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

• URL: http://arxiv.org/abs/2404.04546v1
• Date: Sat, 6 Apr 2024 08:02:18 GMT
• Title: A self-attention model for robust rigid slice-to-volume registration of functional MRI
• Authors: Samah Khawaled, Simon K. Warfield, Moti Freiman,
• Abstract summary: Head motion during fMRI scans can result in distortion, biased analyses, and increased costs. We introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume. Our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods.
• Score: 4.615338063719135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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