Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy
Purpose To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA). Materials and Methods WMSA segmentation was performed on pairs of who...
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| Published in | Journal of magnetic resonance imaging Vol. 15; no. 2; pp. 203 - 209 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Wiley Subscription Services, Inc., A Wiley Company
01.02.2002
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-1807 1522-2586 1522-2586 |
| DOI | 10.1002/jmri.10053 |
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| Abstract | Purpose
To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA).
Materials and Methods
WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method.
Results
The segmentation method combining expectation‐maximization (EM) tissue segmentation, template‐driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z‐score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects).
Conclusion
The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility. J. Magn. Reson. Imaging 2002;15:203–209. © 2002 Wiley‐Liss, Inc. |
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| AbstractList | Purpose
To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA).
Materials and Methods
WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method.
Results
The segmentation method combining expectation‐maximization (EM) tissue segmentation, template‐driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z‐score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects).
Conclusion
The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility. J. Magn. Reson. Imaging 2002;15:203–209. © 2002 Wiley‐Liss, Inc. To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA). WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method. The segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects). The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility. To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA).PURPOSETo assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA).WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method.MATERIALS AND METHODSWMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method.The segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects).RESULTSThe segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects).The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility.CONCLUSIONThe addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility. |
| Author | Guimond, Alexandre Guttmann, Charles R.G. Mugler III, John P. Wolfson, Leslie Weiner, Howard L. Zou, Kelly H. Li, Xiaoming Benson, Randall R. Wu, Ying Wei, Xingchang Warfield, Simon K. |
| Author_xml | – sequence: 1 givenname: Xingchang surname: Wei fullname: Wei, Xingchang organization: Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 2 givenname: Simon K. surname: Warfield fullname: Warfield, Simon K. organization: Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 3 givenname: Kelly H. surname: Zou fullname: Zou, Kelly H. organization: Department of Health Care Policy, Harvard Medical School, and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 4 givenname: Ying surname: Wu fullname: Wu, Ying organization: Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 5 givenname: Xiaoming surname: Li fullname: Li, Xiaoming organization: Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 6 givenname: Alexandre surname: Guimond fullname: Guimond, Alexandre organization: Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 7 givenname: John P. surname: Mugler III fullname: Mugler III, John P. organization: Departments of Radiology and Biomedical Engineering, University of Virginia Health School of Medicine, Charlottesville, Virginia – sequence: 8 givenname: Randall R. surname: Benson fullname: Benson, Randall R. organization: Department of Neurology, University of CT Health Center, University of Connecticut, Farmington, Connecticut – sequence: 9 givenname: Leslie surname: Wolfson fullname: Wolfson, Leslie organization: Department of Neurology, University of CT Health Center, University of Connecticut, Farmington, Connecticut – sequence: 10 givenname: Howard L. surname: Weiner fullname: Weiner, Howard L. organization: Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts – sequence: 11 givenname: Charles R.G. surname: Guttmann fullname: Guttmann, Charles R.G. email: guttmann@bwh.harvard.edu organization: Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/11836778$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Kikinis R, Guttmann CRG, Metcalf D, et al. Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. J Magn Reson Imaging 1999; 9: 519-530. – reference: Paty DW, McFarland H. Magnetic resonance techniques to monitor the long term evolution of multiple sclerosis pathology and to monitor definitive clinical trials. J Neurol Neurosurg Psychiatry 1998; 64: S47-S51. – reference: Wei X, Warfield SK, Mulkern RV, Brookeman JR, Mugler III JP, Guttmann CR. Multi-contrast high-resolution segmentation of the brain: preliminary report [Abstract]. Radiology 2000; 217: 602. – reference: Gawne-Cain ML, Webb S, Tofts P, Miller DH. Lesion volume measurement in multiple sclerosis: how important is accurate repositioning? J Magn Reson Imaging 1996; 6: 705-713. – reference: Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 8476: 307-310. – reference: Miller DH, Grossman RI, Reingold SC, McFarland HF. The role of magnetic resonance techniques in understanding and managing multiple sclerosis. Brain 1998; 121: 3-24. – reference: Zou KH, McDermott MP. Higher-moment approaches to approximate interval estimation for a certain intraclass correlation coefficient. Stat Med 1999; 18: 2051-2061. – reference: Warfield S, Dengler J, Zaers J, Guttmann CRG, et al. Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions. J Image Guid Surg 1995; 1: 326-338. – reference: Weiner HL, Guttmann CRG, Khoury SJ, et al. Serial magnetic resonance imaging in multiple sclerosis: correlation with attacks, disability, and disease stage. J Neuroimmunol 2000; 104: 164-173. – reference: Mugler JP, Bao S, Mulkern RV, Guttmann CRG, et al. Optimized single-slab three-dimensional spin-echo MR imaging of the brain. Radiology 2000; 216: 891-899. – reference: Guttmann CRG, Benson R, Warfield SK, et al. White matter abnormalities in mobility-impaired older persons. Neurology 2000; 54: 1277-1283. – reference: Wells WM, Grimson WEL, Kininis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Med Imaging 1996; 15: 419-442. – reference: Filippi M, Horsfield MA, Ader HJ, et al. Guidelines for using quantitative measures of brain magnetic resonance imaging abnormalities in monitoring the treatment of multiple sclerosis. Ann Neurol 1998; 43: 499-506. – reference: Udupa JK, Wei L, Samarasekera S, Miki Y, van Buchem MA, Grossman RI. Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Trans Med Imaging 1997; 16: 598-609. – reference: Filippi M, Marciano N, Capra R, et al. The effect of imprecise repositioning on lesion volume measurements in patients with multiple sclerosis. Neurology 1997; 49: 274-276. – reference: Warfield SK, Kaus M, Jolesz FA, Kikinis R. Adaptive, template moderated, spatially varying statistical classification. Med Image Anal 2000; 4: 43-55. – reference: Zijdenbos AP, Dawant BM. Brain segmentation and white matter lesion detection in MR images. Crit Rev Biomed Eng 1994; 22: 401-465. – reference: Firbank MJ, Coulthard A, Harrison RM, Williams ED. Partial volume effects in MRI studies of multiple sclerosis. Magn Reson Imaging 1999; 17: 593-601. – reference: Hohol M, Guttmann CRG, Olek M, et al. Roquinimex (Linomide®) treatment in secondary progressive and relapsing remitting multiple sclerosis: results from 48 week randomized, double-blind, placebo-controlled pilot studies [Abstract]. Neurolgy 1997; 48: A174. – reference: Kikinis R, Shenton ME, Gerig G, et al. Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. J Magn Reson Imaging 1992; 2: 619-629. – reference: Guttmann CRG, Kikinis R, Anderson MC, et al. Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J Magn Reson Imaging 1999; 9: 509-518. – volume: 49 start-page: 274 year: 1997 end-page: 276 article-title: The effect of imprecise repositioning on lesion volume measurements in patients with multiple sclerosis publication-title: Neurology – volume: 216 start-page: 891 year: 2000 end-page: 899 article-title: Optimized single‐slab three‐dimensional spin‐echo MR imaging of the brain publication-title: Radiology – volume: 48 start-page: A174 year: 1997 article-title: Roquinimex (Linomide®) treatment in secondary progressive and relapsing remitting multiple sclerosis: results from 48 week randomized, double‐blind, placebo‐controlled pilot studies [Abstract] publication-title: Neurolgy – volume: 6 start-page: 705 year: 1996 end-page: 713 article-title: Lesion volume measurement in multiple sclerosis: how important is accurate repositioning? publication-title: J Magn Reson Imaging – volume: 43 start-page: 499 year: 1998 end-page: 506 article-title: Guidelines for using quantitative measures of brain magnetic resonance imaging abnormalities in monitoring the treatment of multiple sclerosis publication-title: Ann Neurol – volume: 121 start-page: 3 year: 1998 end-page: 24 article-title: The role of magnetic resonance techniques in understanding and managing multiple sclerosis publication-title: Brain – volume: 2 start-page: 619 year: 1992 end-page: 629 article-title: Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging publication-title: J Magn Reson Imaging – volume: 18 start-page: 2051 year: 1999 end-page: 2061 article-title: Higher‐moment approaches to approximate interval estimation for a certain intraclass correlation coefficient publication-title: Stat Med – volume: 9 start-page: 509 year: 1999 end-page: 518 article-title: Quantitative follow‐up of patients with multiple sclerosis using MRI: reproducibility publication-title: J Magn Reson Imaging – volume: 9 start-page: 519 year: 1999 end-page: 530 article-title: Quantitative follow‐up of patients with multiple sclerosis using MRI: technical aspects publication-title: J Magn Reson Imaging – volume: 104 start-page: 164 year: 2000 end-page: 173 article-title: Serial magnetic resonance imaging in multiple sclerosis: correlation with attacks, disability, and disease stage publication-title: J Neuroimmunol – volume: 1 start-page: 326 year: 1995 end-page: 338 article-title: Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions publication-title: J Image Guid Surg – volume: 22 start-page: 401 year: 1994 end-page: 465 article-title: Brain segmentation and white matter lesion detection in MR images publication-title: Crit Rev Biomed Eng – volume: 8476 start-page: 307 year: 1986 end-page: 310 article-title: Statistical methods for assessing agreement between two methods of clinical measurement publication-title: Lancet – start-page: 202 year: 1999 end-page: 209 – volume: 54 start-page: 1277 year: 2000 end-page: 1283 article-title: White matter abnormalities in mobility‐impaired older persons publication-title: Neurology – volume: 16 start-page: 598 year: 1997 end-page: 609 article-title: Multiple sclerosis lesion quantification using fuzzy‐connectedness principles publication-title: IEEE Trans Med Imaging – start-page: 67 year: 1999 end-page: 84 – volume: 217 start-page: 602 year: 2000 article-title: Multi‐contrast high‐resolution segmentation of the brain: preliminary report [Abstract] publication-title: Radiology – start-page: 439 year: 1998 end-page: 448 – volume: 17 start-page: 593 year: 1999 end-page: 601 article-title: Partial volume effects in MRI studies of multiple sclerosis publication-title: Magn Reson Imaging – volume: 64 start-page: S47 year: 1998 end-page: S51 article-title: Magnetic resonance techniques to monitor the long term evolution of multiple sclerosis pathology and to monitor definitive clinical trials publication-title: J Neurol Neurosurg Psychiatry – volume: 4 start-page: 43 year: 2000 end-page: 55 article-title: Adaptive, template moderated, spatially varying statistical classification publication-title: Med Image Anal – volume: 15 start-page: 419 year: 1996 end-page: 442 article-title: Adaptive segmentation of MRI data publication-title: IEEE Trans Med Imaging – ident: e_1_2_6_8_2 doi: 10.1002/(SICI)1522-712X(1995)1:6<326::AID-IGS4>3.0.CO;2-C – ident: e_1_2_6_13_2 doi: 10.1002/jmri.1880020603 – volume: 48 start-page: A174 year: 1997 ident: e_1_2_6_11_2 article-title: Roquinimex (Linomide®) treatment in secondary progressive and relapsing remitting multiple sclerosis: results from 48 week randomized, double‐blind, placebo‐controlled pilot studies [Abstract] publication-title: Neurolgy – ident: e_1_2_6_19_2 doi: 10.1212/WNL.49.1.274 – ident: e_1_2_6_24_2 doi: 10.1007/BFb0056229 – ident: e_1_2_6_14_2 doi: 10.1109/42.511747 – ident: e_1_2_6_17_2 doi: 10.1002/(SICI)1097-0258(19990815)18:15<2051::AID-SIM162>3.0.CO;2-P – ident: e_1_2_6_2_2 doi: 10.1016/S0165-5728(99)00273-8 – volume: 22 start-page: 401 year: 1994 ident: e_1_2_6_15_2 article-title: Brain segmentation and white matter lesion detection in MR images publication-title: Crit Rev Biomed Eng – ident: e_1_2_6_20_2 doi: 10.1016/S0730-725X(98)00210-0 – ident: e_1_2_6_7_2 doi: 10.1002/(SICI)1522-2586(199904)9:4<519::AID-JMRI3>3.0.CO;2-M – ident: e_1_2_6_25_2 doi: 10.1109/42.640750 – ident: e_1_2_6_6_2 doi: 10.1002/ana.410430414 – volume: 64 start-page: S47 year: 1998 ident: e_1_2_6_4_2 article-title: Magnetic resonance techniques to monitor the long term evolution of multiple sclerosis pathology and to monitor definitive clinical trials publication-title: J Neurol Neurosurg Psychiatry – volume: 217 start-page: 602 year: 2000 ident: e_1_2_6_22_2 article-title: Multi‐contrast high‐resolution segmentation of the brain: preliminary report [Abstract] publication-title: Radiology – ident: e_1_2_6_18_2 doi: 10.1002/jmri.1880060502 – ident: e_1_2_6_21_2 doi: 10.1148/radiology.216.3.r00au46891 – ident: e_1_2_6_23_2 doi: 10.1007/10704282_22 – ident: e_1_2_6_3_2 doi: 10.1002/(SICI)1522-2586(199904)9:4<509::AID-JMRI2>3.0.CO;2-S – ident: e_1_2_6_5_2 doi: 10.1093/brain/121.1.3 – ident: e_1_2_6_12_2 doi: 10.1212/WNL.54.6.1277 – ident: e_1_2_6_16_2 doi: 10.1016/S0140-6736(86)90837-8 – ident: e_1_2_6_10_2 doi: 10.1016/S1361-8415(00)00003-7 – ident: e_1_2_6_9_2 doi: 10.1016/B978-012692535-7/50080-X |
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To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance... To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI)... |
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| SubjectTerms | aging Aging - physiology brain Brain - pathology Brain - physiology brain, aging brain, white matter computer-assisted Double-Blind Method Humans Image Interpretation, Computer-Assisted image processing image processing, computer‐assisted magnetic resonance imaging Magnetic Resonance Imaging - methods magnetic resonance imaging, volume measurement multiple sclerosis Multiple Sclerosis - pathology Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted volume measurement white matter |
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| Title | Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy |
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