T₁-mapping methods

First, we initialize a few empty vectors which will be filled with information about each T₁-mapping method:

T1_literature = Float64[]
T1_functions = []
incl_fit = Bool[]
seq_name = String[]
seq_type = Symbol[]

Next, we define the simulations of each pulse sequence as a function and push this function and auxiliary information for plotting to the respective vector.

IR: Stanisz et al.

Inversion-recovery method described by Stanisz et al. (2005). The following function takes qMT parameters as an input, simulates the signal, performs a mono-exponential T₁ fit as described in the publication, and returns the T₁ estimate. Sequence details are extracted from the publications and complemented with information kindly provided by the authors, as well as educated guesses where the corresponding information was not accessible. The latter two sources are indicated by comments in the functions.

function calculate_T1_IRStanisz(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TRF_inv = 10e-6 # s; 10us - 20us according to private conversations
    TRF_exc = TRF_inv # guessed, but has a negligible effect
    TI = exp.(range(log(1e-3), log(32), 35)) # s
    TD = similar(TI)
    TD .= 20 # s

    # simulate signal with an MT model
    u_inv = RF_pulse_propagator(π / TRF_inv, B1, ω0, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
    u_exc = RF_pulse_propagator(π / 2 / TRF_exc, B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    u_rti = [exp(hamiltonian_linear(0, B1, ω0, iTI - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_rtd = [exp(hamiltonian_linear(0, B1, ω0, iTD - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTD ∈ TD]

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_rti[i] * u_inv * u_rtd[i]
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T₁ (in s)
    model3(t, p) = p[1] .- p[2] .* exp.(-p[3] * t)
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

We add the T₁ estimate from Stanisz et al. (2005), the function, and some auxiliary data to the above-initialized vectors:

push!(T1_literature, 1.084) # ± 0.045s in WM
push!(T1_functions, calculate_T1_IRStanisz)
push!(seq_name, "IR Stanisz et al.")
push!(seq_type, :IR)

IR: Stikhov et al.

Inversion-recovery method described by Stikhov et al. (2015).

function calculate_T1_IRStikhov(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    nLobes = 3 # confirmed by authors
    TRF_exc = 3.072e-3 # s; confirmed by authors
    TRF_ref = 3e-3 # s; confirmed by authors
    TI = [30e-3, 530e-3, 1.03, 1.53] # s
    TR = 1.55 # s
    TE = 11e-3 # s

    # simulate signal with an MT model
    # excitation block
    u_exc = RF_pulse_propagator(sinc_pulse(-π / 2, TRF_exc; nLobes=nLobes), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model; spoiler=true)
    u_ref = RF_pulse_propagator(gauss_pulse(π, TRF_ref), B1, ω0, TRF_ref, m0s, R1f, R2f, Rx, R1s, T2s, MT_model; spoiler=false)
    u_te2 = exp(hamiltonian_linear(0, B1, ω0, (TE - TRF_exc - TRF_ref) / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_ref * u_te2 * u_exc

    # adiabatic inversion pulse confirmed by the authors
    ω1, _, φ, TRF_inv = sech_inversion_pulse() # 360 deg, defined by the intgral over the RF's real part.
    u_inv = RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_rti = [exp(hamiltonian_linear(0, B1, ω0, iTI - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_rtd = [exp(hamiltonian_linear(0, B1, ω0, TR - iTI - (TRF_inv + TRF_ref + TE) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_rti[i] * u_sp * u_inv * u_sp * u_rtd[i] * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .- p[2] .* exp.(-p[3] * t)
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 850e-3) # s; peak of histogram
push!(T1_functions, calculate_T1_IRStikhov)
push!(seq_name, "IR Stikhov et al.")
push!(seq_type, :IR)

IR: Preibisch et al.

Inversion-recovery method described by Preibisch et al. (2009).

function calculate_T1_IRPreibisch(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TI = [100, 200, 300, 400, 600, 800, 1000, 1200, 1600, 2000, 2600, 3200, 3800, 4400, 5000] .* 1e-3 # s
    TD = 20 # s
    TE = 27e-3 # s

    # The adiabatic inversion pulse was identical to the one described in http://doi.org/10.1002/mrm.20552 (per private communications with Dr. Deichmann)
    TRF_inv = 8.192e-3 # s
    β = 4.5 # 1/s
    μ = 5 # rad
    ω₁ᵐᵃˣ=13.5*π/TRF_inv # rad/s
    ω1_inv(t) = ω₁ᵐᵃˣ * sech(β * (2t / TRF_inv - 1)) # rad/s
    φ_inv(t)  = μ * log(sech(β * (2t / TRF_inv - 1))) # rad

    # simulate signal with an MT model
    u_inv = RF_pulse_propagator(ω1_inv, B1, φ_inv, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # The shape of the excitation pulse was kindly provided by Dr. Deichmann
    TRF_exc = 2.5e-3 # s
    _ω1_exc(t) = sinc(2 * abs(2t/TRF_exc-1)^0.88) * cos(π/2 * (2t/TRF_exc-1)) # rad/s
    ω1_scale = π/2 / quadgk(_ω1_exc, 0, TRF_exc)[1]
    ω1_exc(t) = _ω1_exc(t) * ω1_scale # rad/s
    u_exc = RF_pulse_propagator(ω1_exc, B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
    u_te = exp(hamiltonian_linear(0, B1, ω0, TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_te * u_exc

    # relaxation blocks
    u_ti = [exp(hamiltonian_linear(0, B1, ω0, iTI - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_td = exp(hamiltonian_linear(0, B1, ω0, TD - TRF_inv / 2 - TE, m0s, R1f, R2f, Rx, R1s, 1))

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_ti[i] * u_sp * u_inv * u_sp * u_td * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    # Fixed κ per private communications with Dr. Deichmann
    κ = 1.964
    model2(t, p) = p[1] .* (1 .- κ .* exp.(-p[2] * t))
    fit = curve_fit(model2, TI, s, [1, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 881e-3) # s; median of WM ROIs; mean is 0.882 s
push!(T1_functions, calculate_T1_IRPreibisch)
push!(seq_name, "IR Preibisch et al.")
push!(seq_type, :IR)

IR: Shin et al.

Inversion-recovery method described by Shin et al. (2009).

function calculate_T1_IRShin(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TI = exp.(range(log(34e-3), log(15), 10)) # s; Authors did not recall the TIs, but said they had at least 3–4 short times
    TR = 30 # s
    TRF_exc = 2.56e-3 # from Shin's memory

    # simulate signal with an MT model
    # EPI readout
    u_exc = RF_pulse_propagator(sinc_pulse(16 / 180 * π, TRF_exc; nLobes=3), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # adiabatic inversion pulse
    ω1, _, φ, TRF_inv = sech_inversion_pulse() # Shin confirmed "standard Siemens" adiabatic inversion pulse
    u_inv = RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_rti = [exp(hamiltonian_linear(0, B1, ω0, iTI - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_rtd = [exp(hamiltonian_linear(0, B1, ω0, TR - iTI - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_rti[i] * u_sp * u_inv * u_sp * u_rtd[i] * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .- p[2] .* exp.(-p[3] * t)
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 0.943) # ± 0.057 s in WM
push!(T1_functions, calculate_T1_IRShin)
push!(seq_name, "IR Shin et al.")
push!(seq_type, :IR)

LL: Shin et al.

Look-Locker method described by Shin et al. (2009).

function calculate_T1_LLShin(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    Nslices = 28 # (inner loop)
    iSlice = Nslices - 18 # guessed from cf. Fig. 6 and 7, the author suggested that the slices were acquired in ascending order

    TI1 = 12e-3 # s; the author suggested < 12ms
    TD = 10 + TI1 # s; time duration of data acquisition per IR period
    TR = 0.4 # s
    TR_slice = TR / Nslices
    TI = (0:TR:TD-TR)

    α_exc = 16 * π / 180 # rad
    TRF_exc = 2.56e-3 # s; from the authors' memory
    nLobes = 3
    Δω0 = (nLobes + 1) * 2π / TRF_exc # rad/s
    ω0slice = ((1:Nslices) .- iSlice) * Δω0

    # simulate signal with an MT model
    ω1, _, φ, TRF_inv = sech_inversion_pulse() # Shin confirmed "standard Siemens" adiabatic inversion pulse
    u_inv = RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    u_exc = Vector{Matrix{Float64}}(undef, length(ω0slice))
    Threads.@threads for is ∈ eachindex(ω0slice)
        if is == iSlice
            u_exc[is] = RF_pulse_propagator(sinc_pulse(α_exc, TRF_exc; nLobes=nLobes), B1, ω0slice[is] + ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
        else # use Graham's model for off-resonant pulses for speed
            u_exc[is] = exp(hamiltonian_linear(0, B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, 1))
            u_exc[is][5, 5] *= exp(-π * quadgk(t -> sinc_pulse(α_exc, TRF_exc; nLobes=nLobes)(t)^2, 0, TRF_exc)[1] * MRIgeneralizedBloch.lineshape_superlorentzian(ω0slice[is] + ω0, T2s))
        end
    end

    U = exp(hamiltonian_linear(0, B1, ω0, TI1 - TRF_inv / 2 - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    for _ ∈ TI, is ∈ eachindex(ω0slice)
        U = u_exc[is] * u_sp * U
        U = exp(hamiltonian_linear(0, B1, -ω0slice[is] + ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, 1)) * U # rewind phase
        U = exp(hamiltonian_linear(0, B1, ω0, TR_slice - 2TRF_exc, m0s, R1f, R2f, Rx, R1s, 1)) * U
    end
    U = u_sp * u_inv * u_sp * U
    m = steady_state(U)

    s = similar(TI)
    m = exp(hamiltonian_linear(0, B1, ω0, TI1 - TRF_inv / 2 - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1)) * m
    for iTI ∈ eachindex(s), is ∈ eachindex(ω0slice)
        m = u_exc[is] * u_sp * m
        m = exp(hamiltonian_linear(0, B1, -ω0slice[is] + ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, 1)) * m # rewind phase
        m = exp(hamiltonian_linear(0, B1, ω0, TR_slice - 2TRF_exc, m0s, R1f, R2f, Rx, R1s, 1)) * m
        if is == iSlice
            s[iTI] = m[1]
        end
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .- p[2] .* exp.(-p[3] * t)
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    R1a_est = fit.param[end] + log(cos(α_exc)) / TR
    return 1 / R1a_est
end

push!(T1_literature, 0.964) # ± 116s in WM
push!(T1_functions, calculate_T1_LLShin)
push!(seq_name, "LL Shin et al.")
push!(seq_type, :LL)

IR: Lu et al.

Inversion-recovery method described by Lu et al. (2005).

function calculate_T1_IRLu(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TRF_exc = 1e-3 # s; 0.5-2 ms according to P Zijl
    TI = [180, 630, 1170, 1830, 2610, 3450, 4320, 5220, 6120, 7010] .* 1e-3 # s
    TD = 8 # s
    TE = 42e-3 # s

    # simulate signal with an MT model
    # excitation block; GRASE RO w/ TSE factor 4
    u_exc = RF_pulse_propagator(sinc_pulse(π / 2, TRF_exc; nLobes=3), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model; spoiler=true)
    u_ref = RF_pulse_propagator(sinc_pulse(π, TRF_exc; nLobes=3), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model; spoiler=false)
    u_te1 = exp(hamiltonian_linear(0, B1, ω0, TE / 4 - TRF_exc, m0s, R1f, R2f, Rx, R1s, 1))
    u_te234 = exp(hamiltonian_linear(0, B1, ω0, TE / 4 - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_te234 * u_ref * u_te234^2 * u_ref * u_te1 * u_exc # 2 refocusing pulses before the RO

    # adiabatic inversion pulse
    ω1, _, φ, TRF_inv = sech_inversion_pulse(ω₁ᵐᵃˣ=4965.910769033364 * 750 / 360) # nom. α = 750deg according to P. Zijl
    u_inv = RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_ti = [exp(hamiltonian_linear(0, B1, ω0, iTI - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]

    u_et = u_te234 * u_ref * u_te234^2 * u_ref * u_te234 # 2 refocusing pulses after the RO
    u_td = [exp(hamiltonian_linear(0, B1, ω0, TD - 2TE - TRF_inv / 2, m0s, R1f, R2f, Rx, R1s, 1)) * u_et for _ ∈ TI]

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_ti[i] * u_sp * u_inv * u_sp * u_td[i] * u_sp
        s[i] = abs(steady_state(U)[1])
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = abs.(p[1] .* (1 .- p[2] .* exp.(-p[3] * t)))
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 0.735) # s; median of WM ROIs; reported T1 = (748 ± 64)ms in the splenium of the CC and (699 ± 38)ms in WM
push!(T1_functions, calculate_T1_IRLu)
push!(seq_name, "IR Lu et al.")
push!(seq_type, :IR)

LL: Stikhov et al.

Look-Locker method described by Stikhov et al. (2015).

function calculate_T1_LLStikhov(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TR = 1.55 # s
    TI = [30e-3, 530e-3, 1.03, 1.53] # s
    TRF_inv = 720e-6 # s; for 180deg pulse, 90deg pulse are half as long

    # simulate signal with an MT model
    u_90  = MRIgeneralizedBloch.xs_destructor(nothing) * RF_pulse_propagator(π / TRF_inv, B1, ω0, TRF_inv / 2, m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=true)
    u_inv = MRIgeneralizedBloch.xs_destructor(nothing) * RF_pulse_propagator(π / TRF_inv, B1, ω0, TRF_inv,     m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=false)
    u_m90 = MRIgeneralizedBloch.xs_destructor(nothing) * RF_pulse_propagator(π / TRF_inv, B1, ω0, TRF_inv / 2, m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=false)
    u_rotp = MRIgeneralizedBloch.z_rotation_propagator(π/2, nothing)
    u_rotm = MRIgeneralizedBloch.z_rotation_propagator(-π/2, nothing)
    u_inv = u_rotp * u_m90 * u_rotm * u_inv * u_rotp * u_90  # 90-180-90 pattern confirmed by authors
    TRF_inv *= 2

    α_exc = 5 * π / 180 # rad
    nLobes = 7 # confirmed by authors
    TRF_exc = 2.56e-3 # s; confirmed by authors
    ω1 = sinc_pulse(α_exc, TRF_exc; nLobes=nLobes)
    u_exc = RF_pulse_propagator(ω1, B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    dTI = TI .- [0; TI[1:end-1]]
    dTI[1] -= (TRF_inv + TRF_exc) / 2
    dTI[2:end] .-= TRF_exc

    u_ir = [exp(hamiltonian_linear(0, B1, ω0, dTI[i], m0s, R1f, R2f, Rx, R1s, 1)) for i in eachindex(dTI)]
    u_fp = exp(hamiltonian_linear(0, B1, ω0, TR - TI[end] - (TRF_inv + TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1))

    U = I
    for i in eachindex(TI)
        U = u_exc * u_ir[i] * U
    end
    U = u_inv * u_fp * U
    m = steady_state(U)

    s = similar(TI)
    for i in eachindex(TI)
        m = u_exc * u_ir[i] * m
        s[i] = m[1]
    end

    # fit mono-expential model and return T1 (in s)
    # model as provided by Stikhov et al. in a private communication
    function model_num(t, p)
        any(t .!= TI) ? error() : nothing

        cα_exc = cos(α_exc)
        TI1 = TI[1]
        TI2 = TI[2] - TI[1]
        Nll = length(TI)

        tr = TR - TI1 - (Nll - 1) .* TI2 # time between last exc and inv pulse


        E1 = exp.(-TI1 ./ p[2])
        E2 = exp.(-TI2 ./ p[2])
        Er = exp.(-tr ./ p[2])

        F = (1 - E2) ./ (1 - cα_exc .* E2)
        Qnom = -F .* cα_exc .* Er .* E1 .* (1 .- (cα_exc .* E2) .^ (Nll - 1)) .- E1 .* (1 .- Er) .- E1 .+ 1
        Qdenom = 1 .+ cα_exc .* Er .* E1 .* (cα_exc .* E2) .^ (Nll - 1)
        Q = Qnom / Qdenom

        Mz = zeros(Nll)
        Msig = zeros(Nll)

        for ii = 1:Nll
            Mz[ii] = F .+ (cα_exc .* E2) .^ (ii - 1) .* (Q - F)
            Msig[ii] = p[1] .* sin(α_exc) .* Mz[ii]
        end
        return Msig
    end

    fit = curve_fit(model_num, TI, s, [1.0, 1.0])
    return fit.param[end]
end

push!(T1_literature, 0.750) # s; peak of histogram; cf. https://doi.org/10.1016/j.mri.2016.08.021
push!(T1_functions, calculate_T1_LLStikhov)
push!(seq_name, "LL Stikhov et al.")
push!(seq_type, :LL)

vFA: Stikhov et al.

Variable flip-angle method described by Stikhov et al. (2015).

function calculate_T1_vFAStikhov(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    α = [3, 10, 20, 30] * π / 180 # rad
    TR = 15e-3 # s
    TRF = 2e-3 # s; confirmed by authors
    nLobes = 9 # confirmed by authors

    # simulate signal with an MT model
    s = similar(α)
    for i in eachindex(α)
        u_exc = RF_pulse_propagator(sinc_pulse(α[i], TRF; nLobes=nLobes), B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
        u_fp = exp(hamiltonian_linear(0, B1, ω0, TR - TRF, m0s, R1f, R2f, Rx, R1s, 1))

        U = u_exc * u_sp * u_fp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    f = lm(@formula(Y ~ X), DataFrame(X=s ./ tan.(α), Y=s ./ sin.(α)))
    T1_est = -TR / log(f.model.pp.beta0[2])
    return T1_est
end

push!(T1_literature, 1.07) # s; peak of histogram (cf. https://doi.org/10.1016/j.mri.2016.08.021)
push!(T1_functions, calculate_T1_vFAStikhov)
push!(seq_name, "vFA Stikhov et al.")
push!(seq_type, :vFA)

vFA: Cheng et al.

Variable flip-angle method described by Cheng et al. (2006).

function calculate_T1_vFACheng(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    α = [2, 9, 19] * π / 180 # rad
    TR = 6.1e-3 # s
    TRF = 1e-3 # s; guessed
    nLobes = 3 # guessed

    # simulate signal with an MT model
    s = similar(α)
    for i in eachindex(α)
        u_exc = RF_pulse_propagator(sinc_pulse(α[i], TRF; nLobes=nLobes), B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
        u_fp = exp(hamiltonian_linear(0, B1, ω0, TR - TRF, m0s, R1f, R2f, Rx, R1s, 1))

        U = u_exc * u_sp * u_fp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    f = lm(@formula(Y ~ X), DataFrame(X=s ./ tan.(α), Y=s ./ sin.(α)))
    T1_est = -TR / log(f.model.pp.beta0[2])
    return T1_est
end

push!(T1_literature, 1.0855) # s; mean of two volunteers
push!(T1_functions, calculate_T1_vFACheng)
push!(seq_name, "vFA Cheng et al.")
push!(seq_type, :vFA)

vFA: Chavez & Stanisz

Variable flip-angle method described by Chavez & Stanisz (2012).

function calculate_T1_vFA_Chavez(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    α = [1, 40, 130, 150] * π / 180 # rad
    TR = 40e-3 # s

    # simulate signal with an MT model
    s = similar(α)
    for i in eachindex(α)
        TRF = α[i] / (π/0.5e-3) # s; guessed, incl. constant ω1 / variable TRF
        u_exc = RF_pulse_propagator(α[i]/TRF, B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, MT_model) # rect. pulse shape guessed because "slab-select gradient [...] [is] turned off"
        u_fp = exp(hamiltonian_linear(0, B1, ω0, TR - TRF, m0s, R1f, R2f, Rx, R1s, 1))

        U = u_exc * u_sp * u_fp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    # NLLS fit as described in the paper
    function vFA_signal(α, p)
        S0, B1, T1 = p
        E1 = exp(-TR / T1)
        return S0 .* sin.(B1 .* α) .* (1 - E1) ./ (1 .- cos.(B1 .* α) .* E1)
    end

    fit_vFA = curve_fit(vFA_signal, α, s, ones(3))
    return fit_vFA.param[end]
end

push!(T1_literature, 1.044) # s; median of corpus callosum ROIs
push!(T1_functions, calculate_T1_vFA_Chavez)
push!(seq_name, "vFA Chavez & Stanisz")
push!(seq_type, :vFA)

vFA: Preibisch et al.

Variable flip-angle method described by Preibisch et al. (2009).

function calculate_T1_vFAPreibisch(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    α = [4, 18] * π / 180 # rad
    TR = 7.6e-3 # s
    TRF = 0.2e-3 # s; confirmed by Dr. Deichmann

    # simulate signal with an MT model
    s = similar(α)
    for i in eachindex(α)
        u_exc = RF_pulse_propagator(α[i] / TRF, B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
        u_fp = exp(hamiltonian_linear(0, B1, ω0, (TR - TRF) / 2, m0s, R1f, R2f, Rx, R1s, 1))

        U = u_fp * u_exc * u_fp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    f = lm(@formula(Y ~ X), DataFrame(X=s .* α, Y=s ./ α))
    T1_est = -2 * TR * f.model.pp.beta0[2]
    return T1_est
end

push!(T1_literature, 0.940) # s; median of ROIs; mean = 0.951s
push!(T1_functions, calculate_T1_vFAPreibisch)
push!(seq_name, "vFA Preibisch et al.")
push!(seq_type, :vFA)

vFA - Hybrid FLASH-EPI: Preibisch et al.

Variable flip-angle method with a Hybrid FLASH-EPI readout described by Preibisch et al. (2009).

function calculate_T1_vFAPreibisch_HYB(m0s, R1f, R2f, Rx, R1s, T2s, α, TR)
    # define sequence parameters
    TRF = 0.2e-3 # s; confirmed by Dr. Deichmann

    # simulate signal with an MT model
    s = similar(α)
    for i in eachindex(α)
        u_exc = RF_pulse_propagator(α[i] / TRF, B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
        u_fp = exp(hamiltonian_linear(0, B1, ω0, (TR - TRF) / 2, m0s, R1f, R2f, Rx, R1s, 1))
        U = u_fp * u_exc * u_fp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    SL = (s[2]/sin(α[2]) - s[1]/sin(α[1])) / (s[2]/tan(α[2]) - s[1]/tan(α[1]))
    T1_est = -TR / log(SL)
    return T1_est
end

For this sequence, we simulate three different settings with different flip angles and repetition times:

push!(T1_literature, 0.955) # s
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFAPreibisch_HYB(m0s, R1f, R2f, Rx, R1s, T2s, [4, 22] .* π ./ 180, 12.5e-3))
push!(seq_name, "vFA HYB12.5ms Preibisch et al.")
push!(seq_type, :vFA)

push!(T1_literature, 0.949) # s
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFAPreibisch_HYB(m0s, R1f, R2f, Rx, R1s, T2s, [4, 24] .* π ./ 180, 15.2e-3))
push!(seq_name, "vFA HYB15.2ms Preibisch et al.")
push!(seq_type, :vFA)

push!(T1_literature, 0.959) # s
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFAPreibisch_HYB(m0s, R1f, R2f, Rx, R1s, T2s, [4, 25] .* π ./ 180, 15.9e-3))
push!(seq_name, "vFA HYB15.9ms Preibisch et al.")
push!(seq_type, :vFA)

vFA: Teixeira et al.

Variable flip-angle method described by Teixeira et al. (2019).

function calculate_T1_vFATeixeira(m0s, R1f, R2f, Rx, R1s, T2s, ω1rms)
    # define sequence parameters
    α = [6, 12, 18] * π / 180 # rad – provided Dr. Teixeira
    TR = 15e-3 # s; provided by Dr. Teixeira for Fig. 7
    TRF = 3e-3 # s; confirmed by Dr. Teixeira
    ω0_CSMT = 6e3 * 2π # 6kHz – confirmed by Dr. Teixeira

    # simulate signal with an MT model
    s = similar(α)
    Threads.@threads for i in eachindex(α)
        u_exc = RF_pulse_propagator(CSMT_pulse(α[i], TRF, TR, ω1rms, ω0=ω0_CSMT), B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
        u_fp = exp(hamiltonian_linear(0, B1, ω0, (TR - TRF) / 2, m0s, R1f, R2f, Rx, R1s, 1))

        U = u_fp * u_exc * u_fp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    f = lm(@formula(Y ~ X), DataFrame(X=s ./ tan.(α), Y=s ./ sin.(α))) # DESPOT1 confirmed by Dr. Teixeira
    T1_est = -TR / log(f.model.pp.beta0[2])
    return T1_est
end

For this sequence, we simulate five different B₁-RMS values:

push!(T1_literature, 0.825) # s; read from Fig. 7
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFATeixeira(m0s, R1f, R2f, Rx, R1s, T2s, 0.4e-6 * 267.522e6)) # rad/s
push!(seq_name, "vFA CSMT w/ B1rms = 0.4uT Teixeira et al.")
push!(seq_type, :vFA)

push!(T1_literature, 0.775) # s; read from Fig. 7
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFATeixeira(m0s, R1f, R2f, Rx, R1s, T2s, 0.8e-6 * 267.522e6)) # rad/s
push!(seq_name, "vFA CSMT w/ B1rms = 0.8uT Teixeira et al.")
push!(seq_type, :vFA)

push!(T1_literature, 0.73) # s; read from Fig. 7
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFATeixeira(m0s, R1f, R2f, Rx, R1s, T2s, 1.2e-6 * 267.522e6)) # rad/s
push!(seq_name, "vFA CSMT w/ B1rms = 1.2uT Teixeira et al.")
push!(seq_type, :vFA)

push!(T1_literature, 0.68) # s; read from Fig. 7
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFATeixeira(m0s, R1f, R2f, Rx, R1s, T2s, 1.6e-6 * 267.522e6)) # rad/s
push!(seq_name, "vFA CSMT w/ B1rms = 1.6uT Teixeira et al.")
push!(seq_type, :vFA)

push!(T1_literature, 0.64) # s; read from Fig. 7
push!(T1_functions, (m0s, R1f, R2f, Rx, R1s, T2s) -> calculate_T1_vFATeixeira(m0s, R1f, R2f, Rx, R1s, T2s, 2e-6 * 267.522e6)) # rad/s
push!(seq_name, "vFA CSMT w/ B1rms = 2uT Teixeira et al.")
push!(seq_type, :vFA)

MP₂RAGE: Marques et al.

MP₂RAGE method described by Marques et al. (2010).

function calculate_T1_MP2RAGE(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    α = [4, 5] .* π / 180 # rad
    TRl = 6.75 # s
    TR_FLASH = 7.9e-3 # s
    TI = [0.8, 3.2] # s
    Nz = 160 ÷ 3

    # simulate signal with an MT model
    # adiabatic inversion pulse
    ω1, _, φ, TRF_inv = sechn_inversion_pulse(n=8, ω₁ᵐᵃˣ=25e-6 * 267.522e6) # HS8 pulse confirmed by Dr. Marques; amplitude chosen close to the max. of a typical 3T system
    u_inv = u_sp * RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model) * u_sp

    ta = TI[1] - Nz / 2 * TR_FLASH - TRF_inv / 2
    tb = TI[2] - TI[1] - Nz * TR_FLASH
    tc = TRl - TI[2] - Nz / 2 * TR_FLASH - TRF_inv / 2

    u_ta = exp(hamiltonian_linear(0, B1, ω0, ta, m0s, R1f, R2f, Rx, R1s, 1))
    u_tb = exp(hamiltonian_linear(0, B1, ω0, tb, m0s, R1f, R2f, Rx, R1s, 1))
    u_tc = exp(hamiltonian_linear(0, B1, ω0, tc, m0s, R1f, R2f, Rx, R1s, 1))

    # excitation blocks
    # binomial water excitation pulses; 1-2-1 pulse scheme confirmed for the Siemens product sequence; not specifically for the prototype.
    TRF_bin = 0.2e-3 # s; guessed, but has little influence the estimated T1
    τ = 1 / (2 * 430) - TRF_bin # s; fat-water shift = 440Hz
    TRF_exc = 2τ + 3TRF_bin # s

    u_1 = RF_pulse_propagator(α[1] / 4 / TRF_bin, B1, ω0, TRF_bin, m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=false)
    u_2 = RF_pulse_propagator(2α[1] / 4 / TRF_bin, B1, ω0, TRF_bin, m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=false)
    u_t = exp(hamiltonian_linear(0, B1, ω0, τ, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc1 = u_1 * u_t * u_2 * u_t * u_1

    u_1 = RF_pulse_propagator(α[2] / 4 / TRF_bin, B1, ω0, TRF_bin, m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=false)
    u_2 = RF_pulse_propagator(2α[2] / 4 / TRF_bin, B1, ω0, TRF_bin, m0s, R1f, R2f, Rx, R1s, T2s, MT_model, spoiler=false)
    u_t = exp(hamiltonian_linear(0, B1, ω0, τ, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc2 = u_1 * u_t * u_2 * u_t * u_1

    u_te = exp(hamiltonian_linear(0, B1, ω0, (TR_FLASH - TRF_exc) / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc1 = u_te * u_exc1 * u_sp * u_te
    u_exc2 = u_te * u_exc2 * u_sp * u_te

    # Propagation matrix in temporal order:
    # U = u_tc * u_exc2^Nz * u_tb * u_exc1^Nz * u_ta * u_inv
    U1 = u_exc1^(Nz / 2) * u_ta * u_inv * u_tc * u_exc2^Nz * u_tb * u_exc1^(Nz / 2)
    U2 = u_exc2^(Nz / 2) * u_tb * u_exc1^Nz * u_ta * u_inv * u_tc * u_exc2^(Nz / 2)

    s1 = steady_state(U1)[1]
    s2 = steady_state(U2)[1]
    sm = s1' * s2 / (abs(s1)^2 + abs(s2)^2)

    # fit mono-expential model and return T1 (in s)
    function MP2RAGE_signal(T1)
        eff_inv = 0.96 # from paper

        E1 = exp(-TR_FLASH / T1)
        EA = exp(-ta / T1)
        EB = exp(-tb / T1)
        EC = exp(-tc / T1)

        mzss = (((((1 - EA) * (cos(α[1]) * E1)^Nz + (1 - E1) * (1 - (cos(α[1]) * E1)^Nz) / (1 - cos(α[1]) * E1)) * EB + (1 - EB)) * (cos(α[2]) * E1)^Nz + (1 - E1) * (1 - (cos(α[2]) * E1)^Nz) / (1 - cos(α[2]) * E1)) * EC + (1 - EC)) / (1 + eff_inv * (cos(α[1]) * cos(α[2]))^Nz * exp(-TRl / T1))

        s1 = sin(α[1]) * ((-eff_inv * mzss * EA + (1 - EA)) * (cos(α[1]) * E1)^(Nz / 2 - 1) + (1 - E1) * (1 - (cos(α[1]) * E1)^(Nz / 2 - 1)) / (1 - cos(α[1]) * E1))
        s2 = sin(α[2]) * ((mzss - (1 - EC)) / (EC * (cos(α[2]) * E1)^(Nz / 2)) - (1 - E1) * ((cos(α[2]) * E1)^(-Nz / 2) - 1) / (1 - cos(α[2]) * E1))

        sm = s1' * s2 / (abs(s1)^2 + abs(s2)^2)
        return sm
    end

    fit = curve_fit((_, T1) -> MP2RAGE_signal.(T1), [1], [sm], [0.5])
    return fit.param[1]
end

push!(T1_literature, 0.81) # ± 0.03 s
push!(T1_functions, calculate_T1_MP2RAGE)
push!(seq_name, "MP2RAGE Marques et al.")
push!(seq_type, :MP2RAGE)

MP-RAGE: Wright et al.

MP-RAGE method described by Wright et al. (2008).

function calculate_T1_MPRAGE_Wright(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TRl = 5 # s
    TR_FLASH = 11e-3 # s
    TE = 6.7e-3 # s
    TI = [160, 190, 285, 441, 680, 1050, 1619, 2100] .* 1e-3 # s
    Nz = 256

    # simulate signal with an MT model
    # adiabatic inversion pulse
    TRF_inv = 13.5e-3 # s; from the paper
    β = 600 # 1/s; picked for 10kHz bandwidth
    μ = 5  # rad – 50 rad would match 10 kHz bandwidth, 5 rad chosen for computation speed (makes little difference)
    ω₁ᵐᵃˣ = 4 * sqrt(μ) * β # rad/s; compromise of appromximating 1.25 >> 1 and keeping B1max in limits
    ω1, _, φ, TRF_inv = sech_inversion_pulse(TRF=TRF_inv, β=β, μ=μ, ω₁ᵐᵃˣ=ω₁ᵐᵃˣ) # standard Philips inverson pulse, likely hyperbolic secant, as confirmed by Dr. Gowland
    u_inv = u_sp * RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # excitation blocks
    α = (8/20:8/20:8) .* π / 180 # rad – pattern confirmed by Dr. Gowland
    TRF_exc = 0.67e-3 # s
    nLobes = 7 # sinc pulses confirmed by Dr. Gowland; number of lobes guessed guessed to approximate the 11.9kHz bandwidth discussed in the paper

    u_exc = [RF_pulse_propagator(sinc_pulse(α[i], TRF_exc; nLobes=nLobes), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model) for i in eachindex(α)]
    u_te = exp(hamiltonian_linear(0, B1, ω0, TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_tr = exp(hamiltonian_linear(0, B1, ω0, TR_FLASH - TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = [u_te * u_exc[i] * u_tr for i in eachindex(u_exc)]
    u_exc_ramp = prod(u_exc[end:-1:1])

    s = similar(TI)
    for iTI in eachindex(TI)
        ti = TI[iTI] - TRF_inv / 2 - (TR_FLASH - TE) - length(α) * TR_FLASH
        tc = TRl - TI[iTI] - Nz * TR_FLASH - TRF_inv / 2 - TE + length(α) * TR_FLASH

        u_ti = exp(hamiltonian_linear(0, B1, ω0, ti, m0s, R1f, R2f, Rx, R1s, 1))
        u_tc = exp(hamiltonian_linear(0, B1, ω0, tc, m0s, R1f, R2f, Rx, R1s, 1))

        # Propagation matrix in temporal order: U = u_tc * u_exc20^(Nz-20) * ... u_exc2 * u_exc1 * u_ti * u_inv
        U = u_exc_ramp * u_ti * u_inv * u_tc * u_exc[end]^(Nz - length(α))
        s[iTI] = steady_state(U)[1] # extract x-magnetization
    end

    # fit mono-expential model and return T1 (in s)
    function MPRAGE_mz(TI, p)
        T1, M0, α_inv = p

        function hamiltonian_T1(T, R1)
            H = @SMatrix [
                -R1  R1;
                  0   0]
            return H * T
        end
        function pulse_propgator(α)
            U = @SMatrix [
                cos(α)  0;
                     0  1]
            return U
        end
        function steady_state_2D(U)
            Q = U - @SMatrix [1 0; 0 0]
            m = Q \ @SVector [0,1]
            return m
        end

        s = similar(TI)
        for iTI in eachindex(TI)
            ti = TI[iTI] - length(α) * TR_FLASH
            tc = TRl - TI[iTI] - Nz * TR_FLASH + length(α) * TR_FLASH

            u_ti = exp(hamiltonian_T1(ti, 1/T1))
            u_tr = exp(hamiltonian_T1(TR_FLASH, 1/T1))
            u_tc = exp(hamiltonian_T1(tc, 1/T1))

            # Propagation matrix in temporal order: U = u_tc * u_exc20^(Nz-20) * ... u_exc2 * u_exc1 * u_ta * u_inv
            U = u_tr * pulse_propgator(α[end])
            for i in (length(α)-1):-1:1
                U = U * u_tr * pulse_propgator(α[i])
            end
            U = U * u_ti * pulse_propgator(α_inv) * u_tc * (u_tr * pulse_propgator(α[end]))^(Nz - length(α))
            s[iTI] = M0 * steady_state_2D(U)[1] # extract z-magnetization
        end
        return s
    end

    fit = curve_fit(MPRAGE_mz, TI, s, [1, sin(α[end]), 0.9π])
    return fit.param[1]
end

push!(T1_literature, 0.84) # s
push!(T1_functions, calculate_T1_MPRAGE_Wright)
push!(seq_name, "MPRAGE Wright et al.")
push!(seq_type, :MP2RAGE)

Adiabatic IR: Wright et al.

Inversion-recovery method with adiabatic inversion pulse described by Wright et al. (2008).

function calculate_T1_IR_EPI_Wright(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TI = [120, 200, 400, 600, 900, 1500, 2100, 3000, 4000] .* 1e-3 # s
    TR = 35 # s
    TE = 45e-3 # s
    TRF_exc = 7.7e-3 # s
    nLobes = 1 # chose to match 395 Hz bandwidth

    # simulate signal with an MT model
    # adiabatic inversion pulse
    TRF_inv = 17.51e-3 # s
    β = 500 # 1/s; chosen to fit 713Hz bandwidth
    μ = 5   # rad – chosen to fit 713Hz bandwidth
    ω₁ᵐᵃˣ = 2 * sqrt(μ) * β # rad/s; compromise of appromximating 2 >> 1 and keeping B1max in limits
    ω1, _, φ, TRF_inv = sech_inversion_pulse(TRF=TRF_inv, β=β, μ=μ, ω₁ᵐᵃˣ=ω₁ᵐᵃˣ)
    u_inv = u_sp * RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_ti = [exp(hamiltonian_linear(0, B1, ω0, iTI - TRF_inv/2 - TRF_exc/2, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_td = [exp(hamiltonian_linear(0, B1, ω0,  TR - TRF_inv/2 - iTI - TE , m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]

    # excitation block
    u_exc = RF_pulse_propagator(sinc_pulse(π / 2, TRF_exc; nLobes=nLobes), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
    u_te = exp(hamiltonian_linear(0, B1, ω0, TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_te * u_exc

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_ti[i] * u_sp * u_inv * u_sp * u_td[i] * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .* (1 .- p[2] .* exp.(-p[3] * t)) # p[2] = (1 - cos(α))
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 0.9) # s; read from Fig. 5
push!(T1_functions, calculate_T1_IR_EPI_Wright)
push!(seq_name, "IR EPI Wright et al.")
push!(seq_type, :IR)

Adiabatic IR: Reynolds et al.

Inversion-recovery method with adiabatic inversion pulse described by Reynolds et al. (2023).

function calculate_T1_IRReynolds_adiabatic(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TRF_exc = 1e-3 # s; guessed
    nLobes = 3 # s; guessed
    TI = [5.5, 10.2, 35.8, 66.9, 125, 234, 598, 818, 1118, 1529, 3910, 5348] .* 1e-3 # s; measured from end to beginning of respective pulse (confirmed by Dr. Reynolds)
    TD = 5 # s
    TE = 10e-3 # s; guessed, but has negligible impact

    # simulate signal with an MT model
    # adiabatic inversion pulse
    ω1, _, φ, TRF_inv = sech_inversion_pulse(TRF=10e-3, ω₁ᵐᵃˣ=13.5e-6 * 267.522e6, μ=1.8380981750265004, β=730)
    u_inv = RF_pulse_propagator(ω1, B1, φ, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_ti = [exp(hamiltonian_linear(0, B1, ω0, iTI, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_td = exp(hamiltonian_linear(0, B1, ω0, TD - TE, m0s, R1f, R2f, Rx, R1s, 1))

    # excitation block
    u_exc = RF_pulse_propagator(sinc_pulse(π / 2, TRF_exc; nLobes=nLobes), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
    u_te = exp(hamiltonian_linear(0, B1, ω0, TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_te * u_exc

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_ti[i] * u_sp * u_inv * u_sp * u_td * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .* (1 .- p[2] .* exp.(-p[3] * t)) # confirmed by Dr. Reynolds
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 0.905) # s
push!(T1_functions, calculate_T1_IRReynolds_adiabatic)
push!(seq_name, "IR ad. Reynolds et al.")
push!(seq_type, :IR)

Sinc IR: Reynolds et al.

Inversion-recovery method with a sinc inversion pulse described by Reynolds et al. (2023).

function calculate_T1_IRReynolds_sinc(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TRF_exc = 1e-3 # s; guessed
    nLobes_exc = 3 # s; guessed
    TI = [5.5, 10.2, 35.8, 66.9, 125, 234, 598, 818, 1118, 1529, 3910, 5348] .* 1e-3 # s; measured from end to beginning of respective pulse (confirmed by Dr. Reynolds)
    TD = 5 # s
    TE = 10e-3 # s; guessed, but has negligible impact

    # simulate signal with an MT model
    # excitation block
    u_exc = RF_pulse_propagator(sinc_pulse(π / 2, TRF_exc; nLobes=nLobes_exc), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
    u_te = exp(hamiltonian_linear(0, B1, ω0, TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_te * u_exc

    # sinc inversion pulse
    TRF_inv = 3e-3 # s
    nLobes_inv = 3
    u_inv = RF_pulse_propagator(sinc_pulse(π, TRF_inv; nLobes=nLobes_inv), B1, ω0, TRF_inv, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_ti = [exp(hamiltonian_linear(0, B1, ω0, iTI, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_td = exp(hamiltonian_linear(0, B1, ω0, TD - TE, m0s, R1f, R2f, Rx, R1s, 1))

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_sp * u_ti[i] * u_sp * u_inv * u_sp * u_td * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .* (1 .- p[2] .* exp.(-p[3] * t)) # confirmed by Rd. Reynolds
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 0.861) # s
push!(T1_functions, calculate_T1_IRReynolds_sinc)
push!(seq_name, "IR sinc Reynolds et al.")
push!(seq_type, :IR)

SR: Reynolds et al.

Saturation recovery method described by Reynolds et al. (2023).

function calculate_T1_SRReynolds(m0s, R1f, R2f, Rx, R1s, T2s)
    # define sequence parameters
    TRF_exc = 1e-3 # s; guessed, but has negligible impact
    nLobes_exc = 5 # guessed, but has negligible impact
    TI = [5.5, 10.2, 35.8, 66.9, 125, 234, 598, 818, 1118, 1529, 3910, 5348] .* 1e-3 # s; measured from end to beginning of respective pulse (confirmed by Dr. Reynolds)
    TD = 5 # s
    TE = 10e-3 # s; guessed, but has negligible impact

    # simulate signal with an MT model
    # saturation pulse
    TRF_sat = 0.5 # s
    ω1 = 10 * 2π # rad/s
    u_sat = RF_pulse_propagator(ω1, B1, ω0, TRF_sat, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)

    # relaxation blocks
    u_ti = [exp(hamiltonian_linear(0, B1, ω0, iTI, m0s, R1f, R2f, Rx, R1s, 1)) for iTI ∈ TI]
    u_td = exp(hamiltonian_linear(0, B1, ω0, TD - TE, m0s, R1f, R2f, Rx, R1s, 1))

    # excitation block
    u_exc = RF_pulse_propagator(sinc_pulse(π / 2, TRF_exc; nLobes=nLobes_exc), B1, ω0, TRF_exc, m0s, R1f, R2f, Rx, R1s, T2s, MT_model)
    u_te = exp(hamiltonian_linear(0, B1, ω0, TE - TRF_exc / 2, m0s, R1f, R2f, Rx, R1s, 1))
    u_exc = u_te * u_exc

    s = similar(TI)
    for i in eachindex(TI)
        U = u_exc * u_ti[i] * u_sp * u_sat * u_td * u_sp
        s[i] = steady_state(U)[1]
    end

    # fit mono-expential model and return T1 (in s)
    model3(t, p) = p[1] .* (1 .- p[2] .* exp.(-p[3] * t)) # confirmed by Dr. Reynolds
    fit = curve_fit(model3, TI, s, [1, 2, 0.8])
    return 1 / fit.param[end]
end

push!(T1_literature, 1.013) # s
push!(T1_functions, calculate_T1_SRReynolds)
push!(seq_name, "SR Reynolds et al.")
push!(seq_type, :SR)