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The Impact of Different Scanning Methods and Reconstruction Algorithms on CT Image Quality

NCT06142539 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2024-03-15

No results posted yet for this study

Summary

Purpose: To evaluate the image quality of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).

Methods: CT images of a phantom were reconstructed with Hybrid iterative reconstruction and deep learning image reconstruction (DLIR). The noise power spectrum (NPS) and task transfer function (TTF) were measured. Two patient groups were included in this study: consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and consecutive patients who underwent unenhanced abdominal LDCT reconstructed of HIR and DLIR (LDCT group). The CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle and abdominal subcutaneous fat were evaluated. Radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. Quantitative and qualitative parameters were compared between SDCT and LDCT groups.

Conditions

  • CT Examination of the Abdomen

Interventions

RADIATION

CT Radiation Doses

Obtaining Low CT Radiation Doses by Adjusting Dose Levels

Sponsors & Collaborators

  • Wei Li

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-13
Primary Completion
2025-02-28
Completion
2025-05-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06142539 on ClinicalTrials.gov