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Confounding factors in breast magnetic resonance fingerprinting: B+1, slice profile, and diffusion effects

: Nolte, T.; Scholten, H.; Gross-Weege, N.; Amthor, T.; Koken, P.; Doneva, M.; Schulz, V.

Fulltext ()

Magnetic resonance in medicine 85 (2021), No.4, pp.1865-1880
ISSN: 0740-3194
ISSN: 1522-2594
European Commission EC
H2020-EU.3.1.3.; 667211; HYPMED
Digital Hybrid Breast PET/MRI for Enhanced Diagnosis of Breast Cancer
Journal Article, Electronic Publication
Fraunhofer MEVIS ()

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T.
Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.
Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane effects occurred in vivo, causing T2 left–right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in −22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions.
Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.