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Found 6 papers, 0 papers with code
On Functional Activations in Deep Neural Networks
These networks were repeatable across repeated performance of related tasks, and correspondence of identified functional networks and activation in tasks not used to define the functional networks was shown to accurately identify the presented task.
Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams
Results: The top predicted protocol in AP mode was correct for 82. 8%, 73. 8%, and 69. 3% of the test cases for "General", "ACR", and "Local" protocol classes, respectively.
Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping
wTFI uses the BFR method RESHARP local fields as supervision to perform a multi-task learning of local tissue fields and QSM, and is capable of recovering magnetic susceptibility estimates near the edges of the brain where are eroded in RESHARP and realize whole brain QSM estimation.
Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction
In this work, we proposed a novel method called Meta-QSM to firstly solve QSM reconstruction of arbitrary image resolution with a single model.
Medical Physics Image and Video Processing
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by ill conditioning of its field-to -susceptibility transformation.
MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue.
