Impact of late gadolinium enhancement image acquisition resolution on neural network based automatic scar segmentation.

ABSTRACT:

Background

Automatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data.

Methods

Thirty-six high-resolution (0.7×0.7×2.0 mm³) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filterin

SUBMITTER: Hoh T 

PROVIDER: S-EPMC10981112 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature