A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI
Objectives: Radiation exposure has been a cause of concern in computed tomography imaging. Reducing radiation dose increases the image noise which can be compensated by using reconstruction techniques. Recently artificial intelligence-based reconstruction technique has been introduced. Therefore, th...
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SAGE Publishing
2025-08-01
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| Series: | SAGE Open Medicine |
| Online Access: | https://doi.org/10.1177/20503121251336046 |
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| author | Thejas Marike Shivakumar Nitika C. Panakkal Shailesh Nayak Rajagopal Kadavigere Tanushree R. Kamath Suresh Sukumar |
| author_facet | Thejas Marike Shivakumar Nitika C. Panakkal Shailesh Nayak Rajagopal Kadavigere Tanushree R. Kamath Suresh Sukumar |
| author_sort | Thejas Marike Shivakumar |
| collection | DOAJ |
| description | Objectives: Radiation exposure has been a cause of concern in computed tomography imaging. Reducing radiation dose increases the image noise which can be compensated by using reconstruction techniques. Recently artificial intelligence-based reconstruction technique has been introduced. Therefore, the purpose of the study was to prospectively compare the image quality between Idose4 and Precise Image in normal BMI individuals. Methods: Sixty-six consecutive patients with a normal body habitus undergoing contrast-enhanced abdomen and pelvis scan were included in the study. All scans were performed using 100 kVp and tube current modulation. The acquired images were reconstructed to iDose4 and precise imaging. Quantitatively images were analyzed by placing regions of interest in different organs to estimate the image noise, signal-to-noise ratio, and contrast-to-noise ratio. Qualitative analysis was done by two radiologists on a five-point Likert scale. Results: Image noise was significantly reduced using Precise Image across the plain (9.11 ± 1.43 vs 8.18 ± 1.2), arterial (14.34 ± 2.1 vs 10.21 ± 1.5), and portovenous phase (14.78 ± 2.30 vs 11.97 ± 2.07) with maximum noise reduction in the arterial and portovenous phases. Signal-to-noise ratio and contrast-to-noise ratio was significantly improved in all the organs across the plain, arterial, and portovenous phases. Qualitative analysis showed no significant difference between Idose4 and Precise Image with regards to visualization of large vessels in the arterial and portovenous phases. However, precise image was graded better than Idose4 with respect to visualization/conspicuity, image noise, and artifacts. Conclusion: Precise Image can be useful in reducing the image noise and improving the signal-to-noise ratio and contrast-to-noise ratio in low-dose computed tomography protocol among normal BMI individuals. |
| format | Article |
| id | doaj-art-8b4454b5ce5341e88e11e53cd0ff0276 |
| institution | Kabale University |
| issn | 2050-3121 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | SAGE Open Medicine |
| spelling | doaj-art-8b4454b5ce5341e88e11e53cd0ff02762025-08-22T12:03:42ZengSAGE PublishingSAGE Open Medicine2050-31212025-08-011310.1177/20503121251336046A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMIThejas Marike Shivakumar0Nitika C. Panakkal1Shailesh Nayak2Rajagopal Kadavigere3Tanushree R. Kamath4Suresh Sukumar5Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Karnataka, IndiaDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Karnataka, IndiaDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Karnataka, IndiaRadio-Diagnosis and Imaging, Department of Radio Diagnosis and Medical Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka, IndiaRadio-Diagnosis and Imaging, Department of Radio Diagnosis and Medical Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka, IndiaDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Karnataka, IndiaObjectives: Radiation exposure has been a cause of concern in computed tomography imaging. Reducing radiation dose increases the image noise which can be compensated by using reconstruction techniques. Recently artificial intelligence-based reconstruction technique has been introduced. Therefore, the purpose of the study was to prospectively compare the image quality between Idose4 and Precise Image in normal BMI individuals. Methods: Sixty-six consecutive patients with a normal body habitus undergoing contrast-enhanced abdomen and pelvis scan were included in the study. All scans were performed using 100 kVp and tube current modulation. The acquired images were reconstructed to iDose4 and precise imaging. Quantitatively images were analyzed by placing regions of interest in different organs to estimate the image noise, signal-to-noise ratio, and contrast-to-noise ratio. Qualitative analysis was done by two radiologists on a five-point Likert scale. Results: Image noise was significantly reduced using Precise Image across the plain (9.11 ± 1.43 vs 8.18 ± 1.2), arterial (14.34 ± 2.1 vs 10.21 ± 1.5), and portovenous phase (14.78 ± 2.30 vs 11.97 ± 2.07) with maximum noise reduction in the arterial and portovenous phases. Signal-to-noise ratio and contrast-to-noise ratio was significantly improved in all the organs across the plain, arterial, and portovenous phases. Qualitative analysis showed no significant difference between Idose4 and Precise Image with regards to visualization of large vessels in the arterial and portovenous phases. However, precise image was graded better than Idose4 with respect to visualization/conspicuity, image noise, and artifacts. Conclusion: Precise Image can be useful in reducing the image noise and improving the signal-to-noise ratio and contrast-to-noise ratio in low-dose computed tomography protocol among normal BMI individuals.https://doi.org/10.1177/20503121251336046 |
| spellingShingle | Thejas Marike Shivakumar Nitika C. Panakkal Shailesh Nayak Rajagopal Kadavigere Tanushree R. Kamath Suresh Sukumar A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI SAGE Open Medicine |
| title | A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI |
| title_full | A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI |
| title_fullStr | A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI |
| title_full_unstemmed | A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI |
| title_short | A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI |
| title_sort | comparison of the image quality between deep learning reconstruction algorithm and idose4 using low dose abdominopelvic computed tomography for individuals with normal bmi |
| url | https://doi.org/10.1177/20503121251336046 |
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