmortem Synthetic MR imaging and MR fingerprinting myelin quantifications in fetal brains older than 15 + 1 gestational age. The strong correlations between quantitative myelin metrics and gestational age indicate the potential of quantitative MR imaging to identify delayed or abnormal states of myelination at prenatal stages of cerebral development.
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Affiliation(s)
- Victor U Schmidbauer
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Intesar-Victoria Malla Houech
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
- Department of Diagnostic Imaging (I.-V.M.H.), Medical University of Sofia, Sofia, Bulgaria
- Alexander R. Margulis Fellowship 2022 (I.-V.M.H., J.M.)
| | - Jakob Malik
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Martin L Watzenboeck
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Rebecca Mittermaier
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Patric Kienast
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Christina Haberl
- Department of Obstetrics and Feto-Maternal Medicine (C. Haberl, T.D., J.B., D.B., H.K.), Medical University of Vienna, Vienna, Austria
| | - Ivana Pogledic
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Christian Mitter
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Gregor O Dovjak
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Astrid Krauskopf
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Florian Prayer
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Marlene Stuempflen
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Tim Dorittke
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
- Department of Obstetrics and Feto-Maternal Medicine (C. Haberl, T.D., J.B., D.B., H.K.), Medical University of Vienna, Vienna, Austria
| | - Nikolai A Gantner
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Julia Binder
- Department of Obstetrics and Feto-Maternal Medicine (C. Haberl, T.D., J.B., D.B., H.K.), Medical University of Vienna, Vienna, Austria
| | - Dieter Bettelheim
- Department of Obstetrics and Feto-Maternal Medicine (C. Haberl, T.D., J.B., D.B., H.K.), Medical University of Vienna, Vienna, Austria
| | - Herbert Kiss
- Department of Obstetrics and Feto-Maternal Medicine (C. Haberl, T.D., J.B., D.B., H.K.), Medical University of Vienna, Vienna, Austria
| | - Christine Haberler
- Division of Neuropathology and Neurochemistry (C. Haberler, E.G.), Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Ellen Gelpi
- Division of Neuropathology and Neurochemistry (C. Haberler, E.G.), Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
| | - Gregor Kasprian
- From the Department of Biomedical Imaging and Image-Guided Therapy (V.U.S., I.-V.M.H., J.M., M.L.W., R.M., P.K., I.P., C.M., G.O.D., A.K., F.P., M.S., T.D., N.A.G., D.P., G.K.), Medical University of Vienna, Vienna, Austria
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Aviles Verdera J, Tomi-Tricot R, Story L, Rutherford MA, Ourselin S, Hajnal JV, Malik SJ, Hutter J. Characterizing T1 in the fetal brain and placenta over gestational age at 0.55T. Magn Reson Med 2024. [PMID: 38968093 DOI: 10.1002/mrm.30193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/10/2024] [Accepted: 05/24/2024] [Indexed: 07/07/2024]
Abstract
PURPOSE T1 mapping and T1-weighted contrasts have a complimentary but currently under utilized role in fetal MRI. Emerging clinical low field scanners are ideally suited for fetal T1 mapping. The advantages are lower T1 values which results in higher efficiency and reduced field inhomogeneities resulting in a decreased requirement for specialist tools. In addition the increased bore size associated with low field scanners provides improved patient comfort and accessibility. This study aims to demonstrate the feasibility of fetal brain T1 mapping at 0.55T. METHODS An efficient slice-shuffling inversion-recovery echo-planar imaging (EPI)-based T1-mapping and postprocessing was demonstrated for the fetal brain at 0.55T in a cohort of 38 fetal MRI scans. Robustness analysis was performed and placental measurements were taken for validation. RESULTS High-quality T1 maps allowing the investigation of subregions in the brain were obtained and significant correlation with gestational age was demonstrated for fetal brain T1 maps (p < 0 . 05 $$ p<0.05 $$ ) as well as regions-of-interest in the deep gray matter and white matter. CONCLUSIONS Efficient, quantitative T1 mapping in the fetal brain was demonstrated on a clinical 0.55T MRI scanner, providing foundations for both future research and clinical applications including low-field specific T1-weighted acquisitions.
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Affiliation(s)
- Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Raphael Tomi-Tricot
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | | | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Shaihan J Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany
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4
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Milos RI, Schmidbauer V, Watzenboeck ML, Stuhr F, Gruber GM, Mitter C, Dovjak GO, Milković-Periša M, Kostovic I, Jovanov-Milošević N, Kasprian G, Prayer D. T1-weighted fast fluid-attenuated inversion-recovery sequence (T1-FFLAIR) enables the visualization and quantification of fetal brain myelination in utero. Eur Radiol 2024; 34:4573-4584. [PMID: 38019312 PMCID: PMC11213743 DOI: 10.1007/s00330-023-10401-z] [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: 03/07/2023] [Revised: 09/03/2023] [Accepted: 09/16/2023] [Indexed: 11/30/2023]
Abstract
OBJECTIVES To investigate the advantage of T1-weighted fast fluid-attenuated inversion-recovery MRI sequence without (T1-FFLAIR) and with compressed sensing technology (T1-FFLAIR-CS), which shows improved T1-weighted contrast, over standard used T1-weighted fast field echo (T1-FFE) sequence for the assessment of fetal myelination. MATERIALS AND METHODS This retrospective single-center study included 115 consecutive fetal brain MRI examinations (63 axial and 76 coronal, mean gestational age (GA) 28.56 ± 5.23 weeks, range 19-39 weeks). Two raters, blinded to GA, qualitatively assessed a fetal myelin total score (MTS) on each T1-weighted sequence at five brain regions (medulla oblongata, pons, mesencephalon, thalamus, central region). One rater performed region-of-interest quantitative analysis (n = 61) at the same five brain regions. Pearson correlation analysis was applied for correlation of MTS and of the signal intensity ratios (relative to muscle) with GA on each T1-weighted sequence. Fetal MRI-based results were compared with myelination patterns of postmortem fetal human brains (n = 46; GA 18 to 42), processed by histological and immunohistochemical analysis. RESULTS MTS positively correlated with GA on all three sequences (all r between 0.802 and 0.908). The signal intensity ratios measured at the five brain regions correlated best with GA on T1-FFLAIR (r between 0.583 and 0.785). T1-FFLAIR demonstrated significantly better correlations with GA than T1-FFE for both qualitative and quantitative analysis (all p < 0.05). Furthermore, T1-FFLAIR enabled the best visualization of myelinated brain structures when compared to histology. CONCLUSION T1-FFLAIR outperforms the standard T1-FFE sequence in the visualization of fetal brain myelination, as demonstrated by qualitative and quantitative methods. CLINICAL RELEVANCE STATEMENT T1-weighted fast fluid-attenuated inversion-recovery sequence (T1-FFLAIR) provided best visualization and quantification of myelination in utero that, in addition to the relatively short acquisition time, makes feasible its routine application in fetal MRI for the assessment of brain myelination. KEY POINTS • So far, the assessment of fetal myelination in utero was limited due to the insufficient contrast. • T1-weighted fast fluid-attenuated inversion-recovery sequence allows a qualitative and quantitative assessment of fetal brain myelination. • T1-weighted fast fluid-attenuated inversion-recovery sequence outperforms the standard used T1-weighted sequence for visualization and quantification of myelination in utero.
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Affiliation(s)
- Ruxandra-Iulia Milos
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Victor Schmidbauer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Martin L Watzenboeck
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Friedrich Stuhr
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Gerlinde Maria Gruber
- Department of Anatomy and Biomechanics, Karl Landsteiner University of Health Sciences, 3500, Krems, Austria
| | - Christian Mitter
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Gregor O Dovjak
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Marija Milković-Periša
- Department of Pathology and Cytology, University Hospital Centre Zagreb, Petrova 13, 10000, Zagreb, Croatia
| | - Ivica Kostovic
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Nataša Jovanov-Milošević
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Biology, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Yildirim MS, Schmidbauer VU, Micko A, Lechner L, Weber M, Furtner J, Wolfsberger S, Malla Houech IV, Cho A, Dovjak G, Kasprian G, Prayer D, Marik W. Multi-Dynamic-Multi-Echo-based MRI for the Pre-Surgical Determination of Sellar Tumor Consistency: a Quantitative Approach for Predicting Lesion Resectability. Clin Neuroradiol 2024:10.1007/s00062-024-01407-1. [PMID: 38639770 DOI: 10.1007/s00062-024-01407-1] [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: 12/26/2023] [Accepted: 03/18/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Pre-surgical information about tumor consistency could facilitate neurosurgical planning. This study used multi-dynamic-multi-echo (MDME)-based relaxometry for the quantitative determination of pituitary tumor consistency, with the aim of predicting lesion resectability. METHODS Seventy-two patients with suspected pituitary adenomas, who underwent preoperative 3 T MRI between January 2020 and January 2022, were included in this prospective study. Lesion-specific T1-/T2-relaxation times (T1R/T2R) and proton density (PD) metrics were determined. During surgery, data about tumor resectability were collected. A Receiver Operating Characteristic (ROC) curve analysis was performed to investigate the diagnostic performance (sensitivity/specificity) for discriminating between easy- and hard-to-remove by aspiration (eRAsp and hRAsp) lesions. A Mann-Whitney-U-test was done for group comparison. RESULTS A total of 65 participants (mean age, 54 years ± 15, 33 women) were enrolled in the quantitative analysis. Twenty-four lesions were classified as hRAsp, while 41 lesions were assessed as eRAsp. There were significant differences in T1R (hRAsp: 1221.0 ms ± 211.9; eRAsp: 1500.2 ms ± 496.4; p = 0.003) and T2R (hRAsp: 88.8 ms ± 14.5; eRAsp: 137.2 ms ± 166.6; p = 0.03) between both groups. The ROC analysis revealed an area under the curve of 0.72 (95% CI: 0.60-0.85) at p = 0.003 for T1R (cutoff value: 1248 ms; sensitivity/specificity: 78%/58%) and 0.66 (95% CI: 0.53-0.79) at p = 0.03 for T2R (cutoff value: 110 ms; sensitivity/specificity: 39%/96%). CONCLUSION MDME-based relaxometry enables a non-invasive, pre-surgical characterization of lesion consistency and, therefore, provides a modality with which to predict tumor resectability.
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Affiliation(s)
- Mehmet Salih Yildirim
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Victor Ulrich Schmidbauer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Alexander Micko
- Department of Neurosurgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | - Lisa Lechner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Julia Furtner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Stefan Wolfsberger
- Department of Neurosurgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria
| | | | - Anna Cho
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Gregor Dovjak
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Wolfgang Marik
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Kim JS, Cho HH, Shin JY, Park SH, Min YS, Park B, Hong J, Park SY, Hahm MH, Hwang MJ, Lee SM. Diagnostic performance of synthetic relaxometry for predicting neurodevelopmental outcomes in premature infants: a feasibility study. Eur Radiol 2023; 33:7340-7351. [PMID: 37522898 DOI: 10.1007/s00330-023-09881-w] [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: 02/18/2023] [Revised: 04/11/2023] [Accepted: 05/08/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVES To investigate the predictability of synthetic relaxometry for neurodevelopmental outcomes in premature infants and to evaluate whether a combination of relaxation times with clinical variables or qualitative MRI abnormalities improves the predictive performance. METHODS This retrospective study included 33 premature infants scanned with synthetic MRI near or at term equivalent age. Based on neurodevelopmental assessments at 18-24 months of corrected age, infants were classified into two groups (no/mild disability [n = 23] vs. moderate/severe disability [n = 10]). Clinical and MRI characteristics associated with moderate/severe disability were explored, and combined models incorporating independent predictors were established. Ultimately, the predictability of relaxation times, clinical variables, MRI findings, and a combination of the two were evaluated and compared. The models were internally validated using bootstrap resampling. RESULTS Prolonged T1-frontal/parietal and T2-parietal periventricular white matter (PVWM), moderate-to-severe white matter abnormality, and bronchopulmonary dysplasia were significantly associated with moderate/severe disability. The overall predictive performance of each T1-frontal/-parietal PVWM model was comparable to that of individual MRI finding and clinical models (AUC = 0.71 and 0.76 vs. 0.73 vs. 0.83, respectively; p > 0.27). The combination of clinical variables and T1-parietal PVWM achieved an AUC of 0.94, sensitivity of 90%, and specificity of 91.3%, outperforming the clinical model alone (p = 0.049). The combination of MRI finding and T1-frontal PVWM yielded AUC of 0.86, marginally outperforming the MRI finding model (p = 0.09). Bootstrap resampling showed that the models were valid. CONCLUSIONS It is feasible to predict adverse outcomes in premature infants by using early synthetic relaxometry. Combining relaxation time with clinical variables or MRI finding improved prediction. CLINICAL RELEVANCE STATEMENT Synthetic relaxometry performed during the neonatal period may serve as a biomarker for predicting adverse neurodevelopmental outcomes in premature infants. KEY POINTS • Synthetic relaxometry based on T1 relaxation time of parietal periventricular white matter showed acceptable performance in predicting adverse outcome with an AUC of 0.76 and an accuracy of 78.8%. • The combination of relaxation time with clinical variables and/or structural MRI abnormalities improved predictive performance of adverse outcomes. • Synthetic relaxometry performed during the neonatal period helps predict adverse neurodevelopmental outcome in premature infants.
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Affiliation(s)
- Ji Sook Kim
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
| | - Hyun-Hae Cho
- Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University Seoul Hospital, 260 Gonghang-daero, Gangseo-gu, Seoul, 07804, South Korea
| | - Ji-Yeon Shin
- Department of Preventive Medicine, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, South Korea
| | - Sook-Hyun Park
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
- Department of Pediatrics, Yonsei University College of Medicine, Eonju-ro, Gangnam-gu, Seoul, 06273, South Korea
| | - Yu-Sun Min
- Department of Rehabilitation Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
| | - Byunggeon Park
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
| | - Jihoon Hong
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
| | - Seo Young Park
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
| | - Myong-Hun Hahm
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea
| | - Moon Jung Hwang
- General Electric (GE) Healthcare Korea, 416 Hangsng-daero, Jung-gu, Seoul, 04637, South Korea
| | - So Mi Lee
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu, 41404, South Korea.
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7
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Payette K, Uus A, Verdera JA, Zampieri CA, Hall M, Story L, Deprez M, Rutherford MA, Hajnal JV, Ourselin S, Tomi-Tricot R, Hutter J. An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field. ARXIV 2023:arXiv:2308.04903v1. [PMID: 37608939 PMCID: PMC10441444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R2 >0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.
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Affiliation(s)
- Kelly Payette
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Megan Hall
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Women & Children’s Health, King’s College London, London, UK: MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Sebastien Ourselin
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Raphael Tomi-Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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8
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Jia F, Liao Y, Li X, Ye Z, Li P, Zhou X, Li Q, Wang S, Ning G, Qu H. Preliminary Study on Quantitative Assessment of the Fetal Brain Using MOLLI T1 Mapping Sequence. J Magn Reson Imaging 2022; 56:1505-1512. [PMID: 35394092 DOI: 10.1002/jmri.28195] [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: 02/17/2022] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Prenatal quantitative evaluation of myelin is important. However, few techniques are suitable for the quantitative evaluation of fetal myelination. PURPOSE To optimize a modified Look-Locker inversion recovery (MOLLI) T1 mapping sequence for fetal brain development study. STUDY TYPE Prospective observational preliminary cohort study. POPULATION A total of 71 women with normal fetuses divided into mid-pregnancy (gestational age 24-28 weeks, N = 25) and late pregnancy (gestational age > 28 weeks, N = 46) groups. FIELD STRENGTH/SEQUENCE A 3 T/MOLLI sequence. ASSESSMENT T1 values were measured in pedunculus cerebri, basal ganglia, thalamus, posterior limb of the internal capsule, temporal white matter, occipital white matter, frontal white matter, and parietal white matter by two radiologists (11 and 16 years of experience, respectively). STATISTICAL TESTS The Kruskal-Wallis test was used for reginal comparison. For each region of interest (ROI), differences in T1 values between the mid and late pregnancy groups were assessed by the Mann Whitney U test. Pearson correlation coefficients (r) were used to evaluate the correlations between T1 values and gestational age for each ROI. Intraobserver and interobserver agreement was determined by the intraclass correlation coefficient (ICC). A P value <0.05 was considered statistically significant. RESULTS Interobserver and intraobserver agreements of T1 were good for all ROIs (all ICCs > 0.700). There were significant differences in T1 values between lobal white matter and deep regions, respectively. Significant T1 values differences were found between middle and late pregnancy groups in pedunculus cerebri, basal ganglion, thalamus, posterior limb of the internal capsule, temporal, and occipital white matter. The T1 values showed significantly negative correlations with gestational weeks in pedunculus cerebri (r = -0.80), basal ganglion (r = -0.60), thalamus (r = -0.68), and posterior limb of the internal capsule (r = -0.77). DATA CONCLUSION The T1 values of fetal brain may be assessed using the MOLLI sequence and may reflect the myelination. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Zhijun Ye
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Pei Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xiaoyue Zhou
- MR Collaborations, Siemens Healthineers, Shanghai, People's Republic of China
| | - Qing Li
- MR Collaborations, Siemens Healthineers, Shanghai, People's Republic of China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, People's Republic of China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
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9
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Abstract
Brain asymmetry is a hallmark of the human brain. Recent studies report a certain degree of abnormal asymmetry of brain lateralization between left and right brain hemispheres can be associated with many neuropsychiatric conditions. In this regard, some questions need answers. First, the accelerated brain asymmetry is programmed during the pre-natal period that can be called “accelerated brain decline clock”. Second, can we find the right biomarkers to predict these changes? Moreover, can we establish the dynamics of these changes in order to identify the right time window for proper interventions that can reverse or limit the neurological decline? To find answers to these questions, we performed a systematic online search for the last 10 years in databases using keywords. Conclusion: we need to establish the right in vitro model that meets human conditions as much as possible. New biomarkers are necessary to establish the “good” or the “bad” borders of brain asymmetry at the epigenetic and functional level as early as possible.
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