Literature DB >> 27385493

Metallic implant geometry and susceptibility estimation using multispectral B0 field maps.

Xinwei Shi1,2, Daehyun Yoon1, Kevin M Koch3, Brian A Hargreaves1,2,4.   

Abstract

PURPOSE: To estimate the susceptibility and the geometry of metallic implants from multispectral imaging (MSI) information, to separate the metal implant region from the surrounding signal loss region. THEORY AND METHODS: The susceptibility map of signal-void regions is estimated from MSI B0 field maps using total variation (TV) regularized inversion. Voxels with susceptibility estimates above a predetermined threshold are identified as metal. The accuracy of the estimated susceptibility and implant geometry was evaluated in simulations, phantom, and in vivo experiments.
RESULTS: The proposed method provided more accurate susceptibility estimation compared with a previous method without TV regularization, in both simulations and phantom experiments. In the phantom experiment where the actual implant was 40% of the signal-void region, the mean estimated susceptibility was close to the susceptibility in literature, and the precision and recall of the estimated geometry was 85% and 93%. In vivo studies in subjects with hip implants also demonstrated that the proposed method can distinguish implants from surrounding low-signal tissues, such as cortical bone.
CONCLUSION: The proposed method can improve the delineation of metallic implant geometry by distinguishing metal voxels from artificial signal voids and low-signal tissues by estimating the susceptibility maps. Magn Reson Med 77:2402-2413, 2017.
© 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  field map; metal implants; multispectral imaging; susceptibility artifacts; susceptibility mapping

Mesh:

Substances:

Year:  2016        PMID: 27385493      PMCID: PMC5529184          DOI: 10.1002/mrm.26313

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


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