PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts

Li, Guang; Huang, Xinhai; Huang, Xinyu; Zong, Yuan; Luo, Shouhua

doi:10.3390/e26020101

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…In recent years, deep learning has been widely applied in the field of CT imaging and has achieved some encouraging results [10][11][12][13], especially in sparse-view reconstruction, showing better imaging results than compressive sensing models [14,15]. Currently, deep learning-based sparse-view reconstruction methods can be categorized into four types: single-domain learning, direct mapping between measurement data and reconstructed images using networks, network models based on iterative reconstruction algorithms and dual-domain learning.…”

Section: Introductionmentioning

confidence: 99%

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction

Li,

Deng,

et al. 2024

Bioengineering

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X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network’s feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement.

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Section: Introductionmentioning

confidence: 99%

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction

Li,

Deng,

et al. 2024

Bioengineering

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Reliable multi‐modal medical image‐to‐image translation independent of pixel‐wise aligned data

Zhou,

2024

Medical Physics

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BackgroundThe current mainstream multi‐modal medical image‐to‐image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel‐wise aligned training data to constrain the model optimization. However, obtaining pixel‐wise aligned multi‐modal medical image datasets is challenging. Unsupervised methods can be trained without paired data, but their reliability cannot be guaranteed. At present, there is no ideal multi‐modal medical image‐to‐image translation method that can generate reliable translation results without the need for pixel‐wise aligned data.PurposeThis work aims to develop a novel medical image‐to‐image translation model that is independent of pixel‐wise aligned data (MITIA), enabling reliable multi‐modal medical image‐to‐image translation under the condition of misaligned training data.MethodsThe proposed MITIA model utilizes a prior extraction network composed of a multi‐modal medical image registration module and a multi‐modal misalignment error detection module to extract pixel‐level prior information from training data with misalignment errors to the largest extent. The extracted prior information is then used to construct a regularization term to constrain the optimization of the unsupervised cycle‐consistent Generative Adversarial Network model, restricting its solution space and thereby improving the performance and reliability of the generator. We trained the MITIA model using six datasets containing different misalignment errors and two well‐aligned datasets. Subsequently, we conducted quantitative analysis using peak signal‐to‐noise ratio and structural similarity as metrics. Moreover, we compared the proposed method with six other state‐of‐the‐art image‐to‐image translation methods.ResultsThe results of both quantitative analysis and qualitative visual inspection indicate that MITIA achieves superior performance compared to the competing state‐of‐the‐art methods, both on misaligned data and aligned data. Furthermore, MITIA shows more stability in the presence of misalignment errors in the training data, regardless of their severity or type.ConclusionsThe proposed method achieves outstanding performance in multi‐modal medical image‐to‐image translation tasks without aligned training data. Due to the difficulty in obtaining pixel‐wise aligned data for medical image translation tasks, MITIA is expected to generate significant application value in this scenario compared to existing methods.

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PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts

Cited by 2 publications

References 41 publications

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction

Reliable multi‐modal medical image‐to‐image translation independent of pixel‐wise aligned data

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