Objective: To determine signal-to-noise (SNR), contrast-to-noise ratio, and segmentation error measurements in various low-dose computed tomographic (CT) acquisitions of an anthropomorphic phantom containing urinary stones before and after implementation of a structure-preserving diffusion (SPD) denoising algorithm, and to compare the measurements with those of standard-dose CT acquisitions.
Methods: After institutional review board approval, written informed consent was waived and 36 calcium oxalate stones were evaluated after CT acquisitions in an anthropomorphic phantom at variable tube currents (33–137 mA s). The SPD denoising algorithm was applied to all images. Signal-to-noise ratio, contrast-to-noise ratio, and expected segmentation error were determined using manually drawn regions of interest to quantify the effect of the noise reduction on the image quality.
Results: The value of segmentation error measurements using the SPD denoising algorithm obtained at tube currents as low as 33 mA s (up to 75% dose reduction level) were similar to standard imaging at 137 mA s. The denoised images at reduced doses up to 75% dose reduction have higher SNR than the standard-dose images without denoising (P < 0.005). Stepwise regression showed significant (P < 0.001) effect of dose length product on SNR, and segmentation error measurements.
Conclusions: Based on objective noise-related image quality metrics, the SPD denoising algorithm may be useful as a robust and fast tool, and it has the potential to improve image quality in low-dose CT ureter protocols.
From the *Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital, Boston, MA; and †Vital Images, A Toshiba Medical Systems Group, Minnetonka, MN.
Received for publication March 21, 2012; accepted May 29, 2012.
Reprints: Frank J. Rybicki, MD, PhD, FAHA, Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115 (e-mail: email@example.com).
No funding was received for this work.
Dr. Rybicki receives grant support from Toshiba Medical Systems Corporation. None of these research funds were related to or used in this project.
Drs. Stefan Atev, Pascal Salazar, and Osama Masoud are employees of Vital Images, a Toshiba Medical Systems Group Company.
Dr. Philippe Raffy was an employee of Vital Images, a Toshiba Medical Systems Group Company, during the data analyses and drafting of the manuscript.
The remaining authors have no potential conflicts of interest.
All image data from this project were generated by, and under the control of, the corresponding author.