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Abstract Introduction The implementation of spatial‐covariance [18F]fluorodeoxyglucose positron emission tomography–based disease‐related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND‐PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores. Methods Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined. Results Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences. Discussion Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

Gamma imaging of the brain in nuclear medicine usually contains a noise distribution owing to attenuation and scattering effects. This study aimed at improving the quality of the image of the brain acquired by single photon emission computed tomography (SPECT) by applying the fast non-local means (FNLM) algorithm, which an excellent noise reduction technique, using a Hoffman 3D brain phantom filled with 99mTc. Gaussian noise was added to the acquired image using MATLAB and the image quality evaluated using conventional noise reduction filters and the proposed FNLM algorithm. The contrast to noise ratio (CNR), peak signal to noise ratio, and root mean square error were used for quantitative evaluation. Comparing the image qualities obtained using various filters and algorithms, the CNR results of the images with Gaussian and median filters, and FNLM algorithm were found to surpass those of the noise image by 1.06, 1.22, and 1.41 times, respectively. In addition, similarity analysis indicated that the image evaluated using the FNLM algorithm showed excellent quality. Thus, our results demonstrated that the FNLM algorithm provided the greatest improvement in image quality based on quantitative evaluation results in the SPECT system. [ABSTRACT FROM AUTHOR]

Introduction: The implementation of spatial-covariance [ 18 F]fluorodeoxyglucose positron emission tomography-based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease-related pattern (PDRP) expression scores., Methods: Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined., Results: Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences., Discussion: Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.