Automated fibrosis segmentation from wideband post-contrast T 1 ∗ $$ {T}_1^{\ast } $$ mapping in an animal model of ischemic heart disease with implantable cardioverter-defibrillators

Abstract

Purpose

Post-contrast $T_1^{\ast }$ mapping has proven promising for automated scar segmentation in subjects without ICDs, but this has not been implemented in patients with ICDs. We introduce an automated cluster-based thresholding method for $T_1^{\ast }$ maps with an ICD present and compare it to manually tuned thresholding of synthetic LGE images with an ICD present and standard LGE without an ICD present.

Methods

Seven swine received an ischemia-reperfusion myocardial infarction and were imaged at 3 T 4–5 weeks post-infarct with and without an ICD. Mapping-based thresholding was performed using synthetic LGE and artifact-corrected cluster-thresholding methods, both employing connected component filtering. Standard pixel signal intensity thresholding was performed on the conventional LGE without an ICD. Volumetric accuracy is relative to conventional LGE and Dice similarity between SynLGE and cluster-based segmentations were evaluated.

Results

No statistical significance was observed between LGE volumes without an ICD and both SynLGE and artifact-corrected cluster-threshold volumes with an ICD, when using connected component filtering. Additionally, Dice alignment between SynLGE and cluster-thresholding was high for healthy myocardium (0.96), dense scar (0.83), and dense scar union gray zone (0.91) when artifact correction and connected component filtering were implemented.

Conclusion

Clustering of $T_1^{\ast }$ maps holds promise for a reproducible approach to scar segmentation in the presence of ICDs.