Dopamine Neuron Activity Visualization From MRI Sensors Through 3-D MRI-to-SPECT Synthesis for Parkinson’s Disease Assessment

This article describes a medical imaging framework that converts MRI (magnetic resonance imaging, a non-invasive scanning technique) scans into synthetic SPECT images (single photon emission computed tomography, a radiological imaging technique that typically requires radioactive drug injections) to help detect Parkinson's disease by analyzing dopamine neuron activity. The proposed 3-D-cycle conversion network uses a machine learning approach inspired by CycleGAN (a type of neural network that learns to transform images from one style to another) to generate SPECT-like images from regular MRI data without needing radioactive injections. The researchers claim their method outperforms similar existing approaches in accuracy.