Paper for JOSS (#200)

* add draft paper.md * update * update * ci: build the pdf please * update affiliations * typo * Add model * update * format * format table * update * unindent table * update with bibliography * move paper in doc * update * update * Apply suggestions from code review Co-authored-by: Chaithya G R <[email protected]> * add lnk to PR * update action * Fill-in affiliation and fix name * fix name * Apply suggestions from code review Co-authored-by: Guillaume Daval-Frérot <[email protected]> * update authors order * smaller figure * Correct names * add orcid Co-authored-by: Chaithya G R <[email protected]> * apply suggestion from guillaume Co-authored-by: Guillaume Daval-Frérot <[email protected]> * Update paper.md * Update paper.md * Update paper.bib add pysap-mri paper * Update paper.md * Update paper.bib * Update paper.md --------- Co-authored-by: Chaithya G R <[email protected]> Co-authored-by: Matteo Cencini <[email protected]> Co-authored-by: LenaOudjman <[email protected]> Co-authored-by: Guillaume Daval-Frérot <[email protected]> Co-authored-by: Asma TANABENE <[email protected]>
mind-inria · Nov 1, 2024 · 19e117b · 19e117b · github-actions · Nov 1, 2024
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diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml
@@ -0,0 +1,42 @@
+name: Draft PDF
+on: 
+  push:
+    paths:
+      - docs/paper-joss/*
+      - .github/workflows/draft-pdf.yml*
+
+permissions:
+  pull-requests: write
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: docs/paper-joss/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: docs/paper-joss/paper.pdf
+
+  link:
+    needs: paper # make sure the artifacts are uploaded first
+    runs-on: ubuntu-latest
+    permissions:
+        contents: write # for commenting on your commit
+        pull-requests: write # for commenting on your pr
+    steps:
+      - uses: beni69/artifact-link@v1
+        with:
+          token: ${{ github.token }}
diff --git a/docs/paper-joss/paper.bib b/docs/paper-joss/paper.bib
@@ -0,0 +1,154 @@
+@unpublished{shih_cufinufft_2021,
+  title = {{{cuFINUFFT}}: A Load-Balanced {{GPU}} Library for General-Purpose Nonuniform {{FFTs}}},
+  shorttitle = {{{cuFINUFFT}}},
+  author = {Shih, Yu-hsuan and Wright, Garrett and Andén, Joakim and Blaschke, Johannes and Barnett, Alex H.},
+  date = {2021-03-25},
+  eprint = {2102.08463},
+  eprinttype = {arXiv},
+  eprintclass = {cs, eess, math},
+  url = {http://arxiv.org/abs/2102.08463},
+  urldate = {2021-12-10},
+  abstract = {Nonuniform fast Fourier transforms dominate the computational cost in many applications including image reconstruction and signal processing. We thus present a generalpurpose GPU-based CUDA library for type 1 (nonuniform to uniform) and type 2 (uniform to nonuniform) transforms in dimensions 2 and 3, in single or double precision. It achieves high performance for a given user-requested accuracy, regardless of the distribution of nonuniform points, via cache-aware point reordering, and load-balanced blocked spreading in shared memory. At low accuracies, this gives on-GPU throughputs around 109 nonuniform points per second, and (even including hostdevice transfer) is typically 4–10× faster than the latest parallel CPU code FINUFFT (at 28 threads). It is competitive with two established GPU codes, being up to 90× faster at high accuracy and/or type 1 clustered point distributions. Finally we demonstrate a 5–12× speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10−12 accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU.},
+  langid = {english},
+  keywords = {{Computer Science - Distributed, Parallel, and Cluster Computing},Computer Science - Mathematical Software,Electrical Engineering and Systems Science - Signal Processing,Mathematics - Numerical Analysis,No DOI found},
+  file = {/volatile/home/pc266769/Zotero/storage/K5LLWXZE/shih_cufinufft_2021.pdf}
+}
+
+@inproceedings{uecker_berkley_2015,
+  title = {Berkley Advanced Reconstruction Toolbox},
+  shorttitle = {Mrirecon/Bart},
+  booktitle = {Proc. {{Intl}}. {{Soc}}. {{Mag}}. {{Reson}}. {{Med}}. 23},
+  author = {Uecker, Martin and Ong, Frank and Tamir, J},
+  date = {2015},
+  location = {Toronto},
+  url = {https://zenodo.org/records/10277939},
+  urldate = {2023-12-19},
+  keywords = {No DOI found},
+  file = {/volatile/home/pc266769/Zotero/storage/LIMD2P5S/10277939.html}
+}
+
+@inproceedings{ong_frank_sigpy_2019,
+  title = {{{SigPy}}: {{A Python Package}} for {{High Performance Iterative Reconstruction}}},
+  booktitle = {{{ISMRM}} 2019},
+  author = {{Ong Frank} and {Lustig Michael}},
+  date = {2019},
+  abstract = {We present SigPy, a Python package designed for high performance iterative reconstruction. Its main features include: - A unified CPU and GPU Python interface to signal processing functions, including convolution, FFT, NUFFT, wavelet transform, and thresholding functions. - Convenient classes (Linop, Prox, Alg, App) to build more complicated iterative reconstruction algorithms. - Commonly used MRI reconstruction methods as Apps, including SENSE, L1-wavelet regularized reconstruction, total-variation regularized reconstruction, and JSENSE. - MRI-specific functions, including poisson-disc sampling, ESPIRiT calibration, and non-Cartesian preconditioners. - Simple installation via pip and conda.},
+  eventtitle = {{{ISMRM}}},
+  keywords = {No DOI found}
+}
+
+@article{sutton_fast_2003,
+  title = {Fast, Iterative Image Reconstruction for {{MRI}} in the Presence of Field Inhomogeneities},
+  author = {Sutton, B.P. and Noll, D.C. and Fessler, J.A.},
+  date = {2003-02},
+  journaltitle = {IEEE Transactions on Medical Imaging},
+  volume = {22},
+  number = {2},
+  pages = {178--188},
+  issn = {1558-254X},
+  doi = {10.1109/TMI.2002.808360},
+  abstract = {In magnetic resonance imaging, magnetic field inhomogeneities cause distortions in images that are reconstructed by conventional fast Fourier transform (FFT) methods. Several noniterative image reconstruction methods are used currently to compensate for field inhomogeneities, but these methods assume that the field map that characterizes the off-resonance frequencies is spatially smooth. Recently, iterative methods have been proposed that can circumvent this assumption and provide improved compensation for off-resonance effects. However, straightforward implementations of such iterative methods suffer from inconveniently long computation times. This paper describes a tool for accelerating iterative reconstruction of field-corrected MR images: a novel time-segmented approximation to the MR signal equation. We use a min-max formulation to derive the temporal interpolator. Speedups of around 60 were achieved by combining this temporal interpolator with a nonuniform fast Fourier transform with normalized root mean squared approximation errors of 0.07\%. The proposed method provides fast, accurate, field-corrected image reconstruction even when the field map is not smooth.},
+  eventtitle = {{{IEEE Transactions}} on {{Medical Imaging}}},
+  keywords = {Biomedical engineering,Frequency,Image reconstruction,Image segmentation,Iterative methods,Magnetic fields,Magnetic resonance imaging,Optical imaging,Reconstruction algorithms,Spirals},
+  file = {/volatile/home/pc266769/Zotero/storage/8XA5ZU44/sutton_fast_2003.pdf}
+}
+
+@article{fessler_nonuniform_2003,
+  title = {Nonuniform Fast Fourier Transforms Using Min-Max Interpolation},
+  author = {Fessler, J.A. and Sutton, B.P.},
+  date = {2003-02},
+  journaltitle = {IEEE Transactions on Signal Processing},
+  shortjournal = {IEEE Trans. Signal Process.},
+  volume = {51},
+  number = {2},
+  pages = {560--574},
+  issn = {1053-587X},
+  doi = {10.1109/tsp.2002.807005},
+  url = {http://ieeexplore.ieee.org/document/1166689/},
+  urldate = {2021-05-03},
+  abstract = {The FFT is used widely in signal processing for efficient computation of the Fourier transform (FT) of finitelength signals over a set of uniformly-spaced frequency locations. However, in many applications, one requires nonuniform sampling in the frequency domain, i.e., a nonuniform FT. Several papers have described fast approximations for the nonuniform FT based on interpolating an oversampled FFT. This paper presents an interpolation method for the nonuniform FT that is optimal in the min-max sense of minimizing the worst-case approximation error over all signals of unit norm. The proposed method easily generalizes to multidimensional signals. Numerical results show that the min-max approach provides substantially lower approximation errors than conventional interpolation methods. The min-max criterion is also useful for optimizing the parameters of interpolation kernels such as the Kaiser-Bessel function.},
+  langid = {english},
+  file = {/volatile/home/pc266769/Zotero/storage/4NDF5834/fessler_nonuniform_2003.pdf}
+}
+
+@article{wang_efficient_2023,
+  title = {Efficient Approximation of {{Jacobian}} Matrices Involving a Non-Uniform Fast {{Fourier}} Transform ({{NUFFT}})},
+  author = {Wang, Guanhua and Fessler, Jeffrey A.},
+  date = {2023},
+  journaltitle = {IEEE Transactions on Computational Imaging},
+  shortjournal = {IEEE Trans. Comput. Imaging},
+  volume = {9},
+  eprint = {2111.02912},
+  eprinttype = {arXiv},
+  eprintclass = {eess},
+  pages = {43--54},
+  issn = {2333-9403, 2334-0118, 2573-0436},
+  doi = {10.1109/TCI.2023.3240081},
+  url = {http://arxiv.org/abs/2111.02912},
+  urldate = {2024-04-11},
+  abstract = {There is growing interest in learning k-space sampling patterns for MRI using optimization approaches [1], [2], [3], [4]. For non-Cartesian sampling patterns, reconstruction methods typically involve non-uniform FFT (NUFFT) operations. A typical NUFFT method contains frequency domain interpolation using Kaiser-Bessel kernel values that are retrieved by nearest neighbor look-up in a finely tabulated kernel [5]. That look-up operation is not differentiable with respect to the sampling pattern, complicating auto-differentiation routines for backpropagation (stochastic gradient descent) for sampling pattern optimization. This paper describes an efficient and accurate approach for computing approximate gradients with respect to the sampling pattern for learning k-space sampling. Various numerical experiments validate the accuracy of the proposed approximation. We also showcase the trajectories optimized for different iterative reconstruction algorithms, including smooth convex regularized reconstruction and compressed sensing-based reconstruction.},
+  langid = {english},
+  keywords = {Electrical Engineering and Systems Science - Image and Video Processing,Electrical Engineering and Systems Science - Signal Processing},
+  file = {/volatile/home/pc266769/Zotero/storage/HU6FNVQU/Wang et Fessler - 2023 - Efficient approximation of Jacobian matrices invol.pdf}
+}
+@inproceedings{knoll_gpunufft_2014,
+  title={gpuNUFFT - An Open Source GPU Library for 3D Regridding with Direct Matlab Interface},
+  author={Florian Knoll and Andreas Schwarzl and Clemens Diwoky and Daniel K. Sodickson},
+  year={2014},
+  url={https://api.semanticscholar.org/CorpusID:53652346}
+}
+@inproceedings{muckley_torchkbnufft_2020,
+  author = {M. J. Muckley and R. Stern and T. Murrell and F. Knoll},
+  title = {{TorchKbNufft}: A High-Level, Hardware-Agnostic Non-Uniform Fast {Fourier} Transform},
+  booktitle = {ISMRM Workshop on Data Sampling \& Image Reconstruction},
+  year = 2020,
+  note = {Source code available at https://github.com/mmuckley/torchkbnufft},
+}
+@inproceedings{comby_snake-fmri_2024,
+  ids = {Comby_Vignaud_Ciuciu_2024},
+  title = {{{SNAKE-fMRI}}: {{A}} Modular {{fMRI}} Simulator from the Space-Time Domain to k-Space Data and Back},
+  booktitle = {{{ISMRM}} Annual Meeting, (in Press)},
+  author = {Comby, P.-A. and Vignaud, A. and Ciuciu, P.},
+  date = {2024},
+  location = {Singapore},
+  keywords = {No DOI found}
+}
+
+@article{farrens_pysap_2020,
+  title = {{{PySAP}}: {{Python Sparse Data Analysis Package}} for Multidisciplinary Image Processing},
+  shorttitle = {{{PySAP}}},
+  author = {Farrens, S. and Grigis, A. and El Gueddari, L. and Ramzi, Z. and G.r., Chaithya and Starck, S. and Sarthou, B. and Cherkaoui, H. and Ciuciu, P. and Starck, J. -L.},
+  date = {2020-07-01},
+  journaltitle = {Astronomy and Computing},
+  shortjournal = {Astronomy and Computing},
+  volume = {32},
+  pages = {100402},
+  issn = {2213-1337},
+  doi = {10.1016/j.ascom.2020.100402},
+  url = {https://www.sciencedirect.com/science/article/pii/S2213133720300561},
+  urldate = {2024-09-27},
+  abstract = {We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. In this paper we present the features available in PySAP and provide practical demonstrations on astrophysical and magnetic resonance imaging data.},
+  keywords = {Convex optimisation,Image processing,Open-source software,Reconstruction},
+  file = {/volatile/home/pc266769/Zotero/storage/X4725MSA/Farrens et al. - 2020 - PySAP Python Sparse Data Analysis Package for multidisciplinary image processing.pdf}
+}
+
+@software{tachella_deepinverse_2023,
+  title = {{{DeepInverse}}: {{A}} Deep Learning Framework for Inverse Problems in Imaging},
+  shorttitle = {{{DeepInverse}}},
+  author = {Tachella, Julian and Chen, Dongdong and Hurault, Samuel and Terris, Matthieu and Wang, Andrew},
+  date = {2023-06},
+  doi = {10.5281/zenodo.7982256},
+  url = {https://github.com/deepinv/deepinv},
+  urldate = {2024-09-27},
+  abstract = {PyTorch library for solving imaging inverse problems using deep learning},
+  version = {latest}
+}
+
+@inproceedings{gueddari_pysap-mri_2020,
+  ids = {gueddari_pysap-mri_2020-1,gueddari_pysap-mri_2020-2},
+  title = {{{PySAP-MRI}}: A Python Package for {{MR}} Image Reconstruction},
+  booktitle = {{{ISMRM}} Workshop on Data Sampling and Image Reconstruction},
+  author = {Gueddari, Loubna and Gr, Chaithya and Ramzi, Zaccharie and Farrens, Samuel and Starck, Sophie and Grigis, Antoine and Starck, Jean-Luc and Ciuciu, Philippe},
+  year = {2020},
+}
+