TITLE: Quality assessment for images obtained using 3D printed stereotaxic frame
AUTHORS: Lucy Liang, David J Schaeffer, Tales Santini, Isabela Zimmermann Rollin, Elvira Pirondini

[DESCRIPTION]
This protocol describes the steps taken to assess whether MR signal noise and distortion is introduced by utilization of a 3D printed plastic stereotaxic frame.

The computer-aided design files and engineering drawings for the frame used in this protocol are publicly available, with the modular design allowing for low cost and manageable manufacturing. 
You can find it here: https://github.com/SchaefferLab/Macaque-Stereotax

This protocol is supplementary to the manuscript: 
Liang, L., Zimmermann Rollin, I., Alikaya, A., Ho, J.C., Santini, T., Bostan, A.C., Schwerdt, H.N., Stauffer, W.R., Ibrahim, T.S., Pirondini, E., Schaeffer, D.J., 2024. An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys. BioRxiv https://doi.org/10.1101/2024.02.17.580767

[GUIDELINES]
The geometric phantom used in this assessment was chosen to match the size of a macaque monkey head, with internal geometric structure the size of a typical monkey brain, our target imaging structure. This protocol can be used also for other imaging targets, but the phantom should be adjusted to obtain accurate assessment results.

[STEPS]
SECTION: Image Acquisitions
1. Acquire MRI using gradient-echo sequences in the 7T MRI scanner (Magnetom, Siemens Healthcare, Erlangen, Germany) with the following parameters:
TE/TR=8.16/40 ms
resolution 400 μm isotropic (same as what you will acquire for your target structure)
matrix size=176×384×384
flip angle=15°
acceleration factor (GRAPPA)=2
SECTION: Image Acquisitions
1.2. With stereotaxic frame: center the geometric phantom in the stereotaxic frame in the same orientation as without the frame, place the stereotaxic frame in the center of the coil, acquire images with same parameters as before
SECTION: Image Acquisitions
1.1. Without stereotaxic frame: place the geometric phantom in the center of the coil, acquire images as described
SECTION: Image Acquisitions
2. Next, acquire CT images of the same phantom in a small animal CT scanner (Si78; Bruker BioSpin GmbH, Ettlingen, Germany) with the following parameters:
resolution 200 μm isotropic, FOV: 79.6×199 mm
Low Dose 1 mm aluminum filter, “step and shoot” method (0.6-degree gantry step) 
reconstruct using filtered back projection algorithm equipped with the
software package ParaVision-360
SECTION: Signal to Noise Ratio (SNR) Assessment
3. In MATLAB, calculate the SNR maps for MRI of the phantom (with and without frame) by dividing the signal in the image by the standard deviation of the background noise. 
SECTION: Signal to Noise Ratio (SNR) Assessment
5. In FSL, visualize (fsleyes) and coregister the SNR maps of the MRIs with and without stereotaxic frame using rigid transformation (flirt).
SECTION: Signal to Noise Ratio (SNR) Assessment
4. Compare the average SNR value of the entire image stack, calculated for each of the two MRIs. They should be very similar. This allows us to assess the overall signal quality.
SECTION: Signal to Noise Ratio (SNR) Assessment
6. Overlay the SNR maps (with and without frame). The hyper/hypo-intensities should match well if there are no spatial distortions introduced by the frame.
SECTION: Geometric Distortion Assessment
7. In FSL, visualize (fsleyes) and coregister the MRIs with the CT image using rigid transformation (flirt).
SECTION: Geometric Distortion Assessment
8. Binarize the phantom (include only internal geometric structure) CT image using thresholding with fslmaths or any equivalent software/function.
SECTION: Geometric Distortion Assessment
9. Overlay the binarized image with the MR SNR maps. Check conformity of the rectilinear geometric structure between the two.