Search Results for author:
Found 17 papers, 7 papers with code
FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model
In this study, we introduce FetalDiffusion, a novel approach utilizing a conditional diffusion model to generate 3D synthetic fetal MRI with controllable pose.
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space
We perform experiments on simulated and retrospective in-vivo data to evaluate the performance of the proposed zero-shot learning method for temporal FSE reconstruction.
Data Consistent Deep Rigid MRI Motion Correction
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.
Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI
Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces.
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI).
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment.
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times.
Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging
Further, it only requires a single noisy image with a few auxiliary observations at different time frames for training and inference.
Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI
Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.
Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy
The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.
Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces.
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning
We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques.
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion.
Temporal Registration in Application to In-utero MRI Time Series
To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.
Temporal Registration in In-Utero Volumetric MRI Time Series
We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series.
