imaging data pulled: 2019-08-29
code written: 2019-08-29
last ran: 2019-08-31
website: http://rpubs.com/navona/NM_SN_thresholds

Here we compare the effect of adjusting the threshold for generating binary maps that visualize potential voxels of the SN.


Method

Chen’s formula is \(I{_{diff}}\) = \(I{_{SN_{voxel}}}\) - \(\mu\)\(_{REF}\) - \(x\) * \(\sigma\)\(_{REF}\). Chen sets \(x\) = 3; here, we review the difference between \(x\) = [1:6], on two example participants: one with ‘good’ data (SEN011), and one with ‘poor’ data (SEN015).

Conclusion

We see that, as \(x\) increases, the SN is more uniquely defined (particiulary for ‘bad’ data), but at the cost of voxels/volume.

Citation

Chen, Huddleston, Langley, Ahn, Barnum, Factor, Levey, & Hu. (2014). Simultaneous imaging of locus coeruleus and substantia nigra with a quantitative neuromelanin MRI approach. Magnetic Resonance Imaging, 32, 1301-1306.