A fully-automated statistical method for characterization of flow artifact presence in cardiac MRI
© Tsaftaris et al; licensee BioMed Central Ltd. 2011
Published: 2 February 2011
Flow artifacts in MR images can appear as ghosts within and outside the body cavity. Current approaches for optimizing sequences for suppressing such artifacts rely on expert scoring or on semi-automated methods for evaluation.
To test a fully-automated statistical image-processing method that can quantify the presence of flow artifacts. The method was evaluated against expert scoring in the setting of cine blood-oxygen-level-dependent (BOLD) MRI with different SSFP imaging strategies.
2D cine SSFP Imaging sequences and parameters used. Shared parameters in text.
Each cine stack I(t) (t denotes trigger time) was loaded in Matlab and the per-pixel mean (IA) and variance (IV) were found across t. Initialized with a rectangle the size of IA, a contour was evolved, using a level-set approach, until it converged to the body-air interface, providing a binary mask M (M=1 for air). Image IR=IV/(IA)2 (pixel-wise division) was calculated and all values of IR in air (M=1) were collected to estimate the excess kurtosis (γ) of their distribution H. The metric QK=γ was used to quantify the presence of flow artifacts.
Three expert reviewers, blinded to sequence type, scored 16 studies for the presence of ghosts [1(least) to 5(most)]. ANOVA was used to test for differences in scores/metric among sequences. QK was correlated with the reviewers’ median choice (QH) to assess agreement.
Statistical comparisons of QK scores identified a difference in the presence of ghost artifacts among the three sequences in full agreement with expert findings. This indicates that this kurtosis-based method can assess the variability of artifact presence in a stack without the need to process each image separately. In contrast to other methods, the proposed approach uses high order statistics (kurtosis) of background pixels to estimate ghost presence and is robust against (coil) bias due to the division with per-pixel mean image IA. Although further studies are needed, the proposed approach may be useful in readily assessing image quality in a clinical/research setting in an unbiased and fully-automated manner.
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