Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) improves chest CT image quality and reduces radiation exposure

Abstract

Objective: To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT).

Methods: Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe's test.

Results: At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.

Conclusion: For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.

Figure 1. Axial plain chest CT images at the upper lung zone (60-year-old male weighing 76 kg).

These images were created from scan data at 240 mA (A, D), 120 mA (B, E) and 60 mA (C, F). The three upper images (A–C) were reconstructed using AIDR3D and the three lower images (D–F) were reconstructed using a conventional reconstruction mode (Boost3D). Each image pair at the same tube current was created from single row data. Image noise was obviously reduced on images with AIDR3D, particularly at lower tube currents.

Figure 2. Reconstructed coronal plain chest CT images (56-year-old male weighing 62 kg).

Images are arranged as in Figure 1 (A–C, with AIDR3D; D–F, without AIDR3D; A and D, at 240 mA; B and E, at 120 mA; C and F, at 60 mA). Severe image noise was observed at the upper lung zones and bottoms at 120 and 60 mA without AIDR3D (E and F), which was obviously improved using AIDR3D (B and C).

Figure 3. Axial plain chest CT images with a solid lung mass in the right middle lobe (75-year-old female weighing 56 kg).

Images are arranged as in Figures 1 and 2. Spiculae were found on all images, while density heterogeneity inside the mass was severe on images at 60 mA without AIDR3D (F).

Figure 4. Axial plain chest CT images showing a ground-glass opacity (GGO) nodule in the left apex (74-year-old female weighing 49 kg).

Images are arranged as in Figures 1 and 2. Nearly homogeneous density of the nodule was accurately depicted on images with AIDR3D at any of three tube currents (A–C). However, on images without AIDR3D (D–F), artificial density heterogeneity due to image noise increased as the tube current decreased from 240 (D) to 60 mA (F).

Figure 5. Axial plain chest CT images with a mediastinal setting to assess streak artifacts (55-year-old male weighing 64 kg). A

, at 240 mA with AIDR3D; B, at 120 mA with AIDR3D; C, at 60 mA with AIDR3D; D, at 240 mA without AIDR3D; E, at 120 mA without AIDR3D; F, at 60 mA without AIDR3D. Many radial streaks from the spine were apparent in the heart, particularly on the image without AIDR3D at 60 mA (F). These streaks were greatly reduced using AIDR3D (C, at 60 mA).