Improved quantitative parameter estimation for prostate T₂ relaxometry using convolutional neural networks.

ABSTRACT:

Objective

Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T₂ in the prostate.

Materials and methods

Large physics-based synthetic datasets simulating T₂ mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentat

SUBMITTER: Bolan PJ 

PROVIDER: S-EPMC11417079 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature