Merge pull request #9 from roflmaostc/rename_iradon

Rename iradon to backproject
roflmaostc · Feb 9, 2024 · 65165e9 · 65165e9 · roflmaostc · Feb 9, 2024
2 parents c4f9521 + 84ed86c
commit 65165e9
Show file tree

Hide file tree

Showing 25 changed files with 121 additions and 121 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "RadonKA"
 uuid = "86de8297-835b-47df-b249-c04e8db91db5"
 authors = ["Felix Wechsler (roflmaostc) <fxw+git@mailbox.org>"]
-version = "0.3.2"
+version = "0.3.3"
 
 [deps]
 Atomix = "a9b6321e-bd34-4604-b9c9-b65b8de01458"

diff --git a/README.md b/README.md
@@ -1,5 +1,5 @@
 # RadonKA.jl
-A simple yet sufficiently fast Radon and inverse Radon (iradon) transform implementation using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl).
+A simple yet sufficiently fast Radon and inverse Radon (backproject) transform implementation using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl).
 It offers multithreading and CUDA support and outperforms any existing Julia Radon transforms (at least the ones we are aware of). 
 On CUDA it is faster much than Matlab and it offers the same or faster speed than ASTRA.
 
@@ -14,13 +14,13 @@ On CUDA it is faster much than Matlab and it offers the same or faster speed tha
 
 # Quick Overview
 * [x] For 2D and 3D arrays 
-* [x] parallel `radon` and `iradon` (`?RadonParallelCircle`)
-* [x] attenuated `radon` and `iradon` (see the parameter `μ`) and see this [paper](https://iopscience.iop.org/article/10.1088/0266-5611/17/1/309/meta) as reference)
+* [x] parallel `radon` and `backproject` (`?RadonParallelCircle`)
+* [x] attenuated `radon` and `backproject` (see the parameter `μ`) and see this [paper](https://iopscience.iop.org/article/10.1088/0266-5611/17/1/309/meta) as reference)
 * [x] arbitrary 2D geometries where starting and endpoint of each ray can be specified (fan beam could be a special case if this) (`?RadonFlexibleCircle`)
 * [x] different strength weighting of rays 
 * [x] based on [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl)
 * [x] tested on `CPU()` and `CUDABackend()`
-* [x] registered adjoint rules for both `radon` and `iradon`
+* [x] registered adjoint rules for both `radon` and `backproject`
 * [x] high performance however not ultra high performance. On par with ASTRA, on CUDA faster than Matlab.
 * [x] simple and extensible API
 
@@ -40,7 +40,7 @@ angles = range(0f0, 2f0π, 500)[begin:end-1]
 # 0.085398 seconds (260 allocations: 1.006 MiB)
 @time sinogram = radon(img, angles);
 # 0.127043 seconds (251 allocations: 1.036 MiB)
-@time backproject = RadonKA.iradon(sinogram, angles);
+@time backproject = RadonKA.backproject(sinogram, angles);
 
 simshow(sinogram)
 simshow(backproject)
@@ -50,10 +50,10 @@ img_c = CuArray(img)
 # 0.003363 seconds (244 CPU allocations: 18.047 KiB) (7 GPU allocations: 1007.934 KiB, 0.96% memmgmt time)
 CUDA.@time sinogram = radon(img_c, angles);
 # 0.005928 seconds (218 CPU allocations: 16.109 KiB) (7 GPU allocations: 1.012 MiB, 0.49% memmgmt time)
-CUDA.@time backproject = RadonKA.iradon(sinogram, angles);
+CUDA.@time backproject = RadonKA.backproject(sinogram, angles);
 ```
 <a  href="docs/src/assets/sinogram.png"><img src="docs/src/assets/sinogram.png"  width="300"></a>
-<a  href="docs/src/assets/radonka_iradon.png"><img src="docs/src/assets/radonka_iradon.png"  width="308"></a>
+<a  href="docs/src/assets/radonka_backproject.png"><img src="docs/src/assets/radonka_backproject.png"  width="308"></a>
 
 # Examples
 See the [documentation](https://roflmaostc.github.io/RadonKA.jl/dev/tutorial).
@@ -69,8 +69,8 @@ julia> using Pluto; Pluto.run()
 ```
 A browser should open.
 The following examples show case the ability of this package:
-* Simple `radon` and `iradon`: [Pluto notebook](examples/0_example_radon_iradon.jl)
-* Different geometries: [Pluto notebook](examples/0_example_radon_iradon.jl)
+* Simple `radon` and `backproject`: [Pluto notebook](examples/0_example_radon_backproject.jl)
+* Different geometries: [Pluto notebook](examples/0_example_radon_backproject.jl)
 * Reconstruction of a CT dataset with an optimizer: [Pluto notebook](examples/2_CT_with_optimizer.jl)
 * How this package is used in Tomographic Volumetric Additive Manufacturing (3D printing): [Pluto notebook](examples/4_Tomographic_Volumetric_Additive_Manufacturing_with_Refraction.jl)
 
@@ -93,4 +93,4 @@ There exists [Sinograms.jl](https://github.com/JuliaImageRecon/Sinograms.jl) and
 Again, no arbitrary geometries can be specified. And also no attenuated Radon transform is possible.
 
 ## Matlab
-Matlab has built-in a `radon` and `iradon` transform which is similar to our lightweight API. However, no CUDA acceleration, no 3D arrays and no attenuated Radon transform.
+Matlab has built-in a `radon` and `backproject` transform which is similar to our lightweight API. However, no CUDA acceleration, no 3D arrays and no attenuated Radon transform.
diff --git a/docs/src/assets/astra_iradon.png → docs/src/assets/astra_backproject.png b/docs/src/assets/astra_iradon.png → docs/src/assets/astra_backproject.png
diff --git a/docs/src/assets/matlab_iradon.png → docs/src/assets/matlab_backproject.png b/docs/src/assets/matlab_iradon.png → docs/src/assets/matlab_backproject.png
diff --git a/docs/src/assets/radonka_iradon.png → docs/src/assets/radonka_backproject.png b/docs/src/assets/radonka_iradon.png → docs/src/assets/radonka_backproject.png
diff --git a/docs/src/assets/sample_iradon.png → docs/src/assets/sample_backproject.png b/docs/src/assets/sample_iradon.png → docs/src/assets/sample_backproject.png
diff --git a/docs/src/benchmark.md b/docs/src/benchmark.md
@@ -30,26 +30,26 @@ Tested on a AMD Ryzen 9 5900X 12-Core Processor with 24 Threads and a NVIDIA GeF
  sinogram = radon(sample_2D, angles);
  @btime sinogram = radon($sample_2D, $angles);
  simshow(sinogram)
- @btime sample_iradon = iradon($sinogram, $angles);
+ @btime sample_backproject = backproject($sinogram, $angles);
 
  @btime CUDA.@sync sinogram_c = radon($sample_2D_c, $angles_c);
  sinogram_c = radon(sample_2D_c, angles_c);
- @btime CUDA.@sync sample_iradon_c = iradon($sinogram_c, $angles_c);
+ @btime CUDA.@sync sample_backproject_c = backproject($sinogram_c, $angles_c);
 
 
  sample_3D = randn(Float32, (512, 512, 100));
  sample_3D_c = CuArray(sample_3D)
 
  sinogram_3D = radon(sample_3D, angles);
  @btime radon($sample_3D, $angles)
- @btime iradon($sinogram_3D, $angles)
+ @btime backproject($sinogram_3D, $angles)
 
  sinogram_3D_c = radon(sample_3D_c, angles_c)
  @btime CUDA.@sync radon($sample_3D_c, $angles_c)
- @btime CUDA.@sync iradon($sinogram_3D_c, $angles_c)
+ @btime CUDA.@sync backproject($sinogram_3D_c, $angles_c)
 ```
 ![](../assets/radonka_sinogram.png)
-![](../assets/radonka_iradon.png)
+![](../assets/radonka_backproject.png)
 
 
 ## Matlab (R2023a)
@@ -65,10 +65,10 @@ R = R / max(R(:));
 imwrite(R, "/tmp/matlab_sinogram.png");
 
 tic; 
-iR = iradon(R, theta, "linear", "none");
+iR = backproject(R, theta, "linear", "none");
 toc;
 iR = iR / max(iR(:));
-imwrite(iR, "/tmp/matlab_iradon.png");
+imwrite(iR, "/tmp/matlab_backproject.png");
 
 
 
@@ -87,13 +87,13 @@ toc;
 
 tic;
 for i = 1:size(y, 1)
-    ix(i, :, :) = iradon(squeeze(y(i, :, :)), theta);
+    ix(i, :, :) = backproject(squeeze(y(i, :, :)), theta);
 end
 toc;
 ```
 
 ![](../assets/matlab_sinogram.png)
-![](../assets/matlab_iradon.png)
+![](../assets/matlab_backproject.png)
 
 ## Astra
 Some of the benchmarks did not run properly or were providing non-sense.
@@ -174,5 +174,5 @@ np.copy(rec)
 ```
 
 ![](../assets/astra_sinogram.png)
-![](../assets/astra_iradon.png)
+![](../assets/astra_backproject.png)
 
diff --git a/docs/src/functions.md b/docs/src/functions.md
@@ -5,7 +5,7 @@ radon
 
 ## IRadon
 ```@docs
-iradon
+backproject
 filtered_backprojection
 ```
 

diff --git a/docs/src/geometries.md b/docs/src/geometries.md
@@ -23,7 +23,7 @@ sinogram[1:5:end] .= 1
 
 geometry_parallel = RadonParallelCircle(N, -(N-1)÷2:(N-1)÷2)
 
-projection_parallel = iradon(sinogram, angles; geometry=geometry_parallel);
+projection_parallel = backproject(sinogram, angles; geometry=geometry_parallel);
 
 simshow(projection_parallel)
 ```
@@ -37,7 +37,7 @@ sinogram_small[1:3:end] .= 1
 
 geometry_small = RadonParallelCircle(200, -49:49)
 
-projection_small = iradon(sinogram_small, angles; geometry=geometry_small);
+projection_small = backproject(sinogram_small, angles; geometry=geometry_small);
 
 simshow(projection_small)
 ```
@@ -54,7 +54,7 @@ The second range indicates the position upon exit of the circle.
 ```julia
 geometry_fan = RadonFlexibleCircle(N, -(N-1)÷2:(N-1)÷2, range(-(N-1)÷4, (N-1)÷4, N-1))
 
-projected_fan = iradon(sinogram, angles; geometry=geometry_fan);
+projected_fan = backproject(sinogram, angles; geometry=geometry_fan);
 
 simshow(projected_fan, γ=0.01)
 ```
@@ -63,7 +63,7 @@ simshow(projected_fan, γ=0.01)
 ```julia
 geometry_extreme = RadonFlexibleCircle(N, -(N-1)÷2:(N-1)÷2, zeros((199,)))
 
-projected_extreme = iradon(sinogram, angles; geometry=geometry_extreme);
+projected_extreme = backproject(sinogram, angles; geometry=geometry_extreme);
 
 simshow(projected_extreme, γ=0.01)
 ```
@@ -74,7 +74,7 @@ For example, if in your application some rays are stronger than others you can i
 
 ```julia
 geometry_weight = RadonParallelCircle(N, -(N-1)÷2:(N-1)÷2, abs.(-(N-1)÷2:(N-1)÷2))
-projection_weight = iradon(sinogram, angles; geometry=geometry_weight);
+projection_weight = backproject(sinogram, angles; geometry=geometry_weight);
 
 simshow(projection_weight)
 ```
@@ -85,7 +85,7 @@ simshow(projection_weight)
 The ray gets some attenuation with `exp(-μ*x)` where `x` is the distance traveled to the entry point of the circle. `μ` is in units of pixel.
 
 ```julia
-projected_exp = iradon(sinogram, angles; geometry=geometry_extreme, μ=0.04);
+projected_exp = backproject(sinogram, angles; geometry=geometry_extreme, μ=0.04);
 
 simshow(projected_exp)
 ```

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,5 +1,5 @@
 # RadonKA.jl
-A simple yet sufficiently fast Radon and inverse Radon (iradon) transform implementation using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl).
+A simple yet sufficiently fast Radon and inverse Radon (backproject) transform implementation using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl).
 
 ```@raw html
 <a  href="assets/RadonKA_logo.png"><img src="assets/RadonKA_logo.png"  width="200"></a>
@@ -11,13 +11,13 @@ A simple yet sufficiently fast Radon and inverse Radon (iradon) transform implem
 
 # Quick Overview
 * [x] For 2D and 3D arrays 
-* [x] parallel `radon` and `iradon` (`?RadonParallelCircle`)
-* [x] attenuated `radon` and `iradon` (see the parameter `μ`)
+* [x] parallel `radon` and `backproject` (`?RadonParallelCircle`)
+* [x] attenuated `radon` and `backproject` (see the parameter `μ`)
 * [x] arbitrary 2D geometries where starting and endpoint of each ray can be specified (fan beam could be a special case if this) (`?RadonFlexibleCircle`)
 * [x] It is restricted to the incircle of radius `N ÷ 2 - 1` if the array has size `(N, N, N_z)`
 * [x] based on [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl)
 * [x] tested on `CPU()` and `CUDABackend`
-* [x] registered adjoint rules for both `radon` and `iradon`
+* [x] registered adjoint rules for both `radon` and `backproject`
 * [x] high performance however not ultra high performance
 * [x] simple API
 
@@ -39,20 +39,20 @@ angles = range(0f0, 2f0π, 500)[begin:end-1]
 @time sinogram = radon(img, angles);
 
 # 0.268649 seconds (147 allocations: 1.015 MiB)
-@time backproject = RadonKA.iradon(sinogram, angles);
+@time backproject = RadonKA.backproject(sinogram, angles);
 
 simshow(sinogram)
 simshow(backproject)
 ```
 
 ```@raw html
 <a  href="assets/sinogram.png"><img src="assets/sinogram.png"  width="300"></a>
-<a  href="assets/radonka_iradon.png"><img src="assets/radonka_iradon.png"  width="308"></a>
+<a  href="assets/radonka_backproject.png"><img src="assets/radonka_backproject.png"  width="308"></a>
 ```
 
 # Examples
 See either the [documentation](https://roflmaostc.github.io/RadonKA.jl/dev/tutorial).
-Otherwise, this [example](https://github.com/roflmaostc/RadonKA.jl/blob/main/examples/example_radon_iradon.jl) shows the main features, including CUDA support.
+Otherwise, this [example](https://github.com/roflmaostc/RadonKA.jl/blob/main/examples/example_radon_backproject.jl) shows the main features, including CUDA support.
 There is one tutorial about [Gradient Descent optimization](https://github.com/roflmaostc/RadonKA.jl/blob/main/examples/CT_with_optimizer.jl).
 Another one covers how the Radon transform is used in [Volumetric Additive Manufacturing](https://github.com/roflmaostc/RadonKA.jl/blob/main/examples/volumetric_printing.jl).
 

diff --git a/docs/src/tutorial.md b/docs/src/tutorial.md
@@ -24,18 +24,18 @@ angles = range(0f0, 2f0π, 500)[begin:end-1]
 ```
 ![](../assets/sinogram.png)
 
-# inverse Radon (iradon) transform
+# inverse Radon (backproject) transform
 ```julia
 # 0.268649 seconds (147 allocations: 1.015 MiB)
-@time backproject = RadonKA.iradon(sinogram, angles);
+@time backproject = RadonKA.backproject(sinogram, angles);
 
 # in Pluto or Jupyter
 simshow(sinogram)
 
 [simshow(img) simshow(backproject)]
 ```
 Left is the original sample and right the simple backprojection.
-![](../assets/sample_iradon.png)
+![](../assets/sample_backproject.png)
 
 
 # Filtered Backprojection

diff --git a/examples/0_example_radon_iradon.jl b/examples/0_example_radon_iradon.jl
@@ -103,16 +103,16 @@ simshow(Array(sinogram_c[:, :, i_z]))
 md"# IRadon Transform"
 
 # ╔═╡ 7e27da5a-1b04-4d4c-8c62-eaffa7f4f9ce
-@time backproject = RadonKA.iradon(sinogram, angles);
+@time backproject = RadonKA.backproject(sinogram, angles);
 
 # ╔═╡ 4e367035-eb2f-4dfa-9646-7c182a111c49
-md"Use this slider to add more and more angles to the iradon transform"
+md"Use this slider to add more and more angles to the backproject transform"
 
 # ╔═╡ b9bf49a0-7320-4269-9a6a-ac2533ab5fde
 @bind angle_limit Slider(1:size(sinogram, 2), default=size(sinogram, 2), show_value=true)
 
 # ╔═╡ 93a7ab4a-b2dc-4fc2-bf69-66e6d615103f
-CUDA.@time CUDA.@sync backproject_cu = RadonKA.iradon(sinogram_c[:, begin:angle_limit, :], togoc(angles[begin:angle_limit]));
+CUDA.@time CUDA.@sync backproject_cu = RadonKA.backproject(sinogram_c[:, begin:angle_limit, :], togoc(angles[begin:angle_limit]));
 
 # ╔═╡ 52a86ed8-4504-4d9e-9ea6-6aeaf8540406
 @bind i_z3 Slider(1:size(sinogram, 3), show_value=true)

diff --git a/examples/1_documentation_different_geometries.jl b/examples/1_documentation_different_geometries.jl
@@ -38,7 +38,7 @@ end
 geometry_parallel = RadonParallelCircle(N, -(N-1)÷2:(N-1)÷2)
 
 # ╔═╡ 07bec511-7802-45e4-b331-0d35e8f850c1
-projection_parallel = iradon(sinogram, angles; geometry=geometry_parallel);
+projection_parallel = backproject(sinogram, angles; geometry=geometry_parallel);
 
 # ╔═╡ 9e5a58be-0de5-4820-bc3b-6dff931271a9
 simshow(projection_parallel)
@@ -56,7 +56,7 @@ end
 geometry_small = RadonParallelCircle(200, -49:49)
 
 # ╔═╡ 27d5aac8-2528-46f2-a0dd-91c3fe5d275c
-projection_small = iradon(sinogram_small, angles; geometry=geometry_small);
+projection_small = backproject(sinogram_small, angles; geometry=geometry_small);
 
 # ╔═╡ 29c9d1ff-694c-4fff-b551-836cc9eaf347
 simshow(projection_small)
@@ -68,7 +68,7 @@ md"# Similar to fan Beam Tomography"
 geometry_fan = RadonFlexibleCircle(N, -(N-1)÷2:(N-1)÷2, range(-(N-1)÷4, (N-1)÷4, N-1))
 
 # ╔═╡ 37d760fa-74e6-47d1-b8e6-3315c1747b4c
-projected_fan = iradon(sinogram, angles; geometry=geometry_fan);
+projected_fan = backproject(sinogram, angles; geometry=geometry_fan);
 
 # ╔═╡ 878121c5-4ad5-477b-88c7-f53df7510052
 simshow(projected_fan, γ=0.01)
@@ -80,7 +80,7 @@ md"# Extreme fan Beam Tomography"
 geometry_extreme = RadonFlexibleCircle(N, -(N-1)÷2:(N-1)÷2, zeros((199,)))
 
 # ╔═╡ 0756e11b-35ca-4eef-be48-52b8b5c098cd
-projected_extreme = iradon(sinogram, angles; geometry=geometry_extreme);
+projected_extreme = backproject(sinogram, angles; geometry=geometry_extreme);
 
 # ╔═╡ 1e22032d-d57d-42d3-a9c5-9f2e94531346
 simshow(projected_extreme, γ=0.01)
@@ -94,7 +94,7 @@ For example, if in your application some rays are stronger than others you can i
 geometry_weight = RadonParallelCircle(N, -(N-1)÷2:(N-1)÷2, abs.(-(N-1)÷2:(N-1)÷2))
 
 # ╔═╡ 45b82171-c581-4fcf-9695-bc51464a2172
-projection_weight = iradon(sinogram, angles; geometry=geometry_weight);
+projection_weight = backproject(sinogram, angles; geometry=geometry_weight);
 
 # ╔═╡ bbb51355-fa72-4064-b10e-46e29f4b2809
 simshow(projection_weight)
@@ -105,7 +105,7 @@ The ray gets some attenuation with `exp(-μ*x)` where `x` is the distance travel
 "
 
 # ╔═╡ b11cb860-3fab-4fc9-8921-bef32a8b7c12
-projected_exp = iradon(sinogram, angles; geometry=geometry_extreme, μ=0.04);
+projected_exp = backproject(sinogram, angles; geometry=geometry_extreme, μ=0.04);
 
 # ╔═╡ 51561641-67a3-46d4-ae87-b896dc351a60
 simshow(projected_exp)
@@ -135,10 +135,10 @@ geometry_extreme2 = RadonFlexibleCircle(N2, -(N2-1)÷2:(N2-1)÷2, zeros((N2-1,))
 simshow(sg_img)
 
 # ╔═╡ 29c25bd9-6359-489d-b0a3-ea767f832a02
-img_iradon = iradon(sg_img, angles2, geometry=geometry_extreme2, μ=0.008);
+img_backproject = backproject(sg_img, angles2, geometry=geometry_extreme2, μ=0.008);
 
 # ╔═╡ 940d3b8b-1797-45f1-b493-02592a39eb19
-simshow(img_iradon)
+simshow(img_backproject)
 
 # ╔═╡ c7dcc341-0afe-4913-b0a8-1869d8f819b4
 md"# How to reconstruct?

diff --git a/examples/2_CT_with_optimizer.jl b/examples/2_CT_with_optimizer.jl
@@ -73,10 +73,10 @@ md"""# Simple Backprojection"""
 img_bp = filtered_backprojection(measurement, angles);
 
 # ╔═╡ 49e59001-0e8d-4872-ae50-47c38486b3fd
-img_iradon = iradon(measurement, angles);
+img_backproject = backproject(measurement, angles);
 
 # ╔═╡ 6fac5606-2350-4f22-9f35-120936114d5a
-[simshow(img_bp) simshow(img_iradon)]
+[simshow(img_bp) simshow(img_backproject)]
 
 # ╔═╡ 1feccdec-cc35-4cc8-9a76-0e0e99bc7be3
 md"# Optimization with gradient descent