I have a large dicom mri dataset for several patients. For each patient, there is a folder including many 2d slices of .dcm files and the data of each patient has different sizes. For example:
patient1: PixelSpacing=0.8mm,0.8mm, SliceThickness=2mm, SpacingBetweenSlices=1mm, 400x400 pixels
patient2: PixelSpacing=0.625mm,0.625mm, SliceThickness=2.4mm, SpacingBetweenSlices=1mm, 512x512 pixels
So my question is how can I convert all of them into {Pixel Spacing} = 1mm,1mm and {Slice Thickness = 1mm}?
Thanks.
These are two different questions:
About harmonizing positions and pixel spacing, these links will be helpful:
Finding the coordinates (mm) of identical slice locations for two MR datasets acquired in the same scanning session
Interpolation between two images with different pixelsize
http://nipy.org/nibabel/dicom/dicom_orientation.html
Basically, you want to build your target volume and interpolate each of its pixels from the nearest neighbors in the source volumes.
About modifying the slice thickness: If you really want to modify the slice thickness rather than the slice distance, I do not see any chance to do this correctly with the source data you have. This is because the thickness says which width of the raw data was used to calculate the values for a slice in your stack (e.g. by averaging or calculating an integral). With a slice thickness of 2 or 2.4mm in the source volumes, you will not be able to reconstruct the gray values with a thickness of 1 mm. If your question was referring to slice distance rather than slice thickness, answer 1 applies.
Related
When you make a line profile of all x-values or all y-values the extraction from each pixel is clear. But when you take a line profile along a diagonal, how does DM choose which pixels to use in the one dimensional readout?
Not really a scripting question, but I'm rather certain that it uses bi-linear interpolation between the grid-points along the drawn line. (And if perpendicular integration is enabled, it does so in an integral.) It's the same interpolation you would get for a "rotate" image.
In fact, you can think of it as a rotate-image (bi-linearly interpolated) with a 'cut-out' afterwards, potentially summed/projected onto the new X-axis.
Here is an example
Assume we have a 5 x 4 image, which gives the grid as shown below.
I'm drawing top-left corners to indicate the coordinates system pixel convention used in DigitalMicrgraph, where
(x/y)=(0/0) is the top-left corner of the image
Now extract a LineProfile from (1/1) to (4/3). I have highlighted the pixels for those coordinates.
Note, that a Line drawn from the corners seems to be shifted by half-a-pixel from what feels 'natural', but that is the consequence of the top-left-corner convention. I think, this is why a LineProfile-Marker is shown shifted compared to f.e. LineAnnotations.
In general, this top-left corner convention makes schematics with 'pixels' seem counter-intuitive. It is easier to think of the image simply as grid with values in points at the given coordinates than as square pixels.
Now the maths.
The exact profile has a length of:
As we can only have profiles with integer channels, we actually extract a LineProfile of length = 4, i.e we round up.
The angle of the profile is given by the arc-tangent of dX and dY.
So to extract the profile, we 'rotate' the grid by that angle - done by bilinear interpolation - and then extract the profile as grid of size 4 x 1:
This means the 'values' in the profile are from the four points:
Which are each bi-linearly interpolated values from four closest points of the original image:
In case the LineProfile is averaged over a certain width W, you do the same thing but:
extract a 2D grid of size L x W centered symmetrically over the line.i.e. the grid is shifted by (W-1)/2 perpendicular to the profile direction.
sum the values along W
I have spectrogram data from an audio analysis which looks like this:
On one axis I have frequencies in Hz and in the other times in seconds. I added the grid over the map to show the actual data points. Due to the nature of the used frequency analysis, the best results never give evenly spaced time and frequency values.
To allow comparison data from multiple sources, I would like to normalize this data. For this reason, I would like to calculate the peak values (maximum and minimum values) for specified areas in the map.
The second visualization shows the areas where I would like to calculate the peak values. I marked an area with a green rectangle to visualize this.
While for the time values, I would like to use equally spaced ranges (e.g 0.0-10.0, 10.0-20.0, 20.0-30.0), The frequency ranges are unevenly distributed. In higher frequencies, they will be like 450-550, 550-1500, 1500-2500, ...
You can download an example data-set here: data.zip. You can unpack the datasets like this:
with np.load(DATA_PATH) as data:
frequency_labels = data['frequency_labels']
time_labels = data['time_labels']
spectrogram_data = data['data']
DATA_PATH has to point to the path of the .npz data file.
As input, I would provide an array of frequency and time ranges. The result should be another 2d NumPy ndarray with either the maximum or the minimum values. As the amount of data is huge, I would like to rely on NumPy as much as possible to speed up the calculations.
How do I calculate the maximum/minimum values of defined areas from a 2d data map?
I would like to calculate the Horizontal and Vertical field of view from the camera intrinsic matrix for the cameras used in the KITTI dataset. The reason I need the Field of view is to convert a depth map into 3D point clouds.
Though this question has been asked quite a long time ago, I felt it needed an answer as I ran into the same issue and was unable to find any info on it.
I have however solved it using the information available in this document and some more general camera calibration documents
Firstly, we need to convert the supplied disparity into distance. This can be done through fist converting the disp map into floats through the method in the dev_kit where they state:
disp(u,v) = ((float)I(u,v))/256.0;
This disparity can then be converted into a distance through the default stereo vision equation:
Depth = Baseline * focal length/ Disparity
Now come some tricky parts. I searched high and low for the focal length and was unable to find it in documentation.
I realised just now when writing that the baseline is documented in the aforementioned source however from section IV.B we can see that it can be found in P(i)rect indirectly.
The P_rects can be found in the calibration files and will be used for both calculating the baseline and the translation from uv in the image to xyz in the real world.
The steps are as follows:
For pixel in depthmap:
xyz_normalised = P_rect \ [u,v,1]
where u and v are the x and y coordinates of the pixel respectively
which will give you a xyz_normalised of shape [x,y,z,0] with z = 1
You can then multiply it with the depth that is given at that pixel to result in a xyz coordinate.
For completeness, as P_rect is the depth map here, you need to use P_3 from the cam_cam calibration txt files to get the baseline (as it contains the baseline between the colour cameras) and the P_2 belongs to the left camera which is used as a reference for occ_0 files.
I have a raster image with multiple polyline feature classes over it. The lines are not overlapping but they are in multiple different orientations. For every pixel in the raster, I want to calculate the length of the line through that pixel so that the result would be a raster with cells assigned a float value of zero to 2^0.5 times the cell size. What's the best way to do this? I'm using ArcPro with an advanced license.
You can have a look at the answers to a similar question here (using R --- but you have a license for that too)
https://gis.stackexchange.com/questions/119993/convert-line-shapefile-to-raster-value-total-length-of-lines-within-cell/120175
I'm interested in extraction of transformation matrix from image coordinate to patient coordinate from DICOM header of a RTDOSE matrix.
I just have a RTDOSE as a single DICOM file.
When I get the imageorigin patient it seems that the origin for just the first slice is given.
How can I get the origin for the last slice?
(or maybe its better to said: I mean how can I extract this .dcm file to its separate slices?)
Any help will appreciated.
Zahra
Grid Frame Offset Vector (3004,000c) defines the offset (in mm) for each dose slice. Combining the Image Position (Patient) (0020,0032), and the offset for the last slice you can calculate the position of the last slice. Direction of the offset is perpendicular to the plane on which the dose has been defined.
More details in chapter C.8.8.3.2 in Part 3 of the DICOM standard.
http://medical.nema.org/medical/dicom/2014a/output/pdf/part03.pdf