92 citations · 118 across the 11 of their papers we have counts for
11 papers
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification
Peng Jiang, Juan Liu, Lang Wang +3
Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time. Thus, making g…
Exploring domain adaptation for deep neural network trained QSM
Juan Liu, Kevin Koch
Quantitative susceptibility mapping (QSM) is a MRI technique that estimates tissue magnetic susceptibility. The generation of QSM requires solving a challenging ill-posed field-to-…
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement
Juan Liu, Masoud Malekzadeh, Niloufar Mirian +3
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurri…
Improved Padding in CNNs for Quantitative Susceptibility Mapping
Juan Liu
Recently, deep learning methods have been proposed for quantitative susceptibility mapping (QSM) data processing: background field removal, field-to-source inversion, and single-st…
Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping
Juan Liu, Kevin M Koch
Quantitative susceptibility mapping (QSM) utilizes MRI phase information to estimate tissue magnetic susceptibility. The generation of QSM requires solving ill-posed background fie…
Model-based Learning for Quantitative Susceptibility Mapping
Juan Liu, Kevin M. Koch
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements…
Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction
Juan Liu, Kevin M. Koch
Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM i…
Deep Quantitative Susceptibility Mapping for Background Field Removal and Total Field Inversion
Juan Liu, Kevin M. Koch
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to estimate local tissue susceptibility, which has been shown useful to provide novel image contrast and as biom…
MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
Juan Liu, Kevin M. Koch
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide n…
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Juan Liu, Kevin M. Koch
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by ill conditioning of its field-to -susceptibility transformation. State-of-art QS…
Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution
Juan Liu, Andrew S. Nencka, L. Tugan Muftuler +3
Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-…