Automatic segmentation of ablation lesions and termination of the image acquisition/analysis process
Journal of Cardiovascular Magnetic Resonance volume 13, Article number: P245 (2011)
Automate the processes of ablation lesion imaging and delineation in order to make them non-expert user friendly.
Visualization of radiofrequency ablation lesions during cardiac electrophysiology procedures would help ensuring their contiguity and inclusiveness, which are essential for the procedures’ long-term success.
The usefulness of dynamic contrast enhancement (DynCE) and cumulative characteristics for ablation lesion visualization has been already demonstrated (1). However, planning of the image acquisition process and interpretation of the resulting images may pose a challenge for electrophysiologists who don’t interpret MRI routinely.
We describe an algorithm allowing automatic discrimination between ablation lesions and surrounding normal tissue during DynCE scans as well automatic termination of the image acquisition and analysis processes as soon as the desired lesion visibility level has been achieved.
56 lesions were ablated in the Latissimus dorsi muscles of 15 rabbits using clinical catheters and time/power settings. The animals underwent MRI at various times after ablations using various imaging techniques. DynCE images were post-processed using original algorithms and software (1).
Lesion non-detectability on early contrast agent wash-in cumulative DynCE images strongly correlated with lack of lesion and normal tissue separation on their histograms (Fig. 1). As wash-in continued and new data was acquired and post-processed, ablation lesions became more apparent (Fig. 2) and separated from normal tissue on histograms (Fig. 3): lower-intensity histogram peaks were formed by lesion core pixels, higher-intensity peaks were formed by normal tissue pixels, and lesion border pixels composed the groove segment between these peaks (Fig. 4).
Our algorithm automatically identified the peaks and groove, and used the information to discriminate between actively and poorly enhancing pixels (Fig. 5). It also compared the peaks’ values to the groove’s one and used the information to terminate image post-processing when satisfactory lesion-to-tissue contrast was detected (Fig. 6). The resulting segmented images demonstrated good correspondence to other lesion depicting MR images acquired during the study (Fig. 7).
Our algorithm demonstrated a good performance in this study and has a potential to prove robust and useful in real clinical conditions. More accurate and noise-resistant histogram analysis and segmentation methods can be implemented, which would result in more robust ablation lesion delineation and the reduction of the DynCE scan time required for it. Automatic lesion detection and scan termination tools based upon this approach have a potential to ease and promote the acceptance of intra-procedural MRI by the clinical electrophysiologist.
Shmatukha A, et al: Journal of Cardiovascular Magnetic Resonance. 2010, 12 (Suppl 1): O25-10.1186/1532-429X-12-S1-O25.
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Shmatukha, A.V., Crystal, E. Automatic segmentation of ablation lesions and termination of the image acquisition/analysis process. J Cardiovasc Magn Reson 13 (Suppl 1), P245 (2011). https://doi.org/10.1186/1532-429X-13-S1-P245