diff --git a/docs/perfusionProcesses.md b/docs/perfusionProcesses.md index 9eefdfc8..6ed95181 100644 --- a/docs/perfusionProcesses.md +++ b/docs/perfusionProcesses.md @@ -1,127 +1,120 @@ - # Section P: Perfusion Processes - ## Native R1 estimation The processes in this section describe commonly used methods to estimate the native relaxation rate *R10* from a given MR signal data set. The resulting native relaxation rate can be used e.g. as input for the conversion from an electromagnetic property to indicator concentration. | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.NR1.001 | Estimate native *R1* | -- | Estimate *R10* | This process returns the native *R1* relaxation rate *R10* derived using a given native *R1* estimation method.
**Input**:
Native *R1* estimation method (select from [Native *R1*-estimation methods](#Native R1 estimation methods))
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10)| -- | +| P.NR1.001 | Estimate native  *R1*  | -- | Estimate *R10* | This process returns the native *R1* relaxation rate *R10* derived using a given native *R1*  estimation method.
**Input**:
Native *R1* estimation method (select from [Native *R1*-estimation methods](#Native R1 estimation methods))
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10)| -- | ### Native *R1*-estimation methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.NR2.001 | Fixed Value | -- | -- | A fixed value of *R10*, e.g. a literature value, rather than a measured value is assumed.
**Input:**
[Fixed *R10*-value (Q.NR1.001)](quantities.md#R10_fixed)
**Output**:
[*R10* (Q.EL1.002)](quantities.md#R10) | Haacke et al. 2007 | -| P.NR2.002 | Variable Flip Angle | -- | VFA| This process estimates the native longitudinal relaxation rate *R10* (and signal scaling factor *S0*) from the MR signal measured at multiple flip angles by inverting the [SPGR model (M.SM2.002)](perfusionModels.md#SPGR model) according to a specified inversion method.
**Input**:
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Signal (Q.MS1.001)](quantities.md#S), [Prescribed excitatory flip angle (Q.MS1.007)](quantities.md#Flip angle)],
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = [SPGR model (M.SM2.002)](perfusionModels.md#SPGR model)
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10),
[*S0* (Q.MS1.010)](quantities.md#S_0) | Wang et al. 1987| -| P.NR2.003 | Multi-delay Saturation Recovery | -- | SR | This process estimates the native longitudinal relaxation rate *R10* (and signal scaling factor *S0*) from the MR signal measured at multiple prepulse delays by inverting the saturation recovery GRE signal model according to a specified inversion method.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Signal (Q.MS1.001)](quantities.md#S), [Prepulse delay time (Q.MS1.008)](quantities.md#PD)],
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = an SR model from [MR signal models](perfusionModels.md#MR signal models)
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10),
[*S0* (Q.MS1.010)](quantities.md#S_0) | Parker et al. 2000 | -| P.NR2.004 | Multi-delay Inversion Recovery | -- | IR | This process estimates the native longitudinal relaxation rate *R10* (and signal scaling factor *S0*) from the MR signal measured at multiple prepulse delays assuming an inversion recovery GRE signal model.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Signal (Q.MS1.001)](quantities.md#S), [Prepulse delay time (Q.MS1.008)](quantities.md#PD)],
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = an IR model from [MR signal models](perfusionModels.md#MR signal models).
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10),
[*S0* (Q.MS1.010)](quantities.md#S_0) | Ordidge et al. 1990| -| P.NR2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | - +| P.NR2.001 | Fixed Value | -- | -- | A fixed value of *R10*, e.g. a literature value, rather than a measured value is assumed.
**Input:**
[Fixed *R10*-value (Q.NR1.001)](quantities.md#R10_fixed)
**Output**:
 [*R10* (Q.EL1.002)](quantities.md#R10) | [Haacke et al. 2007](https://doi.org/10.1002/jmri.22987){target="_blank"} | +| P.NR2.002 | Variable Flip Angle | -- | VFA| This process estimates the native longitudinal relaxation rate *R10* (and signal scaling factor *S0*) from the MR signal measured at multiple flip angles by inverting the [SPGR model (M.SM2.002)](perfusionModels.md#SPGR model) according to a specified inversion method.
**Input**:
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with
 [[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Signal (Q.MS1.001)](quantities.md#S), [Prescribed excitatory flip angle (Q.MS1.007)](quantities.md#Flip angle)],  
 [Forward model (M.GF1.001)](perfusionModels.md#Forward model) = [SPGR model (M.SM2.002)](perfusionModels.md#SPGR model)
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10),
[*S0* (Q.MS1.010)](quantities.md#S_0) | [Wang et al. 1987](https://doi.org/10.1002/mrm.1910050502){target="_blank"} | +| P.NR2.003 | Multi-delay Saturation Recovery | -- | SR | This process estimates the native longitudinal relaxation rate *R10* (and signal scaling factor *S0*) from the MR signal measured at multiple prepulse delays by inverting the saturation recovery GRE signal model according to a specified inversion method.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with  
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Signal (Q.MS1.001)](quantities.md#S), [Prepulse delay time (Q.MS1.008)](quantities.md#PD)],
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = an SR model from [MR signal models](perfusionModels.md#MR signal models)
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10),
[*S0* (Q.MS1.010)](quantities.md#S_0) | [Parker et al. 2000](https://doi.org/10.1002/1522-2594(200011)44:5<783::AID-MRM18>3.0.CO;2-9){target="_blank"} | +| P.NR2.004 | Multi-delay Inversion Recovery | -- | IR | This process estimates the native longitudinal relaxation rate *R10* (and signal scaling factor *S0*) from the MR signal measured at multiple prepulse delays assuming an inversion recovery GRE signal model.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with  
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Signal (Q.MS1.001)](quantities.md#S), [Prepulse delay time (Q.MS1.008)](quantities.md#PD)],
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = an IR model from [MR signal models](perfusionModels.md#MR signal models).
**Output:**
[*R10* (Q.EL1.002)](quantities.md#R10),
[*S0* (Q.MS1.010)](quantities.md#S_0)  | [Ordidge et al. 1990](https://doi.org/10.1002/mrm.1910160205){target="_blank"} | +| P.NR2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Bolus arrival time estimation | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.BA1.001 | Estimate Bolus Arrival Time | -- | EstimateBAT | This process returns the bolus arrival time (BAT) of a data set according to a specified bolus arrival time estimation method.
**Input:**
Bolus arrival time estimation method (select from [BAT estimation methods](#Bolus arrival time estimation methods))
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | -- | +| P.BA1.001 | Estimate Bolus Arrival Time | -- | EstimateBAT | This process returns the bolus arrival time (BAT) of a data set according to a specified bolus arrival time estimation method.
**Input:**
Bolus arrival time estimation method (select from [BAT estimation methods](#Bolus arrival time estimation methods))
**Output**:
 [Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | -- | ### Bolus arrival time estimation methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.BA2.001 | Manually | -- | Manually | The BAT is manually determined by visual inspection.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)]
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | -- | +| P.BA2.001 | Manually | -- | Manually | The BAT is manually determined by visual inspection.
 **Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)]  
**Output**:
 [Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | -- | | P.BA2.002 | Data value exceeds threshold | -- | Exceeds threshold | The BAT is estimated as the minimal data grid point at which the data value exceeds a certain threshold.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Lower threshold (Q.GE1.010)](quantities.md#L)
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | -- | | P.BA2.003 | Derivative of data values exceeds threshold | -- | Derivative exceeds threshold | The BAT is estimated as the minimal data grid point at which the derivative of the data values exceeds a certain threshold.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Lower threshold (Q.GE1.010)](quantities.md#L)
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | -- | -| P.BA2.004 | Intersection-based | -- | Intersection-based | The BAT is determined from calculating the intersection points of the data grid axis and straight lines joining the first N pairs of adjacent points. The BAT is estimated as the maximum of the intersection points.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Intersection-based BAT estimation parameters (Q.BA1.002)](quantities.md#IntersectionBATParms)
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | Galbraith et al. 2002 | -| P.BA2.005 | Model-based | -- | Model-based | A specified model is fitted to the data, yielding the BAT as one of the estimated model parameters.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods))
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | Singh et al. 2009 | - -| P.BA2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.BA2.004 | Intersection-based | -- | Intersection-based | The BAT is determined from calculating the intersection points of the data grid axis and straight lines joining the first N pairs of adjacent points. The BAT is estimated as the maximum of the intersection points.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Intersection-based BAT estimation parameters (Q.BA1.002)](quantities.md#IntersectionBATParms)
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | [Galbraith et al. 2002](https://doi.org/10.1118/1.1491823){target="_blank"} | +| P.BA2.005 | Model-based | -- | Model-based | A specified model is fitted to the data, yielding the BAT as one of the estimated model parameters.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods))
**Output**:
[Bolus arrival time (Q.BA1.001)](quantities.md#BAT) | [Singh et al. 2009](https://doi.org/10.1002/mrm.21838){target="_blank"} | +| P.BA2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Baseline estimation | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.BL1.001 | Estimate Baseline | -- | EstimateBaseline | This process returns the value of the baseline of a data set according to a specified baseline estimation method.
**Input:**
Baseline estimation method (select from [Baseline estimation methods](#Baseline estimation methods))
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | +| P.BL1.001 | Estimate Baseline | -- | EstimateBaseline | This process returns the value of the baseline of a data set according to a specified baseline estimation method.  
**Input:**
Baseline estimation method (select from [Baseline estimation methods](#Baseline estimation methods))
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | ### Baseline estimation methods - | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.BL2.001 | Manually | -- | -- | The baseline is manually determined by visual inspection.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | -| P.BL2.002 | nth data value | -- | -- | The baseline is determined as the data value of the nth data grid point.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Index n (Q.GE1.003)](quantities.md#index)
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | -| P.BL2.003 | Mean baseline of range | -- | Mean baseline | The baseline is determined as the mean of data values in the data grid range (Start, End).
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Start of range (Q.GE1.013)](quantities.md#x_start),
[End of range (Q.GE1.014)](quantities.md#x_end)
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | -| P.BL2.004 | Minimum value | -- | Minimum | The baseline is determined as the minimum of all data values.
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)]
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | -| P.BL2.005 | Model-based | -- | -- | A specified model is fitted to the data, yielding the baseline value as one of the estimated model parameters.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods))
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | Singh et al. 2009 | -| P.BL2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | - +| P.BL2.001 | Manually | -- | -- | The baseline is manually determined by visual inspection.  
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | +| P.BL2.002 | nth data value | -- | -- | The baseline is determined as the data value of the nth data grid point.  
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Index n (Q.GE1.003)](quantities.md#index)
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | +| P.BL2.003 | Mean baseline of range | -- | Mean baseline | The baseline is determined as the mean of data values in the data grid range (Start, End).  
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)],
[Start of range (Q.GE1.013)](quantities.md#x_start),  
[End of range (Q.GE1.014)](quantities.md#x_end)
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | +| P.BL2.004 | Minimum value | -- | Minimum | The baseline is determined as the minimum of all data values.  
**Input:**
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)]
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | -- | +| P.BL2.005 | Model-based | -- | -- | A specified model is fitted to the data, yielding the baseline value as one of the estimated model parameters.  
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods))
**Output**:
[Baseline (Q.BL1.001)](quantities.md#f_BL) | [Singh et al. 2009](https://doi.org/10.1002/mrm.21838){target="_blank"} | +| P.BL2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Signal calibration The processes listed in this section describe commonly used methods to estimate the signal calibration factor *S0* from a given MR signal data set. | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.SC1.001 | Estimate signal scaling factor | -- | Estimate *S0* | In this process the signal scaling factor is determined according to a specified *S0* -estimation method.
**Input:**
Signal scaling factor estimation method ( select from [signal scaling factor estimation methods](#Signal scaling factor estimation methods))
**Output**:
[*S0* (Q.MS1.010)](quantities.md#S_0) | -- | +| P.SC1.001 | Estimate signal scaling factor | -- | Estimate *S0* | In this process the signal scaling factor is determined according to a specified *S0* -estimation method.  
**Input:**
Signal scaling factor estimation method ( select from [signal scaling factor estimation methods](#Signal scaling factor estimation methods))
**Output**:
[*S0* (Q.MS1.010)](quantities.md#S_0) | -- | ### Signal scaling factor estimation methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.SC2.001 | *S0* from native *R1* estimation | -- | -- | In this method *S0* is estimated as described in the native *R1* -estimation methods which have *S0* as output.
**Input:**
Select a [native R1 estimation method](#Native R1 estimation methods) with *S0* as output
**Output**:
[*S0* (Q.MS1.010)](quantities.md#S_0) | -- | -| P.SC2.002 | *S0* from baseline signal of dynamic data | -- | -- | In this method *S0* is estimated by inverting a specified MR signal model according to a specified inversion method for the baseline signal and baseline relaxation rate.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with [Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [MR signal models](perfusionModels.md#MR signal models) with
[*R1* (Q.EL1.001)](quantities.md#R1) = [*R10* (Q.EL1.002)](quantities.md#R10)
or
[*R2* (Q.EL1.004)](quantities.md#R2) = [*R20* (Q.EL1.005)](quantities.md#R20)
or
[*R2** (Q.EL1.007)](quantities.md#R2Star) = [*R20** (Q.E.008)](quantities.md#R2Star0),
[S (Q.MS1.001)](quantities.md#S) = [*SBL*(Q.MS1.002)](quantities.md#S_BL)
**Output**:
[*S0* (Q.MS1.010)](quantities.md#S_0) | -- | -| P.SC2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.SC2.001 | *S0* from native *R1* estimation | -- | -- | In this method *S0*  is estimated as described in the native *R1* -estimation methods which have *S0*  as output.  
**Input:**
 Select a [native R1 estimation method](#Native R1 estimation methods) with *S0* as output  
**Output**:
[*S0* (Q.MS1.010)](quantities.md#S_0) | -- | +| P.SC2.002 | *S0* from baseline signal of dynamic data | -- | -- | In this method *S0*  is estimated by inverting a specified MR signal model according to a specified inversion method for the baseline signal and baseline relaxation rate.
**Input:**
Inversion method (select from [Inversion methods](generalPurposeProcesses.md#Inversion methods)) with [Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [MR signal models](perfusionModels.md#MR signal models) with
[*R1* (Q.EL1.001)](quantities.md#R1) =  [*R10* (Q.EL1.002)](quantities.md#R10)
or
[*R2* (Q.EL1.004)](quantities.md#R2) = [*R20* (Q.EL1.005)](quantities.md#R20)
or
[*R2** (Q.EL1.007)](quantities.md#R2Star) = [*R20** (Q.E.008)](quantities.md#R2Star0),
[S (Q.MS1.001)](quantities.md#S) = [*SBL*(Q.MS1.002)](quantities.md#S_BL)
**Output**:
[*S0* (Q.MS1.010)](quantities.md#S_0) | -- | +| P.SC2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Arterial input function estimation | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.AE1.001 | Estimate arterial input function | -- | Estimate AIF | This process returns the AIF from a given data set, derived using a specified AIF estimation method. Furthermore, it can be optionally specified if an AIF correction method (.e.g. Partial volume correction) will be applied or if a measurement preparation (e.g. dual bolus) has been done for data acquisition.
**Input:**
AIF estimation method (select from [AIF estimation methods](#AIF estimation methods)),
*optional*:
AIF correction or measurement preparation (select from [AIF correction and measurement preparation](#AIF correction and measurement preparation)).
**Output**:
[[Ca,p (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | +| P.AE1.001 | Estimate arterial input function | -- | Estimate AIF | This process returns the AIF from a given data set, derived using a specified AIF estimation method. Furthermore, it can be optionally specified if an AIF correction method (.e.g. Partial volume correction) will be applied or if a measurement preparation (e.g. dual bolus) has been done for data acquisition.  
**Input:**
AIF estimation method (select from [AIF estimation methods](#AIF estimation methods)),
*optional*:
AIF correction or measurement preparation (select from [AIF correction and measurement preparation](#AIF correction and measurement preparation)).  
**Output**:
 [[Ca,p (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | ### AIF estimation methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.AE2.001 | Literature-based AIF | Population-based AIF | -- | The AIF is taken from a published reference or from the average of a population.
**Input:**
--
**Output**:
[[Ca,p (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | -| P.AE2.002 | Mean ROI AIF | -- | -- | In this process the AIF is determined by specifying the mask of a user-defined region of interest (within an artery). This process returns the mean concentration time curve within this masked ROI.
**Input:**
[[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)],
[Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask)
**Output**:
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | -| P.AE2.003 | Model-based AIF | -- | -- | The AIF is derived from fitting a model to the dynamic concentration data.
**Input:**
Inversion method (select from inversion methods) with
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] = [[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)] and
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [AIF models](perfusionModels.md#AIF models) or [descriptive models](perfusionModels.md#Descriptive models)]
**Output**:
[[Ca,p (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | -| P.AE2.004 | Automatic *k*-means-cluster-based | -- | *k*-means | For automatic AIF selection, a k-means cluster algorithm to identify k clusters. The cluster with the lowest first moment represents the AIF.
**Input:**
[[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)],
[Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask),
[*k*-means-cluster-algorithm-parameters (Q.AE1.001)](quantities.md#kMeansParams)
**Output**:
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | -| P.AE2.005 | Automatic fuzzy-c-means-cluster-based | -- | FCM | For automatic AIF selection, a fuzzy-c-means-cluster algorithm with the "fuzziness" parameter *m*, the iterative tolerance level $\epsilon$, the number of clusters *c*, the cluster probability threshold value *Pc* and the initial cluster centroids *vi* are applied. The cluster with maximal $M = \frac{f_{max}}{TTP\cdot FWHM}$ represents the AIF.
**Input:**
[[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)],
[Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask),
[Fuzzy-c-means-cluster-algorithm parameters (Q.AE1.002)](quantities.md#fuzzycMeansParams)
**Output**:
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | -| P.AE2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.AE2.001 | Literature-based AIF | Population-based AIF | -- | The AIF is taken from a published reference or from the average of a population.  
**Input:**
--  
**Output**:
 [[Ca,p (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | +| P.AE2.002 | Mean ROI AIF | -- | -- | In this process the AIF is determined by specifying the mask of a user-defined region of interest (within an artery). This process returns the mean concentration time curve within this masked ROI.
**Input:**
[[Indicator concentration (Q.IC1.001)](quantities.md#C),  [t (Q.GE1.004)](quantities.md#t)],
[Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask)
 **Output**:
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | +| P.AE2.003 | Model-based AIF | -- | -- | The AIF is derived from fitting a model to the dynamic concentration data.
**Input:**
Inversion method (select from inversion methods) with
[[Data (Q.GE1.002)](quantities.md#Data), [Data grid (Q.GE1.001)](quantities.md#DataGrid)] =  [[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)] and
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [AIF models](perfusionModels.md#AIF models) or [descriptive models](perfusionModels.md#Descriptive models)]
 **Output**:
 [[Ca,p (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | +| P.AE2.004 | Automatic *k*-means-cluster-based  | -- | *k*-means | For automatic AIF selection, a k-means cluster algorithm to identify k clusters. The cluster with the lowest first moment represents the AIF.
**Input:**
[[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)],
 [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask),
 [*k*-means-cluster-algorithm-parameters (Q.AE1.001)](quantities.md#kMeansParams)
**Output**:
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | +| P.AE2.005 | Automatic fuzzy-c-means-cluster-based | -- | FCM | For automatic AIF selection, a fuzzy-c-means-cluster algorithm with the "fuzziness" parameter *m*, the iterative tolerance level $\epsilon$, the number of clusters *c*, the cluster probability threshold value *Pc* and the initial cluster centroids *vi*  are applied. The cluster with maximal $M = \frac{f_{max}}{TTP\cdot FWHM}$ represents the AIF.  
**Input:**
[[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)],
 [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask),
 [Fuzzy-c-means-cluster-algorithm parameters (Q.AE1.002)](quantities.md#fuzzycMeansParams)
 **Output**:
[[Ca,b (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- | +| P.AE2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ### AIF correction and measurement preparation | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.AE3.001 | Partial-volume effect corrected | -- | PVE | If this item is set in the [Estimate AIF (P.AE1.001)](#Estimate AIF) method, partial volume effects are accounted for. Otherwise, or if not specified, no partial volume effect correction was performed. | -- | -| P.AE3.002 | Dual Bolus | -- | DB | If this item is set in the [Estimate AIF (P.AE1.001)](#Estimate AIF) method, the full-dose AIF was reconstructed from a pre-bolus injection with a smaller dose. Otherwise, or if not specified, no dual bolus approach was used. | Risse et al. 2006 | -| P.AE3.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | - +| P.AE3.001 | Partial-volume effect corrected | -- | PVE |  If this item is set in the [Estimate AIF (P.AE1.001)](#Estimate AIF) method, partial volume effects are accounted for. Otherwise, or if not specified, no partial volume effect correction was performed. | -- | +| P.AE3.002 | Dual Bolus | -- | DB | If this item is set in the [Estimate AIF (P.AE1.001)](#Estimate AIF) method, the full-dose AIF was reconstructed from a pre-bolus injection with a smaller dose. Otherwise, or if not specified, no dual bolus approach was used. | [Risse et al. 2006](https://doi.org/10.1002/jmri.20747){target="_blank"} | +| P.AE3.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Conversion from signal to concentration | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.SC1.001 | Convert signal to concentration | -- | ConvertSToC | In this process the MR signal is converted to the indicator concentration according to a specified concentration conversion method.
**Input:**
Signal to concentration conversion method (select from [signal to concentration conversion methods](#Signal to concentration conversion methods)).
**Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
| -- | +| P.SC1.001 | Convert signal to concentration | -- | ConvertSToC | In this process the MR signal is converted to the indicator concentration according to a specified concentration conversion method.
**Input:**
Signal to concentration conversion method (select from [signal to concentration conversion methods](#Signal to concentration conversion methods)).
 **Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
 | -- | ### Signal to concentration conversion methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.SC2.001 | Direct conversion from signal concentration | -- | ConvertDirectSToC | In this process the MR signal is directly converted to the indicator concentration by inverting a specified forward model which describes a direct relationship between signal and indicator concentration.
**Input:**
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[Data (Q.GE1.002)](quantities.md#Data) = [Signal (Q.MS1.001)](quantities.md#S),
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select [MR signal model](perfusionModels.md#MR signal models) with direct relationship between signal and indicator concentration
**Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
| -- | -| P.SC2.002 | Conversion via electromagnetic property| -- | ConvertSToCViaEP | In this process the MR signal is first converted to an electromagnetic property, which is in a second step converted to indicator concentration.
**Input:**
Signal to electromagnetic property conversion method (select from [signal to electromagnetic property conversion conversion methods](#Signal to electromagnetic property conversion methods)),
Electromagnetic property to concentration conversion method (select from [electromagnetic property to concentration conversion methods](#Electromagnetic property to concentration conversion methods))
**Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
| -- | -| P.SC2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.SC2.001 | Direct conversion from signal concentration | -- | ConvertDirectSToC | In this process the MR signal is directly converted to the indicator concentration by inverting a specified forward model which describes a direct relationship between signal and indicator concentration.  
**Input:**
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[Data (Q.GE1.002)](quantities.md#Data) = [Signal (Q.MS1.001)](quantities.md#S),
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select [MR signal model](perfusionModels.md#MR signal models) with direct relationship between signal and indicator concentration
  **Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
 | -- | +| P.SC2.002 | Conversion via electromagnetic property| -- | ConvertSToCViaEP | In this process the MR signal is first converted to an electromagnetic property, which is in a second step converted to indicator concentration.  
**Input:**
Signal to electromagnetic property conversion method (select from [signal to electromagnetic property conversion conversion methods](#Signal to electromagnetic property conversion methods)),
Electromagnetic property to concentration conversion method (select from [electromagnetic property to concentration conversion methods](#Electromagnetic property to concentration conversion methods))
  **Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
 | -- | +| P.SC2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ### Signal to electromagnetic property conversion methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.SE1.001 | Model-based | -- | -- | In this process the MR signal is converted to an electromagnetic property (e.g. R1) via inversion of a specified model.
**Input:**
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[Data (Q.GE1.002)](quantities.md#Data) = [Signal (Q.MS1.001)](quantities.md#S),
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [MR signal models](perfusionModels.md#MR signal models)
**Output**:
Quantity from [Electromagnetic quantities](quantities.md#Electromagnetic quantities) ( e.g. *R1*, *R2*, *R2** or $\chi$ )
| -- | -| P.SE1.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.SE1.001 | Model-based | -- | -- | In this process the MR signal is converted to an electromagnetic property (e.g. R1) via inversion of a specified model.  
**Input:**
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[Data (Q.GE1.002)](quantities.md#Data) = [Signal (Q.MS1.001)](quantities.md#S),
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [MR signal models](perfusionModels.md#MR signal models)
 **Output**:
Quantity from [Electromagnetic quantities](quantities.md#Electromagnetic quantities) ( e.g. *R1*, *R2*, *R2** or $\chi$ )
 | -- | +| P.SE1.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ### Electromagnetic property to concentration conversion methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.EC1.001 | Model-based| -- | -- | In this process an electromagnetic property (e.g. R1) is converted to the indicator concentration via inversion of a specified model.
**Input:**
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[Data (Q.GE1.002)](quantities.md#Data) = [Electromagnetic quantities](quantities.md#Electromagnetic quantities),
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [electromagnetic property models](perfusionModels.md#Electromagnetic property models)
**Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
| -- | -| P.EC1.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.EC1.001 | Model-based| -- | -- | In this process an electromagnetic property (e.g. R1) is converted to the indicator concentration via inversion of a specified model.
**Input:**
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with
[Data (Q.GE1.002)](quantities.md#Data) = [Electromagnetic quantities](quantities.md#Electromagnetic quantities),
[Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [electromagnetic property models](perfusionModels.md#Electromagnetic property models)  
 **Output**:
[Indicator concentration (Q.IC1.001)](quantities.md#C)
 | -- | +| P.EC1.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Leakage correction @@ -129,14 +122,14 @@ This group contains methods used to correct for the leakage of an indicator into | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.LC1.001 | Leakage correction | -- | LC | This method is used to correct for the leakage of an indicator into the tissue which is not assumed to leave the vasculature.
**Input**:
Leakage correction method (select from [leakage correction methods](#Leakage correction methods))
**Output**:
[R2* (Q.EL1.007)](quantities.md#R2Star) | -- | +| P.LC1.001 | Leakage correction | -- | LC | This method is used to correct for the leakage of an indicator into the tissue which is not assumed to leave the vasculature.
 **Input**:
Leakage correction method (select from [leakage correction methods](#Leakage correction methods))
 **Output**:
[R2* (Q.EL1.007)](quantities.md#R2Star) | -- | ### Leakage correction methods | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.LC2.001 | Model-based | -- | -- | The leakage correction is done assuming a leakage correction model.
**Input**:
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with a [Forward model (M.GF1.001)](generalPurposeProcesses.md#Forward model) from [leakage correction models](perfusionModels.md#Leakage correction models)
**Output**:
[R2* (Q.EL1.007)](quantities.md#R2Star) | -- | -| P.LC2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | +| P.LC2.001 | Model-based | -- | -- | The leakage correction is done assuming a leakage correction model.
 **Input**:
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with a [Forward model (M.GF1.001)](generalPurposeProcesses.md#Forward model) from [leakage correction models](perfusionModels.md#Leakage correction models)
 **Output**:
[R2* (Q.EL1.007)](quantities.md#R2Star) | -- | +| P.LC2.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | ## Extraction of parameters @@ -144,26 +137,7 @@ In this group methods are listed how to derive physiological or descriptive para | Code | OSIPI name| Alternative names|Notation|Description|Reference| | -- | -- | -- | -- | -- | -- | -| P.EX1.001 | Model-based parameter extraction| -- | Model-based | Parameters are derived by inverting a specified model which provides as output physiological or descriptive model quantities, e.g. via model fitting or deconvolution.
**Input**:
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with a [Forward model (M.GF1.001)](perfusionModels.md#Forward model) from [indicator concentration models](perfusionModels.md#Indicator concentration models) or [descriptive models](perfusionModels.md#Descriptive models).
**Output**:
[[Estimated model parameters (Q.OP1.003)](quantities.md#EMP) from [physiological quantities](quantities.md#Physiological quantities) or [descriptive model quantities](quantities.md#Descriptive model quantities)] | -- | -| P.EX1.002 | Curve descriptive parameter extraction | -- | Descriptive | This process returns the value of a curve descriptive quantity from a given data set on a given data grid according to a specified curve descriptive process.
**Input**:
Method from [curve descriptive processes](generalPurposeProcesses.md#Curve descriptive processes)
**Output**:
[Quantities from [curve descriptive quantities](quantities.md#Curve descriptive quantities)]| -- | -| P.EX1.003 | Derivation of parameters from other parameters | -- | Identity-based | This process returns a quantity from other given quantities and a specified parameter identity model.
**Input**:
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with a [Forward model (M.GF1.001)](perfusionModels.md#Forward model) from [perfusion identity models](perfusionModels.md#Perfusion identity models)
**Output**:
[[Estimated model parameters (Q.OP1.003)](quantities.md#EMP) from [physiological quantities](quantities.md#Physiological quantities)]| -- | -| P.EX1.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | -- | - - - - - - - - - - - - - - - - - - - +| P.EX1.001 | Model-based parameter extraction| -- | Model-based | Parameters are derived by inverting a specified model which provides as output physiological or descriptive model quantities, e.g. via model fitting or deconvolution.
 **Input**:
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with a [Forward model (M.GF1.001)](perfusionModels.md#Forward model) from [indicator concentration models](perfusionModels.md#Indicator concentration models) or [descriptive models](perfusionModels.md#Descriptive models).
 **Output**:
[[Estimated model parameters (Q.OP1.003)](quantities.md#EMP) from [physiological quantities](quantities.md#Physiological quantities) or [descriptive model quantities](quantities.md#Descriptive model quantities)] | -- | +| P.EX1.002 | Curve descriptive parameter extraction | -- | Descriptive | This process returns the value of a curve descriptive quantity from a given data set on a given data grid according to a specified curve descriptive process.
 **Input**:
Method from [curve descriptive processes](generalPurposeProcesses.md#Curve descriptive processes)
 **Output**:
[Quantities from [curve descriptive quantities](quantities.md#Curve descriptive quantities)]| -- | +| P.EX1.003 | Derivation of parameters from other parameters | -- | Identity-based | This process returns a quantity from other given quantities and a specified parameter identity model.
 **Input**:
Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with a [Forward model (M.GF1.001)](perfusionModels.md#Forward model) from [perfusion identity models](perfusionModels.md#Perfusion identity models)
 **Output**:
[[Estimated model parameters (Q.OP1.003)](quantities.md#EMP) from [physiological quantities](quantities.md#Physiological quantities)]| -- | +| P.EX1.999 | Method not listed | -- | -- |This is a custom free-text item, which can be used if a method of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage.  | -- | \ No newline at end of file