Yes, that’s correct. So you’d be following the pipeline over here, but instead replacing step 6 (response function estimation) by:
foreach * : dwi2response dhollander IN/dwi_denoised_preproc_bias_norm.mif IN/response_wm.txt IN/response_gm.txt IN/response_csf.txt
average_response */response_wm.txt ../group_average_response_wm.txt
average_response */response_csf.txt ../group_average_response_csf.txt
…and step 9 (FOD estimation) by:
foreach * : dwi2fod msmt_csd IN/dwi_denoised_preproc_bias_norm_upsampled.mif ../group_average_response_wm.txt IN/fod.mif ../group_average_response_csf.txt IN/csf.mif -mask IN/dwi_mask_upsampled.mif
…and then proceed as normal with the FOD output. This comes down to estimating 3 tissue responses (WM, GM, CSF), but ditching the GM response upon doing MSMT-CSD with only WM and CSF, and then after that ignoring the CSF output (but knowing that the WM FOD will have benefited from its inclusion in the model).
Does that make sense? 