diff --git a/joss.05877/10.21105.joss.05877.crossref.xml b/joss.05877/10.21105.joss.05877.crossref.xml new file mode 100644 index 0000000000..c2f519491c --- /dev/null +++ b/joss.05877/10.21105.joss.05877.crossref.xml @@ -0,0 +1,613 @@ + + + + 20240502T100433-62c9fbfb5f26d615e340358cc5a7c64e0df1fe92 + 20240502100433 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 05 + 2024 + + + 9 + + 97 + + + + GIRFReco.jl: An Open-Source Pipeline for Spiral +Magnetic Resonance Image (MRI) Reconstruction in Julia + + + + Alexander + Jaffray + https://orcid.org/0000-0002-9571-1838 + + + Zhe + Wu + https://orcid.org/0000-0002-2079-5977 + + + S. Johanna + Vannesjo + https://orcid.org/0000-0003-2432-4192 + + + Kâmil + Uludağ + https://orcid.org/0000-0002-2813-5930 + + + Lars + Kasper + https://orcid.org/0000-0001-7667-603X + + + + 05 + 02 + 2024 + + + 5877 + + + 10.21105/joss.05877 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.11075548 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5877 + + + + 10.21105/joss.05877 + https://joss.theoj.org/papers/10.21105/joss.05877 + + + https://joss.theoj.org/papers/10.21105/joss.05877.pdf + + + + + + Comparison of gradient impulse response +functions measured with a dynamic field camera and a phantom-based +technique + Graedel + 2017 + Graedel, N. N., Hurley, S. A., Clare, +S., Miller, K. L., Pruessmann, K. P., & Vannesjo, S. J. (2017). +Comparison of gradient impulse response functions measured with a +dynamic field camera and a phantom-based technique. +378. + + + Open-source model-based reconstruction in +Julia: A pipeline for spiral diffusion imaging + Jaffray + Proc. Intl. Soc. Mag. Reson. Med. +30 + 10.58530/2022/2435 + 2022 + Jaffray, A., Wu, Z., Uludağ, K., +& Kasper, L. (2022). Open-source model-based reconstruction in +Julia: A pipeline for spiral diffusion imaging. Proc. Intl. Soc. Mag. +Reson. Med. 30, 2435. +https://doi.org/10.58530/2022/2435 + + + MR System Stability and Quality Control using +Gradient Impulse Response Functions (GIRF) + Wu + Proc. Intl. Soc. Mag. Reson. Med. +30 + 10.58530/2022/0641 + 2022 + Wu, Z., Jaffray, A., Vannesjo, S. J., +Uludağ, K., & Kasper, L. (2022). MR System Stability and Quality +Control using Gradient Impulse Response Functions (GIRF). Proc. Intl. +Soc. Mag. Reson. Med. 30, 0641. +https://doi.org/10.58530/2022/0641 + + + Simple method for MR gradient system +characterization and k-space trajectory estimation + Addy + Magnetic Resonance in +Medicine + 1 + 68 + 10.1002/mrm.23217 + 1522-2594 + 2012 + Addy, N. O., Wu, H. H., & +Nishimura, D. G. (2012). Simple method for MR gradient system +characterization and k-space trajectory estimation. Magnetic Resonance +in Medicine, 68(1), 120–129. +https://doi.org/10.1002/mrm.23217 + + + Gradient system characterization by impulse +response measurements with a dynamic field camera: Gradient System +Characterization with a Dynamic Field Camera + Vannesjo + Magnetic Resonance in +Medicine + 2 + 69 + 10.1002/mrm.24263 + 2013 + Vannesjo, S. J., Haeberlin, M., +Kasper, L., Pavan, M., Wilm, B. J., Barmet, C., & Pruessmann, K. P. +(2013). Gradient system characterization by impulse response +measurements with a dynamic field camera: Gradient System +Characterization with a Dynamic Field Camera. Magnetic Resonance in +Medicine, 69(2), 583–593. +https://doi.org/10.1002/mrm.24263 + + + On the signal‐to‐noise ratio benefit of +spiral acquisition in diffusion MRI + Lee + Magnetic Resonance in +Medicine + 4 + 85 + 10.1002/mrm.28554 + 0740-3194 + 2021 + Lee, Y., Wilm, B. J., Brunner, D. O., +Gross, S., Schmid, T., Nagy, Z., & Pruessmann, K. P. (2021). On the +signal‐to‐noise ratio benefit of spiral acquisition in diffusion MRI. +Magnetic Resonance in Medicine, 85(4), 1924–1937. +https://doi.org/10.1002/mrm.28554 + + + Spiral imaging: A critical +appraisal + Block + Journal of Magnetic Resonance +Imaging + 6 + 21 + 10.1002/jmri.20320 + 1053-1807 + 2005 + Block, K. T., & Frahm, J. (2005). +Spiral imaging: A critical appraisal. Journal of Magnetic Resonance +Imaging, 21(6), 657–668. +https://doi.org/10.1002/jmri.20320 + + + Fast, iterative image reconstruction for MRI +in the presence of field inhomogeneities + Sutton + IEEE Transactions on Medical +Imaging + 2 + 22 + 10.1109/TMI.2002.808360 + 0278-0062 + 2003 + Sutton, B. P., Noll, D. C., & +Fessler, J. A. (2003). Fast, iterative image reconstruction for MRI in +the presence of field inhomogeneities. IEEE Transactions on Medical +Imaging, 22(2), 178–188. +https://doi.org/10.1109/TMI.2002.808360 + + + Higher order reconstruction for MRI in the +presence of spatiotemporal field perturbations: Higher Order +Reconstruction for MRI + Wilm + Magnetic Resonance in +Medicine + 6 + 65 + 10.1002/mrm.22767 + 2011 + Wilm, B. J., Barmet, C., Pavan, M., +& Pruessmann, K. P. (2011). Higher order reconstruction for MRI in +the presence of spatiotemporal field perturbations: Higher Order +Reconstruction for MRI. Magnetic Resonance in Medicine, 65(6), +1690–1701. https://doi.org/10.1002/mrm.22767 + + + Diffusion MRI with concurrent magnetic field +monitoring: Diffusion MRI with Concurrent Magnetic Field +Monitoring + Wilm + Magnetic Resonance in +Medicine + 4 + 74 + 10.1002/mrm.25827 + 2015 + Wilm, B. J., Nagy, Z., Barmet, C., +Vannesjo, S. J., Kasper, L., Haeberlin, M., Gross, S., Dietrich, B. E., +Brunner, D. O., Schmid, T., & Pruessmann, K. P. (2015). Diffusion +MRI with concurrent magnetic field monitoring: Diffusion MRI with +Concurrent Magnetic Field Monitoring. Magnetic Resonance in Medicine, +74(4), 925–933. +https://doi.org/10.1002/mrm.25827 + + + Advances in sensitivity encoding with +arbitrary k -space trajectories: SENSE With Arbitrary k -Space +Trajectories + Pruessmann + Magnetic Resonance in +Medicine + 4 + 46 + 10.1002/mrm.1241 + 2001 + Pruessmann, K. P., Weiger, M., +Börnert, P., & Boesiger, P. (2001). Advances in sensitivity encoding +with arbitrary k -space trajectories: SENSE With Arbitrary k -Space +Trajectories. Magnetic Resonance in Medicine, 46(4), 638–651. +https://doi.org/10.1002/mrm.1241 + + + Julia: A Fresh Approach to Numerical +Computing + Bezanson + SIAM Review + 1 + 59 + 10.1137/141000671 + 0036-1445 + 2017 + Bezanson, J., Edelman, A., Karpinski, +S., & Shah, V. B. (2017). Julia: A Fresh Approach to Numerical +Computing. SIAM Review, 59(1), 65–98. +https://doi.org/10.1137/141000671 + + + ISMRM Raw data format: A proposed standard +for MRI raw datasets + Inati + Magnetic Resonance in +Medicine + 1 + 77 + 10.1002/mrm.26089 + 0740-3194 + 2017 + Inati, S. J., Naegele, J. D., Zwart, +N. R., Roopchansingh, V., Lizak, M. J., Hansen, D. C., Liu, C., +Atkinson, D., Kellman, P., Kozerke, S., Xue, H., Campbell‐Washburn, A. +E., Sørensen, T. S., & Hansen, M. S. (2017). ISMRM Raw data format: +A proposed standard for MRI raw datasets. Magnetic Resonance in +Medicine, 77(1), 411–421. +https://doi.org/10.1002/mrm.26089 + + + Image reconstruction using a gradient impulse +response model for trajectory prediction: GIRF-Based Image +Reconstruction + Vannesjo + Magnetic Resonance in +Medicine + 1 + 76 + 10.1002/mrm.25841 + 2016 + Vannesjo, S. J., Graedel, N. N., +Kasper, L., Gross, S., Busch, J., Haeberlin, M., Barmet, C., & +Pruessmann, K. P. (2016). Image reconstruction using a gradient impulse +response model for trajectory prediction: GIRF-Based Image +Reconstruction. Magnetic Resonance in Medicine, 76(1), 45–58. +https://doi.org/10.1002/mrm.25841 + + + Gadgetron: An open source framework for +medical image reconstruction: Gadgetron + Hansen + Magnetic Resonance in +Medicine + 6 + 69 + 10.1002/mrm.24389 + 2013 + Hansen, M. S., & Sørensen, T. S. +(2013). Gadgetron: An open source framework for medical image +reconstruction: Gadgetron. Magnetic Resonance in Medicine, 69(6), +1768–1776. https://doi.org/10.1002/mrm.24389 + + + Mrirecon/bart: Version 0.8.00 + Blumenthal + 10.5281/ZENODO.592960 + 2022 + Blumenthal, M., Holme, C., Roeloffs, +V., Rosenzweig, S., Schaten, P., Scholand, N., Tamir, J., Wang, X., +& Uecker, M. (2022). Mrirecon/bart: Version 0.8.00. Zenodo. +https://doi.org/10.5281/ZENODO.592960 + + + Open‐source MR imaging and reconstruction +workflow + Veldmann + Magnetic Resonance in +Medicine + 6 + 88 + 10.1002/mrm.29384 + 0740-3194 + 2022 + Veldmann, M., Ehses, P., Chow, K., +Nielsen, J., Zaitsev, M., & Stöcker, T. (2022). Open‐source MR +imaging and reconstruction workflow. Magnetic Resonance in Medicine, +88(6), 2395–2407. +https://doi.org/10.1002/mrm.29384 + + + MRIReco.jl: An MRI reconstruction framework +written in Julia + Knopp + Magnetic Resonance in +Medicine + 3 + 86 + 10.1002/mrm.28792 + 0740-3194 + 2021 + Knopp, T., & Grosser, M. (2021). +MRIReco.jl: An MRI reconstruction framework written in Julia. Magnetic +Resonance in Medicine, 86(3), 1633–1646. +https://doi.org/10.1002/mrm.28792 + + + ESPIRiT-an eigenvalue approach to +autocalibrating parallel MRI: Where SENSE meets GRAPPA + Uecker + Magnetic Resonance in +Medicine + 3 + 71 + 10.1002/mrm.24751 + 2014 + Uecker, M., Lai, P., Murphy, M. J., +Virtue, P., Elad, M., Pauly, J. M., Vasanawala, S. S., & Lustig, M. +(2014). ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: +Where SENSE meets GRAPPA. Magnetic Resonance in Medicine, 71(3), +990–1001. https://doi.org/10.1002/mrm.24751 + + + Iterative Off-Resonance and Signal Decay +Estimation and Correction for Multi-Echo MRI + Knopp + IEEE Transactions on Medical +Imaging + 3 + 28 + 10.1109/TMI.2008.2006526 + 0278-0062 + 2009 + Knopp, T., Eggers, H., Dahnke, H., +Prestin, J., & Senegas, J. (2009). Iterative Off-Resonance and +Signal Decay Estimation and Correction for Multi-Echo MRI. IEEE +Transactions on Medical Imaging, 28(3), 394–404. +https://doi.org/10.1109/TMI.2008.2006526 + + + Regularized Field Map Estimation in +MRI + Funai + IEEE Transactions on Medical +Imaging + 10 + 27 + 10.1109/TMI.2008.923956 + 0278-0062 + 2008 + Funai, A. K., Fessler, J. A., Yeo, D. +T. B., Olafsson, V. T., & Noll, D. C. (2008). Regularized Field Map +Estimation in MRI. IEEE Transactions on Medical Imaging, 27(10), +1484–1494. +https://doi.org/10.1109/TMI.2008.923956 + + + Correction of B _{\textrm{0}} eddy current +effects in spiral MRI + Robison + Magnetic Resonance in +Medicine + 4 + 81 + 10.1002/mrm.27583 + 0740-3194 + 2019 + Robison, R. K., Li, Z., Wang, D., +Ooi, M. B., & Pipe, J. G. (2019). Correction of B _{\textrm{0}} eddy +current effects in spiral MRI. Magnetic Resonance in Medicine, 81(4), +2501–2513. https://doi.org/10.1002/mrm.27583 + + + Efficient regularized field map estimation in +3D MRI + Lin + IEEE Transactions on Computational +Imaging + 6 + 10.1109/TCI.2020.3031082 + 2333-9403 + 2020 + Lin, C. Y., & Fessler, J. A. +(2020). Efficient regularized field map estimation in 3D MRI. IEEE +Transactions on Computational Imaging, 6, 1451–1458. +https://doi.org/10.1109/TCI.2020.3031082 + + + NIfTI Data Format + NIfTI + 2003 + NIfTI. (2003, September 2). NIfTI +Data Format [National Institute of Mental Health Website]. Neuroimaging +Informatics Technology Initiative. +https://nifti.nimh.nih.gov/ + + + MRI-gradient / GIRF + Vannesjo + 2020 + Vannesjo, S. J., & Graedel, N. N. +(2020). MRI-gradient / GIRF. MRI-gradient. +https://github.com/MRI-gradient/GIRF + + + Concomitant gradient terms in phase contrast +MR: Analysis and correction + Bernstein + Magnetic Resonance in +Medicine + 2 + 39 + 10.1002/mrm.1910390218 + 1522-2594 + 1998 + Bernstein, M. A., Zhou, X. J., +Polzin, J. A., King, K. F., Ganin, A., Pelc, N. J., & Glover, G. H. +(1998). Concomitant gradient terms in phase contrast MR: Analysis and +correction. Magnetic Resonance in Medicine, 39(2), 300–308. +https://doi.org/10.1002/mrm.1910390218 + + + Data Supplement: Open-source model-based +reconstruction in Julia (ISMRM 2022) + Jaffray + 10.5281/zenodo.6510021 + 2022 + Jaffray, A., Wu, Z. (Tim)., Uludağ, +K., & Kasper, L. (2022). Data Supplement: Open-source model-based +reconstruction in Julia (ISMRM 2022) [Data set]. Zenodo. +https://doi.org/10.5281/zenodo.6510021 + + + Single-shot spiral imaging at 7 +T + Engel + Magnetic Resonance in +Medicine + 5 + 80 + 10.1002/mrm.27176 + 1522-2594 + 2018 + Engel, M., Kasper, L., Barmet, C., +Schmid, T., Vionnet, L., Wilm, B., & Pruessmann, K. P. (2018). +Single-shot spiral imaging at 7 T. Magnetic Resonance in Medicine, +80(5), 1836–1846. +https://doi.org/10.1002/mrm.27176 + + + Feasibility of spiral diffusion imaging on a +clinical 3T MR system + Kasper + Proc. Intl. Soc. Mag. Reson. Med. +31 + 2023 + Kasper, L., Wu, Z., Jaffray, A., +Kashyap, S., & Uludağ, K. (2023). Feasibility of spiral diffusion +imaging on a clinical 3T MR system. Proc. Intl. Soc. Mag. Reson. Med. +31, 4164. +https://index.mirasmart.com/ISMRM2023/PDFfiles/4164.html + + + Feasibility of spiral fMRI based on an LTI +gradient model + Graedel + NeuroImage + 245 + 10.1016/j.neuroimage.2021.118674 + 1053-8119 + 2021 + Graedel, N. N., Kasper, L., Engel, +M., Nussbaum, J., Wilm, B. J., Pruessmann, K. P., & Vannesjo, S. J. +(2021). Feasibility of spiral fMRI based on an LTI gradient model. +NeuroImage, 245, 118674. +https://doi.org/10.1016/j.neuroimage.2021.118674 + + + Rapid anatomical brain imaging using spiral +acquisition and an expanded signal model + Kasper + NeuroImage + 168 + 10.1016/j.neuroimage.2017.07.062 + 1053-8119 + 2018 + Kasper, L., Engel, M., Barmet, C., +Haeberlin, M., Wilm, B. J., Dietrich, B. E., Schmid, T., Gross, S., +Brunner, D. O., Stephan, K. E., & Pruessmann, K. P. (2018). Rapid +anatomical brain imaging using spiral acquisition and an expanded signal +model. NeuroImage, 168, 88–100. +https://doi.org/10.1016/j.neuroimage.2017.07.062 + + + Advances in spiral fMRI: A high-resolution +study with single-shot acquisition + Kasper + NeuroImage + 246 + 10.1016/j.neuroimage.2021.118738 + 2022 + Kasper, L., Engel, M., Heinzle, J., +Mueller-Schrader, M., Graedel, N. N., Reber, J., Schmid, T., Barmet, C., +Wilm, B. J., Stephan, K. E., & Pruessmann, K. P. (2022). Advances in +spiral fMRI: A high-resolution study with single-shot acquisition. +NeuroImage, 246, 118738. +https://doi.org/10.1016/j.neuroimage.2021.118738 + + + Michigan Image Reconstruction +Toolbox + Fessler + Fessler, J. A. (n.d.). Michigan Image +Reconstruction Toolbox. Retrieved May 17, 2023, from +https://github.com/JeffFessler/mirt + + + + + + diff --git a/joss.05877/10.21105.joss.05877.jats b/joss.05877/10.21105.joss.05877.jats new file mode 100644 index 0000000000..641f1167d2 --- /dev/null +++ b/joss.05877/10.21105.joss.05877.jats @@ -0,0 +1,1300 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5877 +10.21105/joss.05877 + +GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic +Resonance Image (MRI) Reconstruction in Julia + + + +https://orcid.org/0000-0002-9571-1838 + +Jaffray +Alexander + + + +* + + +https://orcid.org/0000-0002-2079-5977 + +Wu +Zhe + + +* + + +https://orcid.org/0000-0003-2432-4192 + +Vannesjo +S. Johanna + + + + +https://orcid.org/0000-0002-2813-5930 + +Uludağ +Kâmil + + + + + + +https://orcid.org/0000-0001-7667-603X + +Kasper +Lars + + + + + +Krembil Research Institute, University Health Network, +Ontario, Canada + + + + +MRI Research Centre, University of British Columbia, +Vancouver, Canada + + + + +Department of Physics, Norwegian University of Science and +Technology, Trondheim, Norway + + + + +Department of Medical Biophysics, University of Toronto, +Canada + + + + +Physical Sciences, Sunnybrook Research Institute, +Sunnybrook Health Sciences Centre, Toronto, ON, Canada + + + + +* E-mail: +* E-mail: + + +26 +4 +2024 + +9 +97 +5877 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Julia +Magnetic Resonance Imaging +Non-Cartesian Image Reconstruction +Gradient Impulse Response Function (GIRF) +Off-resonance Correction + + + + + + Summary +

Magnetic Resonance Imaging (MRI) acquires data in the frequency + domain (k-space), with the sampling pattern traversed by a path known + as the k-space trajectory. It is desirable to implement MRI data + sampling using k-space trajectories with high acquisition efficiency + (i.e., a fast coverage of k-space). Traditional Cartesian MRI + traverses k-space by acquiring individual lines of the k-space, each + requiring an excitation, a phase-encoding step, and a short readout + gradient. However, it is possible to traverse k-space with an + arbitrary trajectory, achieved by a long sequence of readout + gradients, thus presenting the opportunity to acquire more sampling + points per excitation. Spiral trajectories are a popular and efficient + method for traversing k-space with a long readout, as well as + classical echo-planar imaging (EPI) trajectories. Non-Cartesian + trajectories, such as spiral trajectories, yield significant + reductions in the number of excitations required for the acquisition + of an image, thus offering considerable acceleration and improvements + in signal-to-noise ratio (SNR) per unit time at the cost of + reconstruction complexity. These improvements are particularly + beneficial in diffusion MRI due to sequence timing constraints + (Lee + et al., 2021).

+

The actual k-space trajectory applied during the MRI experiment can + differ from the nominal trajectory due to hardware imperfections, + resulting in image artifacts such as ghosting, blurring or geometric + distortion. This problem is exacerbated in many non-Cartesian + trajectories, such as spirals, because these fast imaging protocols + place high demands on the gradient hardware of the MRI system + (Block + & Frahm, 2005). Accurate characterization of the system + hardware is necessary and can be used for k-space trajectory + correction, for example via a gradient impulse response function + (GIRF) + (Addy + et al., 2012; + Vannesjo + et al., 2013).

+

The high acquisition efficiency of non-Cartesian trajectories + originates, in part, from the prolonged readout duration which allows + for more samples to be acquired per excitation (single-shot or + few-interleave scanning). However, when using such long readouts, the + image encoding scheme is susceptible to static off-resonance (or field + inhomogeneity, B0), resulting in image artifacts that scale + with readout duration. For non-Cartesian trajectories, these artifacts + are difficult to correct in post-processing, but can be effectively + addressed during image reconstruction by incorporating spatial + off-resonance measurements into the signal model + (Sutton + et al., 2003).

+

Therefore, recent spiral imaging approaches often rely on an + expanded signal model incorporating system imperfections and + off-resonance maps + (Engel + et al., 2018; + Graedel + et al., 2021; + Kasper + et al., 2018; + Kasper + et al., 2022; + Lee + et al., 2021; + Robison + et al., 2019; + Vannesjo + et al., 2016; + Wilm + et al., 2011, + 2015), + in combination with parallel imaging acceleration using multiple + receiver coils and iterative non-Cartesian image reconstruction + algorithms, e.g., CG-SENSE + (Pruessmann + et al., 2001).

+

Here, we introduce the open-source + GIRFReco.jl reconstruction pipeline, which + provides a single ecosystem implementation of this state-of-the-art + approach to non-Cartesian MRI in the programming language Julia + (Bezanson + et al., 2017). The core reconstruction routines rely upon the + public Julia package MRIReco.jl, a + comprehensive open-source image reconstruction toolbox. To enable + robust, accessible and fast MRI with spiral gradient waveforms, + GIRFReco.jl is designed as an end-to-end signal + processing pipeline, from open-standard raw MR data ([ISMR]MRD + (Inati + et al., 2017)) to final reconstructed images (NIfTI neuroimage + data format + (NIfTI, + 2003)). It integrates system characterization information via + GIRF correction for accurate representation of the encoding fields, + relevant calibration data (coil sensitivity and static off-resonance + maps) and iterative parallel imaging reconstruction for non-Cartesian + k-space sampling patterns, including spiral trajectories.

+ + + Statement of Need +

Existing open-source solutions for the correction of system + imperfections and static off-resonance in MRI are often implemented + within the framework of mature image reconstruction suites such as + BART + (Blumenthal + et al., 2022), Gadgetron + (Hansen + & Sørensen, 2013) and MIRT + (Fessler, + n.d.).

+

However, the aforementioned complexity of the image reconstruction + task for spiral MRI currently necessitates the integration of tools + from multiple of these software suites in order to establish a + performant and comprehensive image reconstruction workflow (e.g., + (Veldmann + et al., 2022)). With each tool being developed in different + programming languages (C for BART; C++ for Gadgetron; MATLAB, C++ and + C for MIRT, etc.), maintaining and extending such an image + reconstruction pipeline then requires cross-language expertise, adding + significant overhead and complexity to development. This presents a + significant barrier to efficient and reproducible image reconstruction + and limits software accessibility and sustainability, especially for + users without software engineering backgrounds.

+

The programming language Julia + (Bezanson + et al., 2017) provides a practical solution to this + multiple-language problem by using a high-level interface to low-level + compiled code, i.e., enabling fast prototyping with limited resources + in an academic setting, while delivering a near-industrial-level + efficiency of code execution, all within a single development + environment.

+

In this work, we introduce GIRFReco.jl + (initial version presented at the annual meeting of ISMRM 2022 + (Jaffray + et al., 2022b)), which implements an end-to-end, self-contained + processing and image reconstruction pipeline for spiral MR data + completely in Julia. Based on the established + MRIReco.jl package, + GIRFReco.jl incorporates model-based + corrections + (Sutton + et al., 2003; + Vannesjo + et al., 2016; + Wilm + et al., 2011, + 2015) + to achieve high-quality spiral MRI reconstructions. Specifically, this + reconstruction pipeline combines several major steps: (1) ESPIRiT coil + sensitivity map estimation + (Uecker + et al., 2014); (2) Robust off-resonance (B0) map + estimation + (Funai + et al., 2008; + Lin + & Fessler, 2020); (3) Computation of the applied + non-Cartesian k-space trajectory using GIRF correction + (Vannesjo + et al., 2013, + 2016); + (4) Iterative non-Cartesian MRI reconstruction (CG-SENSE) with + off-resonance correction + (Knopp + et al., 2009; + Pruessmann + et al., 2001). Considering software reusability and + sustainability, (1) and (4) of the abovementioned steps are handled by + MRIReco.jl, a comprehensive modular open-source + image reconstruction toolbox in Julia. Step (2), the B0 map + estimation, was developed as a Julia package + MRIFieldmaps.jl by the original authors + (Lin + & Fessler, 2020) with our contribution of implementing an + alternative algorithm + (Funai + et al., 2008) in Julia. Finally, we implemented step (3), the + GIRF correction, in an original Julia package + MRIGradients.jl + (Jaffray + et al., 2022b), porting and refactoring the MATLAB code of the + original authors + (Vannesjo + & Graedel, 2020).

+ + + Functionality + + Required Inputs +

GIRFReco.jl requires raw MRI (k-space) + data (in [ISMR]MRD format + (Inati + et al., 2017)) of the following scans as input:

+ + +

Multi-echo Gradient-echo spin-warp (Cartesian) scan

+ + +

must include at least two echo times (e.g., 4.92 ms and + 7.38 ms at 3T)

+ + + + +

Spiral scan

+ + +

single or multi-interleave

+ + + + +

At the moment, the slice geometry (thickness, field-of-view, and + direction) of the Cartesian and spiral scans must be congruent, + while the resolution does not need to be identical or isotropic.

+ + + Overview of Components +

The following components are utilized within the spiral + reconstruction pipeline of GIRFReco.jl (Fig. + 1), and called from their respective packages. We indicate where the + authors of GIRFReco.jl provided original + contributions to the components by bold font.

+ + +

Core iterative image reconstruction, using the Julia package + MRIReco.jl

+ + +

CG-SENSE + (Pruessmann + et al., 2001) algorithm for iterative non-Cartesian + image reconstruction

+ + +

ESPIRiT + (Uecker + et al., 2014) for sensitivity map estimation

+ + + + +

Model-based correction

+ + +

Static off-resonance (B0 inhomogeneity) + correction

+ + +

Smoothed B0 map estimation, using an + implementation of + (Funai + et al., 2008) and + MRIFieldMaps.jl + (Lin + & Fessler, 2020)

+ + +

Static B0 map correction, accelerated by a + time-segmented implementation + (Knopp + et al., 2009) in + MRIReco.jl + (Knopp + & Grosser, 2021)

+ + + + +

Encoding field (trajectory) correction via Gradient + impulse response function (GIRF) + (Vannesjo + et al., 2013)

+ + +

Measurement with a phantom-based technique + (Addy + et al., 2012; + Graedel + et al., 2017; + Robison + et al., 2019)

+ + +

Estimation using open-source code + (Wu + et al., 2022)

+ + +

Prediction via + MRIGradients.jl + (Jaffray + et al., 2022b)

+ + + + + + +

+ Figure 1 + Figure 1. Overview of the + GIRFReco.jl signal processing and + reconstruction pipeline. Depicted is the workflow from raw acquired + k-space data to the final reconstructed images with the respective + tasks (parallelogram), in/output data (rectangles) and the location + of the processing components within different packages + (colours).

+ + + Detailed Processing Pipeline +

GIRFReco.jl executes the steps required + (depicted in Figure 1) for spiral diffusion reconstruction in the + following order:

+ + +

Conversion of proprietary, vendor-specific raw image data to + an open-source raw data format ([ISMR]MRD, + (Inati + et al., 2017)).

+ + +

Reading of the trajectory or gradient sequence and + synchronization of the k-space trajectory onto the time course + of the sampled k-space data to resolve any sampling rate + differences.

+ + +

Model-based correction of the k-space sampling points (linear + gradient self-terms) and data (k0 eddy currents) + using the gradient impulse response function (GIRF + (Vannesjo + et al., 2013), MRIGradients.jl + (Jaffray + et al., 2022b)).

+ + +

Iterative reconstruction of Cartesian multi-echo gradient + echo (GRE) scan.

+ + +

Coil sensitivity map estimation (ESPIRiT + (Uecker + et al., 2014), MRIReco.jl) from + the first echo of multi-echo Cartesian GRE data.

+ + +

Off-resonance (B0) map estimation and processing + (MRIFieldmaps.jl, + (Funai + et al., 2008; + Lin + & Fessler, 2020)) based on multi-echo Cartesian GRE + data.

+ + +

Non-Cartesian, iterative parallel image reconstruction + (cgSENSE) with off-resonance correction + ((Knopp + et al., 2009; + Pruessmann + et al., 2001), MRIReco.jl + (Knopp + & Grosser, 2021)).

+ + +

Via dedicated configuration files, individual steps can be + selectively applied or skipped during reconstruction, enabling + assessment of the impact of different model-based corrections on + final image quality. We demonstrate this use case by providing + example reconstructions obtained from the + GIRFReco.jl pipeline for a + T2-weighted four-interleave spiral acquisition of a + geometric structure phantom by the American College of Radiology + (ACR). Reconstructions of both fully sampled and accelerated (using + 1 of 4 interleaves, R = 4) datasets are depicted in Figure 2. + In vivo brain images reconstructed from a + T2-weighted single-interleave (R=4) spiral acquisition + are presented in Figure 3 + (Kasper + et al., 2023). In all cases, improved image quality was + obtained by successively increasing the complexity of the applied + model-based corrections (nominal trajectory, added B0 + correction, GIRF-correction of gradients, GIRF correction of + k0 eddy currents). The improvements in quality are best + seen when looking at high-contrast features of the images such as + edges and corners, with subsequent corrections creating sharper edge + contrast and reducing blurring of small features.

+

Note that the reconstruction results from the phantom experiments + (both R = 1 and R = 4 reconstructions in Figure 2) can be fully + reproduced using GIRFReco.jl and the + corresponding dataset made publicly available + (Jaffray + et al., 2022a). For details, see the “Getting Started” + section below.

+

+ Figure 2 + Figure 2. Reconstructed four-interleave + (R=1) and single-interleave (R=4) spiral images of a selected slice + of the ACR phantom. Top row, from left to right: Images + reconstructed from the nominal spiral gradient waveforms (“No + Correction”), with correction for static off-resonance + (“B0 Correction”), B0 + GIRF correction of the + k-space trajectory (“B0+GIRF Correction”), and additional + correction for GIRF k0 eddy currents + (“B0+GIRF+k0 Correction”). Bottom row: + Stepwise difference images between subsequent + corrections.

+

+ Figure 3 + Figure 3. Reconstructed in-vivo spiral + images of a human brain images (single interleave, undersampling + factor R=4). Top row: Images reconstructed from the nominal spiral + gradient waveform (“No correction”), with correction for static + off-resonance (“B0 map”), B0 + GIRF correction + of the k-space trajectory (“GIRF kxyz”), and additional + correction for GIRF k0 eddy currents (“Full Correction”). + Bottom row: Consecutive absolute difference images of top-row + reconstructions (5x scaled, i.e., +/- 20 % max image intensity; or + 50x scaled, i.e., +/- 2% max image intensity). A Cartesian image + (echo 1 from the B0 map scan) is used as the reference; + its edges are overlaid to assess geometric congruency of the spiral + images.

+ + + Quality of Life Features +

In addition to providing an end-to-end reconstruction workflow, + GIRFReco.jl offers methods for plotting + images and calibration data at intermediate steps throughout the + pipeline using PlotlyJS. Furthermore, intermediate reconstruction + results, such as calculated coil sensitivity maps and B0 + maps are optionally stored as NIfTI files, a common neuroimaging + data format supported by various analysis and visualization packages + (NIfTI, + 2003).

+ + + + Getting Started +

Up-to-date information about how to install + GIRFReco.jl, run example reconstructions (e.g., + reproducing Figure 2) and apply it to your own data can be found in + the + README.md + provided in the GitHub repository. Further example scripts and + technical documentation of GIRFReco.jl’s API, + including its current feature set, is provided at + ‘https://brain-to.github.io/GIRFReco.jl’, + automatically generated by + Documenter.jl.

+ + + Conclusion and Outlook +

The presented pipeline, GIRFReco.jl, is an + open-source end-to-end solution for spiral MRI reconstruction. It is + developed in Julia, and allows users to obtain final images directly + from raw MR data acquired by spiral k-space trajectories. Following + best practices of software sustainability and accessibility, we rely + on the established MR image reconstruction package + MRIReco.jl in our pipeline, while extending its + capability to handle the more complex use case of multiple model-based + corrections, necessary for high-quality spiral MRI. Beyond spirals, + GIRFReco.jl can be readily utilized for data + acquired under arbitrary non-Cartesian k-space trajectories; its + features of model-based MRI reconstruction with GIRF and off-resonance + corrections generalize to such sampling patterns in both 2D and 3D. + Furthermore, GIRFReco.jl can be extended to + handle additional model-based corrections (e.g., concomitant or + higher-order encoding fields, + (Bernstein + et al., 1998; + Vannesjo + et al., 2016; + Wilm + et al., 2011, + 2015)), + and act as a self-contained template for generalized image + reconstruction from raw scan and calibration data to interpretable and + accessible images in Julia.

+ + + + + + + + GraedelNadine N. + HurleyS. A. + ClareStuart + MillerKarla L. + PruessmannKlaas P. + VannesjoS. Johanna + + Comparison of gradient impulse response functions measured with a dynamic field camera and a phantom-based technique + Barcelona/ES + 2017 + 378 + + + + + + + JaffrayAlexander + WuZhe + UludağKâmil + KasperLars + + Open-source model-based reconstruction in Julia: A pipeline for spiral diffusion imaging + Proc. Intl. Soc. Mag. Reson. Med. 30 + London, England + 2022 + https://index.mirasmart.com/ISMRM2022/PDFfiles/2435.html + 10.58530/2022/2435 + 2435 + + + + + + + WuZhe + JaffrayAlexander + VannesjoS. Johanna + UludağKâmil + KasperLars + + MR System Stability and Quality Control using Gradient Impulse Response Functions (GIRF) + Proc. Intl. Soc. Mag. Reson. Med. 30 + London, UK + 2022 + https://index.mirasmart.com/ISMRM2022/PDFfiles/0641.html + 10.58530/2022/0641 + 0641 + + + + + + + AddyNii Okai + WuHolden H. + NishimuraDwight G. + + Simple method for MR gradient system characterization and k-space trajectory estimation + Magnetic Resonance in Medicine + 2012 + 20230517 + 68 + 1 + 1522-2594 + https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.23217 + 10.1002/mrm.23217 + 120 + 129 + + + + + + VannesjoS. Johanna + HaeberlinMaximilan + KasperLars + PavanMatteo + WilmBertram J. + BarmetChristoph + PruessmannKlaas P. + + Gradient system characterization by impulse response measurements with a dynamic field camera: Gradient System Characterization with a Dynamic Field Camera + Magnetic Resonance in Medicine + 201302 + 20230124 + 69 + 2 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.24263 + 10.1002/mrm.24263 + 583 + 593 + + + + + + LeeYoojin + WilmBertram J. + BrunnerDavid O. + GrossSimon + SchmidThomas + NagyZoltan + PruessmannKlaas P. + + On the signal‐to‐noise ratio benefit of spiral acquisition in diffusion MRI + Magnetic Resonance in Medicine + 202104 + 20230124 + 85 + 4 + 0740-3194 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.28554 + 10.1002/mrm.28554 + 1924 + 1937 + + + + + + BlockKai Tobias + FrahmJens + + Spiral imaging: A critical appraisal + Journal of Magnetic Resonance Imaging + 200506 + 20230124 + 21 + 6 + 1053-1807 + https://onlinelibrary.wiley.com/doi/10.1002/jmri.20320 + 10.1002/jmri.20320 + 657 + 668 + + + + + + SuttonB. P. + NollD. C. + FesslerJ. A. + + Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities + IEEE Transactions on Medical Imaging + 200302 + 20230124 + 22 + 2 + 0278-0062 + http://ieeexplore.ieee.org/document/1194628/ + 10.1109/TMI.2002.808360 + 178 + 188 + + + + + + WilmBertram J. + BarmetChristoph + PavanMatteo + PruessmannKlaas P. + + Higher order reconstruction for MRI in the presence of spatiotemporal field perturbations: Higher Order Reconstruction for MRI + Magnetic Resonance in Medicine + 201106 + 20230124 + 65 + 6 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.22767 + 10.1002/mrm.22767 + 1690 + 1701 + + + + + + WilmBertram J. + NagyZoltan + BarmetChristoph + VannesjoS. Johanna + KasperLars + HaeberlinMax + GrossSimon + DietrichBenjamin E. + BrunnerDavid O. + SchmidThomas + PruessmannKlaas P. + + Diffusion MRI with concurrent magnetic field monitoring: Diffusion MRI with Concurrent Magnetic Field Monitoring + Magnetic Resonance in Medicine + 201510 + 20230124 + 74 + 4 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.25827 + 10.1002/mrm.25827 + 925 + 933 + + + + + + PruessmannKlaas P. + WeigerMarkus + BörnertPeter + BoesigerPeter + + Advances in sensitivity encoding with arbitrary k -space trajectories: SENSE With Arbitrary k -Space Trajectories + Magnetic Resonance in Medicine + 200110 + 20230124 + 46 + 4 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.1241 + 10.1002/mrm.1241 + 638 + 651 + + + + + + BezansonJeff + EdelmanAlan + KarpinskiStefan + ShahViral B. + + Julia: A Fresh Approach to Numerical Computing + SIAM Review + 201701 + 20230124 + 59 + 1 + 0036-1445 + https://epubs.siam.org/doi/10.1137/141000671 + 10.1137/141000671 + 65 + 98 + + + + + + InatiSouheil J. + NaegeleJoseph D. + ZwartNicholas R. + RoopchansinghVinai + LizakMartin J. + HansenDavid C. + LiuChia‐Ying + AtkinsonDavid + KellmanPeter + KozerkeSebastian + XueHui + Campbell‐WashburnAdrienne E. + SørensenThomas S. + HansenMichael S. + + ISMRM Raw data format: A proposed standard for MRI raw datasets + Magnetic Resonance in Medicine + 201701 + 20230124 + 77 + 1 + 0740-3194 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.26089 + 10.1002/mrm.26089 + 411 + 421 + + + + + + VannesjoS. Johanna + GraedelNadine N. + KasperLars + GrossSimon + BuschJulia + HaeberlinMaximilian + BarmetChristoph + PruessmannKlaas P. + + Image reconstruction using a gradient impulse response model for trajectory prediction: GIRF-Based Image Reconstruction + Magnetic Resonance in Medicine + 201607 + 20230124 + 76 + 1 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.25841 + 10.1002/mrm.25841 + 45 + 58 + + + + + + HansenMichael Schacht + SørensenThomas Sangild + + Gadgetron: An open source framework for medical image reconstruction: Gadgetron + Magnetic Resonance in Medicine + 201306 + 20230124 + 69 + 6 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.24389 + 10.1002/mrm.24389 + 1768 + 1776 + + + + + + BlumenthalMoritz + HolmeChristian + RoeloffsVolkert + RosenzweigSebastian + SchatenPhilip + ScholandNick + TamirJon + WangXiaoqing + UeckerMartin + + Mrirecon/bart: Version 0.8.00 + Zenodo + 202209 + 20230124 + https://zenodo.org/record/592960 + 10.5281/ZENODO.592960 + + + + + + VeldmannMarten + EhsesPhilipp + ChowKelvin + NielsenJon‐Fredrik + ZaitsevMaxim + StöckerTony + + Open‐source MR imaging and reconstruction workflow + Magnetic Resonance in Medicine + 202212 + 20230124 + 88 + 6 + 0740-3194 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.29384 + 10.1002/mrm.29384 + 2395 + 2407 + + + + + + KnoppTobias + GrosserMirco + + MRIReco.jl: An MRI reconstruction framework written in Julia + Magnetic Resonance in Medicine + 202109 + 20230124 + 86 + 3 + 0740-3194 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.28792 + 10.1002/mrm.28792 + 1633 + 1646 + + + + + + UeckerMartin + LaiPeng + MurphyMark J. + VirtuePatrick + EladMichael + PaulyJohn M. + VasanawalaShreyas S. + LustigMichael + + ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA + Magnetic Resonance in Medicine + 201403 + 20230124 + 71 + 3 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.24751 + 10.1002/mrm.24751 + 990 + 1001 + + + + + + KnoppTobias + EggersH. + DahnkeH. + PrestinJ. + SenegasJ. + + Iterative Off-Resonance and Signal Decay Estimation and Correction for Multi-Echo MRI + IEEE Transactions on Medical Imaging + 200903 + 20230124 + 28 + 3 + 0278-0062 + http://ieeexplore.ieee.org/document/4637872/ + 10.1109/TMI.2008.2006526 + 394 + 404 + + + + + + FunaiAmanda K. + FesslerJeffrey A. + YeoDesmond T. B. + OlafssonValur T. + NollDouglas C. + + Regularized Field Map Estimation in MRI + IEEE Transactions on Medical Imaging + 200810 + 20230124 + 27 + 10 + 0278-0062 + http://ieeexplore.ieee.org/document/4494386/ + 10.1109/TMI.2008.923956 + 1484 + 1494 + + + + + + RobisonRyan K. + LiZhiqiang + WangDinghui + OoiMelvyn B. + PipeJames G. + + Correction of B _{\textrm{0}} eddy current effects in spiral MRI + Magnetic Resonance in Medicine + 201904 + 20230124 + 81 + 4 + 0740-3194 + https://onlinelibrary.wiley.com/doi/10.1002/mrm.27583 + 10.1002/mrm.27583 + 2501 + 2513 + + + + + + LinClaire Yilin + FesslerJeffrey A. + + Efficient regularized field map estimation in 3D MRI + IEEE Transactions on Computational Imaging + 2020 + 6 + 2333-9403 + 10.1109/TCI.2020.3031082 + 1451 + 1458 + + + + + + NIfTI + + NIfTI Data Format + Neuroimaging Informatics Technology Initiative + 20030902 + 20230216 + https://nifti.nimh.nih.gov/ + + + + + + VannesjoS. Johanna + GraedelNadine N. + + MRI-gradient / GIRF + MRI-gradient + 20201216 + 20230216 + https://github.com/MRI-gradient/GIRF + + + + + + BernsteinMat A. + ZhouXiaohong Joe + PolzinJason A. + KingKevin F. + GaninAlexander + PelcNorbert J. + GloverGary H. + + Concomitant gradient terms in phase contrast MR: Analysis and correction + Magnetic Resonance in Medicine + 1998 + 39 + 2 + 1522-2594 + http://onlinelibrary.wiley.com/doi/10.1002/mrm.1910390218/abstract + 10.1002/mrm.1910390218 + 300 + 308 + + + + + + JaffrayAlexander + WuZhe (Tim) + UludağKâmil + KasperLars + + Data Supplement: Open-source model-based reconstruction in Julia (ISMRM 2022) + Zenodo + 20220501 + 20230216 + https://zenodo.org/record/6510021 + 10.5281/zenodo.6510021 + + + + + + EngelMaria + KasperLars + BarmetChristoph + SchmidThomas + VionnetLaetitia + WilmBertram + PruessmannKlaas P. + + Single-shot spiral imaging at 7 T + Magnetic Resonance in Medicine + 20181101 + 20180828 + 80 + 5 + 1522-2594 + https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27176 + 10.1002/mrm.27176 + 1836 + 1846 + + + + + + KasperLars + WuZhe + JaffrayAlexander + KashyapSriranga + UludağKâmil + + Feasibility of spiral diffusion imaging on a clinical 3T MR system + Proc. Intl. Soc. Mag. Reson. Med. 31 + Toronto, Canada + 2023 + https://index.mirasmart.com/ISMRM2023/PDFfiles/4164.html + 4164 + + + + + + + GraedelNadine N. + KasperLars + EngelMaria + NussbaumJennifer + WilmBertram J. + PruessmannKlaas P. + VannesjoS. Johanna + + Feasibility of spiral fMRI based on an LTI gradient model + NeuroImage + 20211215 + 20230418 + 245 + 1053-8119 + https://www.sciencedirect.com/science/article/pii/S1053811921009472 + 10.1016/j.neuroimage.2021.118674 + 118674 + + + + + + + KasperLars + EngelMaria + BarmetChristoph + HaeberlinMaximilian + WilmBertram J. + DietrichBenjamin E. + SchmidThomas + GrossSimon + BrunnerDavid O. + StephanKlaas E. + PruessmannKlaas P. + + Rapid anatomical brain imaging using spiral acquisition and an expanded signal model + NeuroImage + 20180301 + 20180317 + 168 + 1053-8119 + http://www.sciencedirect.com/science/article/pii/S1053811917306432 + 10.1016/j.neuroimage.2017.07.062 + 88 + 100 + + + + + + KasperLars + EngelMaria + HeinzleJakob + Mueller-SchraderMatthias + GraedelNadine N. + ReberJonas + SchmidThomas + BarmetChristoph + WilmBertram J. + StephanKlaas Enno + PruessmannKlaas P. + + Advances in spiral fMRI: A high-resolution study with single-shot acquisition + NeuroImage + 202202 + 20211214 + 246 + https://linkinghub.elsevier.com/retrieve/pii/S1053811921010107 + 10.1016/j.neuroimage.2021.118738 + 118738 + + + + + + + FesslerJeffrey A. + + Michigan Image Reconstruction Toolbox + 20230517 + https://github.com/JeffFessler/mirt + + + + +
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