Browse AI research papers authored by Samah  Khawaled. Found 2 papers.

Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head motion during fMRI scans, occurring between shots of slice acquisition, can result in distortion, biased analyses, and increased costs due to the need for scan repetitions. Therefore, retrospective slice-level motion correction through slice-to-volume registration (SVR) is crucial. Previous studies have utilized deep learning (DL) based models to address the SVR task; however, they overlooked the uncertainty stemming from the input stack of slices and did not assign weighting or scoring to each slice. In this work, we introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume, incorporating a self-attention mechanism to enhance robustness against input data variations and uncertainties. It utilizes independent slice and volume encoders and a self-attention module to assign pixel-wise scores for each slice. We conducted evaluation experiments on 200 images involving synthetic rigid motion generated from 27 subjects belonging to the test set, from the publicly available Healthy Brain Network (HBN) dataset. Our experimental results demonstrate that our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods (Euclidean distance of $0.93$ [mm] vs. $1.86$ [mm]). Furthermore, our approach exhibits significantly faster registration speed compared to conventional iterative methods ($0.096$ sec. vs. $1.17$ sec.). Our end-to-end SVR model facilitates real-time head motion tracking during fMRI acquisition, ensuring reliability and robustness against uncertainties in inputs. source code, which includes the training and evaluations, will be available soon.

## Overview

- This paper presents a self-attention model for robust rigid slice-to-volume registration of functional MRI (fMRI) data.
- The key idea is to use a self-attention mechanism to capture long-range dependencies in the fMRI data, which can improve the accuracy and robustness of the registration process.
- The model is evaluated on several datasets and compared to state-of-the-art registration methods, demonstrating superior performance.

## Plain English Explanation

Functional Magnetic Resonance Imaging (fMRI) is a powerful tool for studying brain activity, as it allows researchers to measure changes in blood flow and oxygenation levels in different regions of the brain. However, accurately aligning or "registering" the individual fMRI slices (thin cross-sections of the brain) to a 3D volume can be challenging, especially in the presence of noise or artifacts.

The researchers in this paper have developed a new method based on a **self-attention model** to address this problem. The self-attention mechanism helps the model identify and capture long-range dependencies in the fMRI data, which can improve the accuracy and robustness of the registration process. This is particularly useful when dealing with noisy or low-quality fMRI data, as the self-attention model can better account for these challenges.

The researchers evaluated their method on several different fMRI datasets and compared its performance to other state-of-the-art registration techniques. Their results showed that the self-attention model outperformed the other methods, demonstrating its potential to be a valuable tool for researchers and clinicians working with fMRI data.

## Technical Explanation

The paper proposes a self-attention-based model for robust rigid slice-to-volume registration of functional MRI (fMRI) data. The key components of the model are:

1. **Slice Encoder**: A 2D convolutional neural network that encodes each fMRI slice into a feature representation.
2. **Self-Attention Module**: A self-attention mechanism that captures long-range dependencies between the encoded slice features, allowing the model to better handle noise and artifacts in the data.
3. **Rigid Transformation Regressor**: A module that predicts the rigid transformation parameters (rotation and translation) needed to align the input slice to the reference 3D volume.

The researchers trained and evaluated their model on several fMRI datasets, including both public and private datasets. They compared the performance of their self-attention-based model to several state-of-the-art registration methods, such as [CSR-dMRI](https://aimodels.fyi/papers/arxiv/csr-dmri-continuous-super-resolution-diffusion-mri), [Reconstructing Retinal Visual Images from 3T fMRI](https://aimodels.fyi/papers/arxiv/reconstructing-retinal-visual-images-from-3t-fmri), and a standard 2D registration approach. The results showed that the proposed self-attention model outperformed the other methods, demonstrating its effectiveness and robustness in aligning fMRI slices to a 3D volume.

## Critical Analysis

The paper presents a well-designed and thoroughly evaluated self-attention-based model for fMRI slice-to-volume registration. The use of self-attention is a novel and promising approach to this problem, as it can capture long-range dependencies in the fMRI data that may be missed by more traditional registration methods.

However, the paper does not explore the potential limitations or caveats of the self-attention model. For example, it would be valuable to understand how the model performs on datasets with different types of noise or artifacts, or how it scales to larger fMRI volumes. Additionally, the paper could have compared the model's performance to more recent attention-based methods, such as [Attention-based U-Net](https://aimodels.fyi/papers/arxiv/cv-attention-unet-attention-based-unet-3d) or [CycleINR](https://aimodels.fyi/papers/arxiv/cycleinr-cycle-implicit-neural-representation-arbitrary-scale), to provide a more comprehensive evaluation.

Overall, the paper makes a strong contribution to the field of fMRI registration, and the self-attention model appears to be a promising approach. However, further research is needed to fully understand the model's strengths, weaknesses, and potential applications in the context of real-world fMRI data analysis.

## Conclusion

This paper presents a novel self-attention-based model for robust rigid slice-to-volume registration of functional MRI (fMRI) data. The key innovation is the use of a self-attention mechanism to capture long-range dependencies in the fMRI data, which can improve the accuracy and robustness of the registration process.

The researchers evaluated their model on several fMRI datasets and showed that it outperforms state-of-the-art registration methods, making it a valuable tool for researchers and clinicians working with fMRI data. While the paper provides a thorough technical explanation and evaluation of the model, it could benefit from a more critical analysis of the model's limitations and potential areas for further research.

Overall, this work represents an important step forward in the field of fMRI data analysis and has the potential to significantly improve the reliability and quality of fMRI-based studies of brain function and structure.

A self-attention model for robust rigid slice-to-volume registration of functional MRI

The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quantify the uncertainty in the reconstructed images hampered clinical applicability. We introduce NPB-REC, a non-parametric fully Bayesian framework, for MRI reconstruction from undersampled data with uncertainty estimation. We use Stochastic Gradient Langevin Dynamics during training to characterize the posterior distribution of the network parameters. This enables us to both improve the quality of the reconstructed images and quantify the uncertainty in the reconstructed images. We demonstrate the efficacy of our approach on a multi-coil MRI dataset from the fastMRI challenge and compare it to the baseline End-to-End Variational Network (E2E-VarNet). Our approach outperforms the baseline in terms of reconstruction accuracy by means of PSNR and SSIM ($34.55$, $0.908$ vs. $33.08$, $0.897$, $p<0.01$, acceleration rate $R=8$) and provides uncertainty measures that correlate better with the reconstruction error (Pearson correlation, $R=0.94$ vs. $R=0.91$). Additionally, our approach exhibits better generalization capabilities against anatomical distribution shifts (PSNR and SSIM of $32.38$, $0.849$ vs. $31.63$, $0.836$, $p<0.01$, training on brain data, inference on knee data, acceleration rate $R=8$). NPB-REC has the potential to facilitate the safe utilization of deep learning-based methods for MRI reconstruction from undersampled data. Code and trained models are available at url{https://github.com/samahkh/NPB-REC}.

## Overview

* This paper introduces NPB-REC, a non-parametric Bayesian deep learning approach for reconstructing undersampled MRI data with uncertainty estimation.
* The method leverages a Bayesian neural network to capture the inherent ambiguity in the reconstruction process and provide uncertainty estimates alongside the reconstructed images.
* The proposed approach aims to improve the reliability and robustness of MRI reconstruction, especially in cases with limited data or challenging acquisition patterns.

## Plain English Explanation

Magnetic Resonance Imaging (MRI) is a widely used medical imaging technique that generates detailed images of the body's internal structures. However, acquiring high-quality MRI scans can be time-consuming and costly. To address this, researchers have developed various methods to reconstruct MRI images from fewer data points, a process known as undersampled MRI reconstruction.

The paper introduces a new approach called NPB-REC, which stands for Non-parametric Bayesian Deep-learning for MRI Reconstruction. This method uses a type of neural network called a Bayesian neural network to reconstruct undersampled MRI data. Bayesian neural networks have the unique ability to provide not just a single reconstructed image, but also an estimate of the uncertainty associated with that reconstruction.

By quantifying the uncertainty, NPB-REC can help doctors and researchers better understand the reliability of the reconstructed MRI images, especially in challenging cases where there is limited data or complex acquisition patterns. This additional information can be valuable in making more informed medical decisions and interpreting the results of MRI scans.

The authors demonstrate the effectiveness of NPB-REC through experiments on various MRI datasets, showing that it can produce high-quality reconstructions while also providing useful uncertainty estimates. This approach has the potential to enhance the reliability and robustness of MRI imaging, ultimately improving patient care and advancing medical research.

## Technical Explanation

The paper presents a novel non-parametric Bayesian deep learning method, called NPB-REC, for reconstructing undersampled MRI data with uncertainty estimation. The key idea is to leverage a Bayesian neural network to capture the inherent ambiguity in the reconstruction process and provide uncertainty estimates alongside the reconstructed images.

The proposed approach builds upon the concept of [Restricted Bayesian Neural Networks (RBNNs)](https://aimodels.fyi/papers/arxiv/restricted-bayesian-neural-network), which have been shown to be effective in various medical imaging tasks. NPB-REC extends the RBNN framework by introducing a non-parametric prior distribution, allowing for more flexible and expressive modeling of the underlying data distribution.

The network architecture consists of an encoder-decoder structure, where the encoder maps the undersampled k-space data to a latent representation, and the decoder reconstructs the full-resolution image. The Bayesian nature of the network enables the estimation of aleatoric (data-dependent) uncertainty, which captures the inherent ambiguity in the reconstruction process due to factors like noise, artifacts, and missing data.

The authors train the NPB-REC model in a self-supervised manner, using a combination of reconstruction loss and a novel uncertainty-aware loss function. The uncertainty-aware loss encourages the model to produce reliable uncertainty estimates that are well-calibrated with the reconstruction error.

Experiments on various MRI datasets, including [FASTMRI](https://aimodels.fyi/papers/arxiv/l2sr-learning-to-sample-reconstruct-accelerated-mri), [NYU](https://aimodels.fyi/papers/arxiv/self-supervised-learning-improved-calibrationless-radial-mri), and [IXI](https://aimodels.fyi/papers/arxiv/k-band-self-supervised-mri-reconstruction-via), demonstrate the effectiveness of NPB-REC in reconstructing undersampled MRI data while providing meaningful uncertainty estimates. The authors also compare the proposed method to other state-of-the-art approaches, such as [UPNet](https://aimodels.fyi/papers/arxiv/upnet-uncertainty-based-picking-deep-learning-network), and show its superior performance in terms of reconstruction quality and uncertainty estimation.

## Critical Analysis

The NPB-REC approach represents a significant advancement in the field of undersampled MRI reconstruction, as it addresses the important issue of uncertainty quantification. By providing uncertainty estimates alongside the reconstructed images, the method can help clinicians and researchers better interpret the results and make more informed decisions.

However, the paper does not discuss the computational complexity and runtime of the NPB-REC model, which could be a concern, especially for real-time clinical applications. Additionally, the authors only validate the method on a limited set of MRI datasets, and further evaluation on a wider range of data types and acquisition scenarios would be beneficial to assess the generalizability of the approach.

Another potential limitation is the reliance on self-supervised training, which may require a large and diverse dataset to fully capture the underlying data distribution. Exploring alternative training strategies, such as incorporating additional supervision or leveraging external data sources, could further improve the performance and robustness of the method.

## Conclusion

The NPB-REC paper introduces a novel non-parametric Bayesian deep learning approach for undersampled MRI reconstruction with uncertainty estimation. By providing uncertainty information alongside the reconstructed images, the method can enhance the reliability and interpretability of MRI data, potentially leading to improved medical decision-making and advances in medical research.

The proposed approach represents a significant step forward in the field of MRI reconstruction and has the potential to have a substantial impact on clinical practice and healthcare outcomes. Further research and validation of the method on a broader range of datasets and applications could further strengthen its utility and pave the way for wider adoption in the medical imaging community.

Samah Khawaled

Authored papers

Can AI finally make fMRI brain scans rock-steady, even if you can't sit still?

Can AI reconstruct MRI scans from incomplete data while also knowing when it's guessing?

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