How much computer do you need for MRI processing?

I want to address how much computing power you need when imaging. The short answer is: as much as you can.


If you want to stick with a Mac environment, a Mac Pro would be ideal (although we use iMacs, which work wonderfully). If you plan on running a lot of processes in parallel or doing some intensive visualizations, put as much RAM in the computer as possible. The current generation of Mac Pros can accept 64 GB of RAM; the current iMacs accept 16 GB of RAM. However, if you are not going to do a lot of intensive visualization (if you are not going to use TrackVis, for example), 8 GB of RAM should be the minimum. Again, get as much RAM as possible but get at least 8 GB. I like at least 2 GB per processor/core.

As far as the quality of RAM is concerned, most RAM is pretty good quality now; you do not necessarily have to use really expensive RAM unless your system calls for it. It is a good idea to run a memory test though to check for any problems.


The rule with the CPU is the same as with RAM – get as much as you can. Not all processes are parallelized but some are.¬†Additionally, for something like FreeSurfer, if you have multiple processors (or just multiple cores), you can run multiple¬†instances of the program concurrently. This is another reason why you want as much RAM as you can have – each process uses RAM so the more you run, the more RAM you need. As far as processors go, it doesn’t matter if you use Intel or AMD (or something else); I prefer Intel but I just built an AMD box and in the end, it doesn’t matter too much which company you choose. For imaging, I recommend at least 4 cores/processors but get as many as you can. In our lab we have iMacs with i7 hyperthreaded cores (4 real cores plus 4 virtual cores). You can get a Mac Pro with 12 cores (2 six core processors), which would be great for imaging work. Of course, you could build a Linux machine with similar specifications for cheaper than the Mac Pro but there are good reasons for choosing either path. If money is not an option (and you don’t need really high-end stuff), I’d recommend Macs. You could, alternatively, chain a number of Mac Minis together and build your own little processing farm. Mac Minis don’t have the best processors but if you can parallelize tasks, a stack of Mac Minis might do the trick. Of course, if you have access to a supercomputer, that can be even better (although, there are plenty of reasons to avoid supercomputers too).


In summary, you can never have too much RAM and too much processing power when it comes to neuroimaging. If you think that the costs up front are high, wait until you have to do work over and have to wait for your computers. The less time you spend processing, the more time you have to analyze and write. Generally time that you spend waiting for processes to finish is wasted time. That’s why you want the fastest computer with as much RAM as you can afford. I have to reiterate though that you only need buckets of RAM (>8 GB or so) if you are doing a lot of visualization, otherwise, you don’t have to get too much RAM (>16 GB).

I’ll address graphics cards in a future post, especially as they apply to imaging (e.g., CUDA and FreeSurfer).

About Jared Tanner

I have a PhD in Clinical and Health Psychology with an emphasis in neuropsychology at the University of Florida. I previously studied at Brigham Young University. I am currently a Research Assistant Professor at the University of Florida. I spend the bulk of my research time dealing with structural magnetic resonance images of the brain. My specialty is with traditional structural MR images, such as T1-weighted and T2-weighted images, as well as diffusion weighted images. I also look at the cognitive and behavioral functioning of individuals with PD and older adults undergoing orthopedic surgery. Funding for the images came from NINDS K23NS060660 (awarded to Catherine Price, University of Florida). Brain images may not be used without my written permission (grant and software requirements).