Optimizing A Deep Learning Model for the Prediction of Electric Field
Induced by Transcranial Magnetic Stimulation for Mild to Moderate
Traumatic Brain Injury Patients
Abstract
Transcranial magnetic stimulation (TMS) is a non-invasive method for
treating neurological and psychiatric disorders. It is being tested as
an experimental treatment for patients with mild to moderate traumatic
brain injuries (mTBI). Due to the complex, heterogeneous composition of
the brain, it is difficult to determine if targeted brain regions
receive the correct amount of electric field (E-field) induced by the
TMS coil. E-field distributions can be calculated by running
time-consuming finite element analysis (FEA) simulations of TMS on
patient head models. Using machine learning, the E-field can be
predicted in real-time. Our prior work used a Deep Convolutional Neural
Network (DCNN) to predict the E-field in healthy patients. This study
applies the same DCNN to mTBI patients and investigates how model depth
and color space of E-field images affect model performance. Nine DCNNs
were created using combinations of 3, 4, or 5 encoder and decoder blocks
with the color spaces RGB, LAB, and YCbCr. As depth increased, training
and testing peak signal-to-noise ratios (PSNR) increased and mean
squared errors (MSE) decreased. The depth 5 YCbCr model had the highest
training and testing PSNRs of 34.77 dB and 29.08 dB and lowest training
and testing MSEs of 3.335 * 10-4 and 1.237 *
10-3 respectively. Compared to the model in our prior
work, models of depth 5 have higher testing PSNRs and lower MSEs and,
except for RGB. Thus, DCNNs with depth 5 and alternative color spaces,
despite losing information through color space conversions, resulted in
higher PSNRs and lower MSEs.