Main major development

README.md

@@ -28,14 +28,14 @@ We recommend that lexicon terminology is used in all DCE\DSC manuscripts and doc
 
 In this example, the **a code** is *S* and the section name is *quantities*. So the full hyperlink is  osipi.github.io/OSIPI_CAPLEX/quantities/#S. A # symbol must be added immediately after the slash and prior to the **a code**.
 
-Once you have constructed the hyperlink, test it works by entering the address in your browser. The link should take you directly to the quantity, process or model being referenced. 
+Once you have constructed the hyperlink, test it works by entering the address in your browser. The link should take you directly to the quantity, process or model being referenced.
 
 3. Add the hyperlink to the relevant entry in your manuscript or documentation.
 
 ## Contributing to the lexicon
 The lexicon is designed to the extensible and we actively engage researchers in the DCE/DSC MRI field to engage with its usage and development. If you would like to add or edit content (new entries etc.) of the lexicon, please either:
-1. contact the task force leads (currently Ben Dickie ([email protected]) or Rianne Van der Heijden ([email protected]) with your proposed changes. These will be reviewed at the next Task Force meeting (usually monthly).
-2. OR create a development branch of main and edit the .md files directly. Once done, submit a pull request (do not merge with main!), which will then be reviewed by the Task Force.     
+1. contact the task force leads (currently Ben Dickie ([email protected])) with your proposed changes. These will be reviewed at the next Task Force meeting (usually monthly).
+2. OR create a development branch of main and edit the .md files directly. Once done, submit a pull request (do not merge with main!), which will then be reviewed by the Task Force.
 
 ## Suggesting edits or additions to lexicon entries
 
@@ -57,7 +57,7 @@ The items in this group are related to electromagnetic tissue properties and ele
  | Q.EL1.999 | <a name="not listed EL1"></a> Quantity not listed | -- | -- | This is a custom free-text item, which can be used if a quantity of interest is not listed. Please state a literature reference and request the item to be added to the lexicon for future usage. | [variable] | -- |
 ```
 
-To add a new entry, identify the section and group that makes the most sense, then simply add a new line to the .md file between the last numbered entry and the **not listed** entry. Remember to assign a new code (usually the same as the previous entry but incremented by 1.
+To add a new entry, identify the section and group that makes the most sense, then simply add a new line to the .md file between the last numbered entry and the **not listed** entry. Remember to assign a new code (usually the same as the previous entry but incremented by 1).
 
 For example:
 

docs/contributionTutorial.md

@@ -4,6 +4,8 @@ This is a basic tutorial on how to contribute to the website if you've never use
 
 [Click here](#where-to-find-documentation) to skip the "setting up workspace" tutorial.
 
+**If you prefer suggesting changes to have them implemented by the Taskforce, please either email one of the GitHub leads (currently [Ben Dicke](mailto:[email protected])), [raise an issue](https://github.com/OSIPI/OSIPI_CAPLEX/issues){target = "_blank"} on GitHub or leave feedback using the [feedback form](https://forms.gle/dsfUEZx6P91rBwJe6){target = "_blank"}.**
+
 <font size=5>Work in a remote environment</font>
 
 ## Work in a new development container

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.  | -- |
 

docs/qualityOfLife.md

@@ -4,7 +4,11 @@ This section will introduce you to some tools available on this website to make
 
 ## Copying reference to table data for publications
 
-Inside the data tables you can find a button saying `COPY LINK`. Click on it and the reference to the data cell will be copied into your clipboard. You can use this reference in your publications.
+Inside the data tables you can find a button that says `COPY LINK`. Click on it and the reference to the data cell will be copied into your clipboard. You can use this reference in your publications.
+
+| Header |
+| -- |
+| <button class="md-button md-button--hyperlink">COPY LINK</button> <a id='Placeholder'>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</a> |
 
 ## Copying reference DOI