Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.
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ABSTRACT:
SUBMITTER: Balasubramanian PS
PROVIDER: S-EPMC7522736 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
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Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.
Balasubramanian Priya S PS Spincemaille Pascal P Guo Lingfei L Huang Weiyuan W Kovanlikaya Ilhami I Wang Yi Y
iScience 20200912 10
Adaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously pro ...[more]
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Project description:BackgroundQuantitative susceptibility mapping (QSM) uses prior information to reconstruct maps, but prior information may not show pathology and introduce inconsistencies with susceptibility maps, degrade image quality and inadvertently smoothing image features.PurposeTo develop a local field data-driven QSM reconstruction that does not depend on spatial edge prior information.Study typeRetrospective.Subjects, animal modelsA dataset from 2016 ISMRM QSM Challenge, 11 patients with glioblastoma, a patient with microbleeds and porcine heart.Sequence/field strength3D gradient echo sequence on 3T and 7T scanners.AssessmentAccuracy was compared to Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS), and several published techniques using region of interest (ROI) measurements, root-mean-squared error (RMSE), structural similarity index metric (SSIM), and high-frequency error norm (HFEN). Numerical ranking and semiquantitative image grading was performed by three expert observers to assess overall image quality (IQ) and image sharpness (IS).Statistical testsBland-Altman, Friedman test, and Conover multiple comparisons.ResultsLoss adaptive dipole inversion (LADI) (β = 0.82, R2 = 0.96), morphology-enabled dipole inversion (MEDI) (β = 0.91, R2 = 0.97), and fast nonlinear susceptibility inversion (FANSI) (β = 0.81, R2 = 0.98) had excellent correlation with COSMOS and no bias was detected (bias = 0.006 ± 0.014, P < 0.05). In glioblastoma patients, LADI showed consistently better performance (IQGrade = 2.6 ± 0.4, ISGrade = 2.6 ± 0.3, IQRank = 3.5 ± 0.4, ISRank = 3.9 ± 0.2) compared with MEDI (IQGrade = 2.1 ± 0.3, ISGrade = 2 ± 0.5, IQRank = 2.4 ± 0.5, ISRank = 2.8 ± 0.2) and FANSI (IQGrade = 2.2 ± 0.5, ISGrade = 2 ± 0.4, IQRank = 2.8 ± 0.3, ISRank = 2.1 ± 0.2). Dark artifact visible near the infarcted region in MEDI (InfMEDI = -0.27 ± 0.06 ppm) was better mitigated by FANSI (InfFANSI-TGV = -0.17 ± 0.05 ppm) and LADI (InfLADI = -0.18 ± 0.05 ppm).ConclusionFor neuroimaging applications, LADI preserved image sharpness and fine features in glioblastoma and microbleed patients. LADI performed better at mitigating artifacts in cardiac QSM.Evidence level4 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:823-835.
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Project description:PurposeTo develop a quantitative susceptibility mapping (QSM) method with a consistent zero reference using minimal variation in cerebrospinal fluid (CSF) susceptibility.Theory and methodsThe ventricular CSF was automatically segmented on the R2* map. An L2 -regularization was used to enforce CSF susceptibility homogeneity within the segmented region, with the averaged CSF susceptibility as the zero reference. This regularization for CSF homogeneity was added to the model used in a prior QSM method (morphology enabled dipole inversion [MEDI]). Therefore, the proposed method was referred to as MEDI+0 and compared with MEDI in a numerical simulation, in multiple sclerosis (MS) lesions, and in a reproducibility study in healthy subjects.ResultsIn both the numerical simulations and in vivo experiments, MEDI+0 not only decreased the susceptibility variation within the ventricular CSF, but also suppressed the artifact near the lateral ventricles. In the simulation, MEDI+0 also provided more accurate quantification compared to MEDI in the globus pallidus, substantia nigra, corpus callosum, and internal capsule. MEDI+0 measurements of MS lesion susceptibility were in good agreement with those obtained by MEDI. Finally, both MEDI+0 and MEDI showed good and similar intrasubject reproducibility.ConclusionQSM with a minimal variation in ventricular CSF is viable to provide a consistent zero reference while improving image quality. Magn Reson Med 79:2795-2803, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Project description:To apply quantitative susceptibility mapping (QSM) in the basal ganglia of patients with multiple sclerosis (MS) and relate the findings to R2* mapping with regard to the sensitivity for clinical and morphologic measures of disease severity.The local ethics committee approved this study, and all subjects gave written informed consent. Sixty-eight patients (26 with clinically isolated syndrome, 42 with relapsing-remitting MS) and 23 control subjects underwent 3-T magnetic resonance (MR) imaging. Susceptibility and R2* maps were reconstructed from the same three-dimensional multiecho spoiled gradient-echo sequence. Mean susceptibilities and R2* rates were measured in the basal ganglia and were compared between different phenotypes of the disease (clinically isolated syndrome, MS) and the control subjects by using analysis of variance, and regressing analysis was used to identify independent predictors.Compared with control subjects, patients with MS and clinically isolated syndrome had increased (more paramagnetic) magnetic susceptibilities in the basal ganglia. R2* mapping proved less sensitive than QSM regarding group differences. The strongest predictor of magnetic susceptibility was age. Susceptibilities were higher with increasing neurologic deficits (r = 0.34, P < .01) and lower with normalized volumes of gray matter (r = -0.35, P < .005) and the cortex (r = -0.35, P < .005).QSM provides superior sensitivity over R2* mapping in the detection of MS-related tissue changes in the basal ganglia. With QSM but not with R2* mapping, changes were already observed in patients with clinically isolated syndrome, which suggests that QSM can serve as a sensitive measure at the earliest stage of the disease.
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Project description:Various regularization methods have been proposed for single-orientation quantitative susceptibility mapping (QSM), which is an ill-posed magnetic field to susceptibility source inverse problem. Noise amplification, a major issue in inverse problems, manifests as streaking artifacts and quantification errors in QSM and has not been comparatively evaluated in these algorithms. In this paper, various QSM methods were systematically categorized for noise analysis. Six representative QSM methods were selected from four categories: two non-Bayesian methods with alteration or approximation of the dipole kernel to overcome the ill conditioning; four Bayesian methods using a general mathematical prior or a specific physical structure prior to select a unique solution, and using a data fidelity term with or without noise weighting. The effects of noise in these QSM methods were evaluated by reconstruction errors in simulation and image quality in 50 consecutive human subjects. Bayesian QSM methods with noise weighting consistently reduced root mean squared errors in numerical simulations and increased image quality scores in the human brain images, when compared to non-Bayesian methods and to corresponding Bayesian methods without noise weighting (p ≤ 0.001). In summary, noise effects in QSM can be reduced using Bayesian methods with proper noise weighting.
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