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Which are the 850 images mentioned in your paper?
Inside each experiment folder (SE0, SE1....SE28), there are 906 images. So to get the exact 850 images, you need to, first, reordering the image sequence according to the [SliceLocation] field of the Dicom image (sort in ascending order) and you will get images arranged from pelvis to head. Then just keeping slice 21 to 870 and discard the rest which has almost no content.
What is the train/test split?
After obtaining the 850 ordered images, test images were selected at an interval of 6, i.e. slice 1, 7, 13 … 847.
How do you compute the PSNR and SSIM for the simulated and real dataset?
For the simulated data, the generated noisy image was converted to uint8 using the abdomen window (center: 40, width: 400). The training and evaluation were all conducted using these narrow range 8 bit images.
For the piglet data, the original 16 bit value was used. The training and evaluation was on 16 bit images.
What should I do when my test dicom data is not uint16?
In this case, the correct way to convert the data in preprocess.py is ([Hounsfield units]+1024)*22
Which are the 850 images mentioned in your paper?
Inside each experiment folder (SE0, SE1....SE28), there are 906 images. So to get the exact 850 images, you need to, first, reordering the image sequence according to the [SliceLocation] field of the Dicom image (sort in ascending order) and you will get images arranged from pelvis to head. Then just keeping slice 21 to 870 and discard the rest which has almost no content.
What is the train/test split?
After obtaining the 850 ordered images, test images were selected at an interval of 6, i.e. slice 1, 7, 13 … 847.
How do you compute the PSNR and SSIM for the simulated and real dataset?
For the simulated data, the generated noisy image was converted to uint8 using the abdomen window (center: 40, width: 400). The training and evaluation were all conducted using these narrow range 8 bit images.
For the piglet data, the original 16 bit value was used. The training and evaluation was on 16 bit images.
What should I do when my test dicom data is not uint16?
In this case, the correct way to convert the data in preprocess.py is ([Hounsfield units]+1024)*22