Multi-modality deep learning-based [⁶⁸Ga]Ga-DOTA-FAPI-04 PET polar map generation: potential value in detecting...

Generating polar map (PM) from [⁶⁸Ga]Ga-DOTA-FAPI-04 PET images is challenging and inaccurate using existing automatic methods that rely on the myocardial anatomical integrity in PET images. This study aims to enhance the accuracy of PM generated from [⁶⁸Ga]Ga-DOTA-FAPI-04 PET images and explore the potential value of PM in detecting reactive fibrosis after myocardial infarction and assessing its relationship with cardiac function.

We proposed a deep-learning-based method that fuses multi-modality images to compensate for the cardiac structural information lost in [⁶⁸Ga]Ga-DOTA-FAPI-04 PET images and accurately generated PMs. We collected 133 pairs of [⁶⁸Ga]Ga-DOTA-FAPI-04 PET/MR images from 87 ST-segment elevated myocardial infarction patients for training and evaluation purposes. Twenty-six patients were selected for longitudinal analysis, further examining the clinical value of PM-related imaging parameters.

The quantitative comparison demonstrated that our method was comparable with the manual method and surpassed the commercially available software-PMOD in terms of accuracy in generating PMs for [⁶⁸Ga]Ga-DOTA-FAPI-04 PET images. Clinical analysis revealed the effectiveness of [⁶⁸Ga]Ga-DOTA-FAPI-04 PET PM in detecting reactive myocardial fibrosis. Significant correlations were demonstrated between the difference of baseline PM FAPI% and PM LGE%, and the change in cardiac function parameters (all p < 0.001), including LVESV% (r = 0.697), LVEDV% (r = 0.621) and LVEF% (r = -0.607).

The [⁶⁸Ga]Ga-DOTA-FAPI-04 PET PMs generated by our method are comparable to manually generated and sufficient for clinical use. The PMs generated by our method have potential value in detecting reactive fibrosis after myocardial infarction and were associated with cardiac function, suggesting the possibility of enhancing clinical diagnostic practices.

ClinicalTrials.gov (NCT04723953). Registered 26 January 2021.