跳至主要内容
临床试验/CTRI/2024/03/064056
CTRI/2024/03/064056已完成不适用

Comparison of image quality between hybrid iterative reconstruction and deep learning image reconstruction for low-dose CT of the abdomen and pelvis among low B.M.I individuals

Kasturba Hospital1 个研究点 分布在 1 个国家目标入组 50 人开始时间: 2024年3月22日最近更新:

试验速览

阶段
不适用
状态
已完成
入组人数
50
试验地点
1
主要终点
Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), and image noise

研究概览

简要总结

Informed consent and 50 participants will be taken for the study.

The patients age, height and weight will be measured, and their body mass index (BMI) will be calculated. The scan will be acquired using the standard protocol used for CECT abdomen and pelvis which is the standard of care for all low BMI patients and no additional costs will be borne by the patients. The acquired data will then undergo reconstruction using idose4 and precise imaging techniques. The quantitative assessment of image quality will be performed by calculating signal-to-noise ration (SNR), contrast-to-noise ratio (CNR), and image noise. data analysis will be performed, and outcome measures will be drawn.

研究设计

研究类型
Observational

入排标准

年龄范围
18.00 Year(s) 至 90.00 Year(s)(—)
性别
All

入选标准

  • Patients who are of age above 18 years, referred for CECT abdomen.
  • Low Body Mass Index population (less than 18.5 kg/m²).

排除标准

  • Patients who come above 18.5 kg/m2 Body mass index Population.
  • Uncooperative patients.
  • Fatty, liver cirrhosis patients will be excluded.

结局指标

主要结局

Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), and image noise

时间窗: 16 months

次要结局

未报告次要终点

研究者

申办方类型
Research institution and hospital
责任方
Principal Investigator
主要研究者

Abraham Jacob

Manipal college of health professions MAHE Manipal

研究点 (1)

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