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) Output: R10 (Q.EL1.002) |
-- |
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) Output: R10 (Q.EL1.002) |
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) according to a specified inversion method. Input: Inversion method (select from Inversion methods) with [Data (Q.GE1.002), Data grid (Q.GE1.001)] = [Signal (Q.MS1.001), Prescribed excitatory flip angle (Q.MS1.007)], Forward model (M.GF1.001) = SPGR model (M.SM2.002) Output: R10 (Q.EL1.002), S0 (Q.MS1.010) |
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) with [Data (Q.GE1.002), Data grid (Q.GE1.001)] = [Signal (Q.MS1.001), Prepulse delay time (Q.MS1.008)], Forward model (M.GF1.001) = an SR model from MR signal models Output: R10 (Q.EL1.002), S0 (Q.MS1.010) |
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) with [Data (Q.GE1.002), Data grid (Q.GE1.001)] = [Signal (Q.MS1.001), Prepulse delay time (Q.MS1.008)], Forward model (M.GF1.001) = an IR model from MR signal models. Output: R10 (Q.EL1.002), S0 (Q.MS1.010) |
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. | -- |
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) Output: Bolus arrival time (Q.BA1.001) |
-- |
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), Data grid (Q.GE1.001)] Output: Bolus arrival time (Q.BA1.001) |
-- |
| 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), Data grid (Q.GE1.001)], Lower threshold (Q.GE1.010) Output: Bolus arrival time (Q.BA1.001) |
-- |
| 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), Data grid (Q.GE1.001)], Lower threshold (Q.GE1.010) Output: Bolus arrival time (Q.BA1.001) |
-- |
| 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), Data grid (Q.GE1.001)], Intersection-based BAT estimation parameters (Q.BA1.002) Output: Bolus arrival time (Q.BA1.001) |
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) Output: Bolus arrival time (Q.BA1.001) |
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. | -- |
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) Output: Baseline (Q.BL1.001) |
-- |
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), Data grid (Q.GE1.001) Output: Baseline (Q.BL1.001) |
-- |
| 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), Data grid (Q.GE1.001)], Index n (Q.GE1.003) Output: Baseline (Q.BL1.001) |
-- |
| 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), Data grid (Q.GE1.001)], Start of range (Q.GE1.013), End of range (Q.GE1.014) Output: Baseline (Q.BL1.001) |
-- |
| P.BL2.004 | Minimum value | -- | Minimum | The baseline is determined as the minimum of all data values. Input: [Data (Q.GE1.002), Data grid (Q.GE1.001)] Output: Baseline (Q.BL1.001) |
-- |
| 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) Output: Baseline (Q.BL1.001) |
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. | -- |
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) Output: S0 (Q.MS1.010) |
-- |
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 with S0 as output Output: S0 (Q.MS1.010) |
-- |
| 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) with Forward model (M.GF1.001) = select from MR signal models with R1 (Q.EL1.001) = R10 (Q.EL1.002) or R2 (Q.EL1.004) = R20 (Q.EL1.005) or R2* (Q.EL1.007) = R20* (Q.E.008), S (Q.MS1.001) = SBL(Q.MS1.002) Output: S0 (Q.MS1.010) |
-- |
| 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), optional: AIF correction or measurement preparation (select from AIF correction and measurement preparation). Output: [Ca,p (Q.IC1.001.[a,p]), t (Q.GE1.004)] or [Ca,b (Q.IC1.001.[a,b]), t (Q.GE1.004)] |
-- |
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]), t (Q.GE1.004)] or [Ca,b (Q.IC1.001.[a,b]), t (Q.GE1.004)] |
-- |
| 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), t (Q.GE1.004)], Binary AIF mask (Q.SE1.002) Output: [Ca,b (Q.IC1.001.[a,b]), t (Q.GE1.004)] |
-- |
| 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), Data grid (Q.GE1.001)] = [Indicator concentration (Q.IC1.001), t (Q.GE1.004)] and Forward model (M.GF1.001) = select from AIF models or descriptive models] Output: [Ca,p (Q.IC1.001.[a,p]), t (Q.GE1.004)] or [Ca,b (Q.IC1.001.[a,b]), t (Q.GE1.004)] |
-- |
| 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), t (Q.GE1.004)], Binary AIF mask (Q.SE1.002), k-means-cluster-algorithm-parameters (Q.AE1.001) Output: [Ca,b (Q.IC1.001.[a,b]), t (Q.GE1.004)] |
-- |
| 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), t (Q.GE1.004)], Binary AIF mask (Q.SE1.002), Fuzzy-c-means-cluster-algorithm parameters (Q.AE1.002) Output: [Ca,b (Q.IC1.001.[a,b]), t (Q.GE1.004)] |
-- |
| 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) 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) 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. | -- |
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). Output: Indicator concentration (Q.IC1.001) |
-- |
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) with Data (Q.GE1.002) = Signal (Q.MS1.001), Forward model (M.GF1.001) = select MR signal model with direct relationship between signal and indicator concentration Output: Indicator concentration (Q.IC1.001) |
-- |
| 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), Electromagnetic property to concentration conversion method (select from electromagnetic property to concentration conversion methods) Output: Indicator concentration (Q.IC1.001) |
-- |
| 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) with Data (Q.GE1.002) = Signal (Q.MS1.001), Forward model (M.GF1.001) = select from MR signal models Output: Quantity from 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) with Data (Q.GE1.002) = Electromagnetic quantities, Forward model (M.GF1.001) = select from electromagnetic property models Output: Indicator concentration (Q.IC1.001) |
-- |
| 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¶
This group contains methods used to correct for the leakage of an indicator into the tissue which is not assumed to leave the vasculature.
| 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) Output: R2* (Q.EL1.007) |
-- |
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) with a Forward model (M.GF1.001) from leakage correction models Output: R2* (Q.EL1.007) |
-- |
| 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¶
In this group methods are listed how to derive physiological or descriptive parameters.
| 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) with a Forward model (M.GF1.001) from indicator concentration models or descriptive models. Output: [Estimated model parameters (Q.OP1.003) from physiological quantities or 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 Output: [Quantities from 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) with a Forward model (M.GF1.001) from perfusion identity models Output: [Estimated model parameters (Q.OP1.003) from 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. | -- |