Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images.

Chun-Yi Chiu,Chi-Yuan Wang,Yung-Hui Huang,Wei-Chang Du,Nan-Han Lu,Yun-Shiuan Shiu,Huei-Yong Chen,Tai-Been Chen

doi:10.3390/s21217228

Abstract

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.

Highlights

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole body that effectively detect the presence and distribution of tumors
The lowest background standard deviations were obtained from 3 × 3 optimal response curve (ORC) (2D) and 3 × 3 × 3 (3D) morphological structure operators (MSO), suggesting optimal background denoising merits
Compared with background noise of raw data, the Deluxe Jaszczak phantom and rat of the two-dimensional 3 × 3 MSO background noise were reduced by approximately 85.3% and 33.2%, respectively; the Deluxe Jaszczak phantom and rats of the three-dimensional 3 × 3 × 3 MSO background noise were reduced by approximately 87.1% and 55.3%, respectively

Summary

Introduction

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole body that effectively detect the presence and distribution of tumors. Common noise models in PET scans include Gaussian, Poisson, and mixed noise The generation of these noises will affect image capture, scan time, correction methods, and image reconstruction methods, affecting image quality. Compared to the SNR of raw data without MSO processing, the SNR of the Deluxe Jaszczak phantom and the sublingual gland of the rat increased by approximately 27.2% and 8.2%, respectively, after two-dimensional 3 × 3 MSO processing, and approximately 28.00% and 12.00%, after three-dimensional 3 × 3 × 3 MSO processing. Compared to the CR of raw data without MSO processing, the SNR of the Deluxe Jaszczak phantom and the sublingual gland of the rat increased by approximately 30.02% and 4.62%, respectively, after two-dimensional 3 × 3 MSO processing, and approximately 121.93% and 87.94%, respectively, after three-dimensional 3 × 3 × 3 MSO processing. The estimated FWHM generated by 2D or 3D MSO was close to the designed diameters in the Deluxe Jaszczak phantom

Methods

Results

Discussion

Conclusion