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Comparisons of Hepatobiliary Phase Imaging Using Combinations of Parallel Imaging and Variable Degrees of Compressed Sensing With Use of Parallel Imaging Alone.

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OBJECTIVE This study aimed to compare the image quality in the hepatobiliary phase images of gadoxetic acid-enhanced liver magnetic resonance imaging using parallel imaging (PI) and compressed sensing (CS) reconstruction,… Click to show full abstract

OBJECTIVE This study aimed to compare the image quality in the hepatobiliary phase images of gadoxetic acid-enhanced liver magnetic resonance imaging using parallel imaging (PI) and compressed sensing (CS) reconstruction, using variable CS factors with the standard method using the PI technique. METHODS In this study, 64 patients who underwent gadoxetic acid-enhanced liver magnetic resonance imaging at 3.0 T were enrolled. Hepatobiliary phase images were acquired 6 times using liver acquisition with volume acceleration (LAVA) and CS reconstruction with 5 CS factors 1.4, 1.6, 1.8, 2.0, and 2.5 (LAVA-CS 1.4, 1.6, 1.8, 2.0, and 2.5) and standard LAVA (LAVA-noCS). For objective analysis, the signal intensity ratios (SIRs) of the liver-to-spleen (SIRliver/spleen), liver-to-portal vein (SIRliver/portal vein), and liver-to-fat (SIRliver/fat) were estimated. For subjective analysis, 2 radiologists independently evaluated the quality of all the images. RESULTS The objective analysis demonstrated no significant difference in all evaluation parameters of all the images. Subjective analysis revealed that the scores of all evaluation items were higher for LAVA-noCS images than for LAVA-CS images, and only LAVA-CS 1.4 did not significantly differ from LAVA-noCS in all evaluation items (P = 1.00 in 2 readers). CONCLUSIONS A CS factor of 1.4 in the hepatobiliary phase image with combined PI and CS can reduce the scan time without degrading the image quality compared with the standard method.

Keywords: hepatobiliary phase; imaging using; parallel imaging; analysis; compressed sensing

Journal Title: Journal of computer assisted tomography
Year Published: 2023

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