Search Results for author: Jiazhen Pan

Found 11 papers, 7 papers with code

Direct Cardiac Segmentation from Undersampled K-space Using Transformers

1 code implementation31 May 2024 Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan, Wenqi Huang, Kerstin Hammernik, Daniel Rueckert

The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images.

Cardiac Segmentation Segmentation

Attention-aware non-rigid image registration for accelerated MR imaging

1 code implementation26 Apr 2024 Aya Ghoul, Jiazhen Pan, Andreas Lingg, Jens Kübler, Patrick Krumm, Kerstin Hammernik, Daniel Rueckert, Sergios Gatidis, Thomas Küstner

The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI.

Image Registration Motion Estimation

NISF: Neural Implicit Segmentation Functions

1 code implementation15 Sep 2023 Nil Stolt-Ansó, Julian McGinnis, Jiazhen Pan, Kerstin Hammernik, Daniel Rueckert

Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements.

Cardiac Segmentation Image Segmentation +2

Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations

1 code implementation27 Mar 2023 Julian McGinnis, Suprosanna Shit, Hongwei Bran Li, Vasiliki Sideri-Lampretsa, Robert Graf, Maik Dannecker, Jiazhen Pan, Nil Stolt Ansó, Mark Mühlau, Jan S. Kirschke, Daniel Rueckert, Benedikt Wiestler

Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.

Super-Resolution

Reconstruction-driven motion estimation for motion-compensated MR CINE imaging

no code implementations5 Feb 2023 Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik

Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i. e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization.

Motion Estimation

Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging

no code implementations16 Dec 2022 Wenqi Huang, Hongwei Li, Jiazhen Pan, Gastao Cruz, Daniel Rueckert, Kerstin Hammernik

While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation. We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point.

Image Reconstruction

Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging

no code implementations8 Sep 2022 Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik

Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion.

Image Reconstruction Motion Estimation

Relationformer: A Unified Framework for Image-to-Graph Generation

1 code implementation19 Mar 2022 Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern Menze

We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.

Graph Generation Object +4

Cannot find the paper you are looking for? You can Submit a new open access paper.