A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

• URL: http://arxiv.org/abs/2002.12680v2
• Date: Sat, 25 Apr 2020 02:18:37 GMT
• Title: A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
• Authors: Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang and Jinman Kim
• Abstract summary: We introduce a volumetric motion network (VINS) designed for 4D dynamic medical images. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical methods and natural video methods.
• Score: 18.670134909724723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the dynamic sequence by interpolating the volumes between the acquired image volumes. However, these methods are limited to either 2D images and/or are unable to support large variations in the motion between the image volume sequences. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation methods that have been extended to support volumetric images. Our ablation study further exemplified that our motion network was able to better represent the large functional motion compared with the state-of-the-art unsupervised medical registration methods.

Related papers

• MindFormer: A Transformer Architecture for Multi-Subject Brain Decoding via fMRI [50.55024115943266]
  We introduce a new Transformer architecture called MindFormer to generate fMRI-conditioned feature vectors. MindFormer incorporates two key innovations: 1) a novel training strategy based on the IP-Adapter to extract semantically meaningful features from fMRI signals, and 2) a subject specific token and linear layer that effectively capture individual differences in fMRI signals.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Deep Cardiac MRI Reconstruction with ADMM [7.694990352622926]
  We present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of cardiac imaging. Our method optimize in both the image and k-space domains, allowing for high reconstruction fidelity.
  arXiv  Detail & Related papers  (2023-10-10T13:46:11Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)
• DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method [12.26150675728958]
  We propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures.
  arXiv  Detail & Related papers  (2022-06-14T20:46:11Z)
• 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)
• Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM) [19.70386996879205]
  We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary MRI. We model the image frames in the time series as the deformed version of the 3D template image volume. We assume the deformation maps to be points on a smooth manifold in high-dimensional space.
  arXiv  Detail & Related papers  (2021-12-06T22:04:57Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)
• A deep learning pipeline for identification of motor units in musculoskeletal ultrasound [0.5249805590164902]
  It has been shown that ultrafast ultrasound can be used to record and analyze the mechanical response of individual MUs. We present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences. We train evaluate the model using simulated data mimicking the complex activation pattern and overlapping territories.
  arXiv  Detail & Related papers  (2020-09-23T20:44:29Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• 4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes [69.62333053044712]
  3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single OCT images. We extend 3D CNNs to 4D-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.
  arXiv  Detail & Related papers  (2020-07-02T12:02:20Z)
• Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
  We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
  arXiv  Detail & Related papers  (2020-01-14T22:55:03Z)

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.