Updated cortical thickness figure to include uncertainty.

paper/paper.bib

@@ -214,6 +214,22 @@ @article{jenkinson_fsl_2012
   langid = {english}
 }
 
+@article{korkmaz_clinical_2017,
+  title = {Clinical Significance of Renal Cortical Thickness in Patients with Chronic Kidney Disease},
+  author = {Korkmaz, Mehmet and Aras, Bekir and Güneyli, Serkan and Yılmaz, Mümtaz},
+  date = {2017-05-06},
+  journaltitle = {Ultrasonography},
+  volume = {37},
+  number = {1},
+  eprint = {28618770},
+  eprinttype = {pmid},
+  pages = {50},
+  doi = {10.14366/usg.17012},
+  url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC5769950/},
+  urldate = {2024-11-05},
+  langid = {english}
+}
+
 @article{li_renal_2020,
   title = {Renal {{BOLD MRI}} in Patients with Chronic Kidney Disease: Comparison of the Semi-Automated Twelve Layer Concentric Objects ({{TLCO}}) and Manual {{ROI}} Methods},
   shorttitle = {Renal {{BOLD MRI}} in Patients with Chronic Kidney Disease},

paper/paper.md

@@ -46,16 +46,16 @@ The motivation of `3DQLayers` was to address these limitations of TLCO to provid
 
 The pipeline for defining the layers from the whole kidney mask is outlined in \autoref{fig:flowchart}. Pre-processing steps first fill in the holes in the kidney mask caused by cysts, as the surface of a cyst is not characteristic of the surface of the kidney. Next, the voxel-based representation of the mask is converted to a smoothed mesh-based representation of the kidneys, the distance from the centre of each voxel to the surface of the mesh is calculated to produce a depth map [@dawson-haggerty_trimesh_2023]. Tissue adjacent to the renal pelvis that is not representative of the medulla is then excluded from layer-based analysis. This is achieved by automatically segmenting the renal pelvis then calculating the distance from each voxel to the renal pelvis as described above. Those voxels closer than a specified threshold, typically 10 mm, are excluded from the depth map. Finally, a layer image is generated by quantising the depth map to a desired layer thickness, typically 1 mm.
 
-The layer image and quantitative images are resampled to the same spatial resolution using `NiBabel` [@brett_nipy/nibabel_2019], to allow each layer to be used as an ROI to interrogate each qMR image with statistical measures (e.g. median, standard deviation and kurtosis) across the depth of the kidney. The gradient of the central layers can be calculated to estimate the CMG in qMRI metrics. These metrics can be computed for the left and right kidney separately, or analysed in a combined manner. Additionally, if the renal cortex and medulla ROIs are available, the distribution of tissue types across layer depth can be explored. As the layers are generated from a structural image rather than the quantitative map, using `3DQLayers` stipulates no requirements on quantitative map acquisition, unlike TLCO.
+The layer image and quantitative images are resampled to the same spatial resolution using `NiBabel` [@brett_nipy/nibabel_2019], to allow each layer to be used as an ROI to interrogate each qMR image with statistical measures (e.g. median, standard deviation and kurtosis) across the depth of the kidney. The gradient of the central layers can be calculated to estimate the CMG in qMRI metrics. These metrics can be computed for the left and right kidney separately, or analysed in a combined manner. Additionally, if the renal cortex and medulla ROIs are available, the distribution of tissue types across layer depth can be explored and an estimate of average cortical thickness calculated. As the layers are generated from a structural image rather than the quantitative map, using `3DQLayers` stipulates no requirements on quantitative map acquisition, unlike TLCO.
 
 An object-oriented interface is provided to allow end users to simply generate layers and apply these to qMR images. [Documentation](https://qlayers.readthedocs.io/) is provided to guide users through installation via `PyPI`, `conda` or from [source code on GitHub](https://github.com/alexdaniel654/qlayers); it also includes tutorials and an API reference. An automated test suite with high coverage provides users with confidence in the stability of `3DQLayers` and that there will be no unexpected changes to results unless highlighted in the change-log.
 
 ## Usage Examples
 \autoref{fig:egfr_gradients} shows the use of `3DQLayers` to measure different gradients of R~2~^\*^ in a heathy volunteer with normal renal function and a patient with impaired renal function (an estimated glomerular filtration rate (eGFR) of above 90 ml/min/1.73m^2^ measured from blood samples is considered in the healthy range [@stevens_assessing_2006]). This replicates results shown using TLCO, with a lower gradient in patients compared to healthies, however `3DQLayers` controls for kidney size resulting in the gradient being measured in quantitative units of Hz/mm rather than Hz/layer as in TLCO, thus increasing generalisability.
 
-\autoref{fig:cortical_thickness} shows how `3DQLayers` can be used in combination with cortex and medulla tissue ROIs to analyse the distribution of voxel counts of each tissue as a function of layer depth of the kidney. Here cortex and medulla ROIs are initially generated using a Gaussian mixture model to segment a T~1~-weighted structural image followed by manual ROI correction. From this, average renal cortical thickness can be defined from the depth at which the voxel distribution crosses from cortex to medulla. Cortical thickness has been hypothesised as a potential biomarker of renal disease [@yamashita_value_2015]. 
+\autoref{fig:cortical_thickness} shows how `3DQLayers` can be used in combination with cortex and medulla tissue ROIs to analyse the distribution of voxel counts of each tissue as a function of layer depth of the kidney. Here cortex and medulla ROIs are initially generated using a Gaussian mixture model to segment a T~1~-weighted structural image followed by manual ROI correction. From this, average renal cortical thickness can be defined from the depth at which the voxel distribution crosses from cortex to medulla. Cortical thickness has been hypothesised as a potential biomarker of renal disease [@yamashita_value_2015; @korkmaz_clinical_2017]. 
 
-`3DQLayers` can also be used to analyse ex-vivo kidneys imaged outside the body. \autoref{fig:exvivo_profiles} shows example quantitative maps acquired from a kidney removed for transplant but subsequently deemed unsuitable and the associated layer profiles. \autoref{fig:roi_layers_corr} compares the results of tissue ROI based analysis and layer-based analysis in fifteen transplant kidneys. A significant correlation between outer layers and the cortex, and inner layers and the medulla was shown across all quantitative mapping techniques and a significant correlation between cortico-medullary ratio and layer gradient was shown for T~1~, T~2~, T~2~ ^\*^ and Magnetisation Transfer Ratio (MTR) mapping.
+`3DQLayers` can also be used to analyse *ex-vivo* kidneys imaged outside the body. \autoref{fig:exvivo_profiles} shows example quantitative maps acquired from a kidney removed for transplant but subsequently deemed unsuitable and the associated layer profiles. \autoref{fig:roi_layers_corr} compares the results of tissue ROI based analysis and layer-based analysis in fifteen transplant kidneys. A significant correlation between outer layers and the cortex, and inner layers and the medulla was shown across all quantitative mapping techniques and a significant correlation between cortico-medullary ratio and layer gradient was shown for T~1~, T~2~, T~2~ ^\*^ and Magnetisation Transfer Ratio (MTR) mapping.
 
 # Figures
 ![a) A schematic of the kidneys showing the renal cortex and medullary pyramids. b) A T~1~-weighted structural MR image of the abdomen showing the kidneys with the renal cortex appearing as a light band on the outer edge of the kidney and the medullary pyramids as darker patches on the inner portion of the kidneys. \label{fig:renal_structure}](kidney_overview.png){ width=90% }
@@ -66,9 +66,9 @@ An object-oriented interface is provided to allow end users to simply generate l
 
 ![Exploring the distribution of tissue types through the kidney to measure cortical thickness. \label{fig:cortical_thickness}](cortical_thickness.png){ width=50% }
 
-![Example quantitative maps and associated layer profiles when `3DQLayers` is applied to ex-vivo transplant kidneys. Uncertainty shading shows the 95% confidence interval of each layer. \label{fig:exvivo_profiles}](example_profiles.png)
+![Example quantitative maps and associated layer profiles when `3DQLayers` is applied to *ex-vivo* transplant kidneys. Uncertainty shading shows the 95% confidence interval of each layer. \label{fig:exvivo_profiles}](example_profiles.png)
 
-![ Agreement between tissue ROI-based measures and analogous layer-based measures shown for fifteen ex-vivo transplant kidneys for each qMRI alongside the Pearsons correlation coefficient ($\rho$). * represents a _p_-value between 0.05 and 0.01, ** between 0.01 and 0.001, and *** < 0.001. a) Plots the median within each tissue ROI (cortex or medulla semi-automatically defined) against the equivalent layers (outer layers and inner layers respectively as highlighted in \autoref{fig:exvivo_profiles}) b) Shows the cortico-medullary ratio (calculated by dividing the median within the cortex ROI by the median within the medullary ROI) against central layer gradient profiles calculated using `3DQLayers`. \label{fig:roi_layers_corr}](roi_layers_corr.png)
+![ Agreement between tissue ROI-based measures and analogous layer-based measures shown for fifteen *ex-vivo* transplant kidneys for each qMRI alongside the Pearsons correlation coefficient ($\rho$). * represents a _p_-value between 0.05 and 0.01, ** between 0.01 and 0.001, and *** < 0.001. a) Plots the median within each tissue ROI (cortex or medulla semi-automatically defined) against the equivalent layers (outer layers and inner layers respectively as highlighted in \autoref{fig:exvivo_profiles}) b) Shows the cortico-medullary ratio (calculated by dividing the median within the cortex ROI by the median within the medullary ROI) against central layer gradient profiles calculated using `3DQLayers`. \label{fig:roi_layers_corr}](roi_layers_corr.png)
 
 # Acknowledgements
 We acknowledge the funding support of the ADMIRE project funded through Kidney Research UK (`KS_RP_002_20210111`), and the UKRIN_MAPS project funded by the Medical Research Council (`MR/R02264X/1`), as well as the NIHR Nottingham Biomedical Research Centre during the genesis of this project.