Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks.

ABSTRACT: This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T₂ mapping acquisitions in the prostate. The T₂ estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Large physics-based synthetic datasets simulating T₂ mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, 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 au

SUBMITTER: Bolan PJ 

PROVIDER: S-EPMC9882442 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature