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Co-registration of dissection photographs of coronal slabs into a 3D volume

For FreeSurfer 8.0 or development versions downloaded before May 2025, please see https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon_2024.

If you use this tools in your analysis, please cite:

* Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology. H Gazula, H Tregidgo, B Billot, Y Balbastre, J Williams Ramirez, R Herisse, LJ Deden-Binder, A Casamitjana, EJ Melief, CS Latimer, MD Kilgore, M Montine, E Robinson, E Blackburn, MS Marshall, TR Connors, DH Oakley, MP Frosh, SI Young, K Van Leemput, AV Dalca, B Fischl, CL Mac Donald, CD Keene, B Hyman, and JE Iglesias. Under revision.)

This is a newer version of our older tool with several key improvements:

More flexible framework, accepting a variable number of references (e.g., surface scan and/or MRI scan or MNI atlas etc).
Faster optimization
Easier configuration of parameters: it self configures depending on type(s) of input(s)
Supports slabs of variable thickness
New feature: machine learning interpolation to 1mm slab thickness (fun stuff! See pictures below)

mri_3d_photo_recon -h

--input_photo_dir	Directory with input pixel-corrected photos.
--input_segmentation_dir	Directory with input slab masks/segmentations
--ref_mask	When using a binary volume as a reference.
--ref_surface	Using a 3D surface scan as a reference.
--ref_soft_mask	Using the provided average atlas. Best for using retrospective processing on data without a better reference.
--mesh_reorient_with_indices	Vertex indices of the frontal pole, occipital pole, and top of the central sulcus, separated with commas, for mesh alignment.
--photos_of_posterior_side	Use when photos are taken of the posterior side of slabs (default is anterior side).
--order_posterior_to_anterior	Use when photos are ordered from posterior to anterior (default is anterior to posterior).
--allow_z_stretch	Use to adjust the slice thickness to best match the reference. You should probably *never* use this with soft references (ref_soft_mask).
--rigid_only_for_photos	Switch on if you want photos to deform only rigidly (not affine).
--slice_thickness	Slice thickness in mm.
--photo_resolution	Resolution of the photos in mm.

||--output_directory ||Output directory with reconstructed photo volume and reference ||

In Freeview, you can find the number corresponding to the vertices of each anatomical area to use in the --mesh_reorient_with_indices flag. These indices should be comma-separated in the order: frontal pole, occipital pole, and top of the central sulcus.


Frontal Pole	Occipital Pole	Top of the central sulcus

For example, the --mesh_reorient_with_indices flag might look like this:

–mesh_reorient_with_indices 999999,999999,999999

Example for running reconstruction

mri_3d_photo_recon \
--input_photo_dir ./deformed \
--input_segmentation_dir ./connected_components \
--ref_surface ./mesh/case.stl \
--mesh_reorient_with_indices 999,1010,333333 \
--photos_of_posterior_side --allow_z_stretch \
--slice_thickness 8 \
--photo_resolution 0.1

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-= Co-registration of dissection photographs of coronal slabs into a 3D volume (legacy version, 2024) =
+= Co-registration of dissection photographs of coronal slabs into a 3D volume =
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+If you use this tools in your analysis, please cite:

 

* [[https://elifesciences.org/articles/91398|Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology]]. H Gazula, H Tregidgo, B Billot, Y Balbastre, J Williams Ramirez, R Herisse, LJ Deden-Binder, A Casamitjana, EJ Melief, CS Latimer, MD Kilgore, M Montine, E Robinson, E Blackburn, MS Marshall, TR Connors, DH Oakley, MP Frosh, SI Young, K Van Leemput, AV Dalca, B Fischl, CL Mac Donald, CD Keene,  B Hyman, and JE Iglesias. Under revision.)



This is a newer version of our [[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon_2024|older tool]] with several key improvements:

 * More flexible framework, accepting a variable number of references (e.g., surface scan and/or MRI scan or MNI atlas etc).

 * Faster optimization

 * Easier configuration of parameters: it self configures depending on type(s) of input(s)

 * Supports slabs of variable thickness

 * New feature: machine learning interpolation to 1mm slab thickness (fun stuff! See pictures below)