diff --git a/examples/4.reproducible_notebooks/pluto-03-low-field-boost-optimization.jl b/examples/4.reproducible_notebooks/pluto-03-low-field-boost-optimization.jl
new file mode 100644
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+++ b/examples/4.reproducible_notebooks/pluto-03-low-field-boost-optimization.jl
@@ -0,0 +1,2384 @@
+### A Pluto.jl notebook ###
+# v0.19.46
+
+#> [frontmatter]
+#> image = "https://upload.wikimedia.org/wikipedia/commons/a/a0/Textformatting.svg"
+#> title = "Low-Field CMRA Optimization"
+#> tags = ["CMRA", "Low Field", "Optimization"]
+#> date = "2024-04-16"
+#> description = "Optimizing sequence to improve SNR and fat supression."
+#> 
+#>     [[frontmatter.author]]
+#>     name = "Carlos Castillo Passi"
+#>     url = "https://avatars.githubusercontent.com/u/5957134?s=400&u=fe62a2a899ced18e8b882cebde6b1eefe6a1222c&v=4"
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 7deadd58-b202-4508-b4c7-686f742cb713
+begin
+	begin
+		using Pkg
+		begin
+		  println("OS $(Base.Sys.MACHINE)")    # OS
+		  println("Julia $VERSION")            # Julia version
+		  # Koma sub-packages
+		  for (_, pkg) in filter(((_, pkg),) -> occursin("KomaMRI", pkg.name), Pkg.dependencies())
+		    println("$(pkg.name) $(pkg.version)")
+		  end
+		end
+	end
+end
+
+# ╔═╡ 0b7a405e-bbb5-11ee-05ca-4b1c8567398d
+using KomaMRICore, KomaMRIPlots, PlutoPlotly # Essentials
+
+# ╔═╡ 70dbc2bd-8b93-471d-8340-04d98a008ca6
+using Suppressor, PlutoUI, ProgressLogging # Extras
+
+# ╔═╡ a99c0c47-1b70-4362-a2f6-a7e3259606fa
+md"""# Low-Field BOOST Optimization
+
+This notebook reproduces the simulation experiments performed in our manuscript:
+
+> **"Simultaneous iNAV-Based 3D Whole-Heart Bright-Blood and Black-Blood Imaging at 0.55T"**
+>
+> **Carlos Castillo-Passi**, Karl P. Kunze, Michael G. Crabb, Camila Muñoz, Anastasia Fotaki, Radhouene Neji, Pablo Irarrazaval, Claudia Prieto, and René M. Botnar
+>
+> (2024)
+
+Submitted to Magnetic Resonance in Medicine (MRM).
+"""
+
+# ╔═╡ 1bb3e49b-1a19-4343-ac09-fbaf1cae4ba3
+TableOfContents()
+
+# ╔═╡ fc58640b-c44c-4c25-a4fd-5a0e17d7becd
+md"# 1. Simulation setup"
+
+# ╔═╡ dbf16676-64f2-4d9b-bf1b-8de06b048602
+md"## 1.1. Loading required packages"
+
+# ╔═╡ f49655cc-460e-4981-92ea-dfd6147308bf
+md"Bloch simulations were performed using **KomaMRI.jl** to optimize the proposed whole-heart BOOST parameters."
+
+# ╔═╡ 77153e5c-71bd-42e3-bae9-e4811ffa7a3d
+md"""## 1.2. Scanner
+
+We start by defining the hardware characteristics. The `sys.B0` will be used to calculate the off-resonance of fat."""
+
+# ╔═╡ 9e397426-b60b-4b98-be8b-f7f128621c44
+begin
+	sys = Scanner()
+	sys.B0 = 0.55
+	sys.Gmax = 40.0e-3
+	sys.Smax = 25.0
+	sys
+end
+
+# ╔═╡ 92194fcb-582a-49ce-aad7-20b0145d40d3
+md"## 1.3. Sequence"
+
+# ╔═╡ ae72ffc5-7f8e-4907-a99e-8ad7cb8fddab
+md"""
+The BOOST sequence (`BOOST`) consists of:
+"""
+
+# ╔═╡ cb659118-9f22-43c3-801d-49241dee4df6
+begin
+	# General sequence parameters
+	Trf = 500e-6  			# 500 [ms]
+	B1 = 1 / (360*γ*Trf)    # B1 amplitude [uT]
+	Tadc = 1e-6 			# 1us
+
+	# Prepulses
+	Tfatsat = 26.624e-3     # 26.6 [ms]
+	T2prep_duration = 50e-3 # 50 [ms]
+
+	# Acquisition
+	RR = 1.0 				# 1 [s]
+	dummy_heart_beats = 3 	# Steady-state
+	TR = 5.3e-3             # 5.3 [ms] RF Low SAR
+	TE = TR / 2 			# bSSFP condition
+	iNAV_lines = 6          # FatSat-Acq delay: iNAV_lines * TR
+	iNAV_flip_angle = 3.2   # 3.2 [deg]
+	im_segments = 20        # Acquisitino window: im_segments * TR
+
+	# To be optimized
+	im_flip_angle = [110, 80] # 80 [deg]
+	FatSat_flip_angle = 180   # 180 [deg]
+	IR_inversion_time = 90e-3 # 90 [ms] 
+
+	seq_params = (;
+		dummy_heart_beats,
+		iNAV_lines,
+		im_segments,
+		iNAV_flip_angle,
+		im_flip_angle,
+		T2prep_duration,
+		IR_inversion_time,
+		FatSat_flip_angle,
+		RR
+	)
+
+	seq_params
+end
+
+# ╔═╡ 42417eba-46e0-400e-a874-299533362c41
+md"Below we are showing the **bright-blood contrast** (containing a T2p-IR pulse):"
+
+# ╔═╡ 4aab8a6f-d2ba-46f0-a1bf-ddc0dcf8437f
+md"and **reference contrast** (only containing a FatSat pulse), later used to obtain the black-blood contrast by subtracting the bright-blood contrast. Note that this contrast has a different flip angle."
+
+# ╔═╡ a15d6b64-f8ee-4ee4-812c-d49cf5ea784d
+md"""
+## 1.4. Phantom
+Each tissue was represented with 200 isochromats distributed along the $z$-axis to simulate gradient spoiling effects. The isochromats for each tissue were inside a 1D voxel of size $1.5\,\mathrm{mm}$. The values for $T_1$ and $T_2$ for blood, myocardial muscle, and fat at 0.55T were obtained from the work of Campbell-Washburn, et al. Fat spins were simulated using a chemical shift of $-3.4\,\mathrm{ppm}$, simulating regular fat with $T_1=183\,\mathrm{ms}$, and fast-recovering fat with $T_1=130\,\mathrm{ms}$.
+"""
+
+# ╔═╡ f0a81c9f-5616-4663-948f-a4084e1719af
+begin
+    fat_ppm = -3.4e-6 			# -3.4ppm fat-water frequency shift
+    Niso = 200        			# 200 isochromats in spoiler direction
+    Δx_voxel = 1.5e-3 			# 1.5 [mm]
+    fat_freq = γ*sys.B0*fat_ppm # -80 [Hz]
+    dx = Array(range(-Δx_voxel/2, Δx_voxel/2, Niso))
+	md"- Phantom parameters (show/hide code)"
+end
+
+# ╔═╡ 6b870443-7be5-4287-b957-ca5c14eda89c
+begin
+	function FatSat(α, Δf; sample=false)
+	    # FatSat design
+		# cutoff_freq = sqrt(log(2) / 2) / a where B1(t) = exp(-(π t / a)^2)
+		cutoff = fat_freq / π 			      # cutoff [Hz] => ≈1/10 RF power to water
+		a = sqrt(log(2) / 2) / cutoff         # a [s]
+		τ = range(-Tfatsat/2, Tfatsat/2, 64)  # time [s]
+		gauss_pulse = exp.(-(π * τ / a) .^ 2) # B1(t) [T]
+		# FatSat prepulse
+	    seq = Sequence()
+	    seq += Grad(-8e-3, 3000e-6, 500e-6) #Spoiler1
+	    seq += RF(gauss_pulse, Tfatsat, Δf)
+	    α_ref = get_flip_angles(seq)[2]
+	    seq *= (α/α_ref+0im)
+	    if sample
+	        seq += ADC(1, 1e-6)
+	    end
+	    seq += Grad(8e-3, 3000e-6, 500e-6) #Spoiler2
+	    if sample
+	        seq += ADC(1, 1e-6)
+	    end
+	    return seq
+	end
+
+	function T2prep(TE; sample=false)
+	    seq = Sequence()
+	    seq += RF(90 * B1, Trf)
+	    seq += sample ? ADC(20, TE/2 - 1.5Trf) : Delay(TE/2 - 1.5Trf)
+	    seq += RF(180im * B1 / 2, Trf*2)
+	    seq += sample ? ADC(20, TE/2 - 1.5Trf) : Delay(TE/2 - 1.5Trf)
+	    seq += RF(-90 * B1, Trf)
+	    seq += Grad(8e-3, 6000e-6, 600e-6) #Spoiler3
+	    if sample
+	        seq += ADC(1, 1e-6)
+	    end
+	    return seq
+	end
+
+	function IR(IR_delay; sample=false)
+		# Generating HS pulse
+		# Based on: https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmri.26021
+		# Params
+		flip_angle = 900;    # Peak amplitude (deg)
+		Trf = 10240e-6;      # Pulse duration (ms)
+		β = 6.7e2;           # frequency modulation param (rad/s)
+		μ = 5;               # phase modulation parameter (dimensionless)
+		fmax = μ * β / (2π); # 2fmax = BW
+		# RF pulse
+		t = range(-Trf/2, Trf/2, 201);
+		B1 = sech.(β .* t);
+		Δf = fmax  .* tanh.(β .* t);
+		# Spoiler length
+		spoiler_time = 6000e-6
+		spoiler_rise_fall = 600e-6
+		# Prepulse
+	    seq = Sequence()
+	    seq += RF(B1, Trf, Δf) # FM modulated pulse
+		seq = (flip_angle / get_flip_angles(seq)[1] + 0.0im) * seq # RF scaling
+	    seq += Grad(8e-3, spoiler_time, spoiler_rise_fall) #Spoiler3
+	    if sample
+	        seq += ADC(11, IR_delay - spoiler_time - 2spoiler_rise_fall)
+		else
+			seq += Delay(IR_delay - spoiler_time - 2spoiler_rise_fall)
+		end
+	    return seq
+	end
+
+	function bSSFP(iNAV_lines, im_segments, iNAV_flip_angle, im_flip_angle; sample=false)
+	    k = 0
+	    seq = Sequence()
+	    for i = 0 : iNAV_lines + im_segments - 1
+	        if iNAV_lines != 0
+	            m = (im_flip_angle - iNAV_flip_angle) / iNAV_lines
+	            α = min( m * i + iNAV_flip_angle, im_flip_angle ) * (-1)^k
+	        else
+	            α = im_flip_angle * (-1)^k
+	        end
+	        seq += RF(α * B1, Trf)
+	        if i < iNAV_lines && !sample
+	            seq += Delay(TR - Trf)
+	        else
+	            seq += Delay(TE - Trf/2 - Tadc/2)
+	            seq += ADC(1, Tadc)
+	            seq += Delay(TR - TE - Tadc/2 - Trf/2)
+	        end
+	        k += 1
+	    end
+	    return seq
+	end
+
+	md"- Sequence building blocks: `T2prep`, `IR`, `FatSat`, `bSSFP` (show/hide code)"
+end
+
+# ╔═╡ 7890f81e-cb15-48d2-a80c-9d73f9516056
+begin
+	function BOOST(
+				dummy_heart_beats,
+				iNAV_lines,
+				im_segments,
+				iNAV_flip_angle,
+				im_flip_angle,
+				T2prep_duration=50e-3,
+				IR_inversion_time=90e-3,
+				FatSat_flip_angle=180,
+				RR=1.0;
+				sample_recovery=zeros(Bool, dummy_heart_beats+1)
+				)
+		# Seq init
+	    seq = Sequence()
+	    for hb = 1 : dummy_heart_beats + 1
+			sample = sample_recovery[hb] # Sampling recovery curve for hb
+			# Generating seq blocks
+	        t2p = T2prep(T2prep_duration; sample)
+			ir = IR(IR_inversion_time - iNAV_lines * TR - Trf - TE; sample)
+	        fatsat = FatSat(FatSat_flip_angle, fat_freq; sample)
+	        # Magnetization preparations
+			for contrast = 1:2
+				preps = Sequence()
+				if contrast == 1 # Bright-blood contrast
+		        	preps += t2p
+					preps += ir
+				else # Reference contrast
+		        	preps += fatsat
+				end
+				# Contrst dependant flip angle
+				bssfp = bSSFP(iNAV_lines, im_segments, iNAV_flip_angle,
+					im_flip_angle[contrast]; sample)
+				# Concatenating seq blocks
+				seq += preps
+		        seq += bssfp
+				# RR interval consideration
+				RRdelay = RR  - dur(bssfp) - dur(preps)
+		        seq += sample ? ADC(80, RRdelay) : Delay(RRdelay)
+			end
+	    end
+	    return seq
+	end
+
+	md"""- `BOOST` (show/hide code)
+
+	```julia
+	# Seq init
+	seq = Sequence()
+	for hb = 1 : dummy_heart_beats + 1
+		# Generating seq blocks
+		t2p = T2prep(T2prep_duration)
+		ir = IR(IR_inversion_time)
+		fatsat = FatSat(FatSat_flip_angle, fat_freq)
+		# Magnetization preparations
+		for contrast = 1:2
+			preps = Sequence()
+			if contrast == 1 # Bright-blood contrast
+				preps += t2p
+				preps += ir
+			else # Reference contrast
+				preps += fatsat
+			end
+			# Contrst dependant flip angle
+			bssfp = bSSFP(iNAV_lines, im_segments, iNAV_flip_angle,
+				im_flip_angle[contrast]; sample)
+			# Concatenating seq blocks
+			seq += preps
+			seq += bssfp
+			# RR interval consideration
+			RRdelay = RR  - dur(bssfp) - dur(preps)
+			seq += Delay(RRdelay)
+		end
+	end
+	```"""
+end
+
+# ╔═╡ f57a2b6c-eb4c-45bd-8058-4a60b038925d
+begin
+	function cardiac_phantom(off; off_fat=fat_freq)
+	    myocard = Phantom{Float64}(x=dx, ρ=0.6*ones(Niso), T1=701e-3*ones(Niso),
+	                               T2=58e-3*ones(Niso),    Δw=2π*off*ones(Niso))
+	    blood =   Phantom{Float64}(x=dx, ρ=0.7*ones(Niso), T1=1122e-3*ones(Niso),
+	                               T2=263e-3*ones(Niso),   Δw=2π*off*ones(Niso))
+	    fat1 =    Phantom{Float64}(x=dx, ρ=1.0*ones(Niso), T1=183e-3*ones(Niso),
+	                               T2=93e-3*ones(Niso),    Δw=2π*(off_fat + off)*ones(Niso))
+	    fat2 =    Phantom{Float64}(x=dx, ρ=1.0*ones(Niso), T1=130e-3*ones(Niso),
+	                               T2=93e-3*ones(Niso),    Δw=2π*(off_fat + off)*ones(Niso))
+	    obj = myocard + blood + fat1 + fat2
+	    return obj
+	end
+	md"- Cardiac phantom (show/hide code)"
+end
+
+# ╔═╡ f21e9e59-25c3-4f06-8de4-792cb305eb01
+md"""# 2. Simulation
+
+Two simulation experiments were performed to optimize the sequence parameters, (1) to optimize the imaging flip angle, and (2) to optimize the FatSat flip angle.
+
+"""
+
+# ╔═╡ eceb326a-cab6-465e-8e5c-e835881bd3b0
+md"""
+## 2.0. Magnetization dynamics
+"""
+
+# ╔═╡ d54d6807-444f-4e0e-8fd6-84457974115a
+md"Here we show the magnetization dynamics of the myocardium, blood, and fat signals at 0.55T."
+
+# ╔═╡ d05dcba7-2f42-47bf-a172-6123d0113b3f
+sim_params = Dict{String,Any}(
+	"return_type"=>"mat",
+	"sim_method"=>BlochDict(save_Mz=true),
+	"Δt_rf"=>Trf,
+	"gpu"=>false,
+	"Nthreads"=>1
+)
+
+# ╔═╡ 37f7fd7f-5cb1-48b5-b877-b2bc23a1e7dd
+begin
+    seq = BOOST(seq_params...; sample_recovery=ones(Bool, dummy_heart_beats+1))
+	obj = cardiac_phantom(0)
+    magnetization = @suppress simulate(obj, seq, sys; sim_params)
+	nothing # hide output
+end
+
+# ╔═╡ 0b6c1f72-b040-483c-969b-88bfe09b32c3
+plot_seq(seq; range=[5990, 6280], slider=true)
+
+# ╔═╡ 88eb41a5-d8c2-4f0e-b379-a8b05a341a82
+plot_seq(seq; range=[6900, 7190], slider=true)
+
+# ╔═╡ 1a62ae71-58db-49ea-ae6a-9aea66145963
+begin
+	phantom_T1 = plot(
+		scatter(
+			x=obj.x * 1e3,
+			y=obj.T1 * 1e3,
+			mode="markers",
+			marker=attr(;
+				color=obj.T1 * 1e3,
+				colorscale=[
+					[0.0, "black"],
+					[183.0/maximum(obj.T1 .* 1e3), "green"],
+					[701.0/maximum(obj.T1 .* 1e3), "blue"],
+					[1122.0/maximum(obj.T1 .* 1e3), "red"],
+				],
+				cmin=0.0,
+				cmax=1122.0,
+				colorbar=attr(;ticksuffix="ms", title="T1"),
+				showscale=false
+			),
+			showlegend=false
+		)
+	)
+	relayout!(
+		phantom_T1,
+		yaxis_title="T1 [ms]",
+		xaxis_title="x [mm]",
+		xaxis_tickmode="array",
+		xaxis_tickvals=[-1.5/2, 0.0, 1.5/2],
+		yaxis_tickmode="array",
+		yaxis_tickvals=unique(obj.T1 * 1e3),
+		xaxis_range=[-1.5, 1.5],
+		yaxis_range=[0.0, 1200.0],
+		title="T1 map of 1D Phantom"
+	)
+	phantom_T2 = plot(
+		scatter(
+			x=obj.x * 1e3,
+			y=obj.T2 * 1e3,
+			mode="markers",
+			marker=attr(;
+				color=obj.T2 * 1e3,
+				colorscale=[
+					[0.0, "black"],
+					[58.0/maximum(obj.T2 .* 1e3), "blue"],
+					[93.0/maximum(obj.T2 .* 1e3), "green"],
+					[263.0/maximum(obj.T2 .* 1e3), "red"],
+				],
+				cmin=0.0,
+				cmax=263.0,
+				colorbar=attr(;ticksuffix="ms", title="T2"),
+				showscale=false
+			),
+			showlegend=false
+		)
+	)
+	relayout!(
+		phantom_T2,
+		yaxis_title="T2 [ms]",
+		xaxis_title="x [mm]",
+		xaxis_tickmode="array",
+		xaxis_tickvals=[-1.5/2, 0.0, 1.5/2],
+		yaxis_tickmode="array",
+		yaxis_tickvals=unique(obj.T2 * 1e3),
+		xaxis_range=[-1.5, 1.5],
+		yaxis_range=[0.0, 300.0],
+		title="T2 map of 1D Phantom"
+	)
+	[phantom_T1 phantom_T2]
+end
+
+# ╔═╡ d9715bc1-49cd-4df8-8dbf-c06de42ad550
+begin
+    # Prep plots
+    labs = ["Myocardium", "Blood", "Fat"]
+	cols = ["blue", "red", "green"]
+    spin_group = [(1:Niso)', (Niso+1:2Niso)', (2Niso+1:3Niso)']
+    t = KomaMRICore.get_adc_sampling_times(seq)
+    Mxy(i) = abs.(sum(magnetization[:,spin_group[i],1,1][:,1,:],dims=2)[:]/length(spin_group[i]))
+    Mz(i) = real.(sum(magnetization[:,spin_group[i],2,1][:,1,:],dims=2)[:]/length(spin_group[i]))
+
+    # Plot
+    p0 = make_subplots(
+		rows=2,
+		cols=1,
+		subplot_titles=["Mxy" "Mz" "Sequence"],
+		shared_xaxes=true,
+		vertical_spacing=0.1
+	)
+    for i=eachindex(spin_group)
+        p1 = scatter(
+			x=t, y=Mxy(i),
+			name=labs[i],
+			legendgroup=labs[i],
+			marker_color=cols[i]
+		)
+        p2 = scatter(
+			x=t,
+			y=Mz(i),
+			name=labs[i],
+			legendgroup=labs[i],
+			showlegend=false,
+			marker_color=cols[i]
+		)
+        add_trace!(p0, p1, row=1, col=1)
+        add_trace!(p0, p2, row=2, col=1)
+    end
+	relayout!(p0,
+		yaxis_range=[0, 0.4],
+		xaxis_range=[RR*dummy_heart_beats, RR*dummy_heart_beats+.250]
+	)
+    p0
+end
+
+# ╔═╡ 8dd704a4-bf50-4ddc-a832-d074bd52ad01
+md"Three heartbeats per contrast were considered to achieve steady-state and the fourth heartbeat for each contrast was used to measure the magnetization results in the next sections."
+
+# ╔═╡ 39f44025-2974-4c4c-b0c2-e21399bbdb1f
+md"""
+## 2.1. Reference contrast SNR: Flip angle optimization
+
+For the first simulation experiment, SNR was maximized by varying the imaging flip angle (between 20 and 180 deg). To optimize SNR independently of heart rate, multiple heart rates (between 55 and 85 bpm) were simulated and the mean and standard deviation of the obtained blood signal were calculated.
+"""
+
+# ╔═╡ 7b9ae381-d0fc-4d1d-b1ee-ad31a6445ff6
+begin
+    FAs = 20:10:180          # flip angle [deg]
+	RRs = 60 ./ (55:10:85)  # RR [s]
+    mag1 = zeros(ComplexF64, im_segments, Niso*4, length(FAs), length(RRs))
+    @progress for (m, RR) = enumerate(RRs), (n, α) = enumerate(FAs)
+		seq_params1 = merge(seq_params, (; im_flip_angle=[110, α], RR))
+		sim_params1 = merge(sim_params, Dict("sim_method"=>BlochDict()))
+		seq1        = BOOST(seq_params1...)
+		obj1        = cardiac_phantom(0)
+		magaux = @suppress simulate(obj1, seq1, sys; sim_params=sim_params1)
+		mag1[:, :, n, m] .= magaux[end-im_segments+1:end, :] # Last heartbeat
+    end
+end
+
+# ╔═╡ d0377f9a-680d-4501-90ca-9ea3ab681db4
+md"""## 2.2. Reference contrast fat: FatSat flip angle optimization
+
+For the second simulation experiment, the fat signal was minimized by varying the FatSat flip angle (between 20 and 250 deg) using six iNAV readouts (identified experimentally to result in good fat suppression). To be robust to $B_0$ inhomogeneities, multiple simulations with tissue frequency shifts (between $-1$ and $1\,\mathrm{ppm}$, twice of what was reported by Restivo et al.) were performed, and the mean and standard deviation of the obtained fat signal were calculated."""
+
+# ╔═╡ d750cd5a-3c90-41ea-9942-5723a21da60a
+begin
+    FFAs = 20:20:250 						 # flip angle [deg]
+	Δfs = (-1:0.2:1) .* (γ * sys.B0 * 1e-6)  # off-resonance Δf [s]
+    mag2 = zeros(ComplexF64, im_segments, Niso*4, length(FFAs), length(Δfs))
+    @progress for (m, Δf) = enumerate(Δfs), (n, FatSat_flip_angle) = enumerate(FFAs)
+		seq_params2 = merge(seq_params, (; FatSat_flip_angle))
+		sim_params2 = merge(sim_params, Dict("sim_method"=>BlochDict()))
+		seq2        = BOOST(seq_params2...)
+		obj2        = cardiac_phantom(Δf)
+		magaux = @suppress simulate(obj2, seq2, sys; sim_params=sim_params2)
+		mag2[:, :, n, m] .= magaux[end-im_segments+1:end, :] # Last heartbeat
+    end
+end
+
+# ╔═╡ b0b53632-e6b8-47e9-8d3e-79f1e599315a
+md" ## 2.3. Bright-blood SNR:"
+
+# ╔═╡ 14cf6859-a4b9-4671-b022-659781c55144
+begin
+    mag4 = zeros(ComplexF64, im_segments, Niso*4, length(FAs), length(RRs))
+    @progress for (m, RR) = enumerate(RRs), (n, α) = enumerate(FAs)
+		seq_params4 = merge(seq_params, (; im_flip_angle=[α, 80], RR))
+		sim_params4 = merge(sim_params, Dict("sim_method"=>BlochDict()))
+		seq4        = BOOST(seq_params4...)
+		obj4        = cardiac_phantom(0)
+		magaux = @suppress simulate(obj4, seq4, sys; sim_params=sim_params4)
+		mag4[:, :, n, m] .= magaux[end-2im_segments+1:end-im_segments, :] # Bright-Blood
+    end
+end
+
+# ╔═╡ c64d38cb-2433-4b90-abbf-1fc938f70584
+md"## 2.4. Bright-Blood fat: Inversion delay"
+
+# ╔═╡ c952ecf1-25ef-4b48-9c8f-e53ded302629
+begin
+    TIs = (50:10:130) # Inversion delay [ms]
+    mag3 = zeros(ComplexF64, im_segments, Niso*4, length(TIs), length(Δfs))
+    @progress for (m, Δf) = enumerate(Δfs), (n, TI) = enumerate(TIs)
+		seq_params3 = merge(seq_params, (; IR_inversion_time=TI * 1e-3, RR))
+		sim_params3 = merge(sim_params, Dict("sim_method"=>BlochDict()))
+		seq3        = BOOST(seq_params3...)
+		obj3        = cardiac_phantom(Δf)
+		magaux = @suppress simulate(obj3, seq3, sys; sim_params=sim_params3)
+		mag3[:, :, n, m] .= magaux[end-2im_segments+1:end-im_segments, :] # Bright-Blood
+    end
+end
+
+# ╔═╡ 85834365-238b-4193-a0c0-d1859416ab6c
+md" ## 2.5. Bright-blood contrast: T2prep duration"
+
+# ╔═╡ 50b551d8-a212-4319-b25b-1f8ecdd2a491
+begin
+    T2ps = (20:10:80) # T2prep duration [ms]
+    mag5bb = zeros(ComplexF64, im_segments, Niso*4, length(T2ps), length(RRs))
+	mag5rf = zeros(ComplexF64, im_segments, Niso*4, length(T2ps), length(RRs))
+    @progress for (m, RR) = enumerate(RRs), (n, T2p) = enumerate(T2ps)
+		seq_params5 = merge(seq_params, (; T2prep_duration=T2p * 1e-3, RR))
+		sim_params5 = merge(sim_params, Dict("sim_method"=>BlochDict()))
+		seq5        = BOOST(seq_params5...)
+		obj5        = cardiac_phantom(0)
+		magaux = @suppress simulate(obj5, seq5, sys; sim_params=sim_params5)
+		# Bright-Blood
+		mag5bb[:, :, n, m] .= magaux[end-2im_segments+1:end-im_segments, :]
+		# Reference contrast
+		mag5rf[:, :, n, m] .= magaux[end-im_segments+1:end, :] 
+    end
+end
+
+# ╔═╡ 4b1dc8bf-feb1-42b4-94f7-1b27975d944c
+md"## 2.6. Black-blood contrast: T2prep duration"
+
+# ╔═╡ 8dc11175-ebc8-407a-ab0b-6d543f849a72
+begin
+	# Labels
+	labels = ["Myocardium", "Blood", "Fat (T₁=183 ms)", "Fat (T₁=130 ms)"]
+	colors = ["blue", "red", "green", "purple"]
+	spins = [(1:Niso)', ((Niso + 1):(2Niso))', ((2Niso + 1):(3Niso))', ((3Niso + 1):(4Niso))']
+	mean(x, dim) = sum(x; dims=dim) / size(x, dim)
+	std(x, dim; mu=mean(x, dim)) = sqrt.(sum(abs.(x .- mu) .^ 2; dims=dim) / (size(x, dim) - 1))
+	md"Aux functions (show/hide code)"
+end
+
+# ╔═╡ f73082ff-a6d3-41f8-8796-4114fa89d2bb
+begin
+	# Reducing tissues's signal
+	signal_myoc = reshape(
+	    mean(abs.(mean(mag1[:, spins[1], :, :], 3)), 1), length(FAs), length(RRs)
+	)
+	signal_bloo = reshape(
+	    mean(abs.(mean(mag1[:, spins[2], :, :], 3)), 1), length(FAs), length(RRs)
+	)
+	diff_bloo_myoc = abs.(signal_bloo .- signal_myoc)
+	# Mean
+	mean_myoc = mean(signal_myoc, 2)
+	mean_bloo = mean(signal_bloo, 2)
+	mean_diff = mean(diff_bloo_myoc,2)
+	# Std
+	std_myoc  = std(signal_myoc, 2)
+	std_bloo  = std(signal_bloo, 2)
+	std_diff = std(diff_bloo_myoc,2)
+	# Plotting results
+	# Mean
+	s1 = scatter(;
+	    x=FAs,
+	    y=mean_myoc[:],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    line=attr(; color=colors[1]),
+	)
+	s2 = scatter(;
+	    x=FAs,
+	    y=mean_bloo[:],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    line=attr(; color=colors[2]),
+	)
+	s3 = scatter(;
+		x=FAs,
+		y=mean_diff[:],
+		name="|Blood-Myoc|",
+		legendgroup="|Blood-Myoc|",
+		line=attr(color=colors[4])
+	)
+	# Std
+	s4 = scatter(;
+	    x=[FAs; reverse(FAs)],
+	    y=[(mean_myoc .- std_myoc)[:]; reverse((mean_myoc .+ std_myoc)[:])],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,0,255,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s5 = scatter(;
+	    x=[FAs; reverse(FAs)],
+	    y=[(mean_bloo .- std_bloo)[:]; reverse((mean_bloo .+ std_bloo)[:])],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(255,0,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s6 = scatter(;
+		x=[FAs; reverse(FAs)],
+		y=[(mean_diff .- std_diff)[:]; reverse((mean_diff .+ std_diff)[:])],
+		name="|Blood-Myoc|",legendgroup="|Blood-Myoc|",
+		showlegend=false,
+		fill="toself",
+		fillcolor="rgba(255,0,255,0.2)",
+		line=attr(color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	# Plots
+	fig = plot([s1, s2, s3, s4, s5, s6])
+	relayout!(
+	    fig;
+	    yaxis=attr(; title="Signal [a.u.]", tickmode="array"),
+	    xaxis=attr(;
+	        title="Flip angle [deg]",
+	        tickmode="array",
+	        tickvals=[FAs[1], 80, 110, 130, FAs[end]],
+	        constrain="domain",
+	    ),
+	    font=attr(; family="CMU Serif", size=16, scaleanchor="x", scaleratio=1),
+	    yaxis_range=[0, 0.3],
+		xaxis_range=[FAs[1], FAs[end]],
+	    width=600,
+	    height=400,
+	    hovermode="x unified",
+	)
+	fig
+end
+
+# ╔═╡ 44a31057-7b34-4c80-a273-6621c0773dc7
+begin
+	## Calculating results
+	signal_myoc2 = reshape(
+	    mean(abs.(mean(mag2[:, spins[1], :, :], 3)), 1), length(FFAs), length(Δfs)
+	)
+	signal_bloo2 = reshape(
+	    mean(abs.(mean(mag2[:, spins[2], :, :], 3)), 1), length(FFAs), length(Δfs)
+	)
+	signal_fat2 = reshape(
+	    mean(abs.(mean(mag2[:, spins[3], :, :], 3)), 1), length(FFAs), length(Δfs)
+	)
+	signal_fat22 = reshape(
+	    mean(abs.(mean(mag2[:, spins[4], :, :], 3)), 1), length(FFAs), length(Δfs)
+	)
+	mean_myoc2 = mean(signal_myoc2, 2)
+	mean_bloo2 = mean(signal_bloo2, 2)
+	mean_fat2  = mean(signal_fat2, 2)
+	mean_fat22 = mean(signal_fat22, 2)
+	std_myoc2  = std(signal_myoc2, 2)
+	std_bloo2  = std(signal_bloo2, 2)
+	std_fat2   = std(signal_fat2, 2)
+	std_fat22  = std(signal_fat22, 2)
+	# Plotting results
+	# Mean
+	s12 = scatter(;
+	    x=FFAs,
+	    y=mean_myoc2[:],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    line=attr(; color=colors[1]),
+	)
+	s22 = scatter(;
+	    x=FFAs,
+	    y=mean_bloo2[:],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    line=attr(; color=colors[2]),
+	)
+	s32 = scatter(;
+	    x=FFAs,
+	    y=mean_fat2[:],
+	    name=labels[3],
+	    legendgroup=labels[3],
+	    line=attr(; color=colors[3]),
+	)
+	s52 = scatter(;
+	    x=FFAs,
+	    y=mean_fat22[:],
+	    name=labels[4],
+	    legendgroup=labels[4],
+	    line=attr(; color=colors[3], dash="dash"),
+	)
+	# Std
+	s42 = scatter(;
+	    x=[FFAs; reverse(FFAs)],
+	    y=[(mean_myoc2 .- std_myoc2)[:]; reverse((mean_myoc2 .+ std_myoc2)[:])],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,0,255,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	s62 = scatter(;
+	    x=[FFAs; reverse(FFAs)],
+	    y=[(mean_bloo2 .- std_bloo2)[:]; reverse((mean_bloo2 .+ std_bloo2)[:])],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(255,0,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	s72 = scatter(;
+	    x=[FFAs; reverse(FFAs)],
+	    y=[(mean_fat2 .- std_fat2)[:]; reverse((mean_fat2 .+ std_fat2)[:])],
+	    name=labels[3],
+	    legendgroup=labels[3],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,255,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	s82 = scatter(;
+	    x=[FFAs; reverse(FFAs)],
+	    y=[(mean_fat22 .- std_fat22)[:]; reverse((mean_fat22 .+ std_fat22)[:])],
+	    name=labels[4],
+	    legendgroup=labels[4],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,255,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	# Plots
+	fig2 = plot([s12, s22, s32, s42, s52, s62, s72, s82])
+	relayout!(
+	    fig2;
+	    yaxis=attr(; title="Signal [a.u.]", tickmode="array"),
+	    xaxis=attr(;
+	        title="FatSat flip angle [deg]",
+	        tickmode="array",
+	        tickvals=[FFAs[1], 130, 150, 180, FFAs[end]],
+	        constrain="domain",
+	    ),
+	    font=attr(; family="CMU Serif", size=16, scaleanchor="x", scaleratio=1),
+	    yaxis_range=[0, 0.4],
+		xaxis_range=[FFAs[1], FFAs[end]],
+	    width=600,
+	    height=400,
+	    hovermode="x unified",
+	)
+	fig2
+end
+
+# ╔═╡ 6649f46e-3565-4cd6-86a5-9b03d00cc3db
+begin
+	# Reducing tissues's signal
+	signal_myoc4 = reshape(
+	    mean(abs.(mean(mag4[:, spins[1], :, :], 3)), 1), length(FAs), length(RRs)
+	)
+	signal_bloo4 = reshape(
+	    mean(abs.(mean(mag4[:, spins[2], :, :], 3)), 1), length(FAs), length(RRs)
+	)
+	diff_bloo_myoc4 = abs.(signal_bloo4 .- signal_myoc4)
+	# Mean
+	mean_myoc4 = mean(signal_myoc4, 2)
+	mean_bloo4 = mean(signal_bloo4, 2)
+	mean_diff4 = mean(diff_bloo_myoc4,2)
+	# Std
+	std_myoc4  = std(signal_myoc4, 2)
+	std_bloo4  = std(signal_bloo4, 2)
+	std_diff4 = std(diff_bloo_myoc4,2)
+	# Plotting results
+	# Mean
+	s14 = scatter(;
+	    x=FAs,
+	    y=mean_myoc4[:],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    line=attr(; color=colors[1]),
+	)
+	s24 = scatter(;
+	    x=FAs,
+	    y=mean_bloo4[:],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    line=attr(; color=colors[2]),
+	)
+	s34 = scatter(;
+		x=FAs,
+		y=mean_diff4[:],
+		name="|Blood-Myoc|",
+		legendgroup="|Blood-Myoc|",
+		line=attr(color=colors[4])
+	)
+	# Std
+	s44 = scatter(;
+	    x=[FAs; reverse(FAs)],
+	    y=[(mean_myoc4 .- std_myoc4)[:]; reverse((mean_myoc4 .+ std_myoc4)[:])],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,0,255,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s54 = scatter(;
+	    x=[FAs; reverse(FAs)],
+	    y=[(mean_bloo4 .- std_bloo4)[:]; reverse((mean_bloo4 .+ std_bloo4)[:])],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(255,0,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s64 = scatter(;
+		x=[FAs; reverse(FAs)],
+		y=[(mean_diff4 .- std_diff4)[:]; reverse((mean_diff4 .+ std_diff4)[:])],
+		name="|Blood-Myoc|",legendgroup="|Blood-Myoc|",
+		showlegend=false,
+		fill="toself",
+		fillcolor="rgba(255,0,255,0.2)",
+		line=attr(color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	# Plots
+	fig4 = plot([s14, s24, s34, s44, s54, s64])
+	relayout!(
+	    fig4;
+	    yaxis=attr(; title="Signal [a.u.]", tickmode="array"),
+	    xaxis=attr(;
+	        title="Flip angle [deg]",
+	        tickmode="array",
+	        tickvals=[FAs[1], 110, FAs[end]],
+	        constrain="domain",
+	    ),
+	    font=attr(; family="CMU Serif", size=16, scaleanchor="x", scaleratio=1),
+	    yaxis_range=[0, 0.3],
+		xaxis_range=[FAs[1], FAs[end]],
+	    width=600,
+	    height=400,
+	    hovermode="x unified",
+	)
+	fig4
+end
+
+# ╔═╡ a8751a97-c131-407b-b211-573660452142
+begin
+	## Calculating results
+	signal_myoc3 = reshape(
+	    mean(abs.(mean(mag3[:, spins[1], :, :], 3)), 1), length(TIs), length(Δfs)
+	)
+	signal_bloo3 = reshape(
+	    mean(abs.(mean(mag3[:, spins[2], :, :], 3)), 1), length(TIs), length(Δfs)
+	)
+	signal_fat3 = reshape(
+	    mean(abs.(mean(mag3[:, spins[3], :, :], 3)), 1), length(TIs), length(Δfs)
+	)
+	signal_fat33 = reshape(
+	    mean(abs.(mean(mag3[:, spins[4], :, :], 3)), 1), length(TIs), length(Δfs)
+	)
+	mean_myoc3 = mean(signal_myoc3, 2)
+	mean_bloo3 = mean(signal_bloo3, 2)
+	mean_fat3  = mean(signal_fat3, 2)
+	mean_fat33 = mean(signal_fat33, 2)
+	std_myoc3  = std(signal_myoc3, 2)
+	std_bloo3  = std(signal_bloo3, 2)
+	std_fat3   = std(signal_fat3, 2)
+	std_fat33  = std(signal_fat33, 2)
+	# Plotting results
+	# Mean
+	s13 = scatter(;
+	    x=TIs,
+	    y=mean_myoc3[:],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    line=attr(; color=colors[1]),
+	)
+	s23 = scatter(;
+	    x=TIs,
+	    y=mean_bloo3[:],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    line=attr(; color=colors[2]),
+	)
+	s33 = scatter(;
+	    x=TIs,
+	    y=mean_fat3[:],
+	    name=labels[3],
+	    legendgroup=labels[3],
+	    line=attr(; color=colors[3]),
+	)
+	s53 = scatter(;
+	    x=TIs,
+	    y=mean_fat33[:],
+	    name=labels[4],
+	    legendgroup=labels[4],
+	    line=attr(; color=colors[3], dash="dash"),
+	)
+	# Std
+	s43 = scatter(;
+	    x=[TIs; reverse(TIs)],
+	    y=[(mean_myoc3 .- std_myoc3)[:]; reverse((mean_myoc3 .+ std_myoc3)[:])],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,0,255,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	s63 = scatter(;
+	    x=[TIs; reverse(TIs)],
+	    y=[(mean_bloo3 .- std_bloo3)[:]; reverse((mean_bloo3 .+ std_bloo3)[:])],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(255,0,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	s73 = scatter(;
+	    x=[TIs; reverse(TIs)],
+	    y=[(mean_fat3 .- std_fat3)[:]; reverse((mean_fat3 .+ std_fat3)[:])],
+	    name=labels[3],
+	    legendgroup=labels[3],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,255,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	s83 = scatter(;
+	    x=[TIs; reverse(TIs)],
+	    y=[(mean_fat33 .- std_fat33)[:]; reverse((mean_fat33 .+ std_fat33)[:])],
+	    name=labels[4],
+	    legendgroup=labels[4],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,255,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none",
+	)
+	# Plots
+	fig3 = plot([s13, s23, s33, s43, s53, s63, s73, s83])
+	relayout!(
+	    fig3;
+	    yaxis=attr(; title="Signal [a.u.]", tickmode="array"),
+	    xaxis=attr(;
+	        title="Inversion Time [ms]",
+	        tickmode="array",
+	        tickvals=[TIs[1], 70, 90, TIs[end]],
+	        constrain="domain",
+	    ),
+	    font=attr(; family="CMU Serif", size=16, scaleanchor="x", scaleratio=1),
+	    yaxis_range=[0, 0.3],
+		xaxis_range=[TIs[1], TIs[end]],
+	    width=600,
+	    height=400,
+	    hovermode="x unified",
+	)
+	fig3
+end
+
+
+# ╔═╡ eb0f1e07-24a1-4aa4-b3ef-b63caa97de34
+begin
+	# Reducing tissues's signal
+	signal_myoc5 = reshape(
+	    mean(abs.(mean(mag5bb[:, spins[1], :, :], 3)), 1), length(T2ps), length(RRs)
+	)
+	signal_bloo5 = reshape(
+	    mean(abs.(mean(mag5bb[:, spins[2], :, :], 3)), 1), length(T2ps), length(RRs)
+	)
+	diff_bloo_myoc5 = abs.(signal_bloo5 .- signal_myoc5)
+	# Mean
+	mean_myoc5 = mean(signal_myoc5, 2)
+	mean_bloo5 = mean(signal_bloo5, 2)
+	mean_diff5 = mean(diff_bloo_myoc5,2)
+	# Std
+	std_myoc5  = std(signal_myoc5, 2)
+	std_bloo5  = std(signal_bloo5, 2)
+	std_diff5 = std(diff_bloo_myoc5,2)
+	# Plotting results
+	# Mean
+	s15 = scatter(;
+	    x=T2ps,
+	    y=mean_myoc5[:],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    line=attr(; color=colors[1]),
+	)
+	s25 = scatter(;
+	    x=T2ps,
+	    y=mean_bloo5[:],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    line=attr(; color=colors[2]),
+	)
+	s35 = scatter(;
+		x=T2ps,
+		y=mean_diff5[:],
+		name="|Blood-Myoc|",
+		legendgroup="|Blood-Myoc|",
+		line=attr(color=colors[4])
+	)
+	# Std
+	s45 = scatter(;
+	    x=[T2ps; reverse(T2ps)],
+	    y=[(mean_myoc5 .- std_myoc5)[:]; reverse((mean_myoc5 .+ std_myoc5)[:])],
+	    name=labels[1],
+	    legendgroup=labels[1],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,0,255,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s55 = scatter(;
+	    x=[T2ps; reverse(T2ps)],
+	    y=[(mean_bloo5 .- std_bloo5)[:]; reverse((mean_bloo5 .+ std_bloo5)[:])],
+	    name=labels[2],
+	    legendgroup=labels[2],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(255,0,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s65 = scatter(;
+		x=[T2ps; reverse(T2ps)],
+		y=[(mean_diff5 .- std_diff5)[:]; reverse((mean_diff5 .+ std_diff5)[:])],
+		name="|Blood-Myoc|",legendgroup="|Blood-Myoc|",
+		showlegend=false,
+		fill="toself",
+		fillcolor="rgba(255,0,255,0.2)",
+		line=attr(color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	# Plots
+	fig5 = plot([s15, s25, s35, s45, s55, s65])
+	relayout!(
+	    fig5;
+	    yaxis=attr(; title="Signal [a.u.]", tickmode="array"),
+	    xaxis=attr(;
+	        title="T2prep duration [ms]",
+	        tickmode="array",
+	        tickvals=[T2ps[1], 50, 70, T2ps[end]],
+	        constrain="domain",
+	    ),
+	    font=attr(; family="CMU Serif", size=16, scaleanchor="x", scaleratio=1),
+	    yaxis_range=[0, 0.2],
+		xaxis_range=[T2ps[1], T2ps[end]],
+	    width=600,
+	    height=400,
+	    hovermode="x unified",
+	)
+	fig5
+end
+
+# ╔═╡ 10e68e25-9f68-46c0-b1b0-e9b124706b67
+begin
+	# Reducing tissues's signal
+	signal_myocbb = reshape(
+	    mean(abs.(mean(mag5bb[:, spins[1], :, :], 3)), 1), length(T2ps), length(RRs)
+	)
+	signal_bloodbb = reshape(
+	    mean(abs.(mean(mag5bb[:, spins[2], :, :], 3)), 1), length(T2ps), length(RRs)
+	)
+	signal_myocrf = reshape(
+	    mean(abs.(mean(mag5rf[:, spins[1], :, :], 3)), 1), length(T2ps), length(RRs)
+	)
+	signal_bloodrf = reshape(
+	    mean(abs.(mean(mag5rf[:, spins[2], :, :], 3)), 1), length(T2ps), length(RRs)
+	)
+	# Substracted
+	signal_myoc6 = abs.(signal_myocrf .- signal_myocbb)
+	signal_bloo6 = abs.(signal_bloodrf .- signal_bloodbb)
+	diff_bloo_myoc6 = abs.(signal_bloo6 .- signal_myoc6)
+	# Mean
+	mean_myoc6 = mean(signal_myoc6, 2)
+	mean_bloo6 = mean(signal_bloo6, 2)
+	mean_diff6 = mean(diff_bloo_myoc6,2)
+	# Std
+	std_myoc6  = std(signal_myoc6, 2)
+	std_bloo6  = std(signal_bloo6, 2)
+	std_diff6 = std(diff_bloo_myoc6,2)
+	# Plotting results
+	# Mean
+	s16 = scatter(;
+	    x=T2ps,
+	    y=mean_myoc6[:],
+	    name="Substracted "*labels[1],
+	    legendgroup=labels[1],
+	    line=attr(; color=colors[1]),
+	)
+	s26 = scatter(;
+	    x=T2ps,
+	    y=mean_bloo6[:],
+	    name="Substracted "*labels[2],
+	    legendgroup=labels[2],
+	    line=attr(; color=colors[2]),
+	)
+	s36 = scatter(;
+		x=T2ps,
+		y=mean_diff6[:],
+		name="|Sub. Blood - Sub. Myoc|",
+		legendgroup="|Blood-Myoc|",
+		line=attr(color=colors[4])
+	)
+	# Std
+	s46 = scatter(;
+	    x=[T2ps; reverse(T2ps)],
+	    y=[(mean_myoc6 .- std_myoc6)[:]; reverse((mean_myoc6 .+ std_myoc6)[:])],
+	    name="Substracted "*labels[1],
+	    legendgroup=labels[1],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(0,0,255,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s56 = scatter(;
+	    x=[T2ps; reverse(T2ps)],
+	    y=[(mean_bloo6 .- std_bloo6)[:]; reverse((mean_bloo6 .+ std_bloo6)[:])],
+	    name="Substracted "*labels[2],
+	    legendgroup=labels[2],
+	    showlegend=false,
+	    fill="toself",
+	    fillcolor="rgba(255,0,0,0.2)",
+	    line=attr(; color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	s66 = scatter(;
+		x=[T2ps; reverse(T2ps)],
+		y=[(mean_diff6 .- std_diff6)[:]; reverse((mean_diff6 .+ std_diff6)[:])],
+		name="|Sub. Blood - Sub. Myoc|",legendgroup="|Blood-Myoc|",
+		showlegend=false,
+		fill="toself",
+		fillcolor="rgba(255,0,255,0.2)",
+		line=attr(color="rgba(0,0,0,0)"),
+		hoverinfo="none"
+	)
+	# Plots
+	fig6 = plot([s16, s26, s36, s46, s56, s66])
+	relayout!(
+	    fig6;
+	    yaxis=attr(; title="Signal [a.u.]", tickmode="array"),
+	    xaxis=attr(;
+	        title="T2prep duration [deg]",
+	        tickmode="array",
+	        tickvals=[T2ps[1], 50, 70, T2ps[end]],
+	        constrain="domain",
+	    ),
+	    font=attr(; family="CMU Serif", size=16, scaleanchor="x", scaleratio=1),
+	    yaxis_range=[0, 0.2],
+		xaxis_range=[T2ps[1], T2ps[end]],
+	    width=600,
+	    height=400,
+	    hovermode="x unified",
+	)
+	fig6
+end
+
+
+# ╔═╡ 3d7e7d20-a77a-48b3-ad2e-6b621227be16
+md"""# References
+ - **Castillo-Passi C**, Coronado R, Varela-Mattatall G, Alberola-López C, Botnar R, Irarrazaval P. KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration. Magnetic Resonance in Medicine. 2023;90(1):329-342. [doi:10.1002/mrm.29635](doi:10.1002/mrm.29635)
+ - **Campbell-Washburn AE**, Ramasawmy R, Restivo MC, et al. Opportunities in Interventional and Diagnostic Imaging by Using High-Performance Low-Field-Strength MRI. Radiology. 2019;293(2):384-393. [doi:10.1148/radiol.2019190452](doi:10.1148/radiol.2019190452)
+- **Restivo MC**, Ramasawmy R, Bandettini WP, Herzka DA, Campbell-Washburn AE. Efficient spiral in-out and EPI balanced steady-state free precession cine imaging using a high-performance 0.55T MRI. Magnetic Resonance in Medicine. 2020;84(5):2364-2375. [doi:10.1002/mrm.28278](doi:10.1002/mrm.28278)
+"""
+
+# ╔═╡ abea2c43-d83e-4438-8cd3-4be06b8174b3
+md"""# Reproducibility
+
+This [Pluto notebook](https://plutojl.org/) is reproducible by default, as it has an embedded `Project.toml` and `Manifest.toml`, that store the exact package versions used to create the notebook."""
+
+# ╔═╡ 00000000-0000-0000-0000-000000000001
+PLUTO_PROJECT_TOML_CONTENTS = """
+[deps]
+KomaMRICore = "4baa4f4d-2ae9-40db-8331-a7d1080e3f4e"
+KomaMRIPlots = "76db0263-63f3-4d26-bb9a-5dba378db904"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+PlutoPlotly = "8e989ff0-3d88-8e9f-f020-2b208a939ff0"
+PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
+ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
+Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
+
+[compat]
+PlutoUI = "~0.7.58"
+ProgressLogging = "~0.1.4"
+Suppressor = "~0.2.7"
+"""
+
+# ╔═╡ 00000000-0000-0000-0000-000000000002
+PLUTO_MANIFEST_TOML_CONTENTS = """
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.10.5"
+manifest_format = "2.0"
+project_hash = "9b71b5fa18cd815278316446dec81dab9113a167"
+
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+version = "1.5.0"
+weakdeps = ["ChainRulesCore", "Test"]
+
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+    AbstractFFTsTestExt = "Test"
+
+[[deps.AbstractNFFTs]]
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+git-tree-sha1 = "292e21e99dedb8621c15f185b8fdb4260bb3c429"
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+version = "0.8.2"
+
+[[deps.AbstractPlutoDingetjes]]
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+git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a"
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+    AccessorsTestExt = "Test"
+    AccessorsUnitfulExt = "Unitful"
+
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+    AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
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+    [deps.PlotlyJS.extensions]
+    CSVExt = "CSV"
+    DataFramesExt = ["DataFrames", "CSV"]
+    IJuliaExt = "IJulia"
+    JSON3Ext = "JSON3"
+
+    [deps.PlotlyJS.weakdeps]
+    CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+    DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+    IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
+    JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
+
+[[deps.PlotlyKaleido]]
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+    [deps.PlutoPlotly.extensions]
+    PlotlyKaleidoExt = "PlotlyKaleido"
+    UnitfulExt = "Unitful"
+
+    [deps.PlutoPlotly.weakdeps]
+    PlotlyKaleido = "f2990250-8cf9-495f-b13a-cce12b45703c"
+    Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+
+[[deps.PlutoUI]]
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+[[deps.REPL]]
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+
+[[deps.Random]]
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+
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+weakdeps = ["FixedPointNumbers"]
+
+    [deps.Ratios.extensions]
+    RatiosFixedPointNumbersExt = "FixedPointNumbers"
+
+[[deps.Reexport]]
+git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
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+
+[[deps.SimpleBufferStream]]
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+
+[[deps.SplittablesBase]]
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+[[deps.StaticArrays]]
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+version = "1.9.7"
+weakdeps = ["ChainRulesCore", "Statistics"]
+
+    [deps.StaticArrays.extensions]
+    StaticArraysChainRulesCoreExt = "ChainRulesCore"
+    StaticArraysStatisticsExt = "Statistics"
+
+[[deps.StaticArraysCore]]
+git-tree-sha1 = "192954ef1208c7019899fbf8049e717f92959682"
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+[[deps.SuiteSparse_jll]]
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+
+[[deps.Suppressor]]
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+[[deps.TableTraits]]
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+[[deps.TensorCore]]
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+
+[[deps.ThreadsX]]
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+[[deps.Transducers]]
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+    [deps.Transducers.extensions]
+    TransducersBlockArraysExt = "BlockArrays"
+    TransducersDataFramesExt = "DataFrames"
+    TransducersLazyArraysExt = "LazyArrays"
+    TransducersOnlineStatsBaseExt = "OnlineStatsBase"
+    TransducersReferenceablesExt = "Referenceables"
+
+    [deps.Transducers.weakdeps]
+    BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
+    DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+    LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
+    OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338"
+    Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e"
+
+[[deps.Tricks]]
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+[[deps.URIs]]
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+[[deps.UUIDs]]
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+
+[[deps.UnPack]]
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+[[deps.Unicode]]
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+
+[[deps.UnsafeAtomicsLLVM]]
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+[[deps.WebIO]]
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+[[deps.WebSockets]]
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+[[deps.Widgets]]
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+[[deps.WoodburyMatrices]]
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+[[deps.Zlib_jll]]
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+
+[[deps.libaec_jll]]
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+
+[[deps.libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl"]
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+
+[[deps.nghttp2_jll]]
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+version = "1.52.0+1"
+
+[[deps.oneTBB_jll]]
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+
+[[deps.p7zip_jll]]
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+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+version = "17.4.0+2"
+"""
+
+# ╔═╡ Cell order:
+# ╟─a99c0c47-1b70-4362-a2f6-a7e3259606fa
+# ╟─1bb3e49b-1a19-4343-ac09-fbaf1cae4ba3
+# ╟─fc58640b-c44c-4c25-a4fd-5a0e17d7becd
+# ╟─dbf16676-64f2-4d9b-bf1b-8de06b048602
+# ╟─f49655cc-460e-4981-92ea-dfd6147308bf
+# ╠═0b7a405e-bbb5-11ee-05ca-4b1c8567398d
+# ╠═70dbc2bd-8b93-471d-8340-04d98a008ca6
+# ╟─77153e5c-71bd-42e3-bae9-e4811ffa7a3d
+# ╟─9e397426-b60b-4b98-be8b-f7f128621c44
+# ╟─92194fcb-582a-49ce-aad7-20b0145d40d3
+# ╟─ae72ffc5-7f8e-4907-a99e-8ad7cb8fddab
+# ╟─6b870443-7be5-4287-b957-ca5c14eda89c
+# ╟─7890f81e-cb15-48d2-a80c-9d73f9516056
+# ╟─cb659118-9f22-43c3-801d-49241dee4df6
+# ╟─42417eba-46e0-400e-a874-299533362c41
+# ╠═0b6c1f72-b040-483c-969b-88bfe09b32c3
+# ╟─4aab8a6f-d2ba-46f0-a1bf-ddc0dcf8437f
+# ╠═88eb41a5-d8c2-4f0e-b379-a8b05a341a82
+# ╟─a15d6b64-f8ee-4ee4-812c-d49cf5ea784d
+# ╟─f0a81c9f-5616-4663-948f-a4084e1719af
+# ╟─f57a2b6c-eb4c-45bd-8058-4a60b038925d
+# ╟─1a62ae71-58db-49ea-ae6a-9aea66145963
+# ╟─f21e9e59-25c3-4f06-8de4-792cb305eb01
+# ╟─eceb326a-cab6-465e-8e5c-e835881bd3b0
+# ╟─d54d6807-444f-4e0e-8fd6-84457974115a
+# ╟─d05dcba7-2f42-47bf-a172-6123d0113b3f
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