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NA-MIC Project Weeks


Key Investigators

Jax Luo (BWH & Harvard Medical School)

Loraine Franke (University of Massachusetts Boston)

Raymond Yang (University of Massachusetts Boston)

Daniel Haehn (University of Massachusetts Boston)

Steve Pieper (Isomics, Inc.)

Lipeng Ning (BWH & Harvard Medical School)

Project Description

Transcranial magnetic stimulation (TMS) is a noninvase procedure used for treating depression. In the TMS treatment, a magnetic coil is placed on the subject’s head to induce an electirc field (E-field) to stimulate targeted brain regions.

Our project aims to predict the distribution of the E-field in real-time so that the clinicians can adjust the location of the coil and target the brain ROI with the maximal stimulation strength.


  1. Predicting the distribution of the E-field based on the location of the coil

Approach and Plan

  1. Read a affine transform matrix from the updated (rotated) coil.
  2. Perform an affine transformation to the Coil data and resample it to the subject head model space.
  3. Combine the Coil data and the head model to generate a new nifti file and pre-process it.
  4. Predict the E-field using the generated nifti file and a pre-trained deep network.
  5. Visualize the precition result (.nii)

Progress and Next Steps

  1. Finished step 1-4.
  2. Working on intergrating the code to the visualization module.
  3. Improving the speed of the prediction.


Visualization of the predicted E-field using the developed interface. Visualization of the predicted E-field from another software


Background and References

This is the sister project of Slicer TMS Deep-Learning