Image quality was rated by the SNR, CNR, and the mutual information MI of the intensity×gradient magnitude histogram. Higher values in all quality measures correspond to better image quality. The influence of scanner hardware sh and protocol parameters protocol (tr, te, vs) on image quality (snr, cnr, mi) was examined. Because the MPRAGE protocol was used for all examinations, protocol parameters tr, te, and vs are highly correlated with the scanner type sh.
The SNR is foremost dependent on the scanner hardware sh (see ) and alone explains 74% of the variance. The relative performance of the systems is ranked on the right. Ties are given if results are statistically not different (t test, unequal variance, significance level p=0.05). Array coils (PA) offer a significant advantage over conventional coils (HD). Comparing the SNR on similar devices operating at different field strengths, 1.5T systems are equal or better than 3.0T systems (e.g., Achieva PA, HDx PA, Excite PA). Overall examinations, SNR decreases with field strength (−28.5/T), echo time (−45.2/ms), and increases with repetition time (+11.3/ms) and voxel size (+29.8/mm3).
Fig. 1 Dependency of signal-to-noise ratio (SNR) on scanner hardware, sorted by median. Bars in this boxplot denote the median, boxes the 25%/75% quartiles, whiskers the minimum/maximum range. Rankings are shown on the right. Ties denote non-significant differences (more ...)
The CNR depends on the scanner type only, explaining 65% of the variance. Using array coils leads to a profound contrast increase (see ) (e.g., Achieva PA vs. Achieva HD, Excite PA vs. Excite HD, Sonata PA vs. Sonata HD). Again, 1.5T systems are equal or better than 3.0T systems (e.g., Achieva PA, HDx PA, Excite PA). Over all examinations, CNR decreases with field strength (−0.47/T), echo time (−0.90/ms), and increases with repetition time (+2.36/ms) and voxel size (+3.02/mm3). The marked differences in this ratio across systems are–to a large extent–due to differences in the absolute noise level, although significant differences in the WM/GM contrast ratio are found as well (see below).
Fig. 2 Dependency of contrast-to-noise ratio (CNR) on scanner hardware, sorted by median. Bars in this boxplot denote the median, boxes the 25%/75% quartiles, whiskers the minimum/maximum range. Rankings are shown on the right. Ties denote non-significant differences (more ...)
The overall quality measure MI is compared across scanner hardware in , and again demonstrates the advantage of using array coils, especially on Philips Achieva systems. The scanner hardware explains 85% of the variance alone. Overall examinations, MI decreases with field strength (−0.024/T), echo time (−0.073/ms), and increases with repetition time (+0.021/s) and voxel size (+0.057/mm3).
Fig. 3 Dependency of mutual information (MI) on scanner hardware, sorted by median. Bars in this boxplot denote the median, boxes the 25%/75% quartiles, whiskers the minimum/maximum range. Rankings are shown on the right. Ties denote non-significant differences (more ...)
The white/grey matter contrast ratio wgc in segmented and inhomogeneity-corrected images across scanner hardware is compiled in ). Here, a higher field strength offers a relative advantage on similar systems (e.g., Achieva PA, Genesis HD, HDx PA) while using array coils does not offer an improvement over conventional coils (e.g., Sonata 1.5T, Excite 1.5T, Achieva 1.5T, Symphony 1.5T). Over all examinations, the WM/GM contrast ratio increases with field strength (+0.161/T), echo time (+0.191/ms), and decreases with repetition time (−0.050/ms) and voxel size (−0.157/mm3). The Philips Achieva 3.0T system offers an exceptional contrast.
Fig. 4 White/grey matter contrast ratio (WGC) on scanner hardware, sorted by median. Bars in this boxplot denote the median, boxes the 25%/75% quartiles, whiskers the minimum/maximum range. Rankings are shown on the right. Ties denote non-significant differences (more ...)
In summary, using array coils leads to a remarkable improvement in image quality as measured by SNR, CNR, and MI. Quality measures SNR and CNR in similar systems are equal or slightly better at 1.5T than 3.0T, while the MI and the WM/GM contrast ratio are generally better on high-field systems. The Philips Achieva 3.0T system was ranked best over all measures.
Disregarding the confounded variable weight, subject-related variables (age, gender, clinical group) do not influence image quality parameters.
Impact of imaging protocol on compartment volume precision
Ideally, image content (i.e., brain compartment volumes) should be independent of the scanner hardware and protocol implementation. Compartment volumes icv, brv, gmv, wmv and the ratios brr (brain/intracranial volume) and gwr (GM/WM matter ratio) were tested against hardware-, protocol- and subject-related parameters. Results are compiled in . The most parsimonous models explained between 44 and 68% of the total variance, of which about 22% correspond to subject-related variables, the rest is scanner- and protocol-related.
Dependency of compartment volumes and ratios on subject and protocol parameters.
The intracranial volume depends only on gender and weight–an effect that is explained by their correlation with body volume. The absolute brain volume brv is larger in males, while the relative brain volume brr normalizes against body weight and is gender-independent. There is an age-related loss of brain tissue of about 0.35%/year which is stronger in WM than GM. Degenerative processes in WM lead to a signal decrease in T1-weighted images, and the intensity-based segmentation used here may address any lesions to the GM compartment. This explanation is supported by the finding that the GM loss but not the WM loss is dependent on the clinical group. Compared to the normal group, MCI patients have a loss −1.56% in brain volume, and AD patients a loss of −3.20%.
A major device- and protocol-related influence on compartment volumes is the contrast ratio wgc. A change in the average contrast of 1.48 by 6% (corresponding to the standard deviation of wgc in the sample) leads to a change in computed brain volume by 24 ml (or 2%). Other intensity parameters (e.g., GM or WM intensity) or contrast parameters (e.g., SNR, CNR) may replace wgc here, albeit at a lower significance level. Differences between scanner hardware were not significant, except for Excite PA and HDx PA scanners. The GM/WM ratio gwr is −19% lower in these systems, corresponding to a lower gmv of −7.5%, a higher wmv of +11.5%, and a total increase in the brain ratio brr by 2.7%.
The impact of scanner type on compartment volumes was studied further by focusing on two systems, Excite PA and Achieva PA, for which 426 examinations were available at 1.5T and 3.0T (see ). The WM/GM contrast ratio on both systems is similar at 1.5T, and almost independent of field strength on the Excite system (+0.024/T), in contrast to the Achieva system (+0.161/T). This field-dependent effect could not be explained by differences in TR and TE settings alone. The average brain volume is similar at 1.5T (Excite: 1194 ml, Achieva: 1170 ml, p=0.07), and only slightly different at 3.0T (Excite: 1148 ml, Achieva: 1073 ml, p=0.02). The GMV is (roughly) similar on all systems (Excite: 513 ml (1.5T), 547 ml (3.0T), Achieva: 566 ml (1.5T), 577 ml (3.0T), n.s.), but the WMV differs strongly (Excite: 681 ml (1.5T), 601 ml (3.0T), Achieva: 604 ml (1.5T), 496 ml (3.0T), p=0). The difference in WMV largely explains the difference in the GM/WM ratio described above.
Inspection reveals that the higher WM/GM contrast on Philips systems at 3.0T leads to a better delineation of the grey/white matter boundaries. An example is shown in : The same subject is examined on an Achieva PA 3.0T (top) and an Excite PA 1.5T system (below).
Fig. 5 Axial (column 1) and coronal (column 2) sections of the same subject, examined on a Achieva PA 3.0T (top) and Excite PA 1.5T system (below). Columns 3 and 4 show the corresponding probability images of the GM class. A better WM/GM contrast on the Achieva (more ...)
Except for GE scanners, compartment volumes and ratios are similar over all examinations, and thus, independent of the scanning protocol. GE scanners yield larger compartment volumes and a much lower GM/WM ratio. The well-described age-, gender- and group-related influence on compartment volumes and ratios is replicated and confirmed here. Including protocol-related factors when analyzing compartment volumes and ratios yields regression models that explain a much higher proportion of the variance, and leads to more precise estimates of regression coefficients with tighter error bounds. Now, we render these findings more precisely by analyzing results of repeated examinations of the same subject on the same and on different scanners.
Compartment volume intra-scanner variability
A group of subjects were scanned on the same device using the same protocol within a short timeframe (on average 30 days). Of the 43 subjects in the database with repeated scans, two were excluded for quality annotations. Scanner hardware, sites and demographic data of the remaining 82 examinations in 41 subjects are compiled in , columns ”Retest same scanner”.
Table 4 Retest examinations on the same scanner (columns 2–8) and on different scanners (columns 9–15), detailed per scanner type by number of sites (columns 2, 9), number of examinations (columns 3–5, 10–12), and clinical status (more ...)
Volumetric data and ratios of paired examinations were converted into within-subject variability by dividing the absolute within-subject difference by the within-subject mean for a given parameter d, expressed in percent: dvar=200|d2−d1|/d1+d2, where d1 corresponds to a measure obtained in examination 1, and d2 to the result of the second examination. The within-subject variability of the compartment volumes icv, brv, gmv, wmv and the ratios brr, gwr did not depend on scanner hardware, protocol parameters and subject variables, except for the contrast ratio wgc that had a weak influence (p=0.014) on the variability of GM and WM volumes. Quantiles of the parameter distributions were determined for all variability measures and are compiled in . Although absolute differences are not normally distributed, we included the standard deviation (in %, relative to the mean) for informational purposes.
Absolute within-subject variability (in %) of compartment volumes and ratios for repeated scans on the same scanner. Quantiles of the distributions are tabulated.
This examination-dependent variability of the volumetric and ratio measures can be used as a lower error bound in longitudinal studies. For example, the standard deviation of the intra-subject difference in the brain ratio is 0.21%. Thus, a change in the brain ratio of 0.42% in a longitudinal study of a single subject may be considered as significant based on an error probability of 5%. Comparing this figure with the overall age-related decrease in brr of −0.17%/year, longitudinal changes become significantly detectable after 3 years. The higher variability in GMV and WMV is explained by the influence of the contrast on the segmentation: a greater contrast results in a lower variability of GMV and WMV estimates.
Sorting the median of the parameters included in by clinical status, a typical ordering of Normal<MCI<AD was found, i.e., normal controls have a better retest reliability. However, differences between groups are not significant (Wilcoxon rank sum test, p=0.05). Likewise, the retest reliability is statistically not significantly different across scanners. Note that the number of subjects per system is small for most scanner types, so this result should be taken with care.
Compartment volume inter-scanner variability
A group of subjects were scanned on different scanners within a short timeframe (on average 30 days). Scanner hardware, sites and demographic data of 344 examinations in 172 subjects are compiled in , columns 9–15. Paired results were converted into within-subject variability as described in the previous section are compiled as quantiles in ).
Absolute within-subject variability (in %) of compartment volumes and ratios for repeated scans on different scanners. Quantiles of the distributions are tabulated.
Comparing with , a striking difference is revealed: intra-subject variances are an order of magnitude higher in cross-scan conditions than in repeated scans under the same conditions. If different scanners on the order of those seen here (a 1.5T to 3T upgrade, for example) are used in a longitudinal study, a change in brain ratio of 7.8% is necessary based on a significance level of 5%. This amount corresponds to the expected loss of brain volume in a healthy population over 30 years (Kruggel, 2006
). Now, we re-examine the scanner impact of compartment volumes initially described in section “Impact of imaging protocol on compartment volume precision”.
To allow a fair comparison, systems with less than 10 examinations were excluded (refer to ). To separate within- and between-subject variability, linear mixed effect models (Baayen et al., 2008
) were computed, using age, gender and body weight as covariates, and subject as random factor into account. Results are compiled in .
Within-subject variability of compartment volumes and ratios for repeated scans on different scanners.
Differences in brain compartment volumes of the same subject scanned on different systems are best understood by remembering that compartments have two large boundaries, the WM/GM and GM/CSF interface. A minute shift of 0.1 mm in a cortex of 3 mm thickness leads to a change in the cortical volume of about 3% (or 20 ml). The direction of the differences in compartment volumes found for the Excite PA 1.5T system (see ) can be explained by a relative boundary shift outwards from the WM to the GM and the GM to CSF, leading to a relative increase in wmv and brv at the expense of the GM compartment. The opposite effect is seen on Achieva PA 3.0T and Allegra HD 3.0T systems, with a relative decrease in wmv and brv, without affecting gmv. A shift between the GM/CSF boundary is found on Genesis HD 1.5T, resulting in a decrease in brv and gmv. Finally, a shift of the GM/WM boundary explains results obtained on Trio PA 3.0T systems, with an increase of gmv at the expense of the WM compartment. These boundary shifts also explain the differences found in the brain ratio (dbrr) and grey/white matter volume ratio (dgwr).
The GM/WM volume ratio gwr vs. the brain ratio brr is plotted for these scanner types in . Solid ellipses correspond to the within-scanner variance for repeated scans of the same subject on the same scanner; dotted ellipses correspond to the variance pooled across site with the same scanner hardware, corrected for influences of age, gender, clinical status, and body weight. Note that gwr is about 20% lower on Excite PA 1.5T, and about 20% higher on Achieva PA 3.0T, Allegra HD 3.0T, and Trio PA 3.0T. These scanner-related differences explain the large intra-subject variances found for retests of the same subject on different scanners.
Fig. 6 Grey/white matter volume ratio vs. brain ratio for different scanner hardware. Solid ellipses correspond to the 2σ variance for repeated scans on the same scanner, dotted ellipses correspond to the 2σ variance on the same scanner hardware, (more ...)
A likely reason for these shifts between compartments are the remarkable differences in the tissue contrast. Two 3.0T systems with the highest tissue contrast (Achieva PA, Allegra HD) show similar differences in compartment volumes, while the Excite PA 1.5T system has a low tissue contrast and shows the opposite differences. The much higher variance in compartment volumes across sites than within the same system is best explained by differences in the geometrical mapping of scanners.
Sorting the median of the parameters included in by clinical status as described in the previous section does not lead to a typical ordering, because scanner-dependent influences on compartment volumes and ratios are much larger than disease-related changes.
Summarizing, the intra-subject variability of compartment volumes and ratios for scans on different systems is roughly 10 times higher than repeated scans on the same system. Possible factors explaining this higher variability are scanner-dependent geometrical inaccuracies and protocol-related differences in tissue contrast, resulting in differences in GM/WM volume ratios.