Feasibility of thin-slice abdominal CT in overweight patients using a vendor neutral image-based denoising algorithm: Assessment of image noise, contrast, and quality

Abstract

The purpose of this study was to investigate whether the novel image-based noise reduction software (NRS) improves image quality, and to assess the feasibility of using this software in combination with hybrid iterative reconstruction (IR) in image quality on thin-slice abdominal CT. In this retrospective study, 54 patients who underwent dynamic liver CT between April and July 2017 and had a body mass index higher than 25 kg/m2 were included. Three image sets of each patient were reconstructed as follows: hybrid IR images with 1-mm slice thickness (group A), hybrid IR images with 5-mm slice thickness (group B), and hybrid IR images with 1-mm slice thickness denoised using NRS (group C). The mean image noise and contrast-to-noise ratio relative to the muscle of the aorta and liver were assessed. Subjective image quality was evaluated by two radiologists for sharpness, noise, contrast, and overall quality using 5-point scales. The mean image noise was significantly lower in group C than in group A (p < 0.01), but no significant difference was observed between groups B and C. The contrast-to-noise ratio was significantly higher in group C than in group A (p < 0.01 and p = 0.01, respectively). Subjective image quality was also significantly higher in group C than in group A (p < 0.01), in terms of noise and overall quality, but not in terms of sharpness and contrast (p = 0.65 and 0.07, respectively). The contrast of images in group C was greater than that in group A, but this difference was not significant. Compared with hybrid IR alone, the novel NRS combined with a hybrid IR could result in significant noise reduction without sacrificing image quality on CT. This combined approach will likely be particularly useful for thin-slice abdominal CT examinations of overweight patients.

AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography

“…Other studies have described the potential benefit of AI denoising algorithms on CT in obese patients. For example, Tamura et al described significantly reduced image noise when applying a denoising solution on abdominal CT of obese patients, effectively enabling thin-slice imaging without sacrificing image quality [26]. However, their study focused on conventional CT of obese patients only, so they did not perform BMI subgroup analyses to evaluate the stability of the denoising itself.…”

CT image denoising methods for image quality improvement and radiation dose reduction

“…Evaluating a DL denoising model involves assessing its ability to effectively reduce noise while preserving important image details. Generated denoised images are usually compared against the ground truth images such as FBP, 164 , 165 , 166 IR, 167 , 168 , 169 , 170 or other DL methods. 158 , 159 , 163 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 This comparison can provide insights into the model's relative strengths and weaknesses in terms of denoising performance.…”

Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography

“…CT scans utilize X-rays to generate cross-sectional images or slices of the target organ [2]. These sectional images are reconstructed by measuring the attenuation coefficient of X-rays in the volume of the object under study [3]. Advancements in CT scan devices, including spiral imaging features, multi-slice capability, increased energy supply, and improved detectors, have continuously enhanced image quality and diagnostic value [1].…”

“…Therefore, as the thickness of the slices decreases, the image noise decreases [18,19]. Tamura et al [3] highlighted the use of NRS software for modulating image noise and improving CT scan image quality. Various studies have proposed different software solutions for image reconstruction and enhancement.…”