<html><body><div style="color:#000; background-color:#fff; font-family:times new roman, new york, times, serif;font-size:12pt"><div>Dear Camino experts</div><div><br></div><div>I need to test the effects of a compressive sensing technique on the diffusion MRI data. As I notice that Camino has quite powerful simulation capabilities, I hope I could get some help from forum. <br></div><div><br></div><div>Currently, we want to test the effects of a novel compressive sensing technique on diffusion MRI. The goals are to test how sensitive the compressive sensing to the changes of SNR, b and diffusion encoding direction by comparing the fully sampled DTI-derived measures, such as FA, MD and crossing-fiber delineations with those with compressive factors. Since this novel compressive sensing is sensitive to the proportion of the moving voxels in images across diffusion directions, I think I will have to start from in-vivo images(correct me if I am wrong). What I
am planning to do is as follows:</div><div><br></div><div>1) use modelfit to estimate the model parameters from a set of in-vivo images and use it as gold standard<br></div><div>2) use datasynth to generate a series of images with different SNR, b, diffusion directions and apply compressive sensing with different reduction factors to test the effects on accuracy of the generated measures.<br></div><div><br></div><div>As I am fairly new to Camino, I wonder if someone could show me which model should provide the best trade-off between simulation accuracy and computational time. Currently, I think two-tensor model should be enough for me to test all these two goals, but I am not sure if they are the best, since the usage of the two-tensor model is not that popular.</div><div><br></div><div>Many thanks in advance!</div><div><br></div><div>Leon<br></div></div></body></html>