2016
DOI: 10.1097/rli.0000000000000240
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Six-Fold Acceleration of High-Spatial Resolution 3D SPACE MRI of the Knee Through Incoherent k-Space Undersampling and Iterative Reconstruction—First Experience

Abstract: We demonstrate the successful clinical implementation of 3D TSE MRI with incoherent k-space undersampling and iterative reconstruction for 6-fold accelerated high spatial resolution isotropic 3D MRI data acquisition. Our preliminary assessments suggest similar image quality and diagnostic performance of a comprehensive 10-minute 3D TSE MRI prototype protocol and 20-minute TSE MRI standard protocol.

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Cited by 90 publications
(55 citation statements)
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“…With the use of advanced acceleration techniques, 3D sequences might improve the efficiency of peripheral joint imaging with similar or better image quality than 2D MRI. In addition, 3D sequences acquire a volumetric isotropic data set, which can be used for multiplanar reformation (MPR), eliminating the need to obtain images with identical tissue contrast in multiple planes . 3D sequences also provide thin, continuous sections through the knee joint, which reduces the effects of partial volume averaging .…”
mentioning
confidence: 99%
“…With the use of advanced acceleration techniques, 3D sequences might improve the efficiency of peripheral joint imaging with similar or better image quality than 2D MRI. In addition, 3D sequences acquire a volumetric isotropic data set, which can be used for multiplanar reformation (MPR), eliminating the need to obtain images with identical tissue contrast in multiple planes . 3D sequences also provide thin, continuous sections through the knee joint, which reduces the effects of partial volume averaging .…”
mentioning
confidence: 99%
“…The center of k -space was fully sampled to allow computation of a coil sensitivity map for image reconstruction [23]. A fully sampled k -space center of size 24 × 24 was used for coil sensitivity map estimation in this work.…”
Section: Methodsmentioning
confidence: 99%
“…The reconstruction used in this paper combines CS and PI by minimizing the following cost function as described in [23]: x^=argminx12n=1Nyn-Fu(CSMnx)22+λWx1,…”
Section: Methodsmentioning
confidence: 99%
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