Abstract
Amyloid positron emission tomography (PET) scan is clinically essential for the non-invasive assessment of the presence and spatial distribution of amyloid-beta deposition in subjects with cognitive impairment suspected to have been a result of Alzheimer’s disease. Quantitative assessment can enhance the interpretation reliability of PET scan; however, its clinical application has been limited due to the complexity of preprocessing. This study introduces a novel deep-learning-based approach for SUVR quantification that simplifies the preprocessing step and significantly reduces the analysis time. Using two heterogeneous amyloid ligands, our proposed method successfully distinguished standardized uptake value ratio (SUVR) between amyloidosis-positive and negative groups. The proposed method’s intra-class correlation coefficients were 0.97 and 0.99 against PETSurfer and PMOD, respectively. The difference of global SUVRs between the proposed method and PETSurfer or PMOD were 0.04 and −0.02, which are clinically acceptable. The AUC-ROC exceeded 0.95 for three tools in the amyloid positive assessment. Moreover, the proposed method had the fastest processing time and had a low registration failure rate (1%). In conclusion, our proposed method calculates SUVR that is consistent with PETSurfer and PMOD, and has advantages of fast processing time and low registration failure rate. Therefore, PET quantification provided by our proposed method can be used in clinical practice.
Highlights
Positron emission tomography (PET) neuroimaging tools have been used for in vivo assessment in molecular biology and neuropathology [1]
Amyloid PET is widely used to evaluate the spatial distribution of amyloid-beta plaque in patients with cognitive impairment to rule out Alzheimer’s disease (AD) from other dementia diagnosis [2]
There were no statistical differences in education, sex, activities of daily living (ADL), and instrumental activities of daily living (IADL)
Summary
Positron emission tomography (PET) neuroimaging tools have been used for in vivo assessment in molecular biology and neuropathology [1]. PET techniques have facilitated early and differential dementia diagnosis, and several PET ligands are available to assess dementia biomarkers and, to support key clinical decision-making. An accurate and reliable tool for amyloid PET analysis can advance the research and clinical decision process. There is room for improvement; one can imagine that the disagreement could be observed with less experienced physicians and/or for equivocal cases. These disagreements could lead to misdiagnosis or delayed clinical decisions. To minimize the rater’s bias and improve the reliability of the amyloid PET interpretation, quantitative evaluation of brain PET has been mostly used in research [5]
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