Keywords: Diagnosis/Prediction, Dementia, SPECT
Motivation: 123I-IMP SPECT is useful for diagnosing dementia and Parkinsonism but has drawbacks. We explored whether MPRAGE images could generate SPECT-like images, reducing the need for traditional SPECT.
Goal(s): This study aimed to develop an ML model that generates SPECT-like images from MPRAGE, using a dataset of dementia and Parkinsonism patients to improve model accuracy.
Approach: We used the Pix2PixHD method with various smoothing levels on MPRAGE images and compared models trained on dementia, Parkinsonism, and combined datasets. Quantitative and qualitative assessments were performed.
Results: The combined dataset achieved the highest accuracy, showing that training on diverse cases improves image generation and model generalizability.
Impact: This study shows that a machine learning model trained on a combined dementia and Parkinsonism dataset can generate accurate SPECT-like images from MPRAGE, potentially reducing the need for traditional SPECT imaging in neurological assessments.
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