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Computed Tomography01:10

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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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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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Related Experiment Video

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.

Dongyang Du1,2,3,4, Wenbing Lv1,2,3, Jieqin Lv1,2

  • 1School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China.

European Radiology
|November 10, 2022
PubMed
Summary

Deep learning harmonization using convolutional neural networks (CNNs) significantly improved radiomics model performance across different reconstruction kernels. This approach enhances reproducibility and generalizability for clinical tasks like cancer diagnosis.

Keywords:
Computed tomographyDeep learningHarmonizationRadiomicsReconstruction kernel

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Area of Science:

  • Medical Imaging Analysis
  • Deep Learning in Radiology
  • Radiomics Feature Harmonization

Background:

  • Reconstruction kernels (e.g., B30f, B70f) in CT imaging introduce variability affecting radiomics reproducibility and generalizability.
  • Existing harmonization methods like ComBat, SVD, and ICA have limitations in addressing kernel-induced differences.

Purpose of the Study:

  • To develop and evaluate a deep learning-based harmonization framework using convolutional neural networks (CNNs).
  • To assess the framework's ability to improve radiomics model performance across different clinical tasks and reconstruction kernels.
  • To determine the framework's generalization capability for mitigating effects of new/unobserved kernels on radiomics features.

Main Methods:

  • Developed two CNN models (CNNa, CNNb) to convert images between B70f and B30f kernels.
  • Evaluated model performance using Area Under the Curve (AUC) for lung cancer vs. pulmonary tuberculosis diagnosis and lymph node metastasis prediction.
  • Utilized Patient-Normalized Feature Difference (PNFD) to assess kernel compatibility and CNN harmonization effectiveness on unseen kernels.

Main Results:

  • CNN harmonization significantly improved AUC for lung cancer vs. pulmonary tuberculosis diagnosis (CNNa: 0.85 vs. 0.54-0.74; CNNb: 0.87 vs. 0.54-0.86).
  • Improved AUC for lymph node metastasis prediction (CNNa: 0.68 vs. 0.56-0.61; CNNb: 0.78 vs. 0.70-0.73).
  • Harmonization rendered 85% of investigated unknown kernels comparable to baseline, reducing median PNFD from 1.10-2.31 to 0.23-1.13.

Conclusions:

  • CNN-based harmonization effectively improves radiomics model performance between different reconstruction kernels.
  • The framework demonstrates superior performance compared to traditional methods like ComBat and matrix factorization.
  • CNN harmonization enhances feature reproducibility across specific and unobserved kernels, improving generalizability across scanners and vendors.