diff --git a/2 T1 Mapping/1-1 Inversion Recovery/01-introduction.md b/2 T1 Mapping/1-1 Inversion Recovery/01-introduction.md
index db08747..baefece 100644
--- a/2 T1 Mapping/1-1 Inversion Recovery/01-introduction.md	
+++ b/2 T1 Mapping/1-1 Inversion Recovery/01-introduction.md	
@@ -20,6 +20,6 @@ Inversion recovery was first developed for NMR in the 1940s [@Hahn1949;@Drain194
 
 ```{figure} img/ir_pulsesequences.svg
 :label: irFig1
-:enumeration: 1.1  
+:enumerator: 1.1  
 Pulse sequence of an inversion recovery experiment.
 ```
\ No newline at end of file
diff --git a/2 T1 Mapping/1-1 Inversion Recovery/02-IR_SignalModelling.md b/2 T1 Mapping/1-1 Inversion Recovery/02-IR_SignalModelling.md
index 24fb4b1..3ac9de7 100644
--- a/2 T1 Mapping/1-1 Inversion Recovery/02-IR_SignalModelling.md	
+++ b/2 T1 Mapping/1-1 Inversion Recovery/02-IR_SignalModelling.md	
@@ -33,7 +33,7 @@ The simplicity of the signal model described by Eq. 1.3, both in its equation an
 
 :::{figure} #fig2p2cell
 :label: irPlot1
-:enumeration: 1.2
+:enumerator: 1.2
 Inversion recovery curves (Eq. 1.2) for three different T1 values, approximating the main types of tissue in the brain.
 :::
 
@@ -41,7 +41,7 @@ Practically, Eq. 1.1 is the better choice for simulating the signal of an invers
 
 :::{figure} #fig2p3cell
 :label: irPlot2
-:enumeration: 1.3
+:enumerator: 1.3
 Signal recovery curves simulated using Eq. 1.3 (solid) and Eq. 1.1 (dotted) with a TR = 5 s for T1 values ranging between 0.25 to 5 s.
 :::
 
diff --git a/2 T1 Mapping/1-1 Inversion Recovery/03-IR_DataFitting.md b/2 T1 Mapping/1-1 Inversion Recovery/03-IR_DataFitting.md
index d293411..7317d94 100644
--- a/2 T1 Mapping/1-1 Inversion Recovery/03-IR_DataFitting.md	
+++ b/2 T1 Mapping/1-1 Inversion Recovery/03-IR_DataFitting.md	
@@ -24,7 +24,7 @@ where <i>a</i> and <i>b</i> are complex values. If magnitude-only data is availa
 
 :::{figure} #fig2p4cell
 :label: irPlot3
-:enumeration: 1.4
+:enumerator: 1.4
 Fitting comparison of simulated data (blue markers) with T_1 = 1 s and TR = 1.5 to 5 s, using fitted using RD-NLS & Eq. 1.4 (green) and Levenberg-Marquardt & Eq. 1.2 (orange, long TR approximation).
 :::
 
@@ -34,7 +34,7 @@ Fitting comparison of simulated data (blue markers) with T_1 = 1 s and TR = 1.5
 
 :::{figure} #fig2p5cell
 :label: irPlot4
-:enumeration: 1.5
+:enumerator: 1.5
 Example inversion recovery dataset of a healthy adult brain (left). Inversion times used to acquire this magnitude image dataset were 30 ms, 530 ms, 1030 ms, and 1530 ms, and the TR used was 1550 ms. The T<sub>1</sub> map (right) was fitted using a RD-NLS algorithm.
 :::
 
diff --git a/2 T1 Mapping/1-1 Inversion Recovery/04-IR_BenefitsAndPitfalls.md b/2 T1 Mapping/1-1 Inversion Recovery/04-IR_BenefitsAndPitfalls.md
index b8ca654..affceed 100644
--- a/2 T1 Mapping/1-1 Inversion Recovery/04-IR_BenefitsAndPitfalls.md	
+++ b/2 T1 Mapping/1-1 Inversion Recovery/04-IR_BenefitsAndPitfalls.md	
@@ -21,7 +21,7 @@ One important protocol design consideration is to avoid acquiring at inversion t
 
 :::{figure} #fig2p6cell
 :label: irPlot5
-:enumeration: 1.6
+:enumerator: 1.6
 Monte Carlo simulations (mean and standard deviation (STD), blue markers) and fitted T<sub>1</sub> values (mean and STD, red and green respectively) generated for a T<sub>1</sub> value of 900 ms and 5 TI values linearly spaced across the TR (ranging from 1 to 5 s). A bump in T<sub>1</sub> STD occurs near TR = 3000 ms, which coincides with the TR where the second TI is located near a null point for this T<sub>1</sub> value.
 :::