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CT substitutes derived from MR images reconstructed with parallel imaging
Author(s) -
Johansson Adam,
Garpebring Anders,
Asklund Thomas,
Nyholm Tufve
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4886766
Subject(s) - iterative reconstruction , nuclear medicine , magnetic resonance imaging , cartesian coordinate system , tomography , computer science , computed tomography , artificial intelligence , computer vision , medicine , mathematics , radiology , geometry
Purpose: Computed tomography (CT) substitute images can be generated from ultrashort echo time (UTE) MRI sequences with radial k‐space sampling. These CT substitutes can be used as ordinary CT images for PET attenuation correction and radiotherapy dose calculations. Parallel imaging allows faster acquisition of magnetic resonance (MR) images by exploiting differences in receiver coil element sensitivities. This study investigates whether non‐Cartesian parallel imaging reconstruction can be used to improve CT substitutes generated from shorter examination times. Methods: The authors used gridding as well as two non‐Cartesian parallel imaging reconstruction methods, SPIRiT and CG‐SENSE, to reconstruct radial UTE and gradient echo (GE) data into images of the head for 23 patients. For each patient, images were reconstructed from the full dataset and from a number of subsampled datasets. The subsampled datasets simulated shorter acquisition times by containing fewer radial k‐space spokes (1000, 2000, 3000, 5000, and 10 000 spokes) than the full dataset (30 000 spokes). For each combination of patient, reconstruction method, and number of spokes, the reconstructed UTE and GE images were used to generate a CT substitute. Each CT substitute image was compared to a real CT image of the same patient. Results: The mean absolute deviation between the CT number in CT substitute and CT decreased when using SPIRiT as compared to gridding reconstruction. However, the reduction was small and the CT substitute algorithm was insensitive to moderate subsampling (≥5000 spokes) regardless of reconstruction method. For more severe subsampling (≤3000 spokes), corresponding to acquisition times less than a minute long, the CT substitute quality was deteriorated for all reconstruction methods but SPIRiT gave a reduction in the mean absolute deviation of down to 25 Hounsfield units compared to gridding. Conclusions: SPIRiT marginally improved the CT substitute quality for a given number of radial spokes as compared to gridding. However, the increased reconstruction time of non‐Cartesian parallel imaging reconstruction is difficult to motivate from this improvement. Because the CT substitute algorithm was insensitive to moderate subsampling, data for a CT substitute could be collected in as little as minute and reconstructed with gridding without deteriorating the CT substitute quality.