Susceptibility Distortion Correction of Diffusion MRI with a single Phase-Encoding Direction

• URL: http://arxiv.org/abs/2508.13340v1
• Date: Mon, 18 Aug 2025 19:56:03 GMT
• Title: Susceptibility Distortion Correction of Diffusion MRI with a single Phase-Encoding Direction
• Authors: Sedigheh Dargahi, Sylvain Bouix, Christian Desrosier,
• Abstract summary: We propose a deep learning-based approach to correct susceptibility distortions using only a single acquisition.<n> Experimental results show that our method achieves performance comparable to topup.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion MRI (dMRI) is a valuable tool to map brain microstructure and connectivity by analyzing water molecule diffusion in tissue. However, acquiring dMRI data requires to capture multiple 3D brain volumes in a short time, often leading to trade-offs in image quality. One challenging artifact is susceptibility-induced distortion, which introduces significant geometric and intensity deformations. Traditional correction methods, such as topup, rely on having access to blip-up and blip-down image pairs, limiting their applicability to retrospective data acquired with a single phase encoding direction. In this work, we propose a deep learning-based approach to correct susceptibility distortions using only a single acquisition (either blip-up or blip-down), eliminating the need for paired acquisitions. Experimental results show that our method achieves performance comparable to topup, demonstrating its potential as an efficient and practical alternative for susceptibility distortion correction in dMRI.

Related papers

• Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data [11.174208209806073]
  Undersampling strategies can accelerate image acquisition, but they often result in image artifacts and degraded quality.<n>Recent diffusion models have shown promise for reconstructing high-fidelity images from undersampled data by learning powerful image priors.<n>We introduce a conditional denoising diffusion framework with iterative data-consistency correction.
  arXiv  Detail & Related papers  (2025-10-07T18:01:08Z)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior [29.81704480466466]
  Medical image restoration tasks aim to recover high-quality images from degraded observations, exhibiting emergent desires in many clinical scenarios.<n>Existing deep learning-based restoration methods struggle with rendering computationally-efficient reconstruction results.<n>We present LRformer, a Lightweight Transformer-based method via Reliability-guided learning in the frequency domain.
  arXiv  Detail & Related papers  (2025-04-15T15:26:28Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI [5.393543723150301]
  This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HaITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction. HaITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling.
  arXiv  Detail & Related papers  (2024-06-28T16:40:57Z)
• Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning [2.359373908374829]
  We propose an image translator to restore correspondence between images.<n>We also propose a registration model to align the translated images.<n>This work, to the best of our knowledge, is the first to tackle this problem with deep learning.
  arXiv  Detail & Related papers  (2024-05-17T12:11:58Z)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [39.94162291765236]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence [31.25388976923081]
  Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of inmagnitude water molecules.<n>DWI MRI acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot.<n>These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels.<n>We propose a Physics-Informed Deep DWI reconstruction method to synthesize high-quality paired training data.
  arXiv  Detail & Related papers  (2022-10-20T16:27:54Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss [58.58979566599889]
  We propose a novel self-supervised learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation. The proposed method demonstrates advanced performance in both the quality of synthesized results under a severely misaligned and unpaired data setting.
  arXiv  Detail & Related papers  (2022-01-29T13:45:39Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

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.