## Summary

Goal: Harmonize the dMRI signal differences between scanners

Method: Use a ResNet on 3x3x3 patches to harmonize SH coefficients voxel-wise, by learning a mapping between sites.

Highlights:

• No need for registration (at test time)
• Model-free (spherical harmonics do not impose a model on the data)

## Model

• RISH projection: At test time, RISH features are used to project the harmonized signal and correct for changes in fiber orientation

where $$S_i'$$ are the hamonized SH coefficients and $$S_i$$ the non-harmonized SH coefficients of order $$i$$.

Reminder:

RISH: Squared L2 norm for each order of SH coefficients

## Results

Datasets:

CDMRI harmonization Challenge

• 10 healthy subjects, scanned on a 3T GE Excite-HD and a 3T Siemens Prisma, with 30 directions at b=1200.

Training:

• FAST registration for training subjects
• 5 epochs using Adam with batch size 256, then SGD with batch size 128 and learning rate decay…

Experiments:

• Investigate the sensitivity of the method to the number of ResBlocks n (Table 1)
• Evaluate the ability to reduce inter-scanner variance in the signal itself and metrics like FA/MD (Figures 2,3,4)
• 10-fold cross-validation using the 10 subjects (8 training, 1 validation, 1 test).

The only reported baseline is the Golkov method1, a 3-layer, 150 units neural network with ReLU and Dropout designed, applied on a single voxel at a time; it was “designed” for the estimation of scalar measures from diffusion data, and seems completely unrelated to harmonization.

There is no classical method baseline…

1. Golkov, V., Dosovitskiy, A., Sperl, J.I., Menzel, M.I., Czisch, M., Sämann, P., Brox, T. and Cremers, D., 2016. Q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE transactions on medical imaging, 35(5), pp.1344-1351.