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Imaging Studies I: CT and MRI01:14

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Virtual Monoenergetic CT Imaging via Deep Learning.

Wenxiang Cong1, Yan Xi2, Paul Fitzgerald3

  • 1Biomedical Imaging Center, Center for Biotechnology & Interdisciplinary, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Patterns (New York, N.Y.)
|December 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method to create virtual monoenergetic (VM) images from single-energy CT scans. This approach offers DECT-level accuracy without increased complexity or radiation dose.

Keywords:
CTMMDVMcomputed tomographymachine learningmulti-material decompositionvirtual monoenergetic imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Computational Imaging

Background:

  • Conventional computed tomography (CT) lacks elemental composition information.
  • Dual-energy CT (DECT) provides spectral data but increases complexity and radiation dose.
  • Virtual monoenergetic (VM) and material-selective images are reconstructible from DECT data.

Purpose of the Study:

  • To develop a deep learning model for generating VM images from single-spectrum CT.
  • To enable material decomposition using single-spectrum CT data.
  • To reduce the need for DECT systems in certain applications.

Main Methods:

  • A modified residual neural network (ResNet) was trained on clinical DECT data.
  • The network maps single-spectrum CT images to VM images at specific energy levels.
  • The model was validated against VM images generated by DECT.

Main Results:

  • The deep learning model achieved excellent convergence and image accuracy.
  • Approximations of VM images had a relative error of less than 2%.
  • Multi-material decomposition into three tissue classes was achieved with DECT-comparable accuracy.

Conclusions:

  • Deep learning can accurately generate VM images from single-spectrum CT.
  • This method provides a potential alternative to DECT, reducing system complexity and radiation exposure.
  • The approach facilitates advanced material decomposition from standard CT acquisitions.