mri | 易学教程

Change entire images slices of NIFTI in Python 3.7

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问题 I'm actually work on MRI images using Python. The image format is the NIFTI format I get how to visualise slices on x, y or z axe, but now, I want tu use Sobel filtering on each of them and create a new NIFTI image with those slices. For that: I load the main .nii.gz image (img = nib.load(im_path)) I load again the main .nii.gz image with a new name "img_sobel" (img_sobel = nib.load(im_path)) Create a loop for each slice Sobel filtering the slice Replace this slice on the corresponding slice

MRI ruby threading and performance

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问题 My first question on SO, but I've lurked for a long time now so you'll have to forgive me if I've broken any rules or posted a rubbish question. I'm trying to get a better understanding of threading and I decided to test MRI and see how it performs in general. Given the following code (and output), why are the threaded operations so much slower than the non-threaded variant? code class Benchmarker def self.go puts '----------Benchmark Start----------' start_t = Time.now yield end_t = Time.now

Finding the coordinates (mm) of identical slice locations for two MR datasets acquired in the same scanning session

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问题 I have two MR acquisitions where the first one is a 3D acquisition (1x1x1 mm3) and the second is a 2D acquisition (2.24 x 2.24 x 5.00 mm, axial slices). The high resolution dataset is a full head 3D acquisition that gives 176 slices if resliced in the axial direction (orientation was initially saggital in the acquisition). The 2D acquisition only contains 3 axial slices that were selected to target specific regions in the brain and were acquired continuously. Is it possible to know, which

Are there still benefits to running JRuby vs. the latest MRI with Puma?

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问题 I'm considering updating our ruby interpreter to JRuby, it's been quite a headache because we've had to remove any 2.x specific syntax from our app and resort to ruby 1.9.3 compatibility. Which isn't the end of the world. When it came time to run the app, I found out that we cannot use Puma in clustered mode. The question is, given all the fixes and changes to MRI in the past few years, are the benefits of having "real threads" still valid? update To make this more objective, the question is,

Finding the coordinates (mm) of identical slice locations for two MR datasets acquired in the same scanning session

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I have two MR acquisitions where the first one is a 3D acquisition (1x1x1 mm3) and the second is a 2D acquisition (2.24 x 2.24 x 5.00 mm, axial slices). The high resolution dataset is a full head 3D acquisition that gives 176 slices if resliced in the axial direction (orientation was initially saggital in the acquisition). The 2D acquisition only contains 3 axial slices that were selected to target specific regions in the brain and were acquired continuously. Is it possible to know, which slices in the 3D high resolution dataset correspond accurately to the 3 slices in the 2D dataset assuming