Quantitative analysis of SA functional CMR images across the cardiac cycle requires a complete delineation of the LV myocardium, which cannot be obtained manually within the time constraints of clinical routine. Therefore, clinical software for the analysis of SA functional CMR images should provide automated contouring tools. To date, such automated methods are helpful, but require manual interventions by the user to obtain visually satisfactory LV contours. In this study, we assessed the accuracy and time-effectiveness of a clinical application for the analysis of SA functional CMR images, which contains a combination of automatic and semi-automatic contouring algorithms.
We further have assessed the impact of manual contour corrections on the final outcome of clinical measurements. The addition of basal and apical slices at the ED phase caused a significant increase in the EDV ( and ). Consequently the derived SV and EF both increased significantly. This result confirms the known importance of correct slice selection for the outcome of global volume measurements in SA functional CMR.
Moreover, while the reviewing and correcting the automatically proposed contours doubles the analysis time (), and highlight the limited impact of detailed, manual contour corrections on the outcome of global volume measurements in SA functional CMR. Nevertheless, a review of automated contours remains necessary. Very rarely the automatic contour detection fails, instead producing spurious contours; such instances produce the extreme global-quantification outliers seen in
Finally we have assessed the accuracy of the automatically detected contours with respect to fully manually drawn contours (), as well as the accuracy of the global volumetric results before and after correction with respect to the global volumetric results obtained from fully manual analysis (). Both experiments revealed similar accuracy with respect to our previous experiments (1
), and with respect to many other methods (17
The overall accuracy of complete analysis was high compared with results obtained from fully manual analysis, with excellent correlation between the two methods (). Limits of agreement were tight and comparable to those of inter-observer variation between two fully manual observations, which are reported as mean difference ± SD of 8.07 ± 5.58ml for EDV, 2.77 ± 5.1ml for ESV, −0.53 ± 2.32% for EF, and −2.75 ± 5.97 for LVM (26
). LV mass was slightly but significantly underestimated, by approximately 5%, by complete analysis versus fully manual analysis. Although EDV was significantly overestimated in the statistical sense, the difference (1.0% of mean EDV) is unlikely to be of clinical relevance. LV ESV, SV and perhaps most importantly EF did not differ between complete and fully manual analyses.
Full manual analyses involved hand drawn contouring at ED and ES only. While this was marginally faster than a complete analysis (6.1 minutes vs. 7.6 minutes), the limited impact of elaborate manual corrections, which take a median 4.9 minutes suggests that accurate global LV parameters can be obtained in 3–4 minutes. Moreover, full manual analysis requires manual identification of the ES phase, which is a source of intra- and inter-observer variation that can be eliminated using contour detection on all phases.
Limitations and Future Directions
The timing results reported in this paper are derived from analyses by a single user. It remains to be investigated how these timing results translate to other users, with different levels of experience and in other environments. In many clinical practices, a typical weekly case load might be 10–20 CMR cases, as compared to the 50–100 studies/week performed by this user during this study. Consequently, clinical users may be less experienced with respect to manipulating contours, but also be less fatigued from repetitive analyses. Moreover, in clinical practice, users will likely be more focused on time-efficiency versus absolute accuracy of each contour, as was the case in the present study, as an additional purpose of our study was to generate reference values. Thus the mean analysis times reported here may not be fully generalizable to any given clinical practice for reasons noted above.
It bears mention that our study was conducted on image data obtained from Framingham Offspring cohort participants, the majority of whom were free from clinical cardiovascular disease at the time of CMR study. In clinical practice, analysis of SA functional CMR images is performed for a wide variety of clinical indications. Although similar performance can be expected for many conditions, lower contour accuracy may occur in cases with morphologically deformed ventricles, which may consequently require more analysis time. Additionally, we have shown the impact of detailed corrections on quantification of global functional parameters. The impact of detailed corrections on regional measurements of local myocardial contractile function was not assessed as the vast majority of participants were without wall motion abnormalities. The performance of automated contouring and the effect and time-efficiency of detailed manual correction on assessment of regional function remains to be investigated, and is probably better performed in a population with greater prevalence of both focal and global myocardial dysfunction.
The results reported in this paper are obtained using a particular clinical software application for the analysis of SA functional CMR images, which combines automatic and semi-automatic contouring algorithms with graphics editing tools to enable efficient analysis. It is unknown how timing results may be affected by using different contouring algorithms or other editing paradigms. Similarly, all image data used in this study were acquired with a single CMR scanner. Thus, the time-efficiency of our methods may vary when applied to data from other scanners. Furthermore, our results are not directly comparable to results from benchmarking experiments performed with other algorithms on other image data (27
). That said, the accuracy of automatic contouring in this study was comparable to earlier published results (1
) obtained on image data from other medical centers.
Finally, we did not measure the time expended to upload images for analysis. Although this can be important to clinical workflow, the generalizability of such data are extremely limited and depend strongly on local factors such as whether the analysis software operates on a stand-alone workstation or shares resources with other applications, e.g. a PACS, and of course on the specific hardware configuration. Since any report of data-loading times would apply only to our specific site, and would not provide information of general interest, these data were not obtained.