  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.
