Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations

• URL: http://arxiv.org/abs/2505.09565v1
• Date: Wed, 14 May 2025 17:07:37 GMT
• Title: Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations
• Authors: Maik Dannecker, Thomas Sanchez, Meritxell Bach Cuadra, Özgün Turgut, Anthony N. Price, Lucilio Cordero-Grande, Vanessa Kyriakopoulou, Joseph V. Hajnal, Daniel Rueckert,
• Abstract summary: High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjects.<n>Existing solutions struggle with image artifacts and severe subject motion or require slice pre-alignment to achieve satisfying reconstruction performance.<n>We propose a novel SVR method to enable fast and accurate MRI reconstruction even in cases of severe image and motion corruption.
• Score: 9.749932662063992
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjects, such as fetal brain Magnetic Resonance Imaging (MRI). Existing solutions struggle with image artifacts and severe subject motion or require slice pre-alignment to achieve satisfying reconstruction performance. We propose a novel SVR method to enable fast and accurate MRI reconstruction even in cases of severe image and motion corruption. Our approach performs motion correction, outlier handling, and super-resolution reconstruction with all operations being entirely based on implicit neural representations. The model can be initialized with task-specific priors through fully self-supervised meta-learning on either simulated or real-world data. In extensive experiments including over 480 reconstructions of simulated and clinical MRI brain data from different centers, we prove the utility of our method in cases of severe subject motion and image artifacts. Our results demonstrate improvements in reconstruction quality, especially in the presence of severe motion, compared to state-of-the-art methods, and up to 50% reduction in reconstruction time.

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework [7.639405634241267]
  Motion is estimated to be present in approximately 30% of clinical MRI scans. Deep learning algorithms have been demonstrated to be effective for both the image reconstruction task and the motion correction task. We propose a novel method to simultaneously accelerate imaging and correct motion.
  arXiv  Detail & Related papers  (2024-05-28T02:16:35Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction [25.078280843551322]
  We introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled MRI reconstruction. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets.
  arXiv  Detail & Related papers  (2023-06-01T10:29:58Z)
• Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis [0.0]
  This dissertation aims to provide advanced nonsmooth variational models (Magnetic Resonance Image) MRI reconstruction, efficient learnable image reconstruction algorithms, and deep learning methods for MRI reconstruction and synthesis. The first part introduces a novel based deep neural network whose architecture is inspired by proximal gradient descent for a variational model. The second part is a substantial extension of the preliminary work in the first part by solving the calibration-free fast pMRI reconstruction problem in a discrete-time optimal framework. The third part aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the metalearning framework.
  arXiv  Detail & Related papers  (2023-03-02T18:59:44Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions [0.688204255655161]
  A popular approach to accelerated MRI is to undersample the k-space data. While undersampling speeds up the scan procedure, it generates artifacts in the images, and advanced reconstruction algorithms are needed to produce artifact-free images. In this work, MRI reconstruction from undersampled data was carried out using an optimized neural network using a novel evolutionary neural architecture search algorithm.
  arXiv  Detail & Related papers  (2022-06-15T03:42:18Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction [3.058685580689605]
  We present a novel Deep Learning-based Inverse Problem solver applied to the task of accelerated MRI reconstruction. The RecurrentVarNet consists of multiple blocks, each responsible for one unrolled iteration of the gradient descent algorithm for solving inverse problems. Our proposed method achieves new state of the art qualitative and quantitative reconstruction results on 5-fold and 10-fold accelerated data from a public multi-channel brain dataset.
  arXiv  Detail & Related papers  (2021-11-18T11:44:04Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.