Limits...
A fully-automated statistical method for characterization of flow artifact presence in cardiac MRI

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

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... Fig. 2 shows bar plots of QK values and scores (QH) for each imaging sequence... Correlation coefficient between QK and QH was 0.7 (R=0.49;P<0.01)... 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.

No MeSH data available.


Bar plots (mean ± standard error) for QK (derived using the kurtosis-based method) and QH (the experts median choice per study) grouped by sequence type. Intervals on top indicate statistical significance of individual comparisons (P<0.05). Kruskal-Wallis ANOVA with Tukey-Post-Hoc analysis of QK and expert scores identified a difference in the presence of ghost artifacts in images acquired with sequences A and B or B and C.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3106670&req=5

Figure 2: Bar plots (mean ± standard error) for QK (derived using the kurtosis-based method) and QH (the experts median choice per study) grouped by sequence type. Intervals on top indicate statistical significance of individual comparisons (P<0.05). Kruskal-Wallis ANOVA with Tukey-Post-Hoc analysis of QK and expert scores identified a difference in the presence of ghost artifacts in images acquired with sequences A and B or B and C.

Mentions: Fig. 1 shows a representative case from a study acquired with sequence B. Fig. 2 shows bar plots of QK values and scores (QH) for each imaging sequence. Correlation coefficient between QK and QH was 0.7 (R2=0.49;P<0.01).


A fully-automated statistical method for characterization of flow artifact presence in cardiac MRI
Bar plots (mean ± standard error) for QK (derived using the kurtosis-based method) and QH (the experts median choice per study) grouped by sequence type. Intervals on top indicate statistical significance of individual comparisons (P<0.05). Kruskal-Wallis ANOVA with Tukey-Post-Hoc analysis of QK and expert scores identified a difference in the presence of ghost artifacts in images acquired with sequences A and B or B and C.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3106670&req=5

Figure 2: Bar plots (mean ± standard error) for QK (derived using the kurtosis-based method) and QH (the experts median choice per study) grouped by sequence type. Intervals on top indicate statistical significance of individual comparisons (P<0.05). Kruskal-Wallis ANOVA with Tukey-Post-Hoc analysis of QK and expert scores identified a difference in the presence of ghost artifacts in images acquired with sequences A and B or B and C.
Mentions: Fig. 1 shows a representative case from a study acquired with sequence B. Fig. 2 shows bar plots of QK values and scores (QH) for each imaging sequence. Correlation coefficient between QK and QH was 0.7 (R2=0.49;P<0.01).

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

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... Fig. 2 shows bar plots of QK values and scores (QH) for each imaging sequence... Correlation coefficient between QK and QH was 0.7 (R=0.49;P<0.01)... 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.

No MeSH data available.