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Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers.
Kamesh Iyer, Srikant; Moon, Brianna F; Josselyn, Nicholas; Kurtz, Robert M; Song, Jae W; Ware, Jeffrey B; Nabavizadeh, S Ali; Witschey, Walter R.
Afiliação
  • Kamesh Iyer S; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. kameshiyer.srikant@gmail.com.
  • Moon BF; Perelman Center for Advanced Medicine, South Pavilion, Rm 11-155, Philadelphia, PA, USA. kameshiyer.srikant@gmail.com.
  • Josselyn N; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
  • Kurtz RM; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
  • Song JW; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
  • Ware JB; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
  • Nabavizadeh SA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
  • Witschey WR; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Sci Rep ; 12(1): 21679, 2022 12 15.
Article em En | MEDLINE | ID: mdl-36522372
ABSTRACT
Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using alternating direction method of multiplier framework and applied collaborative filtering denoisers. We apply the technique to the 2016 QSM Challenge and in 10 glioblastoma multiforme datasets. We compared its performance with four published QSM techniques and a multi-orientation QSM method. We analyzed magnetic susceptibility accuracy using brain region-of-interest measurements, and image quality using global error metrics. Reconstructions on glioblastoma data were analyzed using ranked and semiquantitative image grading by three neuroradiologist observers to assess image quality (IQ) and sharpness (IS). PnP-BM4D QSM showed good correlation (ß = 0.84, R2 = 0.98, p < 0.05) with COSMOS and no significant bias (bias = 0.007 ± 0.012). PnP-BM4D QSM achieved excellent quality when assessed using structural similarity index metric (SSIM = 0.860), high frequency error norm (HFEN = 58.5), cross correlation (CC = 0.804), and mutual information (MI = 0.475) and also maintained good conspicuity of fine features. In glioblastoma datasets, PnP-BM4D QSM showed higher performance (IQGrade = 2.4 ± 0.4, ISGrade = 2.7 ± 0.3, IQRank = 3.7 ± 0.3, ISRank = 3.9 ± 0.3) compared to MEDI (IQGrade = 2.1 ± 0.5, ISGrade = 2.1 ± 0.6, IQRank = 2.4 ± 0.6, ISRank = 2.9 ± 0.2) and FANSI-TGV (IQGrade = 2.2 ± 0.6, ISGrade = 2.1 ± 0.6, IQRank = 2.7 ± 0.3, ISRank = 2.2 ± 0.2). We illustrated the modularity of PnP QSM by interchanging two additional patch-based denoisers. PnP QSM reconstruction was feasible, and its flexibility was shown using several patch-based denoisers. This technique may allow rapid prototyping and validation of new denoisers for QSM reconstruction for an array of useful clinical applications.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mapeamento Encefálico / Glioblastoma Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mapeamento Encefálico / Glioblastoma Idioma: En Ano de publicação: 2022 Tipo de documento: Article