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Magnetic Resonance in Medicine, 42(5), 952–962. +https://doi.org/10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s + + + Deep-learning methods for parallel magnetic +resonance imaging reconstruction: A survey of the current approaches, +trends, and issues + Knoll + IEEE Signal Processing +Magazine + 1 + 37 + 10.1109/msp.2019.2950640 + 2020 + Knoll, F., Hammernik, K., Zhang, C., +Moeller, S., Pock, T., Sodickson, D. K., & Akcakaya, M. (2020). +Deep-learning methods for parallel magnetic resonance imaging +reconstruction: A survey of the current approaches, trends, and issues. +IEEE Signal Processing Magazine, 37(1), 128–140. +https://doi.org/10.1109/msp.2019.2950640 + + + A review and experimental evaluation of deep +learning methods for MRI reconstruction + Pal + 10.48550/ARXIV.2109.08618 + 2021 + Pal, A., & Rathi, Y. (2021). A +review and experimental evaluation of deep learning methods for MRI +reconstruction. +https://doi.org/10.48550/ARXIV.2109.08618 + + + + + + From 403fc32a7dc34ec236a8a823039f06f00a95342a Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Mon, 30 May 2022 23:44:36 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.04278.jats --- joss.04278/10.21105.joss.04278.jats | 950 ++++++++++++++++++++++++++++ 1 file changed, 950 insertions(+) create mode 100644 joss.04278/10.21105.joss.04278.jats diff --git a/joss.04278/10.21105.joss.04278.jats b/joss.04278/10.21105.joss.04278.jats new file mode 100644 index 0000000000..f756315f28 --- /dev/null +++ b/joss.04278/10.21105.joss.04278.jats @@ -0,0 +1,950 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +4278 +10.21105/joss.04278 + +DIRECT: Deep Image REConstruction Toolkit + + + + +Yiasemis +George + + + + + + +Moriakov +Nikita + + + + + + +Karkalousos +Dimitrios + + + + + +Caan +Matthan + + + + + +Teuwen +Jonas + + + + + + + +Netherlands Cancer Institute + + + + +University of Amsterdam + + + + +Amsterdam UMC, Biomedical Engineering and +Physics + + + + +Radboud University Medical Center + + + + +30 +10 +2021 + +7 +73 +4278 + +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) + + + +Python +Pytorch +Deep Learning +Inverse Problem Solver +Image Processing +Deep MRI reconstruction +Accelerated MRI + + + + + + Summary +

DIRECT is a Python, end-to-end pipeline for solving inverse + problems emerging in image processing. It is built with PyTorch + (Paszke + et al., 2019) and stores state-of-the-art deep learning imaging + inverse problem solvers for solving inverse problems such as + denoising, dealiasing, and reconstruction. By defining a base forward + linear or non-linear operator, DIRECT can be used for training models + for recovering images such as MRIs from partially observed or noisy + input data. Additionally, it provides the user with the functionality + to load saved weights of pre-trained models to be used for inference. + Furthermore, it offers functions for preparing and pre-processing data + such as .h5 files into PyTorch datasets + compatible with the software’s training pipeline, but also allows for + flexibility to work with any kind of PyTorch dataset. Additionally, in + order for the user to view the process of their experiments, it allows + for continuous visualisation of training and validation metrics as + well as image predictions utilising Tensorboard (examples are + illustrated in Figures 1 and 2).

+ + + + + + + + + + + + + + + +
Figure 1: Visualised reconstructions in + Tensorboard
+
+ + + + + + + + + + + + + + + +
Figure 2: Visualised metrics in + Tensorboard
+
+
+ + Statement of need +

A plethora of image processing problems arising in biology, + chemistry, and medicine can be defined as inverse problems. Inverse + problems aim in recovering a signal + + x𝒳 + (e.g. an image) that cannot be directly observed from a set of + measurements + + y𝒴 + and is subject to a given corruption process known as the forward + model:

+

+ + y=𝒜(x)+n,

+

where + + 𝒜 + denotes the forward operator and + + n + is some measurement noise, often assumed to be additive and normally + distributed. Equation 1 is + usually ill-posed and therefore an explicit solution is hard to find. + Instead, inverse problems in imaging are typically solved by + minimizing an objective function + + 𝒥 + which is consisted of a data-fidelity term and a regularization term + + + + (also known as Variational Problems):

+

+ + x̂=minz𝒳𝒥(z)=minz𝒳12||y𝒜(z)||22+λ(z),λ0.

+ + Accelerated Parallel MRI Reconstruction +

Accelerated Parallel Magnetic Resonance Image (MRI) + Reconstruction, that is, reconstructing an MR image from a set of + partially observed (or sub-sampled) + + k-space + measurements from multiple receiver coils (parallel imaging + (Larkman + & Nunes, 2007)), is par excellence an example of inverse + problems. The base forward operator of accelerated MRI + reconstruction is usually the two or three-dimensional Fast Fourier + Transform (FFT) denoted as + + .

+

More specifically, let

+

+ + y={y1,...,ync},yi=U(Six),i=1,...,nc,

+

be the sub-sampled + + k-space + measurements acquired from + + nc + receiver coils, where where + + Si + denotes a (usually unknown or estimated) coil sensitivity map, + property of each individual coil, and + + U + a retrospective binary sub-sampling mask operator which simulates + the sub-sampling process in clinical settings. Then, the + corresponding inverse problem for Accelerated Parallel MRI + Reconstruction replaces with the following optimization problem

+

+ + x̂=minz𝒳i=1nc12||yiU(Siz)||22+λ(z).

+

Conventional approaches employed for solving include Compressed + Sensing algorithms (CS) + (Candes + et al., 2006; + Donoho, + 2006; + Lustig + et al., 2007), SENSE + (Pruessmann + et al., 1999), and GRAPPA + (Griswold + et al., 2002). Deep learning-based imaging inverse problem + solvers have shown to outperform these conventional techniques by + outputting reconstructed images with higher fidelity from highly + sub-sampled + + k-space + measurements + (Knoll + et al., 2020; + Lønning + et al., 2019; + Pal + & Rathi, 2021).

+
+
+ + Functionality +

DIRECT stores PyTorch MRI datasets and data-loaders, multiple + retrospective sub-sampling schemes, MRI-related transforms and + evaluation metrics, and several state-of-the-art DL + baselines that can + be applied to the task of solving the inverse problem of Accelerated + Parallel MRI Reconstruction, which make it a perfect tool for research + in this domain. In addition to the currently implemented methods and + already-stored baselines, the user can easily incorporate into DIRECT + their own code following the current implementations.

+

DIRECT also allows for easy and flexible experimentation. For an + experiment, the user simply needs to define a configuration file that + comprises the experiment parameters. See + Configuration + File below for a configuration file template. DIRECT can be + employed for training and/or validating models on multiple machines + and GPUs as it is integrated with PyTorch’s + torch.distributed module and NVIDIA’s cuDNN + (Chetlur + et al., 2014).

+ + Configuration File +

All experiment parameters can be specified in a configuration + file. These include model, dataset, sub-sampling scheme, physics, + training, and validation. Each configuration file should be saved + with the .yaml extension. The following is a + template example of a configuration file:

+ model: + model_name: <nn_model_path> + model_parameter_1: <nn_model_paramter_1> + model_parameter_2: <nn_model_paramter_2> + ... +additional_models: + sensitivity_model: + model_name: <nn_sensitivity_model_path> + ... +physics: + forward_operator: fft2(centered=<true_or_false>) + backward_operator: ifft2(centered=<true_or_false>) + ... +training: + datasets: + - name: Dataset1 + lists: + - <path_to_list_1_for_Dataset1> + - <path_to_list_2_for_Dataset1> + transforms: + estimate_sensitivity_maps: <true_or_false> + scaling_key: <scaling_key> + image_center_crop: <true_or_false> + masking: + name: MaskingFunctionName + accelerations: [acceleration_1, accelaration_2, ...] + ... + ... + - name: Dataset2 + ... + optimizer: <optimizer> + lr: <learning_rate> + batch_size: <batch_size> + lr_step_size: <lr_step_size> + lr_gamma: <lr_gamma> + lr_warmup_iter: <num_warmup_iterations> + num_iterations: <num_iterations> + validation_steps: <num_val_steps> + loss: + losses: + - function: <fun1_as_in_model_engine> + multiplier: <multiplier_1> + - function: <fun2_as_in_model_engine> + multiplier: <multiplier_2> + checkpointer: + checkpoint_steps: <num_checkpointer_steps> + metrics: [<metric_1>, <metric_2>, ...] + ... +validation: + datasets: + - name: ValDataset1 + transforms: + ... + masking: + ... + text_description: <val_description_1> + ... + - name: ValDataset2 + ... + batch_size: <val_batch_size> + metrics: + - val_metric_1 + - val_metric_2 + - ... + ... +inference: + dataset: + name: InferenceDataset + lists: ... + transforms: + masking: + ... + ... + text_description: <inference_description> + ... + batch_size: <batch_size> + ... +logging: + tensorboard: + num_images: <num_images> +
+
+ + Baselines Stored + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model NameAlgorithm - Architecture
RecurrentVarNetRecurrent Variational Network + (Yiasemis + et al., 2021)
RIMRecurrent Inference Machine + (Beauferris + et al., 2021; + Lønning + et al., 2019)
LPDNetLearned Primal Dual Network + (Adler + & Oktem, 2018)
EndToEndVarnetEnd-to-end Variational Network + (Sriram + et al., 2020)
XPDNetX - Primal Dual Network + (Ramzi + et al., 2021)
KIKINetKspace-Image-Kspace-Image Network + (Eo + et al., 2018)
JointICNetJoint Deep Model-based MR Image and Coil + Sensitivity Reconstruction Network + (Jun + et al., 2021)
MultiDomainNetFeature-level multi-domain learning with + standardization for multi-channel data + (Muckley + et al., 2021)
UNet2dU-Net for MRI Reconstruction + (Zbontar + et al., 2019)
+
+
+ + Research projects using DIRECT +

DIRECT is the main software used for research by the MRI + Reconstruction team of the Innovation Centre for Artificial + Intelligence (ICAI) - AI for Oncology group of the Netherlands Cancer + Institute (NKI).

+ + Challenges +

DIRECT has been used for MRI Reconstruction result submissions in + the fastMRI challenge + (Muckley + et al., 2021) and the Multi-Coil MRI Reconstruction challenge + (Beauferris + et al., 2021).

+
+ + Publications +

Papers using DIRECT include Yiasemis et al. + (2022) + (presented in SPIE Medical Imaging Conference 2022) and Yiasemis et + al. + (2021) + (to be presented in CVPR Conference 2022).

+
+
+ + + + + + + YiasemisGeorge + ZhangChaoping + SánchezClara I. + SonkeJan-Jakob + TeuwenJonas + + Deep MRI reconstruction with radial subsampling + Medical imaging 2022: Physics of medical imaging + + ZhaoWei + YuLifeng + + International Society for Optics; Photonics; SPIE + 2022 + 12031 + https://doi.org/10.1117/12.2609876 + 10.1117/12.2609876 + 801 + 810 + + + + + + MuckleyMatthew J. + RiemenschneiderBruno + RadmaneshAlireza + KimSunwoo + JeongGeunu + KoJingyu + JunYohan + ShinHyungseob + HwangDosik + MostaphaMahmoud + al. + + Results of the 2020 fastMRI challenge for machine learning MR image reconstruction + IEEE Transactions on Medical Imaging + Institute of Electrical; Electronics Engineers (IEEE) + 202109 + 40 + 9 + 1558-254X + http://dx.doi.org/10.1109/TMI.2021.3075856 + 10.1109/tmi.2021.3075856 + 2306 + 2317 + + + + + + BeauferrisYoussef + TeuwenJonas + KarkalousosDimitrios + MoriakovNikita + CaanMattha + YiasemisGeorge + RodriguesLívia + LopesAlexandre + PedriniHélio + RittnerLetícia + DanneckerMaik + StudenyakViktor + GrögerFabian + VyasDevendra + Faghih-RoohiShahrooz + JethiAmrit Kumar + RajuJaya Chandra + SivaprakasamMohanasankar + LasbyMike + NogovitsynNikita + LoosWallace + FrayneRichard + SouzaRoberto + + Multi-coil MRI reconstruction challenge – assessing brain MRI reconstruction models and their generalizability to varying coil configurations + arXiv + 2021 + https://arxiv.org/abs/2011.07952 + 10.48550/ARXIV.2011.07952 + + + + + + SriramAnuroop + ZbontarJure + MurrellTullie + DefazioAaron + ZitnickC. + YakubovaNafissa + KnollFlorian + JohnsonPatricia + + End-to-end variational networks for accelerated MRI reconstruction + 202009 + 978-3-030-59712-2 + 10.1007/978-3-030-59713-9_7 + 64 + 73 + + + + + + LønningKai + PutzkyPatrick + SonkeJan-Jakob + RenemanLiesbeth + CaanMatthan W. A. + WellingMax + + Recurrent inference machines for reconstructing heterogeneous MRI data + Medical Image Analysis + 2019 + 53 + 1361-8415 + https://www.sciencedirect.com/science/article/pii/S1361841518306078 + 10.1016/j.media.2019.01.005 + 64 + 78 + + + + + + AdlerJonas + OktemOzan + + Learned primal-dual reconstruction + IEEE Transactions on Medical Imaging + Institute of Electrical; Electronics Engineers (IEEE) + 201806 + 37 + 6 + 1558-254X + http://dx.doi.org/10.1109/TMI.2018.2799231 + 10.1109/tmi.2018.2799231 + 1322 + 1332 + + + + + + RamziZaccharie + CiuciuPhilippe + StarckJean-Luc + + XPDNet for MRI reconstruction: An application to the 2020 fastMRI challenge + 2021 + https://arxiv.org/abs/2010.07290 + + + + + + EoTaejoon + JunYohan + KimTaeseong + JangJinseong + LeeHo-Joon + HwangDosik + + KIKI-net: Cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images + Magnetic Resonance in Medicine + 2018 + 80 + 5 + https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27201 + 10.1002/mrm.27201 + 2188 + 2201 + + + + + + JunYohan + ShinHyungseob + EoTaejoon + HwangDosik + + Joint deep model-based MR image and coil sensitivity reconstruction network (joint-ICNet) for fast MRI + Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) + 2021 + 10.1109/cvpr46437.2021.00523 + 5270 + 5279 + + + + + + ZbontarJure + KnollFlorian + SriramAnuroop + MurrellTullie + HuangZhengnan + MuckleyMatthew J. + DefazioAaron + SternRuben + JohnsonPatricia + BrunoMary + ParenteMarc + GerasKrzysztof J. + KatsnelsonJoe + ChandaranaHersh + ZhangZizhao + DrozdzalMichal + RomeroAdriana + RabbatMichael + VincentPascal + YakubovaNafissa + PinkertonJames + WangDuo + OwensErich + ZitnickC. Lawrence + RechtMichael P. + SodicksonDaniel K. + LuiYvonne W. + + fastMRI: An open dataset and benchmarks for accelerated MRI + 2019 + https://arxiv.org/abs/1811.08839 + 10.48550/ARXIV.1811.08839 + + + + + + YiasemisGeorge + SonkeJan-Jakob + SánchezClarisa + TeuwenJonas + + Recurrent variational network: A deep learning inverse problem solver applied to the task of accelerated MRI reconstruction + arXiv + 2021 + https://arxiv.org/abs/2111.09639 + 10.48550/ARXIV.2111.09639 + + + + + + ChetlurSharan + WoolleyCliff + VandermerschPhilippe + CohenJonathan + TranJohn + CatanzaroBryan + ShelhamerEvan + + cuDNN: Efficient primitives for deep learning + 2014 + https://arxiv.org/abs/1410.0759 + + + + + + LarkmanDavid J + NunesRita G + + Parallel magnetic resonance imaging + Physics in Medicine and Biology + IOP Publishing + 200703 + 52 + 7 + https://doi.org/10.1088/0031-9155/52/7/r01 + 10.1088/0031-9155/52/7/r01 + R15 + R55 + + + + + + DonohoD. L. + + Compressed sensing + IEEE Transactions on Information Theory + 2006 + 52 + 4 + 10.1109/TIT.2006.871582 + 1289 + 1306 + + + + + + PaszkeAdam + GrossSam + MassaFrancisco + LererAdam + BradburyJames + ChananGregory + KilleenTrevor + LinZeming + GimelsheinNatalia + AntigaLuca + DesmaisonAlban + KopfAndreas + YangEdward + DeVitoZachary + RaisonMartin + TejaniAlykhan + ChilamkurthySasank + SteinerBenoit + FangLu + BaiJunjie + ChintalaSoumith + + PyTorch: An imperative style, high-performance deep learning library + Advances in neural information processing systems 32 + + WallachH. + LarochelleH. + BeygelzimerA. + dAlché-BucF. + FoxE. + GarnettR. + + Curran Associates, Inc. + 2019 + http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf + 8024 + 8035 + + + + + + LustigMichael + DonohoDavid + PaulyJohn M. + + Sparse MRI: The application of compressed sensing for rapid MR imaging + Magnetic Resonance in Medicine + Wiley + 2007 + 58 + 6 + https://doi.org/10.1002/mrm.21391 + 10.1002/mrm.21391 + 1182 + 1195 + + + + + + CandesE. J. + RombergJ. + TaoT. + + Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information + IEEE Transactions on Information Theory + 2006 + 52 + 2 + 10.1109/TIT.2005.862083 + 489 + 509 + + + + + + GriswoldMark A. + JakobPeter M. + HeidemannRobin M. + NittkaMathias + JellusVladimir + WangJianmin + KieferBerthold + HaaseAxel + + Generalized autocalibrating partially parallel acquisitions (GRAPPA) + Magnetic Resonance in Medicine + Wiley + 200206 + 47 + 6 + https://doi.org/10.1002/mrm.10171 + 10.1002/mrm.10171 + 1202 + 1210 + + + + + + PruessmannKlaas P. + WeigerMarkus + ScheideggerMarkus B. + BoesigerPeter + + SENSE: Sensitivity encoding for fast MRI + Magnetic Resonance in Medicine + Wiley + 199911 + 42 + 5 + https://doi.org/10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s + 10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s + 952 + 962 + + + + + + KnollFlorian + HammernikKerstin + ZhangChi + MoellerSteen + PockThomas + SodicksonDaniel K. + AkcakayaMehmet + + Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues + IEEE Signal Processing Magazine + Institute of Electrical; Electronics Engineers (IEEE) + 202001 + 37 + 1 + https://doi.org/10.1109/msp.2019.2950640 + 10.1109/msp.2019.2950640 + 128 + 140 + + + + + + PalArghya + RathiYogesh + + A review and experimental evaluation of deep learning methods for MRI reconstruction + arXiv + 2021 + https://arxiv.org/abs/2109.08618 + 10.48550/ARXIV.2109.08618 + + + + +