Python implementation of the ENABLE quality-based volume selection algorithm for ASL-MRI data
Project description
# ENABLE - ENhancement of Automated Blood fLow Estimates
ENABLE is a technique to evaulate image quality in multi-PLD ASL data.
*Shirzadi et al, "Enhancement of Automated Blood Flow Estimates (ENABLE) From
Arterial Spin-Labelled MRI" J Magn Reson Imaging. 2017 Jul 6. doi: 10.1002/jmri.25807*
ENABLE performs the following steps:
1. A **preprocessing** step, consisting of tag/control differencing, brain
extraction, T1 co-registration and segmentation
2. A **sorting** step in which quality measures are calculated for each
PLD image, namely:
- Contrast:Noise ratio in GM (CNR)
- Detectibility metric (DETECT, proportion of voxels with significantly greater than zero signal in GM)
- Coefficient of variation of difference data (COV, spatial standard deviation divided by spatial mean in GM)
- Temporal SNR (TSNR, spatial mean of ASL image divided by temporal STD image in GM)
3. A **selection** step in which a combined quality measure is calculated and the best image is identified:
- 0.1*CNR + 1.8*DETECT - COV - TSNR
4. A *CBF generation* step in which a CBF image is generated from the best image
## Usage
oxasl_enable -i <ASL input file> -t1 <T1 image> -n <Noise ROI image> -o <Output dir>
oxasl_enable can also be used as a plug-in for the OXASL ASL processing pipeline - see documentation
for this tool at:
https://oxasl.readthedocs.io/
### Options
--version show program's version number and exit
-h, --help show this help message and exit
-i INFILE ASL data file
--t1=T1 T1 map
-n NOISE Noise ROI
-o OUTPUT Output dir
--debug=DEBUG Debug mode
ENABLE is a technique to evaulate image quality in multi-PLD ASL data.
*Shirzadi et al, "Enhancement of Automated Blood Flow Estimates (ENABLE) From
Arterial Spin-Labelled MRI" J Magn Reson Imaging. 2017 Jul 6. doi: 10.1002/jmri.25807*
ENABLE performs the following steps:
1. A **preprocessing** step, consisting of tag/control differencing, brain
extraction, T1 co-registration and segmentation
2. A **sorting** step in which quality measures are calculated for each
PLD image, namely:
- Contrast:Noise ratio in GM (CNR)
- Detectibility metric (DETECT, proportion of voxels with significantly greater than zero signal in GM)
- Coefficient of variation of difference data (COV, spatial standard deviation divided by spatial mean in GM)
- Temporal SNR (TSNR, spatial mean of ASL image divided by temporal STD image in GM)
3. A **selection** step in which a combined quality measure is calculated and the best image is identified:
- 0.1*CNR + 1.8*DETECT - COV - TSNR
4. A *CBF generation* step in which a CBF image is generated from the best image
## Usage
oxasl_enable -i <ASL input file> -t1 <T1 image> -n <Noise ROI image> -o <Output dir>
oxasl_enable can also be used as a plug-in for the OXASL ASL processing pipeline - see documentation
for this tool at:
https://oxasl.readthedocs.io/
### Options
--version show program's version number and exit
-h, --help show this help message and exit
-i INFILE ASL data file
--t1=T1 T1 map
-n NOISE Noise ROI
-o OUTPUT Output dir
--debug=DEBUG Debug mode
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