antspymm blind QC summary

example images and qc run

import siq

## Failed to import duecredit due to No module named 'duecredit'
## /Users/stnava/Library/Python/3.9/lib/python/site-packages/bids/grabbids/__init__.py:6: FutureWarning:
## 
## grabbids has been renamed to layout in version 0.6.5, and will be removed in version 0.8

import ants
import antspynet
import antspymm
eximg = siq.simulate_image( )
ants.plot( eximg, nslices=8, ncol=4 )

exfn0 = '/tmp/project-sid-date-T1w-uid.nii.gz'
ants.image_write( eximg, exfn0 )
qc = antspymm.blind_image_assessment( exfn0 )
qc2 = antspymm.blind_image_assessment( eximg )
print( qc.keys() )

## Index(['fn', 'noise', 'snr', 'cnr', 'psnr', 'ssim', 'mi', 'reflection_err',
##        'EVR', 'msk_vol', 'spc0', 'spc1', 'spc2', 'org0', 'org1', 'org2',
##        'dimx', 'dimy', 'dimz', 'slice', 'modality', 'mriseries', 'mrimfg',
##        'mrimodel'],
##       dtype='object')

exfns = [ exfn0 ]

simulated images with high noise and high smoothing

# gaussian noise
exfnroot = '/tmp/project-sid-date-T1w-'
nzimg = siq.simulate_image( ).add_noise_to_image( 'additivegaussian', [0,1] )
exfn = exfnroot+'gaussiannoise.nii.gz'
ants.image_write( nzimg, exfn )
exfns.append( exfn )
ants.plot( nzimg )
# salt+pepper

nzimg2 = siq.simulate_image( )
nzimg2 = ants.add_noise_to_image( nzimg2, 'saltandpepper', [0.2,-1,1] )
exfn = exfnroot+'saltpepper.nii.gz'
ants.image_write( nzimg2, exfn )
exfns.append( exfn )
ants.plot( nzimg2 )
# smooth

smimg = siq.simulate_image( ).smooth_image( 3 )
exfn = exfnroot+'smooth.nii.gz'
ants.image_write( smimg, exfn )
exfns.append( exfn )
ants.plot( smimg )
# time series

tsimg = siq.simulate_image( [16,16,16,4] )
exfn = exfnroot+'timeseries.nii.gz'
ants.image_write( tsimg, exfn )
exfns.append( exfn )

qc on all these

import pandas as pd
qcdf = pd.DataFrame()
for k in range(len(exfns)):
    myqc = antspymm.blind_image_assessment( exfns[k] )
    qcdf = pd.concat(  [ qcdf, myqc ], axis=0 )

## /Users/stnava/Library/Python/3.9/lib/python/site-packages/antspymm/mm.py:6968: RuntimeWarning:
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## divide by zero encountered in float_scalars
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## /Users/stnava/Library/Python/3.9/lib/python/site-packages/antspymm/mm.py:6969: RuntimeWarning:
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## divide by zero encountered in float_scalars
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## /Users/stnava/Library/Python/3.9/lib/python/site-packages/antspymm/mm.py:6968: RuntimeWarning:
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## divide by zero encountered in float_scalars
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## /Users/stnava/Library/Python/3.9/lib/python/site-packages/antspymm/mm.py:6969: RuntimeWarning:
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## divide by zero encountered in float_scalars
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## /Users/stnava/Library/Python/3.9/lib/python/site-packages/antspymm/mm.py:6968: RuntimeWarning:
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## divide by zero encountered in float_scalars
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## /Users/stnava/Library/Python/3.9/lib/python/site-packages/antspymm/mm.py:6969: RuntimeWarning:
## 
## divide by zero encountered in float_scalars

qcdf.to_csv("/tmp/qc.csv")    

dynamic sortable table (if rendering available)

qc = read.csv( "/tmp/qc.csv" )[,-1]
DT::datatable( qc )

# knitr::kable( qc, caption='qc data frame')

regular table view

Table continues below

fn	noise	snr	cnr	psnr	ssim
project-sid-date-T1w-uid	0.01206	7.756	5.724	22.57	0.9311
project-sid-date-T1w-gaussiannoise	0.1096	7.883	7.191	15.19	0.6257
project-sid-date-T1w-saltpepper	0.08407	5.394	4.17	12.44	0.481
project-sid-date-T1w-smooth	0.02173	11.07	9.264	26.48	0.9736
project-sid-date-T1w-timeseries	0.09481	10.22	8.452	20.15	0.9104
project-sid-date-T1w-timeseries	0.09979	Inf	Inf	21.94	0.9539
project-sid-date-T1w-timeseries	0.07932	Inf	Inf	22.18	0.9568
project-sid-date-T1w-timeseries	0.07959	Inf	Inf	19.28	0.9099

Table continues below

mi	reflection_err	EVR	msk_vol	spc0	spc1	spc2	org0	org1
-1.022	0.1658	0.7168	25817	1	1	1	0	0
-0.2572	0.2096	0.8457	32756	1	1	1	0	0
-0.6729	0.2433	0.8379	32733	1	1	1	0	0
-1.846	0.2916	0.0332	29081	1	1	1	0	0
-0.9372	0.2214	0.5955	3976	1	1	1	0	0
-1.164	0.2219	0.5882	4080	1	1	1	0	0
-1.258	0.2002	0.576	4083	1	1	1	0	0
-0.9676	0.1844	0.5784	4081	1	1	1	0	0

org2	dimx	dimy	dimz	slice	modality	mriseries	mrimfg	mrimodel
0	32	32	32	0	T1w	NA	NA	NA
0	32	32	32	0	T1w	NA	NA	NA
0	32	32	32	0	T1w	NA	NA	NA
0	32	32	32	0	T1w	NA	NA	NA
0	16	16	16	0	T1w	NA	NA	NA
0	16	16	16	1	T1w	NA	NA	NA
0	16	16	16	2	T1w	NA	NA	NA
0	16	16	16	3	T1w	NA	NA	NA

list of entries and their meaning

• fn - filename

• noise - estimated noise level; just the mean absolute error between the original image and a smoothed version of that image

• snr - signal to noise ratio; defined from an automatically generated background and foreground mask fgmean / bgstd

• cnr - contrast to noise ratio; defined from an automatically generated background and foreground mask ( fgmean - bgmean ) / bgstd

• psnr - peak signal to noise ratio between original and smoothed image

• ssim - structural similarity between original and smoothed image

• mi - mutual information between original and smoothed image

• reflection_error - mean absolute error between original and reflected image

• EVR - eigenvalue ratio - towards zero for smooth images; toward 1 for pure noise; most reasonable medical images around 0.6 to 0.7

• msk_vol - volume of the foreground mask

• spc* - image spacing

• org* - image origin

• dim* - image dimensions

• slice - image slice index (useful for time series)

• modality - inferred image modality - based on filename (could change in future)

• mriseries - series description read from json if available

• mrimfg - manufacturer read from json if available

• mrimodel - read from json if available