Grazie mille per il commento davvero utile. Siamo sempre al lavoro per migliorare la qualità dei video, questi suggerimenti fanno davvero comodo. Per quanto riguarda gli spunti inerenti al voxel, essendo un argomento abbastanza corposo spero di poterci tornare su in futuro e toccare tutte quelle tematiche da te menzionate. Nel frattempo, grazie ancora per il contributo 🙏🏻

Thank you so much for the support

Welcome man.

Thank you so much for the support. Much appreciated

Thank you so much. We will dicuss more about resolution in the future 👌🏻

Thank you. It is something actually we are working on it. So please take the feedback as acknowledged 👌🏻

Have a look at this video realised by the dear colleague Bac: kzfaq.info/get/bejne/r8idjbV5r7CVZ3U.html you will find some very useful info about triggering

I agree. I mainly adopt only isotropic voxels for 3D imaging routinely. Especially considering the advantage of high quality MPRs

​@@everything-mri I see! I am coming at this as a computer graphics dev who inadvertently stumbled into your video looking for rendering techniques of anisotropic voxels. But then I became curious and fascinated that there are anisotropic options for MRI voxel rendering! Isotropic voxels just make so much more sense to me in the vast majority of use cases, but especially for participating media that don't have any clear notion of a surface/normal given how much less data they require and also how their uniformity lends themselves exponentially better-suited for fast compression and decompression. Anyway, I hope you'll forgive a CG programmer's tangent! It's interesting to me that MRIs not only have but largely had anisotropic voxel options when I think anisotropic voxels are so much more costly in general. Might as well stick to rasterizing image slices sorted by depth fragments in those cases if the voxel rep can't be represented in an isotropic fashion.

@@darkengine5931 wow, thank you do much. Your contribution is great!! This bring the discussion to the next level 👏🏻

​@@everything-mri Cheers! Also maybe our terminology is a bit different rewatching the video. I think in CG, we generally consider voxels anistropic even if they aren't uniform in size or directional outputs when viewed from a certain direction/angle, provided that their properties are uniform/constant across all voxels. For example, if all voxels are twice as tall as they are wide or thick, we would generally still call that isotropic in CG provided that all voxels (not just some) are twice as tall or wide, since we can unify their data representations regardless and just scale them all uniformly without having to store the scale differences on a per-voxel basis. Mostly we're analyzing this from a data-oriented standpoint: per-voxel directional data that requires being stored individually per-voxel is anisotropic, while anything else isn't. I don't know if this is of any interest to you or not since I'm in such a different industry focusing on voxels for mostly entertainment purposes (films, games, etc), but there are engines now that can render billions of voxels (ex: 2048^3 voxel space and 100,000^3+ on powerful desktops) at real-time frame rates on a modest computer including real-time manipulation of the voxels, even possibly on mobile phone. Of particular interest to me is the sparse voxel DAG (some really interesting papers and videos there), which is basically just a compressed sparse voxel octree that compresses away the redundant portions of the voxel hierarchy. It's actually an extremely simple concept but can take an engine like Minecraft from being limited to a million voxels to easily rendering, manipulating, and storing hundreds of millions of voxels to billions or even trillions of voxels at the same frame rates. But unlike Minecraft, getting that level of compression usually requires isotropic voxels, but in exchange we can store way more (easily to the level of what Minecraft stores at the texel level and beyond).

@@darkengine5931 wow!! That's great. I think all the people reading this will find this piece of information extremely interesting. Again, thank you for sharing