Automatic delineation algorithm of reference region for amyloid imaging based on kinetics

• Published in: Annals of nuclear medicine 2020-02, Vol.34 (2), p.102-107
• Main Authors: Yamada, Takahiro, Watanabe, Shogo, Nagaoka, Takashi, Nemoto, Mitsutaka, Hanaoka, Kohei, Kaida, Hayato, Ishii, Kazunari, Kimura, Yuichi
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Amyloid - metabolism; Aniline Compounds - chemistry; Carbon Radioisotopes - chemistry; Dose-Response Relationship, Radiation; Electronic Data Processing - methods; Error detection; Humans; Image Interpretation, Computer-Assisted - methods; Imaging; Kinetics; Medicine; Medicine & Public Health; Nuclear Medicine; Original Article; Positron emission; Positron-Emission Tomography - methods; Probabilistic models; Radiology; Radiopharmaceuticals - chemistry; Reproducibility of Results; Shape recognition; Statistical analysis; Systematic errors; Thiazoles - chemistry; Time Factors; Tomography
• ISSN: 0914-7187; 1864-6433
• DOI: 10.1007/s12149-019-01419-9

Objective This study aims to develop an algorithm named AutoRef to delineate a reference region for quantitative PET amyloid imaging. Methods AutoRef sets the reference region automatically using a distinguishing feature in the kinetics of reference region. This is reflected in the shapes of the tissue time activity curve. A statistical shape recognition algorithm of the gaussian mixture model is applied with considering spatial and temporal information on a reference region. We evaluate the BP ND with manually set reference region and AutoRef using 86 cases (43 positive cases, 10 equivocal cases, and 33 negative cases) of dynamically scanned 11 C-Pittsburgh Compound-B. Results From the Bland–Altman plot, the difference between two BP ND is 0.099 ± 0.21 as standard deviation, and no significant systematic error is observed between the BP ND with AutoRef and with manual definition of a reference region. Although a proportional error is detected, it is smaller than the 95% limits of agreement. Therefore, the proportional error is negligibly small. Conclusions AutoRef presents the same performance as the manual definition of the reference region. Further, since AutoRef is more algorithmic than the ordinary manual definition of the reference region, there are few operator-oriented uncertainties in AutoRef. We thus conclude that AutoRef can be applied as an automatic delineating algorithm for the reference region in amyloid imaging.