Feasibility of dose reduction using novel denoising techniques for low kV (80 kV) CT enterography: optimization and validation

Abstract

Rationale and objectives: The aim of this study was to optimize and validate projection-space denoising (PSDN) strategies for application to 80-kV computed tomographic (CT) data to achieve 50% dose reduction.

Materials and methods: Image data obtained at 80 kV (mean CT dose index volume, 7.9 mGy) from dual-source, dual-energy CT enterographic (CTE) exams in 42 patients were used. For each exam, nine 80 kV image data sets were reconstructed using PSDN (three levels of intensity) with or without image-based denoising and compared to commercial reconstruction kernels. For optimization, qualitative analysis selected optimal denoising strategies, with quantitative analysis measuring image contrast, noise, and sharpness (full width at half maximum bowel wall thickness, maximum CT number gradient). For validation, two radiologists examined image quality, comparing low-dose 80-kV optimally denoised images to full-dose mixed-voltage images.

Results: PSDN algorithms generated the best 80-kV image quality (41 of 42 patients), while the commercial kernels produced the worst (39 of 42) (P < .001). Overall, 80-kV PSDN approaches resulted in higher contrast (mean, 332 vs 290 Hounsfield units), slightly less noise (mean, 20 vs 26 Hounsfield units), but slightly decreased image sharpness (relative bowel wall thickness, 1.069 vs 1.000) compared to full-dose mixed-voltage images. Mean image quality scores for full-dose CTE images were 4.9 compared to 4.5 for optimally denoised half-dose 80-kV CTE images and 3.1 for nondenoised 80-kV CTE images (P < .001).

Conclusion: Optimized denoising strategies improve the quality of 80-kV CTE images such that CT data obtained at 50% of routine dose levels approaches the image quality of full-dose exams.

Figure 1

The process for obtaining images of projection space denoising and/or image space denoising with commercial kernels, illustrating the 9 different datasets evaluated. The commercial kernels used to reconstruct images after projection space denoising are listed in parentheses. (3D ORA = three-dimensional non-linear optimized reconstruction algorithm)

Figure 2

Selected CTE images exemplifying the four kinds of datasets analyzed by the two validation readers. A) 80 kV non-denoised dataset, B) dataset considered the best for this specific patient, C) dataset considered the best for this patient’s size group (≤ 35 cm), D) full-dose dataset. Note the significant decrease in image noise from A to B and C. Readers slightly preferred the full-dose, but gave to B) the same image quality score (5). In this patient, there is mild mucosal hyperenhancement in the ascending colon (arrows).