thank you for your message – however it’s a bit hard to find out what (you think) went wrong.
There are so many ways to use Lead-DBS to analyze your data and it is very important that you understand what it actually does to interpret results. For us to help you / explain what’s happening, it’s is similarly important that you let us know what you exactly did.
Some basics that might help:
1. Lead-DBS uses atlases to define anatomy. There is no option to subdivide the STN in single patients (although we’re losely working on such things).
2. It is a big difference whether you visualize results in native or template space. In the first option, the atlas is co-registered into native space, in the second case the MRI data of the patient is co-registered into template space (where the atlas is defined).
3. These warps (from native to template and back) are performed using the best nonlinear methods available in the field (e.g. see Klein 2009 NeuroImage) that have been specially tuned to be precise in the subcortex. Still, as in any scientific model, the model has limitations.
4. These limitations are largely driven by the quality of your preoperative imaging data and quality. For instance you can supply 10 different MRI sequences including some special Basal ganglia sequences (FGATIR, QSM etc) of the same patient to Lead-DBS and use *all of them* simultaneously to define the warp e.g. using the Unified Segmentation approach by John Ashburner on a specialized tissue prior model featuring regions of interest (STN, GPi) included in Lead-DBS. If you did that, I’d guess that the nonlinear transform would be really accurate, really showing a precise location of electrodes in relationship to the atlas STN. However, this is an ideal scenario and most Universities don’t have such great preoperative imaging protocols. Rather, in a “standard use case”, most centers have a T1 sequence and a T2 sequence or similar. Often in poor resolution. If you supply this data to Lead-DBS, it will still give you a result but the accuracy is just way worse than in the first case. So part of getting good results are not dependent on the *data* and not the methods of Lead-DBS.
5. A good rule of thumb to find out how precise results could optimally be is for instance whether you can clearly discern the structures of interest on the preoperative acquisition. Say, you cannot clearly see the STN yourself on an image, you’re not totally sure where it starts and where it ends. In such a case, Lead-DBS will not be able to apply some magic and still see the structure, let alone the STN subdivisions. It will make a “best guess” based on surrounding anatomy and the segmentation results but that’s all you can get with poor data.
6. Finally, please be aware that none of the segmentation/normalization techniques have been developed by ourselves. We use freely available tools and sometimes tweak them a bit for subcortical anatomy. All these methods are published and well documented. It is highly recommended to study how these methods work, what their benefits are and their limitations.
Now getting maybe a bit closer to your problem: You described “all images looked the same”. If you mean the visualization of anatomy in the 3D viewer, it could have been that the MNI template was displayed instead of patient specific anatomy? This is the default setting. You can visualize patient anatomy by choosing what to display in the anatomy slices panel. Maybe this helps?