Predicting 4D Liver MRI for MR-guided Interventions

• URL: http://arxiv.org/abs/2202.12628v1
• Date: Fri, 25 Feb 2022 11:34:25 GMT
• Title: Predicting 4D Liver MRI for MR-guided Interventions
• Authors: Gino Gulamhussene, Anneke Meyer, Marko Rak, Oleksii Bashkanov, Jazan Omari, Maciej Pech, Christian Hansen
• Abstract summary: Organ motion poses an unresolved challenge in image-guided interventions. We propose a novel approach for real-time, high-resolution 4D MRI with large fields of view. We show that small training sizes with short acquisition times down to 2min can already achieve promising results.
• Score: 2.9699476315275772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organ motion poses an unresolved challenge in image-guided interventions. In the pursuit of solving this problem, the research field of time-resolved volumetric magnetic resonance imaging (4D MRI) has evolved. However, current techniques are unsuitable for most interventional settings because they lack sufficient temporal and/or spatial resolution or have long acquisition times. In this work, we propose a novel approach for real-time, high-resolution 4D MRI with large fields of view for MR-guided interventions. To this end, we trained a convolutional neural network (CNN) end-to-end to predict a 3D liver MRI that correctly predicts the liver's respiratory state from a live 2D navigator MRI of a subject. Our method can be used in two ways: First, it can reconstruct near real-time 4D MRI with high quality and high resolution (209x128x128 matrix size with isotropic 1.8mm voxel size and 0.6s/volume) given a dynamic interventional 2D navigator slice for guidance during an intervention. Second, it can be used for retrospective 4D reconstruction with a temporal resolution of below 0.2s/volume for motion analysis and use in radiation therapy. We report a mean target registration error (TRE) of 1.19 $\pm$0.74mm, which is below voxel size. We compare our results with a state-of-the-art retrospective 4D MRI reconstruction. Visual evaluation shows comparable quality. We show that small training sizes with short acquisition times down to 2min can already achieve promising results and 24min are sufficient for high quality results. Because our method can be readily combined with earlier methods, acquisition time can be further decreased while also limiting quality loss. We show that an end-to-end, deep learning formulation is highly promising for 4D MRI reconstruction.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors [39.58317527488534]
  We developed a deep learning-based method for MRI reconstruction from undersampled data acquired with RT-specific receiver coil arrangements. DeepMRIRec reduced scanning time by a factor of four producing a structural similarity score surpassing the evaluated state-of-the-art method.
  arXiv  Detail & Related papers  (2023-11-22T16:01:44Z)
• 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)
• Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI [1.207455285737927]
  Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements and a real 4D flow MRI scan. Our optimized SIREN architecture outperformed state-of-the-art techniques, producing denoised and super-resolved velocity fields from clinical data.
  arXiv  Detail & Related papers  (2023-02-24T08:42:04Z)
• Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution [21.75329634476446]
  We develop a novel deep learning framework called the coarse-super-resolution-fine network (CoSF-Net) to achieve simultaneous motion estimation and excavating super-resolution in a unified model. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformable vector fields between the respiratory phases of 4D-MRI but also simultaneously improved the spatial resolution of 4D-MRI with enhanced anatomic features.
  arXiv  Detail & Related papers  (2022-11-21T01:42:51Z)
• 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)
• DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI [0.27998963147546135]
  Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. MRI with high temporal resolution suffers from limited spatial resolution. Deep learning based super-resolution approaches have been proposed to mitigate this trade-off. This research addresses the problem by creating a deep learning model which attempts to learn both spatial and temporal relationships.
  arXiv  Detail & Related papers  (2022-02-10T22:20:58Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)
• Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives [5.3148259096171175]
  One drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities. Deep Neural Networks (DNNs) have been used in sparse MRI reconstruction models to recreate relatively high-quality images. Generative Adversarial Networks (GAN) based methods are proposed to solve fast MRI with enhanced image perceptual quality.
  arXiv  Detail & Related papers  (2021-05-04T23:59:00Z)
• 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)
• 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation [49.32653090178743]
  We investigate whether extending this problem to full 4D deep learning using a history of MRI volumes can improve performance. We find that our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
  arXiv  Detail & Related papers  (2020-04-20T11:41:01Z)

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.