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Abstract Details

Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals
Aging, Dementia, Cognitive, and Behavioral Neurology
P1 - Poster Session 1 (8:00 AM-9:00 AM)
Game theory-inspired deep learning using a GAN provides an environment where neural networks competitively interact to accomplish a goal. A classical GAN contains a generator and discriminator that work together to take images from one domain (e.g., low-quality brain MRIs) and create images similar to real training data (e.g., high-quality brain MRIs). Most published work in medical imaging has focused on singular tasks like super-resolution and segmentation.
To design and train a dual-objective generative adversarial network (GAN) to (1) reconstruct higher quality brain MRIs that (2) accurately retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD).
We obtained 3T T1-weighted brain MRIs (i.e., original scans) of participants with MCI from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer's Coordinating Center (NACC, N=190). We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The inputs to the generator were diced scans. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) to encourage the generator to encode AD-related information during reconstruction. We assessed the quality of the generated images and their utility in distinguishing pMCI from sMCI. The framework was trained and internally validated on ADNI data and externally validated on NACC data.
In the independent NACC cohort, generated scans had better image quality than the diced scans (structural similarity [SSIM]: 0.553 ± 0.116 versus 0.348 ± 0.108). Furthermore, a classifier utilizing the generated scans distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: 0.634 ± 0.019 versus 0.573 ± 0.028).
Competitive deep learning frameworks show promise in facilitating disease-oriented image reconstruction in those at risk of developing Alzheimer's disease.
Akshara Balachandra, MD (Stanford Health Care)
Dr. Balachandra has nothing to disclose.
Xiao Zhou No disclosure on file
Michael Romano No disclosure on file
Vijaya B. Kolachalama, PhD, FAHA (Boston University) The institution of Vijay Kolachalama, PhD, FAHA has received research support from NIH. Vijay Kolachalama, PhD, FAHA has received personal compensation in the range of $5,000-$9,999 for serving as a Expert opinion with Mass Mutual.