Apply JuliaFormatter

docs/make.jl

@@ -1,27 +1,24 @@
 using Documenter, Perfusion
 
-makedocs(
-    ;
+makedocs(;
     modules = [Perfusion],
-    format = Documenter.HTML(
-        prettyurls = get(ENV, "CI", nothing) == "true"
-    ),
-    pages=[
+    format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
+    pages = [
         "Introduction" => "index.md",
         "Pre-processing" => Any[
-            "Loading DICOM" => "preprocessing/loading.md",
-            "T1 mapping" => "preprocessing/T1mapping.md",
-            "Signal to Concentration" => "preprocessing/signal2concentration.md"
+            "Loading DICOM"=>"preprocessing/loading.md",
+            "T1 mapping"=>"preprocessing/T1mapping.md",
+            "Signal to Concentration"=>"preprocessing/signal2concentration.md",
         ],
         "AIF-based Models" => Any[
-            "AIF" => "models/aif.md",
-            "Tofts" => "models/tofts.md",
-            "Extended Tofts" => "models/extendedtofts.md",
-            "Exchange" => "models/exchange.md",
-            "Uptake" => "models/uptake.md",
-            "Filtration" => "models/filtration.md"
+            "AIF"=>"models/aif.md",
+            "Tofts"=>"models/tofts.md",
+            "Extended Tofts"=>"models/extendedtofts.md",
+            "Exchange"=>"models/exchange.md",
+            "Uptake"=>"models/uptake.md",
+            "Filtration"=>"models/filtration.md",
         ],
-        "Functions" => "functions.md"
+        "Functions" => "functions.md",
     ],
     repo = "https://github.com/notZaki/Perfusion.jl/blob/{commit}{path}#L{line}",
     sitename = "Perfusion.jl",

src/Perfusion.jl

@@ -12,11 +12,11 @@ using LsqFit
 using Statistics: mean
 include("relaxation.jl")
 export signal_to_concentration,
-       concentration_to_signal,
-       signal_to_R1,
-       R1_to_signal,
-       concentration_to_R1,
-       R1_to_concentration
+    concentration_to_signal,
+    signal_to_R1,
+    R1_to_signal,
+    concentration_to_R1,
+    R1_to_concentration
 export fit_relaxation, fit_relaxation_nls, fit_relaxation_despot, fit_relaxation_novifast
 
 using SpecialFunctions: gamma

src/aif.jl

@@ -18,21 +18,18 @@ The hematocrit `hct` is used to convert the concentration in blood to plasma and
 """
 function aif_parker(t; params = _parker_params, hct = 0.42)
     @extract (A, T, σ, α, β, s, τ) params
-    cb = [(A[1] / (σ[1] * sqrt(2 * pi))) * exp(-(t - T[1])^2 / (2 * (σ[1])^2)) +
-          (A[2] / (σ[2] * sqrt(2 * pi))) * exp(-(t - T[2])^2 / (2 * (σ[2])^2)) +
-            α * exp(-β * t) / (1.0 + exp(-s * (t - τ))) for t in t]
-    cb[t .<= 0] .= 0
+    cb = [
+        (A[1] / (σ[1] * sqrt(2 * pi))) * exp(-(t - T[1])^2 / (2 * (σ[1])^2)) +
+        (A[2] / (σ[2] * sqrt(2 * pi))) * exp(-(t - T[2])^2 / (2 * (σ[2])^2)) +
+        α * exp(-β * t) / (1.0 + exp(-s * (t - τ))) for t in t
+    ]
+    cb[t.<=0] .= 0
     ca = cb ./ (1 - hct)
     return ca
 end
 
-const _georgiou_params = (
-    A = [0.37, 0.33, 10.06],
-    m = [0.11, 1.17, 16.02],
-    α = 5.26,
-    β = 0.032,
-    τ = 0.129,
-)
+const _georgiou_params =
+    (A = [0.37, 0.33, 10.06], m = [0.11, 1.17, 16.02], α = 5.26, β = 0.032, τ = 0.129)
 
 """
     aif_georgiou(t; params, hct = 0.35)
@@ -61,7 +58,7 @@ function aif_georgiou(t; params = _georgiou_params, hct = 0.35)
         end
     end
     cb = exponential_part .* gammavariate_part
-    cb[t .<= 0] .= 0
+    cb[t.<=0] .= 0
     ca = cb ./ (1 - hct)
     return ca
 end
@@ -88,7 +85,7 @@ The default value is set to to zero, i.e. no conversion takes place.
 function aif_biexponential(t; params, hct = 0)
     @extract (D, a, m) params
     cb = [D * (a[1] * exp(-m[1] * t) + a[2] * exp(-m[2] * t)) for t in t]
-    cb[t .<= 0] .= 0
+    cb[t.<=0] .= 0
     ca = cb ./ (1 - hct)
     return ca
 end
@@ -129,16 +126,12 @@ The timepoints `t` are in minutes with the bolus arriving at 0.
 The model parameters from the paper will be used by default unless an optional `params` input is provided.
 The `hct` is used to convert the concentration in blood to plasma and has default value of 0.42.
 """
-function aif_orton1(
-    t;
-    params = (aB = 10.4, μB = 12.0, aG = 1.24, μG = 0.169),
-    hct = 0.42,
-)
+function aif_orton1(t; params = (aB = 10.4, μB = 12.0, aG = 1.24, μG = 0.169), hct = 0.42)
     @extract (aB, μB, aG, μG) params
     AB = aB - aB * aG / (μB - μG)
     AG = aB * aG / (μB - μG)
     cb = [AB * exp(-μB * t) + AG * exp(-μG * t) for t in t]
-    cb[t .< 0] .= 0
+    cb[t.<0] .= 0
     ca = cb ./ (1 - hct)
     return ca
 end
@@ -152,16 +145,12 @@ The timepoints `t` are in minutes with the bolus arriving at 0.
 The model parameters from the paper will be used by default unless an optional `params` input is provided.
 The `hematocrit` is used to convert the concentration in blood to plasma and has default value of 0.42.
 """
-function aif_orton2(
-    t;
-    params = (aB = 344, μB = 20.2, aG = 1.24, μG = 0.172),
-    hct = 0.42,
-)
+function aif_orton2(t; params = (aB = 344, μB = 20.2, aG = 1.24, μG = 0.172), hct = 0.42)
     @extract (aB, μB, aG, μG) params
     AB = aB - aB * aG / (μB - μG)
     AG = aB * aG / (μB - μG)^2
     cb = [AB * t * exp(-μB * t) + AG * (exp(-μG * t) - exp(-μB * t)) for t in t]
-    cb[t .<= 0] .= 0
+    cb[t.<=0] .= 0
     ca = cb ./ (1 - hct)
     return ca
 end
@@ -175,11 +164,7 @@ The timepoints `t` are in minutes with the bolus arriving at `t = 0`.
 The model parameters from the paper will be used by default unless an optional `params` input is provided.
 The `hct` is used to convert the concentration in blood to plasma and has default value of 0.42.
 """
-function aif_orton3(
-    t;
-    params = (aB = 2.84, μB = 22.8, aG = 1.36, μG = 0.171),
-    hct = 0.42,
-)
+function aif_orton3(t; params = (aB = 2.84, μB = 22.8, aG = 1.36, μG = 0.171), hct = 0.42)
     @extract (aB, μB, aG, μG) params
     tb = 2 * pi / μB
     cb = zeros(length(t))
@@ -190,7 +175,7 @@ function aif_orton3(
             cb[i] = aB * aG * _orton3_f(tb, μG, μB) * exp(-μG * (t - tb))
         end
     end
-    cb[t .<= 0] .= 0
+    cb[t.<=0] .= 0
     ca = cb ./ (1 - hct)
     return ca
 end

src/dce_models/exchange.jl

@@ -45,16 +45,16 @@ function fit_exchange_nls(
             continue
         end
         initialvalue = [init_fp[idx], init_ps[idx], init_ve[idx], init_vp[idx]]
-        fp[idx], ps[idx], ve[idx], vp[idx] = curve_fit(model, t, ct[idx, :], initialvalue).param
+        fp[idx], ps[idx], ve[idx], vp[idx] =
+            curve_fit(model, t, ct[idx, :], initialvalue).param
     end
     T = @. (vp + ve) / fp
     Tp = @. vp / fp
     Te = @. ve / ps
     return (; est = (; fp, ps, ve, vp, T, Tp, Te))
 end
 
-function fit_exchange_lls(
-    ;
+function fit_exchange_lls(;
     t::AbstractVector,
     ca::AbstractVector,
     ct::AbstractArray,

src/dce_models/filtration.jl

@@ -43,7 +43,8 @@ function fit_filtration_nls(
             continue
         end
         initialvalue = [init_fp[idx], init_ps[idx], init_ve[idx], init_vp[idx]]
-        fp[idx], ps[idx], ve[idx], vp[idx] = curve_fit(model, t, ct[idx, :], initialvalue).param
+        fp[idx], ps[idx], ve[idx], vp[idx] =
+            curve_fit(model, t, ct[idx, :], initialvalue).param
     end
     T = @. (vp + ve) / fp
     Tp = @. vp / fp
@@ -54,8 +55,7 @@ function fit_filtration_nls(
     return (; est = (; fp, ps, ve, vp, T, Tp, Te))
 end
 
-function fit_filtration_lls(
-    ;
+function fit_filtration_lls(;
     t::AbstractVector,
     ca::AbstractVector,
     ct::AbstractArray,

src/dce_models/reference_region.jl

@@ -16,30 +16,37 @@ function fit_rrm_nls(
     ct::AbstractArray,
     mask = true,
 )
-    (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca=crr, ct, mask)
+    (t, ct, mask, num_timepoints, volume_size) =
+        resolve_fitting_inputs(; t, ca = crr, ct, mask)
     rel_kt, kep, kep_rr = (fill(NaN, volume_size...) for _ = 1:3)
     lls_est = fit_rrm_lls(; t, crr, ct, mask).est
     init_rel_kt, init_kep, init_kep_rr = select(lls_est, (:rel_kt, :kep, :kep_rr))
-    model(x, p) = model_referenceregion(conv; t = x, crr, params = (rel_kt = p[1], kep = p[2], kep_rr = p[3]))
+    model(x, p) = model_referenceregion(
+        conv;
+        t = x,
+        crr,
+        params = (rel_kt = p[1], kep = p[2], kep_rr = p[3]),
+    )
     for idx in eachindex(IndexCartesian(), mask)
         if mask[idx] == false
             continue
         end
         initialvalues = [init_rel_kt[idx], init_kep[idx], init_kep_rr[idx]]
-        rel_kt[idx], kep[idx], kep_rr[idx] = curve_fit(model, t, ct[idx, :], initialvalues).param
+        rel_kt[idx], kep[idx], kep_rr[idx] =
+            curve_fit(model, t, ct[idx, :], initialvalues).param
     end
     rel_ve = @. rel_kt * kep_rr / kep
     return (; est = (; rel_kt, rel_ve, kep, kep_rr))
 end
 
-function fit_rrm_lls(
-    ;
+function fit_rrm_lls(;
     t::AbstractVector,
     crr::AbstractVector,
     ct::AbstractArray,
     mask = true,
 )
-    (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca = crr, ct, mask)
+    (t, ct, mask, num_timepoints, volume_size) =
+        resolve_fitting_inputs(; t, ca = crr, ct, mask)
     x1, x2, x3 = (fill(NaN, volume_size...) for _ = 1:3)
 
     M = zeros(num_timepoints, 3)
@@ -68,9 +75,10 @@ function fit_crrm_nls(
     mask = true,
     kep_rr = 0.0,
 )
-    (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca = crr, ct, mask)
+    (t, ct, mask, num_timepoints, volume_size) =
+        resolve_fitting_inputs(; t, ca = crr, ct, mask)
     rel_kt, kep = (fill(NaN, volume_size...) for _ = 1:2)
-    
+
     if kep_rr <= 0
         rrm_est = fit_rrm_nls(conv; t, crr, ct, mask).est
         viable_est = positive_only_mask(rrm_est)
@@ -79,10 +87,11 @@ function fit_crrm_nls(
     lls_est = fit_crrm_lls(; t, crr, ct, kep_rr, mask = mask).est
     init_rel_kt, init_kep = select(lls_est, (:rel_kt, :kep))
     model(x, p) = model_referenceregion(
-    conv;
-    t = x,
-    crr,
-    params = (rel_kt = p[1], kep = p[2], kep_rr = kep_rr))
+        conv;
+        t = x,
+        crr,
+        params = (rel_kt = p[1], kep = p[2], kep_rr = kep_rr),
+    )
     for idx in eachindex(IndexCartesian(), mask)
         if mask[idx] == false
             continue
@@ -94,15 +103,15 @@ function fit_crrm_nls(
     return (; est = (; rel_kt, rel_ve, kep, kep_rr))
 end
 
-function fit_crrm_lls(
-    ;
+function fit_crrm_lls(;
     t::AbstractVector,
     crr::AbstractVector,
     ct::AbstractArray,
     mask = true,
     kep_rr = 0.0,
 )
-    (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca = crr, ct, mask)
+    (t, ct, mask, num_timepoints, volume_size) =
+        resolve_fitting_inputs(; t, ca = crr, ct, mask)
     rel_kt, kep = (fill(NaN, volume_size...) for _ = 1:2)
 
     if kep_rr <= 0
@@ -124,14 +133,14 @@ function fit_crrm_lls(
     return (est = (rel_kt = rel_kt, rel_ve = rel_ve, kep = kep, kep_rr = kep_rr),)
 end
 
-function fit_errm_lls(
-    ;
+function fit_errm_lls(;
     t::AbstractVector,
     crr::AbstractVector,
     ct::AbstractArray,
     mask = true,
 )
-    (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca = crr, ct, mask)
+    (t, ct, mask, num_timepoints, volume_size) =
+        resolve_fitting_inputs(; t, ca = crr, ct, mask)
     x1, x2, x3, x4 = (fill(NaN, volume_size...) for _ = 1:4)
 
     M = zeros(num_timepoints, 4)
@@ -159,15 +168,15 @@ function fit_errm_lls(
     return (; est = (; rel_kt, rel_ve, rel_vp, kep, kep_rr))
 end
 
-function fit_cerrm_lls(
-    ;
+function fit_cerrm_lls(;
     t::AbstractVector,
     crr::AbstractVector,
     ct::AbstractArray,
     mask = true,
     kep_rr = 0.0,
 )
-    (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca = crr, ct, mask)
+    (t, ct, mask, num_timepoints, volume_size) =
+        resolve_fitting_inputs(; t, ca = crr, ct, mask)
     x1, rel_vp, kep = (fill(NaN, volume_size...) for _ = 1:3)
 
     if kep_rr <= 0
@@ -212,8 +221,7 @@ function fit_rrift_with_crrm(; crr, cp, t, ct, tail_start, kep_rr = 0.0, mask =
     return (est = (; est..., kep_rr, kt_rr, ve_rr))
 end
 
-function fit_rrift(
-    ;
+function fit_rrift(;
     crr::AbstractVector,
     cp::AbstractVector,
     t::AbstractVector,
@@ -225,8 +233,9 @@ function fit_rrift(
     crr_tail = crr[tail_start:end]
     cp_tail = cp[tail_start:end]
     t_tail = t[tail_start:end]
-    numerator = crr_tail .- crr_tail[1] .+
-                kep_rr .* cumul_integrate(t_tail, crr_tail, TrapezoidalFast())
+    numerator =
+        crr_tail .- crr_tail[1] .+
+        kep_rr .* cumul_integrate(t_tail, crr_tail, TrapezoidalFast())
     denominator = cumul_integrate(t_tail, cp_tail, TrapezoidalFast())
     kt_rr = denominator \ numerator
     return kt_rr

src/dce_models/tofts.jl

@@ -127,8 +127,7 @@ function fit_extendedtofts_nls(
     return (; est)
 end
 
-function fit_tofts_lls(
-    ;
+function fit_tofts_lls(;
     t::AbstractVector,
     ca::AbstractVector,
     ct::AbstractArray,
@@ -150,8 +149,7 @@ function fit_tofts_lls(
     return (; est)
 end
 
-function fit_extendedtofts_lls(
-    ;
+function fit_extendedtofts_lls(;
     t::AbstractVector,
     ca::AbstractVector,
     ct::AbstractArray,

src/dce_models/uptake.jl

@@ -24,8 +24,7 @@ function fit_uptake_nls(
 )
     (t, ct, mask, num_timepoints, volume_size) = resolve_fitting_inputs(; t, ca, ct, mask)
     fp, ps, vp = (fill(NaN, volume_size...) for _ = 1:3)
-    model(x, p) =
-        model_uptake(; t = x, ca, params = (fp = p[1], ps = p[2], vp = p[3]))
+    model(x, p) = model_uptake(; t = x, ca, params = (fp = p[1], ps = p[2], vp = p[3]))
     lls_est = fit_uptake_lls(; t, ca, ct, mask).est
     init_fp, init_ps, init_vp = select(lls_est, (:fp, :ps, :vp))
     for idx in eachindex(IndexCartesian(), mask)
@@ -38,8 +37,7 @@ function fit_uptake_nls(
     return (; est = (; fp, ps, vp))
 end
 
-function fit_uptake_lls(
-    ;
+function fit_uptake_lls(;
     t::AbstractVector,
     ca::AbstractVector,
     ct::AbstractArray,