6 papers
DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction
Qiaoxin Li, Caini Pan, Pierre-Antoine Comby +2
Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging with high k-space undersampli…
Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices
Daniel del Pozo Bueno, Serge Brosset, Theo Monniez +3
Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited…
Combining Cartesian and non-Cartesian acceleration techniques with SPARKLING for 1mm isotropic whole-brain MPRAGE in a minute
Chaithya Giliyar Radhakrishna, Aurélien Massire, Blanche Bapst +2
Purpose: T1-weighted MPRAGE remains a cornerstone of clinical anatomical imaging, yet its long acquisition times constrain routine use. Established acceleration techniques, namely…
Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography
Serge Brosset, Daniel del Pozo Bueno, Thomas David +3
Electron tomography (ET) plays an important role in the three-dimensional (3D) characterization of nanomaterials. However, under limited-angle and sparse-view conditions, conventio…
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction
Asma Tanabene, Chaithya Giliyar Radhakrishna, Aurélien Massire +2
Deep learning has shown great promise for MRI reconstruction from undersampled data, yet there is a lack of research on validating its performance in 3D parallel imaging acquisitio…
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction
Pierre-Antoine Comby, Benjamin Lapostolle, Matthieu Terris +1
Achieving high-quality Magnetic Resonance Imaging (MRI) reconstruction at accelerated acquisition rates remains challenging due to the inherent ill-posed nature of the inverse prob…