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SNAKE-fMRI: A modular fMRI simulator from the space-time domain to k-space data and back
Pierre-Antoine Comby1,2,3, Alexandre Vignaud1,2, and Philippe Ciuciu1,2,3
1CEA/Neurospin, Gif-Sur-Yvette, France, 2Université Paris-Saclay, Gif-sur-Yvette, France, 3Inria Saclay, Palaiseau, France

Synopsis

Keywords: fMRI Acquisition, Simulations

Motivation: fMRI is a powerful tool for neuroimaging, but its optimization in vivo is complicated due to limited reproducibility. Simulation setup can help, but current solutions lack flexibility, control, or efficiency.

Goal(s): Develop a versatile and efficient fMRI simulator generating realistic fMRI data under various conditions: SNR levels, acceleration factors, spatiotemporal resolutions, noise, and artifact sources.

Approach: We propose SNAKE-fMRI, a modular, open-source fMRI simulator that operates from image to k-space and vice-versa. It can generate 3D+time k-space and images, mimicking the entire fMRI acquisition process, with potential k-t accelerations.

Results: We showcase the use of SNAKE-fMRI on a non-Cartesian k-t accelerated scenario.

Impact: With SNAKE-fMRI as an open-source, versatile, and efficient fMRI simulator, researchers can generate realistic fMRI data under a controlled and fully reproducible setup, enabling new advancements in fMRI acquisition and reconstruction methods.

Introduction

Functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful non-invasive tool in neuroscience, enabling scientists to understand human brain function.

However, fMRI acquisition and reconstruction techniques are complex to optimize and benchmark due to the lack of ground truth that produces absolute and quantitative metrics. Repeating in-vivo experiments may face the issue of limited reproducibility [1] and is time-consuming and expensive.

fMRI simulators have been developed to generate synthetic fMRI time series, where brain responses are artificially added to existing or synthetic data. However, they often lack flexibility, integration with post-processing, and computational efficiency (summarized in Table 1). Exploring new acceleration schemes in the acquisition setting and innovative reconstruction methods with these tools is not feasible.

To address these unmet needs, we propose SNAKE-fMRI (Simulator from NeuroActivation to Kspace Evaluation for Methodology and Reconstruction of Images). This open-source fMRI simulator operates in the image and k-space domain to yield the data. Its flexibility allows us to investigate various scenarios regarding SNR and acceleration factors to reach higher spatial and temporal resolution and validate reconstruction methods against those scenarios.

Key Features

Simplicity with modularity

SNAKE-FMRI has a modular approach for the simulation of fMRI data (Figure 2): A core simulation object will be modified and enriched by so-called handlers, each of them being responsible for a specific part of the model (for instance, defining the anatomical base volume from a phantom or adding noise to a time-series). They are chained together to produce complex behaviors from simple operations. Once the simulation has been done, It is passed down to a reconstruction and statistical analysis pipeline.

Different noise and artifact sources can be modeled and superimposed on one another before or after k-space generation. However, the virtue of SNAKE-fMRI is that it can output both k-space and image-space data, so it can be interfaced with many different image analysis or reconstruction algorithms for validating them.

Efficient K-space data Generation
The acquisition process in SNAKE-fMRI is modeled using a Fourier Transform, which is a simplification that neglects the relaxation aspect of MR physics. In the context of a segmented (or multishot) acquisition, SNAKE-fMRI simulates a new image of the state of the signal at each shot, representing the ideal magnetization after the RF-pulse excitation. The shot is then acquired using the Fourier model, as detailed in Figure 3.

Example Scenario

We illustrate the capabilities of SNAKE-fMRI by simulating fMRI data under a high spatial resolution (e.g., 1mm iso) in a non-Cartesian, k-t accelerated setup consisting of a stack-of-spirals (SoS); the slices are ordered in the “center-out” methods, as described in Figure 3.

We simulated a retinotopy mapping by adding brain activation consisting of a 5-minute paradigm of block-on(20s)/block-off(20s) pattern convoluted with canonical HRF that creates a 3% change of contrast in a region of interest, defined as a mask intersecting the gray matter. The high temporal resolution image simulation was then acquired under 3 different acceleration factors 2, 4, and 8 (resulting in volumetric TR of 3.6, 2.2, and 1.5 seconds) and input SNR of 100.

The reconstruction was done using a sequential, per-frame Compressed Sensing-based reconstruction with L1-wavelet regularisation, implemented in PySAP-MRI[10], using density-compensated gpuNUFFT for the data-consistency gradient step. To reduce the number of study parameters, the regularization parameters were estimated using Stein's unbiased Risk Estimate (SURE)[3].

We show an end-to-end process in Figure 4. Using a higher acceleration factor, the effective SNR drops and the sequential reconstruction cannot retrieve the activations in the right region of interest (ROI). This calls for more elaborated methods, with time-aware priors, such as a low-rank and sparsity in time domain one, that fully leverage the k-t aspect of the acquisition.

Conclusion

SNAKE-fMRI is a versatile and efficient fMRI simulator that provides researchers with a valuable tool for generating synthetic fMRI data under various conditions. The simulator can be used to develop and validate new fMRI acquisition and reconstruction methods, including training neural networks for fMRI reconstruction on synthetic data, as well as an educational tool.

SNAKE-fMRI will be made available at https://github.com/snake-fmri/snake-fmri

Acknowledgements

No acknowledgement found.

References

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[7] E. B. Erhardt, E. A. Allen, Y. Wei, T. Eichele, and V. D. Calhoun, “SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability,” NeuroImage, vol. 59, no. 4, pp. 4160–4167, Feb. 2012, doi: 10.1016/j.neuroimage.2011.11.088.

[8] C. T. Ellis, C. Baldassano, A. C. Schapiro, M. B. Cai, and J. D. Cohen, “Facilitating open-science with realistic fMRI simulation: validation and application,” PeerJ, vol. 8, p. e8564, Feb. 2020, doi: 10.7717/peerj.8564.

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Figures

Table 1: Comparative analysis of available simulators in the literature. MRI simulators, like Jemris[4], Odin[5], and MRILab[6] (top), mainly produce k-space data and target MR physicists interested in sequence programming. In contrast, fMRI simulators (bottom), like SimTB[7], fMRISim[8], or NeuroRsim[9], focus more on image-domain signal generation and augmentation. SNAKE-fMRI joins the best of both worlds and focuses on acquisition and reconstruction.

Figure 1: Modular design of the simulator and ecosystem of packages in SNAKE-fMRI. 1) A SimulationData object containing all the data and metadata of the simulation is fed into a chain of handlers, modifying the simulation. 2) the completed simulation (with k-space data) is used as input for reconstruction methods. 3) The reconstructed data is compared to the ground truth using standard statistical tools, such as first-level GLM analysis and statistical testing.

Figure 2: Acquisition Method implemented in SNAKE-fMRI: The case represented is simplified to a 2D Cartesian case (e.g., a projected view of a 3D non-accelerated EPI scheme). Each line (or plane in 3D) in the k-space sampling trajectory is acquired separately from an on-purpose generated simulation volume.

Figure 3: Example of a stack of spiral trajectories used during the readout in Simulated Scenario. (A) Acquired k-space for generating time frames. The acceleration factor is magnified for visualization purposes. (B) Different strategies for slice order acquisition. The central slices of the k-space are always collected. However, three distinct orders are possible: (a) top-down: kz coordinates order the slices; (b) center-out: the slices are acquired starting from the center; (c) random: the slices are acquired in random order.

Figure 4: Obtained statistics after simulation, acquisition (center-out SoS) and reconstruction: The downstream analysis and testing against ground truth allow for a clear comparison between different acquisition or reconstruction strategies. Activations (a) are compared with ground-truth ROI; (b) ROC Curve can provide Overall classification metrics such as AUC or Balanced Accuracy (BACC); Finally, time series after the different strategies can be compared (c).

Here, we compared 3 Acceleration factors for the same SNR level and reconstruction method (sequential).


Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
3408
DOI: https://doi.org/10.58530/2024/3408