Edit flatfield equation formatting

docs/flatfield/flatfield.md

@@ -6,28 +6,28 @@ nav_order: 3
 
 # Flatfield Correction
 
-The `flatfield` module performs a flatfield correction as described in Hobson, et al.$^1$ The function outputs $I_{Corrected}$ using the following equation:
+The `flatfield` module performs a flatfield correction as described in [Hobson, et al.]((#reference)) The function outputs $$I_{Corrected}$$ using the following equation:
 
 $$I_{Corrected} = \frac{I_{Measured} - I_{Dark}}{I_N}$$
 
-The input $I_{Measured}$ is the acquired image, while $I_{Dark}$ and $I_N$ are calibration images. To prepare these calibration images, see [Flatfield Inputs](#flatfield-inputs) below.  The paths to these calibration images must be specified in the paths section of the configuration file. Enable flatfield correction by adding a flatfield section to the configuration file with the image bit depth specified.
+The input $$I_{Measured}$$ is the acquired image, while $$I_{Dark}$$ and $$I_N$$ are calibration images. To prepare these calibration images, see [Flatfield Inputs](#flatfield-inputs) below.  The paths to these calibration images must be specified in the paths section of the configuration file. Enable flatfield correction by adding a flatfield section to the configuration file with the image bit depth specified.
 
 ## Flatfield Inputs
-Several flatfield calibration images should be collected for each channel to be corrected. Similarly, several dark images (i.e., camera aperture closed) should be collected for the system. Experimental details on collecting these images can be found in Hobson, et al.$^1$ Once the sets of images are collected, they are averaged to create $I_{flatfield}$ and $I_{dark}$, respectively. The ImageJ macro described below performs this averaging while creating the required $I_N$ input image. $I_N$ is a normalized flatfield image, calculated as:
+Several flatfield calibration images should be collected for each channel to be corrected. Similarly, several dark images (i.e., camera aperture closed) should be collected for the system. Experimental details on collecting these images can be found in [Hobson, et al.]((#reference)) Once the sets of images are collected, they are averaged to create $$I_{flatfield}$$ and $$I_{dark}$$, respectively. The ImageJ macro described below performs this averaging while creating the required $$I_N$$ input image. $$I_N$$ is a normalized flatfield image, calculated as:
 
 $$I_N = \frac{I_{Flatfield}-I_{Dark}}{<I_{Flatfield}-I_{Dark}>}$$
 
-where <> indicates the mean intensity value of the enclosed image.
+where $$<>$$ indicates the mean intensity value of the enclosed image.
 
 ### ImageJ Macro
-When averaging the collected calibration images, it is best to use ImageJ's 32-bit type for more precision. Additionally, $I_N$ **must** be saved as a 32-bit image as fractional values are necessary for the flatfield correction. To help keep the type conversions consistent, we provide an [ImageJ macro](https://github.com/aicjanelia/LLSM/blob/master/src/imagej/flatfield_inputs.ijm) that will perform all necessary calculations. This plugin takes as input all collected dark images as a multi-image tiff file (e.g., 100 dark images saved as a single tiff file) and a single tiff stack per channel of flatfield images.
+When averaging the collected calibration images, it is best to use ImageJ's 32-bit type for more precision. Additionally, $$I_N$$ **must** be saved as a 32-bit image as fractional values are necessary for the flatfield correction. To help keep the type conversions consistent, we provide an [ImageJ macro](https://github.com/aicjanelia/LLSM/blob/master/src/imagej/flatfield_inputs.ijm) that will perform all necessary calculations. This plugin takes as input all collected dark images as a multi-image tiff file (e.g., 100 dark images saved as a single tiff file) and a single tiff stack per channel of flatfield images.
 
-To use, run the `flatfield_inputs.ijm` macro. A GUI will open, as shown in the figure below. You can specify the darkfield image in multiple ways: (i) use the `Browse` button to find the stack of darkfield images, (ii) copy the full path into the box, or (iii) simply drag and drop the image file onto the darkfield line of the GUI. Repeat this process for the flatfield stacks. Any channel entries without files will be ignored. The macro will average the stacks and save a file called *DarkAverage.tif* in the same folder as the input darkfield stack. The macro will then continue to perform the $I_N$ calculation, saving one *I_N_###.tif* file per channel in the same folder, with *###* specified by the values in the Channel input line of the macro GUI. These files can then be specified in the paths section of the pipeline configuration file to run the flatfield correction module.
+To use, run the `flatfield_inputs.ijm` macro. A GUI will open, as shown in the figure below. You can specify the darkfield image in multiple ways: (i) use the `Browse` button to find the stack of darkfield images, (ii) copy the full path into the box, or (iii) simply drag and drop the image file onto the darkfield line of the GUI. Repeat this process for the flatfield stacks. Any channel entries without files will be ignored. The macro will average the stacks and save a file called *DarkAverage.tif* in the same folder as the input darkfield stack. The macro will then continue to perform the $$I_N$$ calculation, saving one *I_N_###.tif* file per channel in the same folder, with *###* specified by the values in the Channel input line of the macro GUI. These files can then be specified in the paths section of the pipeline configuration file to run the flatfield correction module.
 
 ![](https://github.com/aicjanelia/LLSM/blob/master/docs/flatfield/FlatFieldGUI.png)
 
 ## Reference
-$^1$ Hobson, C. M., Guo, M., Vishwasrao, H. D., Wu, Y., Shroff, H. and Chew, T.-L. (2022). Practical considerations for quantitative light sheet fluorescence microscopy. Nat Methods 1-12. [doi:10.1038/s41592-022-01632-x](https://doi.org/10.1038/s41592-022-01632-x)
+Hobson, C. M., Guo, M., Vishwasrao, H. D., Wu, Y., Shroff, H. and Chew, T.-L. (2022). Practical considerations for quantitative light sheet fluorescence microscopy. Nat Methods 1-12. [doi:10.1038/s41592-022-01632-x](https://doi.org/10.1038/s41592-022-01632-x)
 
 # Usage