Alzheimer 's disease identification from 3D SPECT brain scans by variational analysis. (February 2023)

• Record Type: Journal Article
• Authors: Sedlakova, Zuzana; Nachtigalova, Iva; Rusina, Robert; Matej, Radoslav; Buncova, Marie; Kukal, Jaromir
• Is Part Of: Biomedical signal processing and control. Volume 80:Part 2(2023)
• Publication Date: 2023-02
• Subjects: Variational classifier -- Alzheimer's disease -- Identification -- 3D classifier -- SPECT scans; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104385
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: The application of a radioactive tracer and following brain Single Positron Emission Computed Tomography (SPECT) is a standard technique used in neurodegenerative disease investigation. Alzheimer's disease is the most common form of neurodegenerative disease. In this paper, a novel 3D linear classifier is developed to classify Alzheimer's disease. The classification problem is formulated as the variational task with periodic boundary conditions, which is easy to discretize and solve using Fast Fourier Transform, and therefore, the resulting learning algorithm is very fast. Thanks to linearity of the classifier, weights obtained by 3D classifier learning are easy to visualize and bring understanding the most important features. The proposed classifier exhibits accuracy, sensitivity, and specificity of at least 90%. Highlights: New linear classifier standardized 3D images. Regularized learning process generates smooth 3D weight maps. Fast implementations of variational classifier using FFT. Classifier application to 3D SPECT scans of human brain with intuitive visualization of brain activity changes. High sensitivity and specificity in diagnosis of Alizheimer's disease.