Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL.

ABSTRACT:

Purpose

The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths.

Methods

A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data f

SUBMITTER: Pramanik A 

PROVIDER: S-EPMC10524947 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature