Chaithya G R
Chaithya G R
Recently, tensorflow-nufft came up, which is based out of cufiNUFFT, which is known to be computationally and memory efficient. I will proceed with initial tests for this and get a...
A quick run of following code: ```python platform = api.get_platforms()[platform_number] device = platform.get_devices()[device_number] thr_free = api.Thread(device) thr_compile = api.Thread(device) #Compile with something thr_compile.compile() #Free memory thr_free.release() thr_compile.release() ``` You would...
Currently the way we handle multiple batches is to send repeated trajectories as input. This is memory consuming and also might cause degradation in performance as deep inside we use...
Adding @zaccharieramzi to do this as you are using this in some codes. When you are comfortable, feel free to remove the codes. The old codes work only for radial...
Currently, the estimator for decompensator is only checked for a good run and not for any semantics. We can ideally generate a VDS trajectory and ensure that the density compensated...
Hello I was curious if there are any roadmap or plans on a GPU implementation of this algorithm? Thank you for the wonderful project!
This resolves #306 Also I have a bunch of fixes which did not make through for the earlier refactoring PR.
Add Smaps estimation module in mri-nufft to makes creation of Fourier operator streamlined. This should be merged after #89
Currently the `pipe` density needs normalization. The best I feel is to take a template data, do FH ( D * F (x)) and normalize based on difference in means...
There are a bunch of scripts to better view k-space sampling trajectories, particularly but not limited to 3D: - [ ] Temporal colored plots: Helps to verify how good trajectory...