Impact of variable helical pitch (vHP) on radiation dose and image quality in computed tomography (CT) examinations
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Date
2024-07-15
Authors
Κοντομίχη, Ελένη
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Abstract
The study aims to optimize Chest Abdomen Pelvis (CAP) protocols in a modern CT equipped with deep learning reconstruction algorithm in post-processing. The population consisted of 30 patients who underwent CAP CT examination in Arterial and Venous Phase. The patients were categorized into 3 weight groups: Group 1≤50kg, 50kg; Group 2≤80kg and Group 3>80kg. All studies were performed on a 320-slice CT scanner. Volume CT dose index (CTDIvol) and dose length product (DLP) were recorded from the CT dose report. The effective dose (ED) was calculated by multiplying DLP with appropriate k conversion factors. Subjective image quality was evaluated by experienced radiologists using the Likert scale (1 for perfect diagnostic image, 3 for threshold, and 5 for non-diagnostic). For objective evaluation, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for specific regions of interest (ROIs) from DICOM images. The mean ED was 10.73±2.81mSv for Group 1, 9.12±4.27mSv for Group 2 and 20.70±5.45mSv for Group 3. Regarding the subjective image quality in chest region, noise, contrast, diagnostic confidence, and artifacts were 1.8 ±0.4, 1.9±0.2, 2.0±0.0 and 1.4 ±0.3, respectively for Group1. In the same group, the results for abdomen-pelvis (AP) were: 2.0± 0.5, 2.0± 0.0, 2.1± 0.2 and 1.4 ±0.3. In Group 2, the same parameters were calculated as: 2.3± 0.6, 1.7± 0.6, 1.9 ±0.6 and 2.5 ±0.3 for chest and 2.6 ±0.4, 2.2± 0.5, 2.1 ±0.4 and 1.6± 0.5 for AP. For Group 3, the corresponding values were: 2.4±0.4, 1.7 ±0.4, 2.7± 0.6 ,2.9±0.7 for chest and 2.3±0.4, 1.3±0.3, 2.1±0.6 and 2.0±0.7 for AP. In objective evaluation, SNR=11.27±2.61 and CNR=4.70±1.97 for Group 1, SNR=11.41±2.09 and CNR=4.58±1.30 for Group 2, and SNR=11.81±5.26 and CNR=5.73±2.85 for Group 3. CAP CT protocols for Group 1 needs further optimization to achieve a diagnostically acceptable image quality with the lowest possible radiation exposure.
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Keywords
Optimization, Computed tomography