Practical considerations for noise power spectra estimation for clinical CT scanners
| Autor: | Hua Li, S Dolly, Mark A. Anastasio, Hsin-Chen Chen, Sasa Mutic |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Tomography Scanners
X-Ray Computed Image quality image quality assessment Image subtraction Image processing Iterative reconstruction Signal-To-Noise Ratio Window function 030218 nuclear medicine & medical imaging Background noise 03 medical and health sciences 0302 clinical medicine Medical Imaging Region of interest Image noise Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Computer Simulation Instrumentation Mathematics Radiation business.industry Phantoms Imaging Pattern recognition computed tomography noise power spectrum background removal iterative CT reconstruction 030220 oncology & carcinogenesis Artificial intelligence business Tomography X-Ray Computed |
| Zdroj: | Journal of Applied Clinical Medical Physics |
| ISSN: | 1526-9914 |
| Popis: | Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ∼0.15 mm−1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low‐frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task‐based image quality assessment. PACS number(s): 87.57.C‐, 87.57.Q‐ |
| Databáze: | OpenAIRE |
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