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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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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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The important convolution properties include width, area, differentiation, and integration properties.
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Convolution computations can be simplified by utilizing their inherent properties.
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Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network.

Satoshi Kida1, Takahiro Nakamoto1, Masahiro Nakano2

  • 1Radiology, The University of Tokyo Hospital.

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A deep convolutional neural network (DCNN) improves cone beam computed tomography (CBCT) image quality by reducing artifacts. This DCNN method enhances spatial uniformity, peak-signal-to-noise ratio, and structural similarity for better image-guided radiation therapy.

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cone beam ctconvolutional neural networkdeep learningdeformable image registrationimage qualityplanning ct

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Cone beam computed tomography (CBCT) is crucial for image-guided radiation therapy (IGRT).
  • CBCT suffers from shading artifacts due to scatter and truncated projections.
  • Existing correction methods have limitations in improving CBCT image quality.

Purpose of the Study:

  • To develop a deep convolutional neural network (DCNN) for enhancing CBCT image quality.
  • To address shading artifacts and improve image fidelity in CBCT.

Main Methods:

  • A 39-layer DCNN was trained on paired CBCT and registered planning CT (pCT_r) images from 20 prostate cancer patients.
  • The DCNN learned a direct mapping from CBCT to pCT_r.
  • The trained model generated improved CBCT (i-CBCT) images from new CBCT data.

Main Results:

  • The i-CBCT images demonstrated significantly improved spatial uniformity compared to original CBCT.
  • Peak-signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were substantially enhanced in i-CBCT.
  • The DCNN method outperformed existing pCT-based correction techniques.

Conclusions:

  • A DCNN-based method effectively improves CBCT image quality.
  • The proposed method shows potential for direct application across various commercial CBCT scanners.
  • This advancement can benefit image-guided radiation therapy through superior CBCT imaging.