NeSVoR is a package for GPU-accelerated slice-to-volume reconstruction (both rigid and deformable).
This package is the accumulation of the following works:
[1] SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI (Springer | Arxiv)
[2] NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI (IEEE | TechRxiv)
NeSVoR is a deep learning package for solving slice-to-volume reconstruction problems (i.e., reconstructing a 3D isotropic high-resolution volume from a set of motion-corrupted low-resolution slices) with application to fetal/neonatal MRI, which provides
- Motion correction by mapping 2D slices to a 3D canonical space using Slice-to-Volume Registration Transformers (SVoRT).
- Volumetric reconstruction of multiple 2D slices using implicit neural representation (NeSVoR).
Figure 1. SVoRT: an iterative Transformer for slice-to-volume registration. (a) The k-th iteration of SVoRT. (b) The detailed network architecture of the SVT module.
Figure 2. NeSVoR: A) The forward imaging model in NeSVoR. B) The architecture of the implicit neural network in NeSVoR.
To make our reconstruction tools more handy, we incorporate several preprocessing and downstream analysis tools in this package. The next figure shows our overall reconstruction pipeline.
Figure 3. The reconstruction pipeline.
The full documentation is available at Read the Docs.
We recommend to use our docker image to run nesvor.
You may follow this guide to install Docker and NVIDIA Container Toolkit
docker pull junshenxu/nesvor
Note: our latest image was built with CUDA 11.7.
You may run a container in an interactive way.
docker run -it --gpus all --ipc=host junshenxu/nesvor
nesvor -h
You may also run the nesvor command directly as follows.
docker run --rm --gpus all --ipc=host \
-v <path-to-inputs>:/incoming:ro -v <path-to-outputs>:/outgoing:rw \
junshenxu/nesvor \
nesvor reconstruct \
--input-stacks /incoming/stack-1.nii.gz ... /incoming/stack-N.nii.gz \
--thicknesses <thick-1> ... <thick-N> \
--output-volume /outgoing/volume.nii.gz
Check out our documentation if you want to install NeSVoR from source.
This example reconstruct a 3D fetal brain from mutiple stacks of 2D images in the following steps:
- Segment fetal brain from each image using a CNN (
--segmentation). - Correct bias field in each stack using the N4 algorithm (
--bias-field-correction). - Register slices using SVoRT (
--registration svort). - Reconstruct a 3D volume using NeSVoR.
nesvor reconstruct \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--thicknesses <thick-1> ... <thick-N> \
--output-volume volume.nii.gz \
--output-resolution 0.8 \
--registration svort \
--segmentation \
--bias-field-correction
This example reconstruct a 3D neonatal brain from mutiple stacks of 2D images in the following steps:
- Removal background (air) with Otsu thresholding (
--otsu-thresholding). - Correct bias field in each stack using the N4 algorithm (
--bias-field-correction). - Register slices using SVoRT (
--registration svort). - Reconstruct a 3D volume using NeSVoR.
nesvor reconstruct \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--thicknesses <thick-1> ... <thick-N> \
--output-volume volume.nii.gz \
--output-resolution 0.8 \
--registration svort \
--otsu-thresholding \
--bias-field-correction
This is an example for deformable NeSVoR which consists of the following steps:
- Create an ROI based on the intersection of all input stacks (
--stacks-intersection). - Perform stack-to-stack registration (
--registration stack). - Reconstruct a 3D volume using Deformable NeSVoR (
--deformable).
nesvor reconstruct \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--thicknesses <thick-1> ... <thick-N> \
--output-volume volume.nii.gz \
--output-resolution 1.0 \
--stacks-intersection \
--registration stack \
--deformable \
--weight-transformation 1 \
--weight-deform 0.1 \
--weight-image 0.1 \
--single-precision \
--log2-hashmap-size 22 \
--batch-size 8192
This section provides the basic usage of the commands in NeSVoR. Please refer to our document for details. NeSVoR currently supports the following commands:
nesvor reconstruct: reconstruct a 3D volume (i.e., train a NeSVoR model) from either multiple stacks of slices (NIfTI) or a set of motion-corrected slices (the output ofregister). It can also perform multiple preprocessing steps, including brain segmentation, bias field correction, and registration.nesvor register: register stacks of slices using a pretrained SVoRT model or stack-to-stack registration.svr: a classical slice-to-volume registration/reconstruciton method.nesvor sample-volume: sample a volume from a trained NeSVoR model.nesvor sample-slices: simulate slices from a trained NeSVoR model.nesvor segment-stack: 2D fetal brain segmentation/masking in input stacks.nesvor correct-bias-field: bias field correction using the N4 algorihtm.nesvor assess: quality and motion assessment of input stacks.
run nesvor <command> -h for a full list of parameters of a command.
The reconstruct command can be used to reconstruct a 3D volume from N stacks of 2D slices (in NIfTI format, i.e. .nii or .nii.gz).
A basic usage of reconstruct is as follows, where mask-i.nii.gz is the ROI mask of the i-th input stack.
nesvor reconstruct \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--stack-masks mask-1.nii.gz ... mask-N.nii.gz \
--output-volume volume.nii.gz
A more elaborate example could be
nesvor reconstruct \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--stack-masks mask-1.nii.gz ... mask-N.nii.gz \
--thicknesses <thick-1> ... <thick-N> \
--output-volume volume.nii.gz \
--output-resolution 0.8 \
--output-model model.pt \
--weight-image 1.0 \
--image-regularization edge \
--delta 0.2 \
--n-iter 5000 \
--level-scale 1.38 \
--coarsest-resolution 16.0 \
--finest-resolution 0.5 \
--n-levels-bias 0 \
--n-samples 128
Run nesvor reconstruct --h to see the meaning of each parameter.
Given multiple stacks at inputs, reconstruct first corrects the motion in the input stacks using SVoRT (the same as what register did),
and then trains a NeSVoR model that implicitly represent the underlying 3D volume, from which a discretized volume (i.e., a 3D tensor) can be sampled.
reconstruct can also take a folder of motion-corrected slices as inputs.
nesvor reconstruct \
--input-slices <path-to-slices-folder> \
--output-volume volume.nii.gz
This enables the separation of registration and reconstruction. That is, you may first run register to perform motion correction, and then use reconstruct to reconstruct a volume from a set of motion-corrected slices.
NeSVoR can now reconstruct data with deformable (non-rigid) motion! To enable deformable motion, use the flag --deformable.
nesvor reconstruct \
--deformable \
......
This feature is still experimental.
register takes mutiple stacks of slices as inputs, performs motion correction, and then saves the motion-corrected slices to a folder.
nesvor register \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--stack-masks mask-1.nii.gz ... mask-N.nii.gz \
--registration <method> \
--output-slices <path-to-save-output-slices>
run nesvor register -h to see a full list of supported registration methods.
svr implements a classical slice-to-volume registration/reconstruction method combined with SVoRT
motion correction. The usage of svr is similar to reconstruct.
svr currently only supports rigid motion.
Upon training a NeSVoR model with the reconstruct command, you can sample a volume at arbitrary resolutions with the sample-volume command.
nesvor sample-volume \
--output-volume volume.nii.gz \
--input-model model.pt \
--output-resolution 0.5
You may sample slices from the model using the sample-slices command. For each slice in <path-to-slices-folder>, the command simulates a slice from the NeSVoR model at the corresponding slice location.
nesvor sample-slices \
--input-slices <path-to-slices-folder> \
--input-model model.pt \
--simulated-slices <path-to-save-simulated-slices>
For example, after running reconstruct, you can use sample-slices to simulate slices at the motion-corrected locations and evaluate the reconstruction results by comparing the input slices and the simulated slices.
We integrate a deep learning based fetal brain segmentation model (MONAIfbs) into our pipeline to extract the fetal brain ROI from each input image.
Check out their repo and paper for details.
The segment-stack command generates brain mask for each input stack as follows.
nesvor segment-stack \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--output-stack-masks mask-1.nii.gz ... mask-N.nii.gz \
You may also perform brain segmentation in the reconstruct command by setting --segmentation.
We also provide a wrapper of the N4 algorithm in SimpleITK for bias field correction.
The correct-bias-field command correct the bias field in each input stack and output the corrected stacks.
nesvor correct-bias-field \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--stack-masks mask-1.nii.gz ... mask-N.nii.gz \
--output-corrected-stacks corrected-stack-1.nii.gz ... corrected-stack-N.nii.gz
You may perform bias field correction in the reconstruct command by setting --bias-field-correction
The assess command evalutes the image quality/motion of input stacks.
This information can be used to find a template stack with the best quality or filter out low-quality data.
An example is as follows.
nesvor assess \
--input-stacks stack-1.nii.gz ... stack-N.nii.gz \
--stack-masks mask-1.nii.gz ... mask-N.nii.gz \
--metric <metric> \
--output-json result.json
run nesvor assess -h to see a full list of supported metrics.
SVoRT
@inproceedings{xu2022svort,
title={SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI},
author={Xu, Junshen and Moyer, Daniel and Grant, P Ellen and Golland, Polina and Iglesias, Juan Eugenio and Adalsteinsson, Elfar},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={3--13},
year={2022},
organization={Springer}
}
NeSVoR
@article{10015091,
author={Xu, Junshen and Moyer, Daniel and Gagoski, Borjan and Iglesias, Juan Eugenio and Ellen Grant, P. and Golland, Polina and Adalsteinsson, Elfar},
journal={IEEE Transactions on Medical Imaging},
title={NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/TMI.2023.3236216}
}
Fetal IQA
@inproceedings{xu2020semi,
title={Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency},
author={Xu, Junshen and Lala, Sayeri and Gagoski, Borjan and Abaci Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={386--395},
year={2020},
organization={Springer}
}
This project has been greatly inspired by the following list of fantastic works.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent