Name: | BASIL | |

Type: | Module | |

Short URL: | http://bit.ly/1B7Ehot | |

Description: | This is the core tool that performs kinetic curve model-fitting to the data using a Bayesian algorithm. You should only need to use it directly for more custom analyses than that offered by oxford_asl. BASIL: Bayesian Inference for Arterial Spin Labeling MRI It is becoming increasingly common to aquire ASL data at multiple times post-inversion and fit the resultant data to a kinetic curve model. This permits problems in perfusion estimation associated with variable bolus arrival time to be avoided, since this becomes a paramter of the model whose value is determined from the data. Commonly the model fitting will be performed with a least squares technique providing parameter estimates, e.g. perfusion and bolus arrival time. Here we provide details on the use of BASIL on resting-state ASL data, as an alternative to least squares methods, which performs fast Bayesian inference offering: Voxel-wise estimation of perfusion and bolus arrival time along with parameter variance (allowing confidence intervals to be calculated). Incorporation of natural varaibility of other model parameters, e.g. values of T1, T1b and bolus duration. Spatial regularization of the estimated perfusion image. Correction for partial volume effects (where the appropriate segmentation information is available). If you have resting-state ASL data with only a single inversion time then you can still use the tools in BASIL for perfusion quantification. If you want to perform analysis of a functional experiment with ASL data, i.e. one where you want to use a GLM, then you should consult the perfusion section of FEAT, or if you have dual-echo (combined BOLD and ASL) data then consult FABBER. | |

Executable: | /dual_regression | |

Input Parameters: | - Brain mask file
- Parameter options file
- Analysis model
- least squares analysis
- infer on bolus length
- Input file
- uncertainty in T1 values
- infer on arterial compartment
- adaptive spatial smoothing
- Single step
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Output Parameters: | - Output directory
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File size: | 21.75 KB | |

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