Article Figures & Data
Figures
- Fig 1.
Illustration of the basis functions for each of the tissue subsets. Gaussian curves represent each of the three tissues in voxels containing a single tissue type. Reflected triangle pairs convolved with gaussian curves represent the content of voxels containing mixtures of tissues. Three levels of shading identify the contribution of each of the three tissues in both pure and partial volume voxels.
- Fig 2.
Algorithm at work.
A, Contrast-enhanced T1-weighted image of a vestibular schwannoma.
B, Histogram of the region of interest fit with the theoretical basis functions.
C, Once the fit is optimized, the relative probability that vestibular schwannoma tissue could produce each voxel’s intensity is determined from the function. This should be directly related to the proportion of the tissue in that voxel.
- Fig 3.
Computer-generated phantom contains similar intensity and noise characteristics of pure and partial volume voxels of CSF, brain tissue, and tumor tissue. The artificial tumors ranged from 12 to 80 voxels in total volume. The phantom is constructed so that the absolute true volumes of the tumors are known before the effects of noise and partial volume averaging are added.
- Fig 4.
A, Illustration of the problem of taking the mean diameter as an indicator of tumor size. Although there is some correlation between volume calculations and averaged diameter measurements at large volume, this correlation breaks down at small volumes.
B, Expansion of the plot of manual volume estimation versus Bayesian partial volume segmentation for smaller tumors shows good linear correlation with manual volume estimates at all tumor sizes.
- Fig 5.
Example of follow-up study of vestibular schwannoma, illustrating a small tumor with no evidence of growth.
A, Initial image of a 0.8-cm vestibular schwannoma.
B, Magnified view of initial image.
C, Follow-up image obtained after 11 months.
D, Probability map of initial tumor location.
E, Probability map of final tumor location.
F, Subtraction map shows differences of more than +25% (open squares) and less than −25% (shaded squares) in voxel volume change after subtraction of the two probability maps shown in D and E. This shows that the tumor has moved in the second image relative to the first, that all differences are due to partial volume changes, and that the number of voxels with apparent volume loss is matched by the number with volume gain. Overall difference in volume of the whole tumor is one voxel, well within the accuracy of the measurement.
- Fig 6.
Example of follow-up study of vestibular schwannoma, illustrating a tumor with significant growth.
A, Initial image of a 2-cm vestibular schwannoma.
B, Magnified view of the initial image.
C, Follow-up image obtained after 7 months.
D, Tumor probability map of initial maximal diameter.
E, Tumor probability map of final maximal diameter.
F, Subtraction map of the two probability images shows voxels with a volume change of more than 50%. All voxels that show change show an increase in tumor volume. There is no evidence of significant misregistration between the images, and the overall volume increase is 38%.
Tables
- TABLE 1:
Repeat measurement accuracy of estimation methods in 20 randomly selected vestibular schwannomas*
Measurement Method Error in Units of Measurement Average % Error Relative to Size Maximal diameter 1.8 mm (8.5–0.2) 15 (114–0.9) Manually segmented volume 150 mm3 (430–4) 18 (112–2.0) Bayesian classification volume 70 mm3 (342–1) 7 (22–0.3) * Percentages in parentheses are the ranges in measurement errors with respect to diameter or volume of each initial tumor measurement.
- TABLE 2:
Correlation between measurement methods and the standard manual segmentation estimate of tumor volume
Measurement Method R2 Value (95% CI) Mean diameter 0.708(0.5572–0.814) Maximal diameter 0.732(0.592–0.830) Perimeter 0.764(0.637–0.851) Elliptical area 0.859(0.776–0.912) Manually segmented area 0.873(0.798–0.851) Seeded volume 0.949(0.917–0.970) Bayesian partial volume 0.994(0.989–0.996) - TABLE 3:
Growth rates determined from Bayesian measurements of tumor size in 38 patients who underwent serial MR imaging
Rate of Growth Significance Level 1ς Significance Level 3ς No. with Growth Volume Doubling Time <1 y/<2 y/>2 y No. with Growth (3ς) Volume Doubling Time(3ς) <1 y/<2 y/>2 y Significant growth 19 7/7/5 12 6/5/1 “No growth” 13 0/0/13 24 1/2/21 Significant shrinkage 6 0/0/6 2 0/0/2 Note.—Data show the number of vestibular schwannomas demonstrating measurable growth greater than the estimated measurement error of 70 mm3 (1ς) and 3 times the measurement error (3ς). Measurements at 1ς indicate possible growth and measurements at 3ς indicate definite growth.
- TABLE 4:
Distribution by each growth category for both the manual segmentation and Bayesian tissue classification with a cutoff of 1ς (possible growth)
Manual Segmentation Bayesian Classification Significant Growth “No Growth” Significant Shrinkage Significant growth 9 2 0 “No growth” 10 8 3 Significant shrinkage 0 3 3 - TABLE 5:
Distribution by growth category for both the manual segmentation and Bayesian tissue classification with cutoff of 3ς (definite growth)
Manual Bayesian Classification Significant Growth “No Growth” Significant Shrinkage Significant growth 2 0 0 “No growth” 10 23 0 Significant shrinkage 0 1 2
