Merge pull request #79 from MartinKjunior/main-major-development

Main major development

Author: MRdep

docs/perfusionProcesses.md

@@ -79,11 +79,11 @@ The processes listed in this section describe commonly used methods to estimate
 ### <a id="AIF estimation methods"></a> AIF estimation methods
 | Code | OSIPI name| Alternative names|Notation|Description|Reference|
 | -- | -- | -- | -- | -- | -- |
-| P.AE2.001 <button class="md-button md-button--hyperlink">COPY LINK</button> | Literature-based AIF | Population-based AIF | -- | The AIF is taken from a published reference or from the average of a population.  <br /> **Input:** <br /> --  <br /> **Output**: <br />  [[C<sub>a,p</sub> (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
-| P.AE2.002 <button class="md-button md-button--hyperlink">COPY LINK</button> | 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. <br /> **Input:** <br /> [[Indicator concentration (Q.IC1.001)](quantities.md#C),  [t (Q.GE1.004)](quantities.md#t)], <br /> [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask) <br />  **Output**: <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
-| P.AE2.003 <button class="md-button md-button--hyperlink">COPY LINK</button> | Model-based AIF | -- | -- | The AIF is derived from fitting a model to the dynamic concentration data. <br /> **Input:** <br /> Inversion method (select from inversion methods) with <br /> [[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 <br /> [Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [AIF models](perfusionModels.md#AIF models) or [descriptive models](perfusionModels.md#Descriptive models)] <br />  **Output**: <br />  [[C<sub>a,p</sub> (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
-| P.AE2.004 <button class="md-button md-button--hyperlink">COPY LINK</button> | 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. <br /> **Input:** <br /> [[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)], <br />  [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask), <br />  [*k*-means-cluster-algorithm-parameters (Q.AE1.001)](quantities.md#kMeansParams) <br /> **Output**: <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
-| P.AE2.005 <button class="md-button md-button--hyperlink">COPY LINK</button> | 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 *P<sub>c</sub>* and the initial cluster centroids *v<sub>i</sub>*  are applied. The cluster with maximal $M = \frac{f_{max}}{TTP\cdot FWHM}$ represents the AIF.  <br /> **Input:** <br /> [[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)], <br />  [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask), <br />  [Fuzzy-c-means-cluster-algorithm parameters (Q.AE1.002)](quantities.md#fuzzycMeansParams) <br />  **Output**: <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
+| P.AE2.001 <button class="md-button md-button--hyperlink">COPY LINK</button> | <a id="Literature-based_AIF"></a> Literature-based AIF | Population-based AIF | -- | The AIF is taken from a published reference or from the average of a population.  <br /> **Input:** <br /> --  <br /> **Output**: <br />  [[C<sub>a,p</sub> (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
+| P.AE2.002 <button class="md-button md-button--hyperlink">COPY LINK</button> | <a id="Mean_ROI_AIF"></a> 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. <br /> **Input:** <br /> [[Indicator concentration (Q.IC1.001)](quantities.md#C),  [t (Q.GE1.004)](quantities.md#t)], <br /> [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask) <br />  **Output**: <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
+| P.AE2.003 <button class="md-button md-button--hyperlink">COPY LINK</button> | <a id="Model-based_AIF"></a> Model-based AIF | -- | -- | The AIF is derived from fitting a model to the dynamic concentration data. <br /> **Input:** <br /> Inversion method (select from inversion methods) with <br /> [[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 <br /> [Forward model (M.GF1.001)](perfusionModels.md#Forward model) = select from [AIF models](perfusionModels.md#AIF models) or [descriptive models](perfusionModels.md#Descriptive models)] <br />  **Output**: <br />  [[C<sub>a,p</sub> (Q.IC1.001.[a,p])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] or <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
+| P.AE2.004 <button class="md-button md-button--hyperlink">COPY LINK</button> | <a id="Automatic_k-means-cluster-based_AIF"></a> 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. <br /> **Input:** <br /> [[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#t)], <br />  [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask), <br />  [*k*-means-cluster-algorithm-parameters (Q.AE1.001)](quantities.md#kMeansParams) <br /> **Output**: <br /> [[C<sub>a,b</sub> (Q.IC1.001.[a,b])](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)] | -- |
+| P.AE2.005 <button class="md-button md-button--hyperlink">COPY LINK</button> | <a id="Automatic_fuzzy-c-means-cluster-based_AIF"></a> 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 *P<sub>c</sub>* and the initial cluster centroids *v<sub>i</sub>*  are applied. The cluster with maximal $M = \frac{f_{max}}{TTP\cdot FWHM}$ represents the AIF.  <br /> **Input:** <br /> [[Indicator concentration (Q.IC1.001)](quantities.md#C), [t (Q.GE1.004)](quantities.md#time)], <br />  [Binary AIF mask (Q.SE1.002)](quantities.md#BinAIFMask), <br />  [Fuzzy-c-means-cluster-algorithm parameters (Q.AE1.002)](quantities.md#fuzzycMeansParams) <br />  **Output**: <br /> [[C<sub>a,b</sub> (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.  | -- |
 
 
@@ -104,7 +104,7 @@ The processes listed in this section describe commonly used methods to estimate
 ### <a id="Signal to concentration conversion methods"></a> Signal to concentration conversion methods
 | Code | OSIPI name| Alternative names|Notation|Description|Reference|
 | -- | -- | -- | -- | -- | -- |
-| P.SC2.001 <button class="md-button md-button--hyperlink">COPY LINK</button> | 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.  <br /> **Input:** <br /> Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with <br /> [Data (Q.GE1.002)](quantities.md#Data) = [Signal (Q.MS1.001)](quantities.md#S), <br /> [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 <br />   **Output**: <br /> [Indicator concentration (Q.IC1.001)](quantities.md#C) <br />  | -- |
+| P.SC2.001 <button class="md-button md-button--hyperlink">COPY LINK</button> | <a id="Direct_conversion_from_signal_concentration"></a> 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.  <br /> **Input:** <br /> Inversion method (select from [inversion methods](generalPurposeProcesses.md#Inversion methods)) with <br /> [Data (Q.GE1.002)](quantities.md#Data) = [Signal (Q.MS1.001)](quantities.md#S), <br /> [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 <br />   **Output**: <br /> [Indicator concentration (Q.IC1.001)](quantities.md#C) <br />  | -- |
 | P.SC2.002 <button class="md-button md-button--hyperlink">COPY LINK</button> | 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.  <br /> **Input:** <br /> Signal to electromagnetic property conversion method (select from [signal to electromagnetic property conversion conversion methods](#Signal to electromagnetic property conversion methods)), <br /> Electromagnetic property to concentration conversion method (select from [electromagnetic property to concentration conversion methods](#Electromagnetic property to concentration conversion methods)) <br />   **Output**: <br /> [Indicator concentration (Q.IC1.001)](quantities.md#C) <br />  | -- |
 | 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.  | -- |