JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing
This work addresses the need for streamlined and efficient multimodal registration in neuroimaging, particularly for large-scale datasets, though it appears incremental as it builds on existing learning-based techniques.
The authors tackled the problem of multimodal neuroimaging registration by proposing a joint pipeline that reduces preprocessing complexity and enables fast inference, demonstrating its predictive power in a case-control study and cross-modal relationship analysis.
We present a pipeline for unbiased and robust multimodal registration of neuroimaging modalities with minimal pre-processing. While typical multimodal studies need to use multiple independent processing pipelines, with diverse options and hyperparameters, we propose a single and structured framework to jointly process different image modalities. The use of state-of-the-art learning-based techniques enables fast inferences, which makes the presented method suitable for large-scale and/or multi-cohort datasets with a diverse number of modalities per session. The pipeline currently works with structural MRI, resting state fMRI and amyloid PET images. We show the predictive power of the derived biomarkers using in a case-control study and study the cross-modal relationship between different image modalities. The code can be found in https: //github.com/acasamitjana/JUMP.