Home > Publications database > Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging. > print

001     182640
005     20240229145726.0
024 7 _ |a 10.1002/mrm.29476
|2 doi
024 7 _ |a pmid:36373186
|2 pmid
024 7 _ |a 0740-3194
|2 ISSN
024 7 _ |a 1522-2594
|2 ISSN
024 7 _ |a altmetric:138515908
|2 altmetric
037 _ _ |a DKFZ-2022-02817
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Ruck, Laurent
|0 0000-0002-5978-3383
|b 0
245 _ _ |a Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging.
260 _ _ |a New York, NY [u.a.]
|c 2023
|b Wiley-Liss
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1672319100_18455
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a #LA:E020# / 2023 Mar;89(3):1102-1116
520 _ _ |a To evaluate the classifiability of small multiple sclerosis (MS)-like lesions in simulated sodium (23 Na) MRI for different 23 Na MRI contrasts and reconstruction methods.23 Na MRI and 23 Na inversion recovery (IR) MRI of a phantom and simulated brain with and without lesions of different volumes (V = 1.3-38.2 nominal voxels) were simulated 100 times by adding Gaussian noise matching the SNR of real 3T measurements. Each simulation was reconstructed with four different reconstruction methods (Gridding without and with Hamming filter, Compressed sensing (CS) reconstruction without and with anatomical 1 H prior information). Based on the mean signals within the lesion volumes of simulations with and without lesions, receiver operating characteristics (ROC) were determined and the area under the curve (AUC) was calculated to assess the classifiability for each lesion volume.Lesions show higher classifiability in 23 Na MRI than in 23 Na IR MRI. For typical parameters and SNR of a 3T scan, the voxel normed minimal classifiable lesion volume (AUC > 0.9) is 2.8 voxels for 23 Na MRI and 19 voxels for 23 Na IR MRI, respectively. In terms of classifiability, Gridding with Hamming filter and CS without anatomical 1 H prior outperform CS reconstruction with anatomical 1 H prior.Reliability of lesion classifiability strongly depends on the lesion volume and the 23 Na MRI contrast. Additional incorporation of 1 H prior information in the CS reconstruction was not beneficial for the classification of small MS-like lesions in 23 Na MRI.
536 _ _ |a 315 - Bildgebung und Radioonkologie (POF4-315)
|0 G:(DE-HGF)POF4-315
|c POF4-315
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a X-nuclei MRI
|2 Other
650 _ 7 |a anatomical prior information
|2 Other
650 _ 7 |a compressed sensing (CS)
|2 Other
650 _ 7 |a lesion classification
|2 Other
650 _ 7 |a multiple sclerosis (MS)
|2 Other
650 _ 7 |a sodium (23Na)
|2 Other
700 1 _ |a Mennecke, Angelika
|b 1
700 1 _ |a Wilferth, Tobias
|0 0000-0002-2520-9025
|b 2
700 1 _ |a Lachner, Sebastian
|b 3
700 1 _ |a Müller, Max
|b 4
700 1 _ |a Egger, Nico
|b 5
700 1 _ |a Doerfler, Arnd
|b 6
700 1 _ |a Uder, Michael
|b 7
700 1 _ |a Nagel, Armin
|0 P:(DE-He78)054fd7a5195b75b11fbdc5c360276011
|b 8
|e Last author
|u dkfz
773 _ _ |a 10.1002/mrm.29476
|g p. mrm.29476
|0 PERI:(DE-600)1493786-4
|n 3
|p 1102-1116
|t Magnetic resonance in medicine
|v 89
|y 2023
|x 0740-3194
909 C O |p VDB
|o oai:inrepo02.dkfz.de:182640
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 8
|6 P:(DE-He78)054fd7a5195b75b11fbdc5c360276011
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-315
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Bildgebung und Radioonkologie
|x 0
914 1 _ |y 2022
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2021-01-27
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-27
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2023-10-21
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MAGN RESON MED : 2022
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2023-10-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-10-21
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2023-10-21
920 2 _ |0 I:(DE-He78)E020-20160331
|k E020
|l E020 Med. Physik in der Radiologie
|x 0
920 1 _ |0 I:(DE-He78)E020-20160331
|k E020
|l E020 Med. Physik in der Radiologie
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)E020-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21