beautifying

Author: Ivo Maatman

ext/MRISubspaceReconCUDAExt/BackProjection.jl

@@ -124,7 +124,7 @@ end
 function MRISubspaceRecon.calculate_backprojection(data::CuArray{Tc}, trj::CuArray{<:Integer,3}, img_shape; U=cu(I(size(trj)[end])), sample_mask=CUDA.ones(Bool, size(trj)[2:end])) where {Tc <: Complex}
     trj_cpu = Array(trj)
     mask_cpu = Array(sample_mask)
-    @assert all(d -> all((@view(trj_cpu[d, :, :])[mask_cpu]) .>= 1) && all((@view(trj_cpu[d, :, :])[mask_cpu]) .<= img_shape[d]), 1:size(trj, 1)) "Cartesian trajectory values must be in the range 1:img_shape[d] for each dimension d. Consider shifting the trajectory."
+    @assert all(d -> all((@view(trj_cpu[d, :, :])[mask_cpu]) .>= 1) && all((@view(trj_cpu[d, :, :])[mask_cpu]) .<= img_shape[d]), 1:size(trj, 1)) "Cartesian trajectory values must be in the range 1:img_shape[d] for each dimension d."
     Ncoeff = size(U, 2)
     Ncoil = size(data, 3)
     img_idx = CartesianIndices(img_shape)

ext/MRISubspaceReconCUDAExt/CoilMaps.jl

@@ -19,7 +19,7 @@ function MRISubspaceRecon.calculate_coil_maps(
     mask_calib .&= sample_mask # update mask to only take calib region of k-space in CoilwiseCG
 
     x = MRISubspaceRecon.reconstruct_coilwise(data, trj_calib, calib_size; U, sample_mask=mask_calib, Niter_cg)
-    
+
     imdims = ntuple(i -> i, length(img_shape))
     kbp = fftshift(x, imdims)
     fft!(kbp, imdims)

src/BackProjection.jl

@@ -132,7 +132,7 @@ function calculate_backprojection(data::AbstractArray{Tc}, trj::AbstractArray{<:
 end
 
 function calculate_backprojection(data::AbstractArray{Tc}, trj::AbstractArray{<:Integer,3}, img_shape; U=I(size(trj)[end]), sample_mask=trues(size(trj)[2:end])) where {Tc <: Complex}
-    @assert all(d -> all((@view(trj[d, :, :])[sample_mask]) .>= 1) && all((@view(trj[d, :, :])[sample_mask]) .<= img_shape[d]), 1:size(trj, 1)) "Cartesian trajectory values must be in the range 1:img_shape[d] for each dimension d. Consider shifting the trajectory."
+    @assert all(d -> all((@view(trj[d, :, :])[sample_mask]) .>= 1) && all((@view(trj[d, :, :])[sample_mask]) .<= img_shape[d]), 1:size(trj, 1)) "Cartesian trajectory values must be in the range 1:img_shape[d] for each dimension d."
     Ncoeff = size(U, 2)
     Ncoil = size(data, 3)
     img_idx = CartesianIndices(img_shape)