Dataset Information

Trajectory Auto-Corrected image reconstruction.

ABSTRACT: To estimate k-space trajectory errors in non-Cartesian acquisitions and reconstruct distortion-free images, without trajectory measurements or gradient calibrations.The Trajectory Auto-Corrected image Reconstruction method jointly estimates k-space trajectory errors and images, based on SENSE and SPIRiT parallel imaging reconstruction. The underlying idea is that parallel imaging and oversampling in the center of k-space provides data redundancy that can be exploited to simultaneously reconstruct images and correct trajectory errors. Trajectory errors are represented as weighted sums of trajectory-dependent error basis functions, the coefficients of which are estimated using gradient-based optimization.Trajectory Auto-Corrected image Reconstruction was applied to reconstruct images and errors in golden angle radial, center-out radial, and spiral in vivo 7 Tesla brain acquisitions in five subjects. Compared to reconstructions using nominal trajectories, Trajectory auto-corrected image reconstructions contained considerably less blurring and streaking and were of similar quality to images reconstructed using measured k-space trajectories in the center-out radial and spiral cases. Reconstruction cost function reductions and improvements in normalized image gradient squared were also similar to those for images reconstructed using measured trajectories.Trajectory Auto-Corrected image Reconstruction enables non-Cartesian image reconstructions free from trajectory errors without the need for separate gradient calibrations or trajectory measurements. Magn Reson Med 76:757-768, 2016. © 2015 Wiley Periodicals, Inc.

SUBMITTER: Ianni JD

PROVIDER: S-EPMC4788989 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Trajectory Auto-Corrected image reconstruction.

Ianni Julianna D JD Grissom William A WA

Magnetic resonance in medicine 20150912 3

<h4>Purpose</h4>To estimate k-space trajectory errors in non-Cartesian acquisitions and reconstruct distortion-free images, without trajectory measurements or gradient calibrations.<h4>Theory and methods</h4>The Trajectory Auto-Corrected image Reconstruction method jointly estimates k-space trajectory errors and images, based on SENSE and SPIRiT parallel imaging reconstruction. The underlying idea is that parallel imaging and oversampling in the center of k-space provides data redundancy that ca ...[more]

PMID: 26362967

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Project description:BackgroundRespiratory motions may cause artifacts on positron emission tomography (PET) images that degrade image quality and quantification accuracy. This study aimed to evaluate the effect of a respiratory motion-corrected image reconstruction (MCIR) algorithm on image quality and tumor quantification compared with nongated/nonmotion-corrected reconstruction.MethodsWe used a phantom consisting of 5 motion spheres immersed in a chamber driven by a motor. The spheres and the background chamber were filled with 18F solution at a sphere-to-background ratio of 5:1. We enrolled 42 and 16 patients undergoing 2-deoxy-2-[18F]fluoro-D-glucose {2-[18F]FDG} and 68Ga-labeled [1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid]-1-Nal3-octreotide {[68Ga]Ga-DOTA-NOC} PET/computed tomography (CT) from whom 74 and 30 lesions were segmented, respectively. Three reconstructions were performed: data-driven gating-based motion correction (DDGMC), external vital signal module-based motion correction (VSMMC), and noncorrection reconstruction. The standardized uptake values (SUVs) and the volume of the spheres and the lesions were measured and compared among the 3 reconstruction groups. The image noise in the liver was measured, and the visual image quality of motion artifacts was scored by radiologists in the patient study.ResultsIn the phantom study, the spheres' SUVs increased by 26-36%, and the volumes decreased by 35-38% in DDGMC and VSMMC compared with the noncorrection group. In the 2-[18F]FDG PET patient study, the lesions' SUVs had a median increase of 10.87-12.65% while the volumes had a median decrease of 14.88-15.18% in DDGMC and VSMMC compared with those of noncorrection. In the [68Ga]Ga-DOTA-NOC PET patient study, the lesions' SUVs increased by 14.23-15.45%, and the volumes decreased by 19.11-20.94% in DDGMC and VSMMC. The image noise in the liver was equal between the DDGMC, VSMMC, and noncorrection groups. Radiologists found improved image quality in more than 45% of the cases in DDGMC and VSMMC compared with the noncorrection group. There was no statistically significant difference in SUVs, volumes, or visual image quality scores between DDGMC and VSMMC.ConclusionsMCIR improves tumor quantification accuracy and visual image quality by reducing respiratory motion artifacts without compromised image noise performance or elongated acquisition time in 2-[18F]FDG and [68Ga]Ga-DOTA-NOC PET/CT tumor imaging. The performance of DDG-driven MCIR is as good as that of the external device-driven solution.

Dataset Information

Trajectory Auto-Corrected image reconstruction.

Publications

Trajectory Auto-Corrected image reconstruction.

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