Biomed Phys Eng Express. 2024 Apr 10. doi: 10.1088/2057-1976/ad3cdd. Online ahead of print.
ABSTRACT
Thoracoabdominal MRI is limited by respiratory motion, especially in neonates who cannot perform breath-holds. To reduce motion blurring, radially-acquired MRI can be reconstructed from data acquired during specific respiratory phases (“hard-gating”), but this reduces image SNR. Various “soft-gating” reconstruction schemes have been proposed that incorporate data acquired outside the period of interest into reconstruction with diminished weighting. However, the choice of soft-gated weighting algorithm and parameters, and effect on image SNR and motion blurring, has not previously been explored. 
Methods: Purpose of this study is to map how variable data inclusion and weighting affect SNR and motion blurring in respiratory-gated reconstructions of neonatal radial lung MRI, using existing and novel soft-gated weighting functions. Ten neonatal subjects with respiratory abnormalities from the neonatal intensive care unit were imaged using a 1.5T neonatal-sized scanner and 3D radial ultrashort echo-time (UTE) sequence. The apparent SNR and motion blurring of retrospectively respiratory-gated UTE-MRI were compared between images reconstructed using ungated, hard-gated, and several soft-gating weighting algorithms and parameters (using exponential, sigmoid, inverse, and linear weighting decay outside the period of interest). Motion blurring was measured by the maximum derivative of image intensity at the diaphragm (MDD). 
Results: Soft-gating functions produce higher aSNR than hard-gated images using equivalent numbers of projections (%Nproj), but with lower MDD. While aSNR was approximately linear with %Nproj for each algorithm, MDD performance diverged between functions as %Nproj decreased. Algorithm performance was relatively consistent between subjects, except in images with high noise, where function performance differed.
Conclusion: The temporal pattern of undersampling has a significant effect on image quality; for the same %Nproj, a wider temporal distribution of included data produces higher aSNR, a narrower temporal distribution increases MDD. Therefore, timing strategy of undersampling schemes can be optimized depending on the required application’s compromise between aSNR and MDD.
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PMID:38599190 | DOI:10.1088/2057-1976/ad3cdd
