Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework

• URL: http://arxiv.org/abs/2312.08343v2
• Date: Mon, 18 Dec 2023 03:50:53 GMT
• Title: Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework
• Authors: Zhuoyao Xin, Christopher Wu, Dong Liu, Chunming Gu, Jia Guo, Jun Hua
• Abstract summary: We propose a versatile multi-task neural network framework, based on an enhanced Transformer U-Net architecture. We decompose the traditional problem of synthesizing CT images into distinct subtasks. To enhance the framework's versatility in handling multi-modal data, we expand the model with multiple image channels.
• Score: 16.864720020158906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image segmentation, real-value prediction, and cross-modal translation are critical challenges in medical imaging. In this study, we propose a versatile multi-task neural network framework, based on an enhanced Transformer U-Net architecture, capable of simultaneously, selectively, and adaptively addressing these medical image tasks. Validation is performed on a public repository of human brain MR and CT images. We decompose the traditional problem of synthesizing CT images into distinct subtasks, which include skull segmentation, Hounsfield unit (HU) value prediction, and image sequential reconstruction. To enhance the framework's versatility in handling multi-modal data, we expand the model with multiple image channels. Comparisons between synthesized CT images derived from T1-weighted and T2-Flair images were conducted, evaluating the model's capability to integrate multi-modal information from both morphological and pixel value perspectives.

Related papers

• Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser [12.402947207350394]
  We propose a transformer-based modality infuser designed to synthesize multimodal brain MR images. In our method, we extract modality-agnostic features from the encoder and then transform them into modality-specific features. We carried out experiments on the BraTS 2018 dataset, translating between four MR modalities.
  arXiv  Detail & Related papers  (2024-02-01T06:34:35Z)
• CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation [5.662694302758443]
  Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. It frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition.
  arXiv  Detail & Related papers  (2023-09-06T19:01:58Z)
• MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data [0.0]
  Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues. ViTs are proven to outperform traditional deep learning models, such as convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2022-11-12T19:07:25Z)
• 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)
• Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution [55.52779466954026]
  Multi-contrast super-resolution (SR) reconstruction is promising to yield SR images with higher quality. Existing methods lack effective mechanisms to match and fuse these features for better reconstruction. We propose a novel network to address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques.
  arXiv  Detail & Related papers  (2022-03-26T01:42:59Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• ResViT: Residual vision transformers for multi-modal medical image synthesis [0.0]
  We propose a novel generative adversarial approach for medical image synthesis, ResViT, to combine local precision of convolution operators with contextual sensitivity of vision transformers. Our results indicate the superiority of ResViT against competing methods in terms of qualitative observations and quantitative metrics.
  arXiv  Detail & Related papers  (2021-06-30T12:57:37Z)
• Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation [9.659642285903418]
  Cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists. We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
  arXiv  Detail & Related papers  (2021-03-05T16:22:31Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis [143.55901940771568]
  We propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis. In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality. A multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality.
  arXiv  Detail & Related papers  (2020-02-11T08:26:42Z)

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.