Vector Quantized Convolutional Autoencoder Network for LDCT Image Reconstruction with Hybrid Loss
Medical image reconstruction is the process of creating high-quality and accurate images. During acquisition, these devices capture raw measurements or signals that represent the internal structures of the human body. However, these raw measurements are often noisy or incomplete. Low-dose CT is a me...
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| Vydáno v: | SN computer science Ročník 5; číslo 1; s. 2 |
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Springer Nature Singapore
01.01.2024
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| Abstract | Medical image reconstruction is the process of creating high-quality and accurate images. During acquisition, these devices capture raw measurements or signals that represent the internal structures of the human body. However, these raw measurements are often noisy or incomplete. Low-dose CT is a medical imaging technique that uses a reduced amount of radiation to obtain detailed cross-sectional images of the body. Deep learning for low-dose CT is an emerging field that utilizes advanced artificial intelligence techniques to enhance the image quality and diagnostic accuracy of CT scans acquired with reduced radiation doses. A convolutional autoencoder for low-dose CT is a specific type of deep-learning architecture designed to address the challenges of reducing radiation doses while maintaining image quality. They are effectively trained on large datasets of both low-dose and standard-dose CT images to learn patterns and features that can compensate for the noise and artifacts present in low-dose scans. This research offers a new vector quantization-based convolutional autoencoder network strategy for CT image reconstruction. In vector quantization, discrete data elements are mapped to a set of representative vectors known as codebook entries. Each data element is associated with the nearest codebook entry based on a defined distance metric. This mapping allows for the efficient representation of discrete data by replacing each element with its corresponding codebook entry. Discrete data representation is essential for the efficient storage and transmission of information during the image reconstruction task. The results’ quality is assessed based on the perceptual and bias-reducing loss functions. On the LoDoPaB-CT benchmark dataset, experimental evaluations are done. Its findings demonstrated that, in terms of quantitative and visual evaluation, respectively, the proposed network obtained better performance metric values and better noise reduction results. |
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| AbstractList | Medical image reconstruction is the process of creating high-quality and accurate images. During acquisition, these devices capture raw measurements or signals that represent the internal structures of the human body. However, these raw measurements are often noisy or incomplete. Low-dose CT is a medical imaging technique that uses a reduced amount of radiation to obtain detailed cross-sectional images of the body. Deep learning for low-dose CT is an emerging field that utilizes advanced artificial intelligence techniques to enhance the image quality and diagnostic accuracy of CT scans acquired with reduced radiation doses. A convolutional autoencoder for low-dose CT is a specific type of deep-learning architecture designed to address the challenges of reducing radiation doses while maintaining image quality. They are effectively trained on large datasets of both low-dose and standard-dose CT images to learn patterns and features that can compensate for the noise and artifacts present in low-dose scans. This research offers a new vector quantization-based convolutional autoencoder network strategy for CT image reconstruction. In vector quantization, discrete data elements are mapped to a set of representative vectors known as codebook entries. Each data element is associated with the nearest codebook entry based on a defined distance metric. This mapping allows for the efficient representation of discrete data by replacing each element with its corresponding codebook entry. Discrete data representation is essential for the efficient storage and transmission of information during the image reconstruction task. The results’ quality is assessed based on the perceptual and bias-reducing loss functions. On the LoDoPaB-CT benchmark dataset, experimental evaluations are done. Its findings demonstrated that, in terms of quantitative and visual evaluation, respectively, the proposed network obtained better performance metric values and better noise reduction results. |
| ArticleNumber | 2 |
| Author | Ramasundaram, Mohan Ramanathan, Shalini |
| Author_xml | – sequence: 1 givenname: Shalini orcidid: 0000-0002-4954-8013 surname: Ramanathan fullname: Ramanathan, Shalini email: 406916001@nitt.edu organization: Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli – sequence: 2 givenname: Mohan surname: Ramasundaram fullname: Ramasundaram, Mohan organization: Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli |
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| Keywords | Deep learning Quantization Convolutional neural network medical image reconstruction Autoencoders Low-dose computed tomography |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence Codes Computed tomography Computer Imaging Computer Science Computer Systems Organization and Communication Networks COVID-19 Data compression Data Structures and Information Theory Datasets Deep learning Dictionaries Image acquisition Image enhancement Image quality Image reconstruction Imaging techniques Information Systems and Communication Service Machine learning Medical imaging Neural networks Noise reduction Original Research Pattern Recognition and Graphics Quality assessment Radiation Radiation dosage Representations Research Trends in Computational Intelligence Software Engineering/Programming and Operating Systems Vision |
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| Title | Vector Quantized Convolutional Autoencoder Network for LDCT Image Reconstruction with Hybrid Loss |
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