Streaking artifact suppression of quantitative susceptibility mapping reconstructions via L1-norm data fidelity optimization (L1-QSM)

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dc.contributor.authorMilovic C.
dc.contributor.authorLambert M.
dc.contributor.authorIrarrazaval P.
dc.contributor.authorTejos C.
dc.contributor.authorMilovic C.
dc.contributor.authorLambert M.
dc.contributor.authorIrarrazaval P.
dc.contributor.authorTejos C.
dc.contributor.authorMilovic C.
dc.contributor.authorLambert M.
dc.contributor.authorIrarrazaval P.
dc.contributor.authorTejos C.
dc.contributor.authorMilovic C.
dc.contributor.authorLangkammer C.
dc.contributor.authorLangkammer C.
dc.contributor.authorBredies K.
dc.contributor.authorBredies K.
dc.contributor.authorIrarrazaval P.
dc.date.accessioned2024-01-10T14:22:44Z
dc.date.available2024-01-10T14:22:44Z
dc.date.issued2021
dc.description.abstract© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in MedicinePurpose: The presence of dipole-inconsistent data due to substantial noise or artifacts causes streaking artifacts in quantitative susceptibility mapping (QSM) reconstructions. Often used Bayesian approaches rely on regularizers, which in turn yield reduced sharpness. To overcome this problem, we present a novel L1-norm data fidelity approach that is robust with respect to outliers, and therefore prevents streaking artifacts. Methods: QSM functionals are solved with linear and nonlinear L1-norm data fidelity terms using functional augmentation, and are compared with equivalent L2-norm methods. Algorithms were tested on synthetic data, with phase inconsistencies added to mimic lesions, QSM Challenge 2.0 data, and in vivo brain images with hemorrhages. Results: The nonlinear L1-norm-based approach achieved the best overall error metric scores and better streaking artifact suppression. Notably, L1-norm methods could reconstruct QSM images without using a brain mask, with similar regularization weights for different data fidelity weighting or masking setups. Conclusion: The proposed L1-approach provides a robust method to prevent streaking artifacts generated by dipole-inconsistent data, renders brain mask calculation unessential, and opens novel challenging clinical applications such asassessing brain hemorrhages and cortical layers.
dc.description.funderANID
dc.description.funderNational Agency for Research and Development
dc.description.funderCancer Research UK
dc.description.funderAustrian Science Fund
dc.description.funderFONDECYT
dc.fechaingreso.objetodigital2024-04-09
dc.fuente.origenScopus
dc.identifier.doi10.1002/mrm.28957
dc.identifier.eissn15222594
dc.identifier.issn15222594 07403194
dc.identifier.pubmedidMEDLINE:34350634
dc.identifier.scopusidSCOPUS_ID:85111718971
dc.identifier.urihttps://doi.org/10.1002/mrm.28957
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79992
dc.identifier.wosidWOS:000681206800001
dc.information.autorucFacultad de Ingeniería; Irarrazaval Mena, Pablo; S/I; 57376
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherJohn Wiley and Sons Inc
dc.revistaMagnetic Resonance in Medicine
dc.rightsacceso abierto
dc.subjectFANSI
dc.subjecthemorrhage
dc.subjectL1-norm
dc.subjectQSM
dc.subjecttotal variation
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleStreaking artifact suppression of quantitative susceptibility mapping reconstructions via L1-norm data fidelity optimization (L1-QSM)
dc.typeartículo
sipa.codpersvinculados57376
sipa.indexWos
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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