JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910307889184768 |
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| author | Casamitjana, Adria Iglesias, Juan Eugenio Tudela, Raul Ninerola-Baizan, Aida Sala-Llonch, Roser |
| author_facet | Casamitjana, Adria Iglesias, Juan Eugenio Tudela, Raul Ninerola-Baizan, Aida Sala-Llonch, Roser |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14250 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing Casamitjana, Adria Iglesias, Juan Eugenio Tudela, Raul Ninerola-Baizan, Aida Sala-Llonch, Roser Computer Vision and Pattern Recognition 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. |
| title | JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.14250 |