Acquisition Data

There are two different forms of acquisition data types in MRIReco:

• RawAcquisitionData
• AcquisitionData

While the former is used to hold the data in the form, how it will be written out from the scanner, the later has already performed some data permutations bringing the data into the shape how the reconstruction expects it.

Raw Data

The RawAcquisitionData is a data type that closely resembles the ISMRMRD data format. It looks like

mutable struct RawAcquisitionData
  params::Dict{String, Any}
  profiles::Vector{Profile}
end

with

mutable struct Profile
  head::AcquisitionHeader
  traj::Array{Float32,2}
  data::Array{Complex{Float32},2}
end

The params member of RawAcquisitionData is basically the a flattened dictionary derived from the XML part of an ISMRMRD file. A Profile describes the data measured after a single excitation during an MRI experiment. It has members head, traj, and data, which exactly resemble the structures specified by the ISMRMRD file format.

Preprocessed Data

The RawAcquisitionData can be preprocessed into a form, which makes it more convenient for reconstruction algorithms. The AcquisitionData type looks like

mutable struct AcquisitionData
  sequenceInfo::Dict{Symbol,Any}
  traj::Vector{Trajectory}
  kdata::Array{Matrix{ComplexF64},3}
  subsampleIndices::Vector{Array{Int64}}
  encodingSize::Vector{Int64}
  fov::Vector{Float64}
end

It consists of the sequence informations stored in a dictionary, the k-space trajectory, the k-space data, and several parameters describing the dimension of the data and some additional index vectors.

The k-space data kdata has three dimensions encoding

1. dim : contrasts/echoes
2. dim : slices
3. dim : repetitions

Each element is a matrix encoding

1. dim : k-space nodes
2. dim : channels/coils

In case of undersampled data, the subsampling indices are stored in subsampleIndices. One check if the data is undersampled by checking if isempty(subsampleIndices).

The encoded space is stored in the field encodingSize. It is especially relevant for non-Cartesian trajectories where it is not clear upfront, how large the grid size for reconstruction should be chosen. Finally fov describes the physical lengths of the encoding grid.