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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| SDCT group | |||
| LDCT group |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CT Radiation Doses | Radiation | Obtaining Low CT Radiation Doses by Adjusting Dose Levels |
|
| Measure | Description | Time Frame |
|---|---|---|
| Results of phantom research | Compare the changes in spatial resolution (TTF curve) and noise (NPS curve) between different algorithms | up to six months |
| Results of human clinical study | General information of clinical trial personnel Compare the general information of two groups of subjects, such as age, weight（kg）, height（m）, gender, and BMI (kg/m2). Quantitative image analysis The standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle were evaluated. Qualitative image analysis Two radiologists qualitatively assessed the overall image noise and overall image quality depiction. | up to six months |
| Measure | Description | Time Frame |
|---|---|---|
| Patient demographics | Participant demographics: Age (year)/Gender/Body weight (kg) / Body mass index (kg/m2) | up to six months |
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Inclusion Criteria:
Abdominal CT examination
Exclusion Criteria:
pregnancy and lactation for women unstable breath holding
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Abdominal CT examination
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| ((Wei Li[Author]) | Contact | 13869190655 | lwqfsh@126.com | |
| Hui Qi | Contact | 13210607228 | 1604158620@qq.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| uCT960+ | Recruiting | Shandong | Jinan Shandong | 250000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39707032 | Derived | Qi H, Cui D, Xu S, Li W, Zeng Q. Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction. Abdom Radiol (NY). 2025 Jul;50(7):3353-3362. doi: 10.1007/s00261-024-04760-4. Epub 2024 Dec 21. |
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