Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling

Abstract

Purpose: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers.

Methods: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity.

Results: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images.

Conclusion: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Figure 1

(A) Coronal MR image of pseudodynamic phantom consisting of three 75-ml containers. Object 1 (mineral oil) and object 2 (0.25% gadoteridol) were unaltered to provide constant signal for all acquired series. Object 3 container was manually switched to provide variable signal between series in the following order: 0% gadoteridol for series 1 through 4, 0.5% gadoteridol for series 5, 1.0%gadoteridol for series 6, and 2.0% gadoteridol for series 7 through 10. (B) Expected signal change in objects 1, 2, and 3 relative to last series based on MRI acquisition conditions [18]. Simulated data are depicted with symbols, whereas connecting lines are used as guides.

Figure 2

Apparent signal change in objects 1, 2, and 3 relative to last series using representative image analysis package SW7 (Table 1) for MR image sources (A) Philips 3.0T (scanner 1), (B) GE 1.5T (scanner 2), (C) Siemens 3T (scanner 3), and (D) GE 3T (scanner 4). Symbols mark the measured data, whereas connecting lines are used as guides. Despite the fact that objects 1 and 2 were unaltered and acquisition conditions were held constant for all 10 series, measured Philips image signal (A) in objects 1 and 2 appears to change by more than 80% relative to the last series. Also note, the shape of object 3 signal change curve is distorted relative to the expected shape shown in Figure 1B. Signal changes measured from the GE and Siemens images (B–D) of objects 1, 2, and 3 are as expected for the experimental conditions (Figure 1B).

Figure 3

Apparent signal change in object 1 (A), object 2 (B), and object 3 (C) relative to the last series as measured by all SW packages applied to Philips 3.0T MR images. Symbols mark the measured data, whereas connecting lines are used as guides. As exemplified by SW7 in Figure 2, SW packages SW1 through SW13 (Table 1) reveal apparent strong signal changes in (A) object 1 and (B) object 2 even though these objects should exhibit constant signal. Apparent signal change shape by SW1 through SW13 is deviated from expected shape (Figure 1B) for (C) object 3. These observations clearly indicate that SW1 through SW13 do not properly account for image scaling from this image source. SW packages SW14, SW15, and SW16, modified to perform image source-specific scaling, exhibit proper apparent signal changes for all three objects in A, B, and C.