Measurement Consistency from Magnetic Resonance Images

Rationale and Objectives

In quantifying medical images, length-based measurements are still obtained manually. Due to possible human error, a measurement protocol is required to guarantee the consistency of measurements. In this work, we review various statistical techniques that can be used in determining measurement consistency. The focus is on detecting a possible measurement bias and determining the robustness of the procedures to outliers.

Materials and Methods

We review correlation analysis, linear regression, Bland-Altman method, paired t -test, and analysis of variance (ANOVA). These techniques were applied to measurements, obtained by two raters, of head and neck structures from magnetic resonance images.

Results

The correlation analysis and the linear regression were shown to be insufficient for detecting measurement inconsistency. They are also very sensitive to outliers. The widely used Bland-Altman method is a visualization technique, so it lacks the numeric quantification. The paired t -test tends to be sensitive to small measurement bias. In contrast, ANOVA performs well even under small measurement bias.

Conclusions

In almost all cases, using only one method is insufficient and it is recommended that several methods be used simultaneously. In general, ANOVA performs the best.

We were motivated in part by the need to establish a reliable measurement protocol of head and neck structures involving both bony and soft tissue structures from magnetic resonance (MR) images collected for the purpose of quantifying the growth pattern of various oral and pharyngeal structures or vocal tract structures ( ). Figure 1 depicts a select set of such measurements obtained manually from MR imaging.

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Figure 1

Mid-sagittal head and neck magnetic resonance images with the six measurements used for measurement consistency comparison: ( a ) Head length (HL); ( b ) lower anterior face height (LFH); ( c ) anterior tongue length (ATL); ( d ) hyoid vertical distance from posterior nasal spine (HVP); ( e ) vocal tract length (VTL); and ( f ) soft palate length (SP). See text for the definition of variables and tissue type and measurement type of each variable.

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Materials and methods

Description of Head and Neck Imaging Data

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ARE=1n∑ni=1∣RDi−CCi∣∣RDi+CCi∣/2, A

∑

❘

−

❘

where RDi R

i and CCi C

i are the i th measurement of RD and CC, respectively, and n=30 n

30 , the number of measurements obtained by each rater. The average relative errors for HL, LFH, ATL, HVP, VTL, and SP are 0.016, 0.036, 0.041, 0.070, 0.046, and 0.1, respectively. The fairly large ARE of SP is caused by an outlier ( Fig 2 ).

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Correlation Analysis and Linear Regression

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T=rn−2√1−r2√. T

−

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RDi=β0+β1×CCi+εi. R

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Bland-Altman Method and Paired t - Test

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T=d¯S2d/n√, T

which is distributed as the t -distribution with n−1 n

−

1 degrees of freedom.

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ANOVA and Within-Rater Consistency

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Xijk=μ+αi+βj+αβij+εijk. X

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Results

Correlation Analysis and Linear Regression

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Bland-Altman Method and Paired t - Test

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ANOVA and Within-Rater Consistency

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Discussion

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Table 1

Summary of Statistical Method Used in Determining the Measurement Consistency

Method Strength Weakness Agreement Disagreement Correlation and regression

VTL, SP

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Visualization technique

The method does not provide a decision. Paired t -test

Fails under systematic bias

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LFH, HVP ANOVA

Complicated procedure

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ANOVA: analysis of variance.

The last two columns show whether the method agrees with the ANOVA result for the six variables: head length (HL), lower anterior face height (LFH), anterior tongue length (ATL), hyoid vertical distance from posterior nasal spine (HVP), vocal tract length (VTL), and soft palate length (SP).

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Acknowledgments

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Measurement Consistency from Magnetic Resonance Images

Rationale and Objectives

Materials and Methods

Results

Conclusions

Materials and methods

Description of Head and Neck Imaging Data

Correlation Analysis and Linear Regression

Bland-Altman Method and Paired t - Test

ANOVA and Within-Rater Consistency

Results

Correlation Analysis and Linear Regression

Bland-Altman Method and Paired t - Test

ANOVA and Within-Rater Consistency

Discussion

Acknowledgments

References

Further Reading

Abstracts of Funded National Institutes of Health Grants

Accurate Automatic Papillary Muscle Identification for Quantitative Left Ventricle Mass Measurements in Cardiac Magnetic Resonance Imaging

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