Search Results for author: Elfar Adalsteinsson

Found 17 papers, 7 papers with code

FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model

no code implementations29 Mar 2024 Molin Zhang, Polina Golland, Patricia Ellen Grant, Elfar Adalsteinsson

In this study, we introduce FetalDiffusion, a novel approach utilizing a conditional diffusion model to generate 3D synthetic fetal MRI with controllable pose.

Pose Estimation

Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space

1 code implementation3 Mar 2023 Molin Zhang, Junshen Xu, Yamin Arefeen, Elfar Adalsteinsson

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.

Self-Supervised Learning SSIM +3

Data Consistent Deep Rigid MRI Motion Correction

1 code implementation25 Jan 2023 Nalini M. Singh, Neel Dey, Malte Hoffmann, Bruce Fischl, Elfar Adalsteinsson, Robert Frost, Adrian V. Dalca, Polina Golland

Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.

Image Reconstruction

Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI

1 code implementation27 Aug 2022 Yamin Arefeen, Junshen Xu, Molin Zhang, Zijing Dong, Fuyixue Wang, Jacob White, Berkin Bilgic, Elfar Adalsteinsson

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.

Decoder

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

1 code implementation22 Jun 2022 Junshen Xu, Daniel Moyer, P. Ellen Grant, Polina Golland, Juan Eugenio Iglesias, Elfar Adalsteinsson

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.

3D Reconstruction

Rapid head-pose detection for automated slice prescription of fetal-brain MRI

no code implementations8 Oct 2021 Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, Leah Morgan, Paul Wighton, M. Dylan Tisdall, Martin Reuter, Elfar Adalsteinsson, P. Ellen Grant, Lawrence L. Wald, André J. W. van der Kouwe

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.

Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging

1 code implementation23 Jun 2021 Junshen Xu, Elfar Adalsteinsson

Further, it only requires a single noisy image with a few auxiliary observations at different time frames for training and inference.

Image Denoising

Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI

no code implementations2 Apr 2021 Yamin Arefeen, Onur Beker, Jaejin Cho, Heng Yu, Elfar Adalsteinsson, Berkin Bilgic

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

no code implementations16 Jul 2020 Molin Zhang, Junshen Xu, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson

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.

Anatomy Decision Making

Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis

1 code implementation2 Jul 2020 Nalini M. Singh, Juan Eugenio Iglesias, Elfar Adalsteinsson, Adrian V. Dalca, Polina Golland

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.

Image Denoising MRI Reconstruction

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

no code implementations23 Jun 2020 Junshen Xu, Sayeri Lala, Borjan Gagoski, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson

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.

Image Quality Assessment

Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning

no code implementations30 Sep 2019 Daniel Polak, Itthi Chatnuntawech, Jaeyeon Yoon, Siddharth Srinivasan Iyer, Jong-Ho Lee, Peter Bachert, Elfar Adalsteinsson, Kawin Setsompop, Berkin Bilgic

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

no code implementations10 Jul 2019 Junshen Xu, Molin Zhang, Esra Abaci Turk, Larry Zhang, Ellen Grant, Kui Ying, Polina Golland, Elfar Adalsteinsson

The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion.

Pose Estimation Time Series +1

Temporal Registration in Application to In-utero MRI Time Series

no code implementations6 Mar 2019 Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, Elfar Adalsteinsson, P. Ellen Grant, Polina Golland

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.

Time Series Time Series Alignment

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