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Enhancing MR imaging driven Alzheimers disease classification performance using generative adversarial learning

Background: Generative adversarial networks (GAN) can generate images of improved quality but their ability to augment image-based classification tasks is not fully explored. Purpose: We evaluated if a modified GAN can learn from MRI scans of multiple magnetic field strength to enhance Alzheimers disease (AD) classification performance. Materials and methods: T1-weighted brain MRI scans from 151 participants of the Alzheimers Disease Neuroimaging Initiative (ADNI), who underwent both 1.5 Tesla (1.5T) and 3 Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (1.5T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Data from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=107), and the National Alzheimers Coordinating Center (NACC, n=565) was used for model validation. Results: The mean quality of the generated (1.5T*) images was consistently higher than the 1.5T images, as measured using SNR, BRISQUE and NIQE on the validation datasets. The 1.5T*-based FCN classifier performed better than the FCN model trained using the 1.5T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940 and from 0.870 to 0.907 on the ADNI test, AIBL and NACC datasets, respectively. Conclusion: This study demonstrates that GAN frameworks can be constructed to simultaneously improve image quality and augment classification performance.