Search Results for author:
Found 9 papers, 6 papers with code
DCE-FORMER: A Transformer-based Model With Mutual Information And Frequency-based Loss Functions For Early And Late Response Prediction In Prostate DCE-MRI
Dynamic Contrast Enhanced Magnetic Resonance Imaging aids in the detection and assessment of tumor aggressiveness by using a Gadolinium-based contrast agent (GBCA).
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction
Experiments reveal that our approach 1) adapts on the fly to various unseen configurations up to 32 coils when trained on lower numbers (i. e. 7 to 11) of randomly varying coils, and to 120 deviated unseen configurations when trained on 18 configurations in a single model, 2) matches the performance of coil configuration-specific models, and 3) outperforms configuration-invariant models with improvement margins of around 1 dB / 0. 03 and 0. 3 dB / 0. 02 in PSNR / SSIM for knee and brain data.
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks
The hypernetworks and the reconstruction network in the GBML setting provide discriminative mode-specific features and low-level image features, respectively.
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning
Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts.
SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction
We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the structure and capacity of the student and enable aligning the representations of the teacher with the student.
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction
In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction.
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction
We propose a multiple acquisition context based network, called MAC-ReconNet for MRI reconstruction, flexible to multiple acquisition contexts and generalizable to unseen contexts for applicability in real scenarios.
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations.
DC-WCNN: A deep cascade of wavelet based convolutional neural networks for MR Image Reconstruction
Among them, U-Net has shown to be the baseline architecture for MR image reconstruction.
