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MR scanner systems should be adequately characterized in diffusion-MRI of the breast.

ABSTRACT: Breast imaging represents a relatively recent and promising field of application of quantitative diffusion-MRI techniques. In view of the importance of guaranteeing and assessing its reliability in clinical as well as research settings, the aim of this study was to specifically characterize how the main MR scanner system-related factors affect quantitative measurements in diffusion-MRI of the breast. In particular, phantom acquisitions were performed on three 1.5 T MR scanner systems by different manufacturers, all equipped with a dedicated multi-channel breast coil as well as acquisition sequences for diffusion-MRI of the breast. We assessed the accuracy, inter-scan and inter-scanner reproducibility of the mean apparent diffusion coefficient measured along the main orthogonal directions () as well as of diffusion-tensor imaging (DTI)-derived mean diffusivity (MD) measurements. Additionally, we estimated spatial non-uniformity of (NU) and MD (NUMD) maps. We showed that the signal-to-noise ratio as well as overall calibration of high strength diffusion gradients system in typical acquisition sequences for diffusion-MRI of the breast varied across MR scanner systems, introducing systematic bias in the measurements of diffusion indices. While and MD values were not appreciably different from each other, they substantially varied across MR scanner systems. The mean of the accuracies of measured and MD was in the range [-2.3%,11.9%], and the mean of the coefficients of variation for and MD measurements across MR scanner systems was 6.8%. The coefficient of variation for repeated measurements of both and MD was < 1%, while NU and NUMD values were <4%. Our results highlight that MR scanner system-related factors can substantially affect quantitative diffusion-MRI of the breast. Therefore, a specific quality control program for assessing and monitoring the performance of MR scanner systems for diffusion-MRI of the breast is highly recommended at every site, especially in multicenter and longitudinal studies.

SUBMITTER: Giannelli M

PROVIDER: S-EPMC3904912 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

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MR scanner systems should be adequately characterized in diffusion-MRI of the breast.

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