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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge.

Yongshuai Ge1,2,3, Ting Su1, Jiongtao Zhu1

  • 1Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Quantitative Imaging in Medicine and Surgery
|March 20, 2020
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Summary

A new deep learning model, ADAPTIVE-NET, reconstructs high-quality low-dose computed tomography (LDCT) images directly from sinograms. This method integrates analytical domain transformation for improved image reconstruction performance compared to existing networks.

Keywords:
CT reconstructionComputed tomography (CT)convolutional neural network (CNN)domain transformationlow dose CT reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Reconstruction

Background:

  • The field of computed tomography (CT) reconstruction is rapidly advancing with deep learning techniques.
  • Traditional CT reconstruction methods face challenges, particularly with low-dose protocols.

Purpose of the Study:

  • To introduce ADAPTIVE-NET, a novel convolutional neural network for direct CT image reconstruction from sinograms.
  • To leverage analytical domain transformation knowledge within the deep learning framework.

Main Methods:

  • ADAPTIVE-NET utilizes a custom network layer for analytical back-projection, transforming sinograms to the CT image domain.
  • Simultaneous feature extraction is performed in both the sinogram and CT image domains.
  • The network was validated using Mayo low-dose CT (LDCT) data and compared against the RED-CNN, evaluating Mean Square Error (MSE) and VGG-based perceptual loss.

Main Results:

  • ADAPTIVE-NET successfully reconstructs clinically relevant 512x512 CT images from sinograms on a single GPU.
  • The proposed network outperforms the RED-CNN when using the same MSE loss function.
  • Jointly applying VGG loss enhances image quality, producing more natural-looking CT images.

Conclusions:

  • The end-to-end supervised ADAPTIVE-NET effectively reconstructs high-quality LDCT images directly from sinograms.
  • This deep learning approach offers a promising advancement in CT image reconstruction technology.