JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing

• URL: http://arxiv.org/abs/2401.14250v1
• Date: Thu, 25 Jan 2024 15:40:19 GMT
• Title: JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing
• Authors: Adria Casamitjana and Juan Eugenio Iglesias and Raul Tudela and Aida Ninerola-Baizan and Roser Sala-Llonch
• Abstract summary: We present a pipeline for unbiased and robust registration of neuroimaging modalities with minimal pre-processing. 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.
• Score: 1.3549498237473223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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