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JuliaHealth / KomaMRI.jl

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.
https://JuliaHealth.github.io/KomaMRI.jl
MIT License
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Reconstruction for 3D sequences #323

Open katyabrui opened 8 months ago

katyabrui commented 8 months ago

What happened?

When using a 3D pulse sequence (MPRAGE) with brain_phantom3D, the recostruction is not performed propperly. Looks like a 3D k-space is considered as a set of 2D k-spaces or something like this. I attach the .seq file as an example (actually, I couldn't attach it as it is, so you will need to remove "txt" after you save it) MPRAGE_brain_25cmfov.seq.txt mprage

Environment

OS x86_64-w64-mingw32
Julia 1.9.3
KomaMRIPlots 0.8.0
KomaMRIFiles 0.8.0
KomaMRI 0.8.0
KomaMRICore 0.8.0
KomaMRIBase 0.8.1
curtcorum commented 8 months ago

@katyabrui

Great that you are using KomaMRI, and thanks for reporting the issue!

An immediate work around might be to use:

your_obj = brain_phantom3D(; ss=your_ss, start_end=[177, 183]);

if using KomaUI:

obj_ui[] = brain_phantom3D(; ss=your_ss, start_end=[177, 183]);

This is a 3D phantom that is very thin along Z, will take a bit longer to simulate, but less so than the default brain_phantom3D(). ss defaults to 4 for both 2D and 3D.

Hope this helps while the issue is investigated!

=====================

This unfortunately does not fix the issue.

curtcorum commented 8 months ago

@katyabrui

I am trying the seq now. Nice job getting it through github!

cncastillo commented 8 months ago

This is related to #308, but instead of the "echo dimension," the problem is in the "slice dimension." Right now, we are guessing how to reconstruct stuff (the dimensions Nx, Ny, Nz); a proper way would be to specify the label counters in the "[EXTENSION]" section of the pulseq file. I believe previously, there was a warning that told the user we were doing this. We should put it back (@beorostica). For now, for the echoes, we could add a seq.DEF["Necho"] to reconstruct interleaved echoes.

In the meantime, you could specify the correct sequence dimensions in the REPL before reconstructing.

seq_ui[].DEF["Nx"] = 32
seq_ui[].DEF["Ny"] = 32
seq_ui[].DEF["Nz"] = 32

I can not test it right now, but I think it should work. For convenience, this also can be specified in the sequence file

[DEFINITIONS]
AdcRasterTime 6.25e-05 
BlockDurationRaster 1e-05 
GradientRasterTime 1e-05 
RadiofrequencyRasterTime 1e-06 
Nx 32
Ny 32
Nz 32
curtcorum commented 8 months ago

@cncastillo @katyabrui

This seems to be the issue, the current seq_ui[].DEF below has Nz=1:

julia> seq_ui[].DEF
Dict{String, Any} with 12 entries:
  "signature"                => "9adc63c8e1f8331d67b3fb1c9bdeac98"
  "AdcRasterTime"            => 6.25e-5
  "GradientRasterTime"       => 1.0e-5
  "Nz"                       => 1
  "Nx"                       => 32
  "Ny"                       => 1024
  "PulseqVersion"            => 1004000
  "BlockDurationRaster"      => 1.0e-5
  "extension"                => [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
  "FileName"                 => "MPRAGE_brain_25cmfov.seq"
  "RadiofrequencyRasterTime" => 1.0e-6
  "additional_text"          => "# Format of extension lists:\n# id type ref next_id\n# next_id of 0 terminates the list\n# Extension list is followed by extension specification…

The Nz, Nx, Ny parts of DEF are generated by KomaMRI(?) and are not in the current seq file:

[DEFINITIONS]
AdcRasterTime 6.25e-05 
BlockDurationRaster 1e-05 
GradientRasterTime 1e-05 
RadiofrequencyRasterTime 1e-06

I saw a similar issue with:

https://github.com/pulseq/pulseq/blob/master/matlab/demoSeq/writeGradientEcho3D.m

but had not gotten around to resolving yet. Will try the above fix, thanks!

curtcorum commented 8 months ago

@cncastillo @katyabrui

Changing the Nx-z parameters in seq.DEF:

julia> seq.DEF
Dict{String, Any} with 12 entries:
  "signature"                => "9adc63c8e1f8331d67b3fb1c9bdeac98"
  "AdcRasterTime"            => 6.25e-5
  "GradientRasterTime"       => 1.0e-5
  "Nz"                       => 32
  "Nx"                       => 32
  "Ny"                       => 32
  "PulseqVersion"            => 1004000
  "BlockDurationRaster"      => 1.0e-5
  "extension"                => [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
  "FileName"                 => "MPRAGE_brain_25cmfov.seq"
  "RadiofrequencyRasterTime" => 1.0e-6
  "additional_text"          => "# Format of extension lists:\n# id type ref next_id\n# next_id of 0 terminates the list\n# Extension list is followed by extension specifications\n[EXTENSIONS]\n\n#…

And running the simulation, results in:

julia> raw = simulate(obj, seq, sys);
┌ Info: Running simulation in the GPU (Quadro RTX 8000)
│   koma_version = v"0.8.1"
│   sim_method = Bloch()
│   spins = 78126
│   time_points = 81897
└   adc_points = 32768
Progress: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| Time: 0:01:04
  simulated_blocks:  2264
  rf_blocks:         1124
  acq_samples:       32768
 65.210778 seconds (65.12 M allocations: 3.968 GiB, 2.15% gc time)

which seems to be simulating everything:

MPRAGE_brain_raw

however:

julia> raw.params
Dict{String, Any} with 23 entries:
  "protocolName"                   => "NoName"
  "institutionName"                => "Pontificia Universidad Catolica de Chile"
  "reconSize"                      => [32, 32, 1]
  "enc_lim_repetition"             => Limit(0, 0, 0)
  "enc_lim_set"                    => Limit(0, 0, 0)
  "enc_lim_segment"                => Limit(0, 0, 0)
  "userParameters"                 => Dict{String, Any}("Nblocks"=>2264, "gpu"=>1, "gpu_device"=>0, "type_sim_parts"=>Bool[0, 1, 0, 1, 0, 1, 0, 1, 0, 1  …  1, 0, 1, 0, 1, 0, 1, 0, 1, 0], "precision…
  "enc_lim_phase"                  => Limit(0, 0, 0)
  "enc_lim_average"                => Limit(0, 0, 0)
  "enc_lim_slice"                  => Limit(0, 0, 0)
  "reconFOV"                       => Float32[258.065, 258.065, 1.0]
  "systemModel"                    => "v0.8.1"
  "H1resonanceFrequency_Hz"        => 63866203
  "patientName"                    => "brain3D"
  "enc_lim_contrast"               => Limit(0, 0, 0)
  "trajectory"                     => "other"
  "systemFieldStrength_T"          => 1.5
  "enc_lim_kspace_encoding_step_0" => Limit(0, 31, 16)
  "enc_lim_kspace_encoding_step_1" => Limit(0, 31, 16)
  "encodedFOV"                     => Float32[258.065, 258.065, 1.0]
  "enc_lim_kspace_encoding_step_2" => Limit(0, 0, 0)
  "systemVendor"                   => "KomaMRI.jl"
  "encodedSize"                    => [32, 32, 1]

with "encodedSize" => [32, 32, 1] it is not taking the values from seq.DEF as far as I can tell. and the acq parameters get generated correspondingly:


julia> acq = AcquisitionData( raw);
julia> acq.encodingSize
(32, 32)
julia> acq.fov
(258.06451416015625, 258.0649108886719, 1.0)

Trying to manually set the recon size fails with:

julia> Nx, Ny, Nz = [32, 32, 32];

julia> reconParams = Dict{Symbol,Any}(:reco=>"direct", :reconSize=>(Nx, Ny, Nz));

julia> image = reconstruction(acq, reconParams);
ERROR: ArgumentError: Nodes x have dimension 2 != 3

Next step is to fill in the encoded size correctly in raw.

cncastillo commented 8 months ago

with "encodedSize" => [32, 32, 1] it is not taking the values from seq.DEF as far as I can tell. and the acq parameters get generated correspondingly:

Ok, this should work. So it is an unrelated bug (@beorostica can you create an issue?).

julia> image = reconstruction(acq, reconParams);
ERROR: ArgumentError: Nodes x have dimension 2 != 3

Last time I tried using 3D k-spaces (nodes) with MRIReco it always gave an error, so we are just storing 2D k-spaces in the raw data (or at least that is the default ndims=2 of signal_to_raw_data). This could create this dimension mismatch. Maybe we just need to check if :reconSize has 3 components to save a 3D k-space, otherwise a 2D k-space. This is the main thing we need to explore to fix this issue.

curtcorum commented 8 months ago

Manually editing raw.params:

julia> raw.params
Dict{String, Any} with 23 entries:
  "protocolName"                   => "NoName"
  "institutionName"                => "Pontificia Universidad Catolica de Chile"
  "reconSize"                      => [32, 32, 32]
  "enc_lim_repetition"             => Limit(0, 0, 0)
  "enc_lim_set"                    => Limit(0, 0, 0)
  "enc_lim_segment"                => Limit(0, 0, 0)
  "userParameters"                 => Dict{String, Any}("Nblocks"=>2264, "gpu"=>1, "gpu_device"=>0, "type_sim_parts"=>Bool[0, 1, 0, 1, 0, 1, 0, 1, 0, 1  …  1, 0, 1, 0, 1, 0, 1, 0, 1, 0], "precision…
  "enc_lim_phase"                  => Limit(0, 0, 0)
  "enc_lim_average"                => Limit(0, 0, 0)
  "enc_lim_slice"                  => Limit(0, 0, 0)
  "reconFOV"                       => [258.065, 258.065, 258.065]
  "systemModel"                    => "v0.8.1"
  "H1resonanceFrequency_Hz"        => 63866203
  "patientName"                    => "brain3D"
  "enc_lim_contrast"               => Limit(0, 0, 0)
  "trajectory"                     => "other"
  "systemFieldStrength_T"          => 1.5
  "enc_lim_kspace_encoding_step_0" => Limit(0, 31, 16)
  "enc_lim_kspace_encoding_step_1" => Limit(0, 31, 16)
  "encodedFOV"                     => [258.065, 258.065, 258.065]
  "enc_lim_kspace_encoding_step_2" => Limit(0, 31, 16)
  "systemVendor"                   => "KomaMRI.jl"
  "encodedSize"                    => [32, 32, 32]

gives mixed results:

julia> acq = AcquisitionData( raw);

julia> acq.encodingSize
(32, 32)

julia> acq.fov
(258.065, 258.065, 258.065)

julia> acq.traj
1-element Vector{MRIBase.Trajectory{Float32}}:
 MRIBase.Trajectory{Float32}("Custom", Float32[-0.20536378 -0.19211452 … 0.19211386 0.20536311; 0.21198764 0.21198764 … 0.21198764 0.21198764], Float32[0.0, 6.2f-5, 0.000124, 0.000186, 0.000248, 0.00031, 0.000372, 0.000434, 0.000496, 0.000558  …  0.001364, 0.001426, 0.001488, 0.00155, 0.001612, 0.001674, 0.001736, 0.001798, 0.00186, 0.001922], 0.0f0, 0.001f0, 32, 32, 1, false, true)

and unfortunately still does not reconstruct:

julia> Nx, Ny, Nz = raw.params["reconSize"][1:3]
3-element Vector{Int64}:
 32
 32
 32

julia> reconParams = Dict{Symbol,Any}(:reco=>"direct", :reconSize=>(Nx, Ny, Nz));

julia> reconParams
Dict{Symbol, Any} with 2 entries:
  :reconSize => (32, 32, 32)
  :reco      => "direct"

julia> image = reconstruction(acq, reconParams)
ERROR: ArgumentError: Nodes x have dimension 2 != 3
Stacktrace:
  [1] initParams(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}, dims::UnitRange{Int64}; kargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:20
  [2] initParams
    @ ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:3 [inlined]
  [3] NFFT.NFFTPlan(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}; dims::UnitRange{Int64}, fftflags::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:78
  [4] NFFTPlan
    @ ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:73 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:49 [inlined]
  [6] macro expansion
    @ ./timing.jl:393 [inlined]
  [7] #plan_nfft#130
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:48 [inlined]
  [8] plan_nfft
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:46 [inlined]
  [9] #plan_nfft#2
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [10] plan_nfft
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [11] samplingDensity(acqData::AcquisitionData{Float32, 2}, shape::Tuple{Int64, Int64, Int64})
    @ MRIBase ~/.julia/packages/MRIBase/oNfYy/src/Datatypes/AcqData.jl:288
 [12] setupDirectReco(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/RecoParameters.jl:19
 [13] reconstruction(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/Reconstruction.jl:37
 [14] top-level scope
    @ REPL[80]:1

probably because of acq.traj and other incorrect parameters or dimensions of trajectory and other related data?

curtcorum commented 8 months ago

The data is getting there but in the wrong dimensionality/indexing:

julia> acq.kdata
1×32×1 Array{Matrix{ComplexF32}, 3}:
[:, :, 1] =
 [-5.97573-6.46294im; -21.4331+0.217442im; … ; 21.9411-4.22948im; -11.1961-2.9045im;;]  …  [22.9858-1.1932im; 5.25461-2.63809im; … ; 19.4627-5.74995im; -4.43083-8.30492im;;]

julia> size( acq.kdata[1, 1, 1])
(1024, 1)

julia> size( acq.kdata[1, 2, 1])
(1024, 1)

julia> size( acq.kdata[1, 32, 1])
(1024, 1)
curtcorum commented 8 months ago

@cncastillo @beorostica

Line 448 suggests it may work if the DEF items are placed in the seq file. Line 456 may be the problem when not present in 3D sequence? I will try the Nx-z DEF items in the seq file.

https://github.com/curtcorum/KomaMRI.jl/blob/0a8a93de7c30fec886d27a052b1eff34ceca3a45/KomaMRIFiles/src/Sequence/Pulseq.jl#L448

if !haskey(seq.DEF,"Nx")

https://github.com/curtcorum/KomaMRI.jl/blob/0a8a93de7c30fec886d27a052b1eff34ceca3a45/KomaMRIFiles/src/Sequence/Pulseq.jl#L456

seq.DEF["Nz"] = Nz #Number of unique RF frequencies, in a 3D acquisition this should not work

curtcorum commented 8 months ago

This is a test where the Nx-z parameters are in the [DEFINITIONS] block of the seq file:

[DEFINITIONS]
AdcRasterTime 6.25e-05 
BlockDurationRaster 1e-05 
GradientRasterTime 1e-05 
RadiofrequencyRasterTime 1e-06
Nx 32
Ny 32
Nz 32 
(base) curt@green:~/src/KomaMRI$ julia
               _
   _       _ _(_)_     |  Documentation: https://docs.julialang.org
  (_)     | (_) (_)    |
   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 1.9.4 (2023-11-14)
 _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
|__/                   |

julia> using Revise

julia> using NativeFileDialog, MAT, MRIReco

julia> using KomaMRIBase, KomaMRICore, KomaMRIFiles, KomaMRIPlots

julia> sys = Scanner();

julia> seq = read_seq( seq_file)
[ Info: Loading sequence gre3d_cac.seq ...
Sequence[ τ = 42992.22 ms | blocks: 6444 | ADC: 1024 | GR: 7290 | RF: 1074 | DEF: 15 ]

julia> seq.DEF
Dict{String, Any} with 15 entries:
  "Nz"                       => 32.0
  "Nx"                       => 32.0
  "signature"                => ""
  "Ny"                       => 32.0
  "AdcRasterTime"            => 1.0e-7
  "BlockDurationRaster"      => 1.0e-5
  "extension"                => [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
  "PulseqVersion"            => 1004001
  "FileName"                 => "gre3d_cac.seq"
  "GradientRasterTime"       => 1.0e-5
  "TotalDuration"            => 42.9922
  "FOV"                      => [0.19, 0.19, 0.19]
  "Name"                     => "gre3d_cac"
  "RadiofrequencyRasterTime" => 1.0e-6
  "additional_text"          => "# Format of extension lists:\n# id type ref next_id\n# next_id of 0 terminates the list\n# Extension list is followed by extension spec…

julia> get_flip_angles( seq[1])
1-element Vector{Float64}:
 8.0

julia> raw = simulate(obj, seq, sys, sim_params=sim_params);
┌ Info: Running simulation in the GPU (Quadro RTX 8000)
│   koma_version = v"0.8.1"
│   sim_method = Bloch()
│   spins = 78126
│   time_points = 74945
└   adc_points = 32768
Progress: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| Time: 0:00:10
  simulated_blocks:  2168
  rf_blocks:         1075
  acq_samples:       32768
 10.773805 seconds (9.39 M allocations: 498.809 MiB, 3.71% gc time)

julia> raw.params
Dict{String, Any} with 23 entries:
  "protocolName"                   => "gre3d_cac"
  "institutionName"                => "Pontificia Universidad Catolica de Chile"
  "reconSize"                      => [32, 32, 1]
  "enc_lim_repetition"             => Limit(0, 0, 0)
  "enc_lim_set"                    => Limit(0, 0, 0)
  "enc_lim_segment"                => Limit(0, 0, 0)
  "userParameters"                 => Dict{String, Any}("Nblocks"=>2168, "gpu"=>1, "gpu_device"=>0, "type_sim_parts"=>Bool[0, 1, 0, 1, 0, 1, 0, 1, 0, 1  …  1, 0, 1, 0, …
  "enc_lim_phase"                  => Limit(0, 0, 0)
  "enc_lim_average"                => Limit(0, 0, 0)
  "enc_lim_slice"                  => Limit(0, 0, 0)
  "reconFOV"                       => Float32[190.0, 190.0, 1.0]
  "systemModel"                    => "v0.8.1"
  "H1resonanceFrequency_Hz"        => 63866203
  "patientName"                    => "brain3D"
  "enc_lim_contrast"               => Limit(0, 0, 0)
  "trajectory"                     => "other"
  "systemFieldStrength_T"          => 1.5
  "enc_lim_kspace_encoding_step_0" => Limit(0, 30, 16)
  "enc_lim_kspace_encoding_step_1" => Limit(0, 30, 16)
  ⋮                                => ⋮

julia> acq = AcquisitionData( raw);

julia> reconParams = Dict{Symbol,Any}(:reco=>"direct", :reconSize=>(32, 32, 32));

julia> image = reconstruction(acq, reconParams);
ERROR: ArgumentError: Nodes x have dimension 2 != 3
Stacktrace:
  [1] initParams(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}, dims::UnitRange{Int64}; kargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:20
  [2] initParams
    @ ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:3 [inlined]
  [3] NFFT.NFFTPlan(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}; dims::UnitRange{Int64}, fftflags::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:78
  [4] NFFTPlan
    @ ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:73 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:49 [inlined]
  [6] macro expansion
    @ ./timing.jl:393 [inlined]
  [7] #plan_nfft#130
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:48 [inlined]
  [8] plan_nfft
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:46 [inlined]
  [9] #plan_nfft#2
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [10] plan_nfft
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [11] samplingDensity(acqData::AcquisitionData{Float32, 2}, shape::Tuple{Int64, Int64, Int64})
    @ MRIBase ~/.julia/packages/MRIBase/oNfYy/src/Datatypes/AcqData.jl:288
 [12] setupDirectReco(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/RecoParameters.jl:19
 [13] reconstruction(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/Reconstruction.jl:37
 [14] top-level scope
    @ REPL[24]:1
curtcorum commented 8 months ago

Here is some more testing:

julia> raw.params["encodedFOV"]
3-element Vector{Float32}:
 190.0
 190.0
   1.0

julia> raw.params["encodedFOV"]=[190.0, 190.0, 190.0]
3-element Vector{Float64}:
 190.0
 190.0
 190.0

julia> raw.params["encodedSize"]=[32, 32, 32]
3-element Vector{Int64}:
 32
 32
 32

julia> raw.params
Dict{String, Any} with 23 entries:
  "protocolName"                   => "gre3d_cac"
  "institutionName"                => "Pontificia Universidad Catolica de Chile"
  "reconSize"                      => [32, 32, 32]
  "enc_lim_repetition"             => Limit(0, 0, 0)
  "enc_lim_set"                    => Limit(0, 0, 0)
  "enc_lim_segment"                => Limit(0, 0, 0)
  "userParameters"                 => Dict{String, Any}("Nblocks"=>2168, "gpu"=>1, "gpu_device"=>0, "type_sim_parts"=>Bool[0, 1, 0, 1, 0, 1, 0, 1, 0, 1  …  1, 0, 1, 0, 1, 0, 1, 0, 1, 0…
  "enc_lim_phase"                  => Limit(0, 0, 0)
  "enc_lim_average"                => Limit(0, 0, 0)
  "enc_lim_slice"                  => Limit(0, 0, 0)
  "reconFOV"                       => [190.0, 190.0, 190.0]
  "systemModel"                    => "v0.8.1"
  "H1resonanceFrequency_Hz"        => 63866203
  "patientName"                    => "brain3D"
  "enc_lim_contrast"               => Limit(0, 0, 0)
  "trajectory"                     => "other"
  "systemFieldStrength_T"          => 1.5
  "enc_lim_kspace_encoding_step_0" => Limit(0, 30, 16)
  "enc_lim_kspace_encoding_step_1" => Limit(0, 30, 16)
  "encodedFOV"                     => [190.0, 190.0, 190.0]
  "enc_lim_kspace_encoding_step_2" => Limit(0, 30, 16)
  "systemVendor"                   => "KomaMRI.jl"
  "encodedSize"                    => [32, 32, 32]

julia> acq = AcquisitionData( raw);

julia> reconParams = Dict{Symbol,Any}(:reco=>"direct", :reconSize=>(32, 32, 32));

julia> image = reconstruction(acq, reconParams);
ERROR: ArgumentError: Nodes x have dimension 2 != 3
Stacktrace:
  [1] initParams(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}, dims::UnitRange{Int64}; kargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:20
  [2] initParams
    @ ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:3 [inlined]
  [3] NFFT.NFFTPlan(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}; dims::UnitRange{Int64}, fftflags::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:78
  [4] NFFTPlan
    @ ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:73 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:49 [inlined]
  [6] macro expansion
    @ ./timing.jl:393 [inlined]
  [7] #plan_nfft#130
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:48 [inlined]
  [8] plan_nfft
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:46 [inlined]
  [9] #plan_nfft#2
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [10] plan_nfft
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [11] samplingDensity(acqData::AcquisitionData{Float32, 2}, shape::Tuple{Int64, Int64, Int64})
    @ MRIBase ~/.julia/packages/MRIBase/oNfYy/src/Datatypes/AcqData.jl:288
 [12] setupDirectReco(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/RecoParameters.jl:19
 [13] reconstruction(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/Reconstruction.jl:37
 [14] top-level scope
    @ REPL[39]:1

julia> acq.
encodingSize      fov               kdata             sequenceInfo      subsampleIndices  traj
julia> size(acq.kdata)
(1, 32, 1)

julia> acq.kdata[1,1,1]
1024×1 Matrix{ComplexF32}:
 -20.856276f0 + 7.8534117f0im
 -23.674004f0 + 0.69720984f0im
 -31.270561f0 - 11.370958f0im
  -24.04995f0 + 42.965137f0im
   39.66914f0 + 48.67775f0im
  57.792076f0 + 25.581245f0im
  21.598516f0 - 4.7730045f0im
  -82.14332f0 - 29.625975f0im
   -40.8282f0 - 20.949701f0im
  13.959399f0 + 4.1346235f0im
 -11.851673f0 + 28.035168f0im
  24.585815f0 + 9.329832f0im
   56.90302f0 + 7.7491198f0im
  17.272808f0 + 13.794415f0im
   -1.95158f0 - 26.888247f0im
   8.668823f0 - 80.32206f0im
              ⋮
 -20.988113f0 - 13.841843f0im
   28.07649f0 - 22.968506f0im
  45.602043f0 + 26.476562f0im
   55.74817f0 + 22.142883f0im
  14.360283f0 + 28.204256f0im
   17.00085f0 + 1.0903955f0im
   5.857233f0 - 23.596966f0im
 -25.678976f0 + 40.5332f0im
  20.144512f0 + 9.556927f0im
  15.072937f0 - 12.6632595f0im
   9.912712f0 + 7.5607147f0im
 -23.415226f0 - 46.04351f0im
  6.5762672f0 + 27.526182f0im
 -19.309336f0 + 33.920307f0im
  -28.33278f0 - 93.11535f0im
  55.864494f0 + 14.333662f0im

julia> acq.kdata[1,32,1]
1024×1 Matrix{ComplexF32}:
 -23.373734f0 + 28.150126f0im
  34.316868f0 + 29.423656f0im
  1.5463567f0 - 3.6292593f0im
 -52.664368f0 - 23.4733f0im
 -17.954971f0 - 32.63766f0im
  7.2143764f0 - 14.636225f0im
 -15.502787f0 + 27.550005f0im
  -10.73413f0 + 2.437481f0im
 -19.018562f0 + 26.472218f0im
  -18.13644f0 + 36.484882f0im
   8.976894f0 + 4.1743774f0im
  41.983356f0 + 26.89516f0im
   46.48584f0 - 12.104265f0im
   41.22889f0 - 8.658623f0im
  40.798172f0 + 39.47073f0im
  5.5201626f0 + 19.247066f0im
              ⋮
  -2.829985f0 - 8.459925f0im
   32.78006f0 - 21.172215f0im
  38.438377f0 + 26.038582f0im
   58.80835f0 + 18.670595f0im
  20.287176f0 + 25.217157f0im
  14.318125f0 + 8.467522f0im
  16.699306f0 - 23.905882f0im
 -20.987934f0 + 35.578873f0im
   15.35615f0 + 21.750748f0im
   12.56637f0 - 13.1215515f0im
  14.982704f0 + 2.081315f0im
  -32.10189f0 - 36.650608f0im
 -4.2777576f0 + 27.28927f0im
 -5.1820927f0 + 39.759125f0im
 -43.646645f0 - 87.57974f0im
   39.00147f0 + 13.069844f0im

julia> size( acq.kdata[1,32,1])
(1024, 1)

It looks like fixing the parameters is not enough, the kdata is still packed up wrong?

curtcorum commented 8 months ago

Testing 3D non-Cartesian, apparently has the same issues as 3D Cartesian.

(base) curt@green:~/src/KomaMRI$ julia
               _
   _       _ _(_)_     |  Documentation: https://docs.julialang.org
  (_)     | (_) (_)    |
   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 1.9.4 (2023-11-14)
 _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
|__/                   |

julia> using Markdown

julia> using InteractiveUtils

julia> using Revise

julia> using NativeFileDialog, MAT, MRIReco

julia> using KomaMRIBase, KomaMRICore, KomaMRIFiles, KomaMRIPlots

julia> sys = Scanner()
Scanner
  B0: Float64 1.5
  B1: Float64 1.0e-5
  Gmax: Float64 0.06
  Smax: Int64 500
  ADC_Δt: Float64 2.0e-6
  seq_Δt: Float64 1.0e-5
  GR_Δt: Float64 1.0e-5
  RF_Δt: Float64 1.0e-6
  RF_ring_down_T: Float64 2.0e-5
  RF_dead_time_T: Float64 0.0001
  ADC_dead_time_T: Float64 1.0e-5

julia> sys.RF_dead_time_T=1e-5
1.0e-5

julia> sys.RF_ring_down_T=10e-6
1.0e-5

julia> sys
Scanner
  B0: Float64 1.5
  B1: Float64 1.0e-5
  Gmax: Float64 0.06
  Smax: Int64 500
  ADC_Δt: Float64 2.0e-6
  seq_Δt: Float64 1.0e-5
  GR_Δt: Float64 1.0e-5
  RF_Δt: Float64 1.0e-6
  RF_ring_down_T: Float64 1.0e-5
  RF_dead_time_T: Float64 1.0e-5
  ADC_dead_time_T: Float64 1.0e-5

julia> seq_file = pick_file( "/home/curt/src"; filterlist="seq")
Gtk-Message: 19:49:26.593: Failed to load module "canberra-gtk-module"
Gtk-Message: 19:49:26.593: Failed to load module "canberra-gtk-module"

(julia:37505): GLib-GIO-WARNING **: 19:49:26.671: Failed to create file monitor for /home/curt/.config/glib-2.0/settings/keyfile: Unable to find default local file monitor type
"/home/curt/src/pulseq_champaign_imaging_llc/matlab/pulseq_m/zte_petra_cac.seq"

julia> seq = read_seq( seq_file)
[ Info: Loading sequence zte_petra_cac.seq ...
Sequence[ τ = 4328.22 ms | blocks: 1052 | ADC: 515 | GR: 3145 | RF: 536 | DEF: 16 ]

julia> seq.DEF
Dict{String, Any} with 16 entries:
  "signature"                => "e4359a04de6485703a97f6d7c56107ae"
  "AdcRasterTime"            => 1.0e-7
  "GradientRasterTime"       => 1.0e-5
  "TotalDuration"            => 4.32822
  "FOV"                      => [0.256, 0.256, 0.256]
  "Name"                     => "petra"
  "Nz"                       => 1
  "Nx"                       => 200
  "Ny"                       => 515
  "SamplesPerShell"          => 515.0
  "PulseqVersion"            => 1004001
  "BlockDurationRaster"      => 1.0e-5
  "extension"                => [0, 0, 0, 0, 0, 0, 0, 0, 0, 0  …  0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
  "FileName"                 => "zte_petra_cac.seq"
  "additional_text"          => "# Format of extension lists:\n# id type ref next_id\n# next_id of 0 terminates the list\n# Extension list is followed by extension specifications\n[EXTENSIONS]\n\n# Exten…
  "RadiofrequencyRasterTime" => 1.0e-6

julia> seq_plot = plot_seq(seq; range=[0,100], darkmode=true, slider=true,)
[ Info: Listening on: 127.0.0.1:7953, thread id: 1

julia> get_flip_angles( seq[3])
1-element Vector{Float64}:
 3.996

julia> sim_params = KomaMRICore.default_sim_params()
Dict{String, Any} with 9 entries:
  "return_type" => "raw"
  "Nblocks"     => 20
  "gpu"         => true
  "Nthreads"    => 1
  "gpu_device"  => 0
  "sim_method"  => Bloch()
  "precision"   => "f32"
  "Δt"          => 0.001
  "Δt_rf"       => 5.0e-5

julia> sim_params.
age       count     idxfloor  keys      maxprobe  ndel      slots     vals
julia> sim_params["Δt_rf"]
5.0e-5

julia> sim_params["Δt_rf"]=1e-6
1.0e-6

julia> obj = brain_phantom3D(; ss=4, start_end=[178, 182]);

julia> sim_params
Dict{String, Any} with 9 entries:
  "return_type" => "raw"
  "Nblocks"     => 20
  "gpu"         => true
  "Nthreads"    => 1
  "gpu_device"  => 0
  "sim_method"  => Bloch()
  "precision"   => "f32"
  "Δt"          => 0.001
  "Δt_rf"       => 1.0e-6

julia> raw = simulate(obj, seq, sys, sim_params=sim_params);
┌ Info: Running simulation in the GPU (Quadro RTX 8000)
│   koma_version = v"0.8.1"
│   sim_method = Bloch()
│   spins = 13027
│   time_points = 117639
└   adc_points = 103000
Progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| Time: 0:00:13
  simulated_blocks:  1091
  rf_blocks:         536
  acq_samples:       103000
 13.879795 seconds (9.95 M allocations: 548.532 MiB, 0.90% gc time)

julia> raw.params
Dict{String, Any} with 23 entries:
  "protocolName"                   => "petra"
  "institutionName"                => "Pontificia Universidad Catolica de Chile"
  "reconSize"                      => [26, 26, 1]
  "enc_lim_repetition"             => Limit(0, 0, 0)
  "enc_lim_set"                    => Limit(0, 0, 0)
  "enc_lim_segment"                => Limit(0, 0, 0)
  "userParameters"                 => Dict{String, Any}("Nblocks"=>1091, "gpu"=>1, "gpu_device"=>0, "type_sim_parts"=>Bool[0, 1, 0, 1, 0, 1, 0, 1, 0, 1  …  1, 0, 1, 0, 1, 0, 1, 0, 1, 0], "precision"=>"f3…
  "enc_lim_phase"                  => Limit(0, 0, 0)
  "enc_lim_average"                => Limit(0, 0, 0)
  "enc_lim_slice"                  => Limit(0, 0, 0)
  "reconFOV"                       => Float32[256.0, 256.0, 1.0]
  "systemModel"                    => "v0.8.1"
  "H1resonanceFrequency_Hz"        => 63866203
  "patientName"                    => "brain3D"
  "enc_lim_contrast"               => Limit(0, 0, 0)
  "trajectory"                     => "other"
  "systemFieldStrength_T"          => 1.5
  "enc_lim_kspace_encoding_step_0" => Limit(0, 24, 13)
  "enc_lim_kspace_encoding_step_1" => Limit(0, 25, 13)
  "encodedFOV"                     => Float32[256.0, 256.0, 1.0]
  "enc_lim_kspace_encoding_step_2" => Limit(0, 0, 0)
  "systemVendor"                   => "KomaMRI.jl"
  "encodedSize"                    => [25, 26, 1]

julia> acq = AcquisitionData( raw);

julia> begin
               Nx, Ny = raw.params["reconSize"][1:2];
               reconParams = Dict{Symbol,Any}(:reco=>"direct", :reconSize=>(Nx, Ny));
               image = reconstruction(acq, reconParams);;
       end

6-dimensional AxisArray{ComplexF32,6,...} with axes:
    :x, (0.0:10.24:256.0) mm
    :y, (0.0:9.846153846153847:246.15384615384616) mm
    :z, (0.0:1.0:0.0) mm
    :echos, 1:1
    :coils, 1:1
    :repetitions, 1:1
And data, a 26×26×1×1×1×1 Array{ComplexF32, 6}:
[:, :, 1, 1, 1, 1] =
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im        …          0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im                   0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im                   0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im          -0.0236505+0.0992867im             0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im        -0.00777292-0.0254325im      0.012173+0.253888im         0.115967+0.127116im          0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im         -0.0793708+0.124642im       0.239155-0.130121im   …    0.0784993+0.198522im          0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im         -0.0569608+0.0734885im    -0.132711+0.00239755im  -0.00624282+0.0227152im       -0.401928-0.0373109im   -0.136928+0.164801im         0.0+0.0im              0.0+0.0im
  -0.0222453+0.124349im       0.14743+0.162326im     0.0811325+0.282061im      -0.248402+0.172537im          0.51513+0.188474im     0.265433+0.066368im    0.257239+0.142123im         0.0+0.0im
    -0.12643-0.103366im    -0.0969422-0.0783985im    0.0239488+0.0854764im     0.0508624-0.0679528im       0.0785211+0.757174im    -0.132184-0.129531im  -0.0516888-0.192597im         0.0+0.0im
   0.0145846+0.0472643im   -0.0715923-0.0530962im    0.0238834+0.221171im      -0.228209+0.151941im        -0.311803+1.07776im     0.0541115+0.721079im   0.0380341-0.126004im         0.0+0.0im
 -0.00197875+0.0977978im   -0.0290478+0.0666364im    0.0956486+0.0605195im     -0.020544+0.442739im   …   -0.0646548+1.2661im      -0.303796+1.2412im     -0.133189-0.060337im         0.0+0.0im
    0.037106+0.107172im      0.184824+0.0822097im    -0.133251-0.186752im       0.141473+0.988213im        -0.092847+1.7226im      -0.342548+1.21904im    0.0885918+0.442872im         0.0+0.0im
  -0.0738069-0.167435im     0.0514082+0.150021im     0.0903598-0.265952im      0.0595301+0.800169im         0.033736+2.22846im     0.0331009+1.57372im     0.261175+0.729681im   0.0829206-0.137986im
    0.129208+0.00561913im   -0.171928+0.324073im      0.142662-0.406718im       -0.24646+1.12177im         0.0174301+2.41918im    -0.0122723+2.06395im     0.196475+0.885627im         0.0+0.0im
   -0.194472-0.135396im     -0.157046+0.263632im      -0.23762-0.0996091im     -0.270387+1.47725im         -0.029394+2.2901im     -0.0107559+1.50242im    0.0865599+0.541747im         0.0+0.0im
    0.079245-0.0244986im     0.206436+0.0963664im    0.0358069-0.0455092im      0.218636+0.809432im   …     0.168469+2.19265im     -0.274536+1.46205im   -0.0206982-0.0357301im        0.0+0.0im
    0.111329-0.0116827im    0.0941668+0.186362im     0.0414217-0.0693443im      0.261088+0.25694im         0.0234841+1.6421im      -0.298419+1.49593im    0.0610674+0.0813188im        0.0+0.0im
   -0.099452+0.189673im     0.0295172+0.201996im    -0.0141953+0.182957im       0.151031+0.0302758im       -0.117989+1.24655im     0.0375125+0.718611im   0.0532909-0.0469774im        0.0+0.0im
         0.0+0.0im          -0.132238-0.202624im    -0.0268976+0.13931im       0.0730343-0.285966im      -0.00140481+0.758735im    0.0919741+0.324555im         0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im          0.0678213-0.0374102im      0.077156+0.133285im        -0.115163-0.118061im          0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im          0.0567113-0.04648im       0.0242621+0.280348im   …    -0.061529+0.135817im          0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im           0.0612392+0.0382043im             0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im                   0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im                   0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im                   0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im
         0.0+0.0im                0.0+0.0im                0.0+0.0im                 0.0+0.0im        …          0.0+0.0im               0.0+0.0im              0.0+0.0im              0.0+0.0im

julia> image_plot = plot_image( abs.( image[:, :, 1]);)

(this does give a 3d to 2d projection image)

julia> reconParams = Dict{Symbol,Any}(:reco=>"direct", :reconSize=>(32, 32, 32));

julia> image = reconstruction(acq, reconParams);
ERROR: ArgumentError: Nodes x have dimension 2 != 3
Stacktrace:
  [1] initParams(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}, dims::UnitRange{Int64}; kargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:20
  [2] initParams
    @ ~/.julia/packages/NFFT/RT2hs/src/precomputation.jl:3 [inlined]
  [3] NFFT.NFFTPlan(k::Matrix{Float64}, N::Tuple{Int64, Int64, Int64}; dims::UnitRange{Int64}, fftflags::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol, Symbol}, NamedTuple{(:m, :σ), Tuple{Int64, Int64}}})
    @ NFFT ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:78
  [4] NFFTPlan
    @ ~/.julia/packages/NFFT/RT2hs/src/implementation.jl:73 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:49 [inlined]
  [6] macro expansion
    @ ./timing.jl:393 [inlined]
  [7] #plan_nfft#130
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:48 [inlined]
  [8] plan_nfft
    @ ~/.julia/packages/NFFT/RT2hs/src/NFFT.jl:46 [inlined]
  [9] #plan_nfft#2
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [10] plan_nfft
    @ ~/.julia/packages/AbstractNFFTs/Xd3qS/src/derived.jl:13 [inlined]
 [11] samplingDensity(acqData::AcquisitionData{Float32, 2}, shape::Tuple{Int64, Int64, Int64})
    @ MRIBase ~/.julia/packages/MRIBase/oNfYy/src/Datatypes/AcqData.jl:288
 [12] setupDirectReco(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/RecoParameters.jl:19
 [13] reconstruction(acqData::AcquisitionData{Float32, 2}, recoParams::Dict{Symbol, Any})
    @ MRIReco ~/src/MRIReco.jl/src/Reconstruction/Reconstruction.jl:37
 [14] top-level scope
    @ REPL[32]:1
curtcorum commented 8 months ago

@cncastillo @beorostica

Last time I tried using 3D k-spaces (nodes) with MRIReco it always gave an error, so we are just storing 2D k-spaces in the raw data (or at least that is the default ndims=2 of signal_to_raw_data). This could create this dimension mismatch. Maybe we just need to check if :reconSize has 3 components to save a 3D k-space, otherwise a 2D k-space. This is the main thing we need to explore to fix this issue.

KomaMRICore/src/rawdata/ISMRMRD.jl definitely is only doing 2D.

function signal_to_raw_data(
    signal, seq;
    phantom_name="Phantom", sys=Scanner(), sim_params=Dict{String,Any}(), ndims=2
)
    version = string(VersionNumber(Pkg.TOML.parsefile(joinpath(@__DIR__, "..", "..", "Project.toml"))["version"]))
    #Number of samples and FOV
    _, ktraj = get_kspace(seq) #kspace information
    mink = minimum(ktraj, dims=1)
    maxk = maximum(ktraj, dims=1)
    Wk = maxk .- mink
    Δx = 1 ./ Wk[1:2] #[m] Only x-y
    Nx = get(seq.DEF, "Nx", 1)
    Ny = get(seq.DEF, "Ny", 1)
    Nz = get(seq.DEF, "Nz", 1)
    if haskey(seq.DEF, "FOV")
        FOVx, FOVy, _ = seq.DEF["FOV"] #[m]
        if FOVx > 1 FOVx *= 1e-3 end #mm to m, older versions of Pulseq saved FOV in mm
        if FOVy > 1 FOVy *= 1e-3 end #mm to m, older versions of Pulseq saved FOV in mm
        Nx = round(Int64, FOVx / Δx[1])
        Ny = round(Int64, FOVy / Δx[2])
    else
        FOVx = Nx * Δx[1]
        FOVy = Ny * Δx[2]
    end

Is the idea to have it do 3D again, or fool it at least?

curtcorum commented 8 months ago

Functions so far that handle 2D only at the moment.

setup_raw()

https://github.com/curtcorum/KomaMRI.jl/blob/3028ce9699b5479a9e39f4f1490d8e521174df6a/src/ui/ExportUIFunctions.jl#L123

handle(w, "recon")

https://github.com/curtcorum/KomaMRI.jl/blob/3028ce9699b5479a9e39f4f1490d8e521174df6a/src/KomaUI.jl#L202

function signal_to_raw_data(
    signal, seq;
    phantom_name="Phantom", sys=Scanner(), sim_params=Dict{String,Any}(), ndims=2
)

https://github.com/curtcorum/KomaMRI.jl/blob/3028ce9699b5479a9e39f4f1490d8e521174df6a/KomaMRICore/src/rawdata/ISMRMRD.jl#L78