Xinwei Shi1,2, Daehyun Yoon1, Kevin M Koch3, Brian A Hargreaves1,2,4. 1. Department of Radiology, Stanford University, Stanford, California, USA. 2. Department of Electrical Engineering, Stanford University, Stanford, California, USA. 3. Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA. 4. Department of Bioengineering, Stanford University, Stanford, California, USA.
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.
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.
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