DIRECT: Deep Image REConstruction Toolkit

Yiasemis, George; Moriakov, Nikita; Karkalousos, Dimitrios; Caan, Matthan W.A.; Teuwen, Jonas

doi:10.21105/joss.04278

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…To obtain our results, data preparation, retrospective subsampling generation, and model training we used the Deep Image Reconstruction Toolkit (DIRECT) [38].…”

Section: Resultsmentioning

confidence: 99%

On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction

Yiasemis¹,

Sánchez²,

Sonke³

et al. 2023

Preprint

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Purpose: The MRI k-space acquisition is time consuming. Traditional techniques aim to acquire accelerated data, which in conjunction with recent DL methods, aid in producing high-fidelity images in truncated times. Conventionally, subsampling the k-space is performed by utilizing Cartesianrectilinear trajectories, which even with the use of DL, provide imprecise reconstructions, though, a plethora of non-rectilinear or non-Cartesian trajectories can be implemented in modern MRI scanners. This work investigates the effect of the k-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models. Methods:The RecurrentVarNet was used as the DL-based MRI-reconstruction architecture. Cartesian fully-sampled multi-coil k-space measurements from three datasets with different accelerations were retrospectively subsampled using eight distinct subsampling schemes (four Cartesian-rectilinear, two Cartesian non-rectilinear, two non-Cartesian). Experiments were conducted in two frameworks: Scheme-specific, where a distinct model was trained and evaluated for each dataset-subsampling scheme pair, and multi-scheme, where for each dataset a single model was trained on data randomly subsampled by any of the eight schemes and evaluated on data subsampled by all schemes.Results: In the scheme-specific setting RecurrentVarNets trained and evaluated on non-rectilinearly subsampled data demonstrated superior performance especially for high accelerations, whilst in the multi-scheme setting, reconstruction performance on rectilinearly subsampled data improved when compared to the scheme-specific experiments. Conclusion:Training DL-based MRI reconstruction algorithms on non-rectilinearly subsampled measurements can produce more faithful reconstructions. Our findings demonstrate the potential for using DL-based methods trained on prospective acquisitions with non-rectilinearly subsampled measurements to optimize scan time and image quality.

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“…To obtain our results, data preparation, retrospective subsampling generation, and model training we used the Deep Image Reconstruction Toolkit (DIRECT) [38].…”

Section: Resultsmentioning

confidence: 99%

On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction

Yiasemis¹,

Sánchez²,

Sonke³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…These models are the zero-filled reconstruction, the U-Net model (Jin et al, 2017 ), the WW-net model (Souza et al, 2020b ), and the hybrid-cascade model (Souza et al, 2019 ). To date, Track 01 has received six independent submissions from ResoNNance (Yiasemis et al, 2022a ) (two different models), The Enchanted (two different models), TUMRI, and M-L UNICAMP teams.…”

Section: Methodsmentioning

confidence: 99%

Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations

et al. 2022

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Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

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