Mitigating analytical variability in fMRI results with style transfer
- URL: http://arxiv.org/abs/2404.03703v1
- Date: Thu, 4 Apr 2024 07:49:39 GMT
- Title: Mitigating analytical variability in fMRI results with style transfer
- Authors: Elodie Germani, Elisa Fromont, Camille Maumet,
- Abstract summary: We make the assumption that pipelines can be considered as a style component of data and propose to use different generative models to convert data between pipelines.
We design a new DM-based unsupervised multi-domain image-to-image transition framework and constrain the generation of 3D fMRI statistic maps.
- Score: 0.9217021281095907
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines can be considered as a style component of data and propose to use different generative models, among which, Diffusion Models (DM) to convert data between pipelines. We design a new DM-based unsupervised multi-domain image-to-image transition framework and constrain the generation of 3D fMRI statistic maps using the latent space of an auxiliary classifier that distinguishes statistic maps from different pipelines. We extend traditional sampling techniques used in DM to improve the transition performance. Our experiments demonstrate that our proposed methods are successful: pipelines can indeed be transferred, providing an important source of data augmentation for future medical studies.
Related papers
- Ambient Diffusion Posterior Sampling: Solving Inverse Problems with
Diffusion Models trained on Corrupted Data [56.81246107125692]
Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption.
We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance.
We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
arXiv Detail & Related papers (2024-03-13T17:28:20Z) - JUMP: A joint multimodal registration pipeline for neuroimaging with
minimal preprocessing [1.3549498237473223]
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.
arXiv Detail & Related papers (2024-01-25T15:40:19Z) - Source-Free Collaborative Domain Adaptation via Multi-Perspective
Feature Enrichment for Functional MRI Analysis [55.03872260158717]
Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis.
Many methods have been proposed to reduce fMRI heterogeneity between source and target domains.
But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies.
We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
arXiv Detail & Related papers (2023-08-24T01:30:18Z) - Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion
Generative Models [75.52575380824051]
We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI.
We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process.
Our method requires as few as five training images to learn effective sampling patterns.
arXiv Detail & Related papers (2023-06-05T22:09:06Z) - Robust Fiber ODF Estimation Using Deep Constrained Spherical
Deconvolution for Diffusion MRI [7.9283612449524155]
A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF)
measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI.
Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling.
We propose a novel data-driven deep constrained spherical deconvolution method to
arXiv Detail & Related papers (2023-06-05T14:06:40Z) - f-DM: A Multi-stage Diffusion Model via Progressive Signal
Transformation [56.04628143914542]
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains.
We propose f-DM, a generalized family of DMs which allows progressive signal transformation.
We apply f-DM in image generation tasks with a range of functions, including down-sampling, blurring, and learned transformations.
arXiv Detail & Related papers (2022-10-10T18:49:25Z) - Pipeline-Invariant Representation Learning for Neuroimaging [5.502218439301424]
We evaluate how preprocessing pipeline selection can impact the downstream performance of a supervised learning model.
We propose two pipeline-invariant representation learning methodologies, MPSL and PXL, to improve robustness in classification performance.
These results suggest that our proposed models can be applied to mitigate pipeline-related biases, and to improve prediction robustness in brain-phenotype modeling.
arXiv Detail & Related papers (2022-08-27T02:34:44Z) - Mode decomposition-based time-varying phase synchronization for fMRI
Data [0.0]
One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time.
This requires the textita priori choice of type and cut-off frequencies for the bandpass filter needed to perform the analysis.
Here we explore alternative approaches based on the use of various mode decomposition (MD) techniques that circumvent this issue.
arXiv Detail & Related papers (2022-03-26T01:04:28Z) - Reference-based Magnetic Resonance Image Reconstruction Using Texture
Transforme [86.6394254676369]
We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction.
We formulate the under-sampled data and reference data as queries and keys in a transformer.
The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
arXiv Detail & Related papers (2021-11-18T03:06:25Z) - A Variational Bayesian Approach to Learning Latent Variables for
Acoustic Knowledge Transfer [55.20627066525205]
We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models.
Our proposed VB approach can obtain good improvements on target devices, and consistently outperforms 13 state-of-the-art knowledge transfer algorithms.
arXiv Detail & Related papers (2021-10-16T15:54:01Z) - Robust partial Fourier reconstruction for diffusion-weighted imaging
using a recurrent convolutional neural network [5.3580471186206005]
A neural network architecture is derived which alternates between data consistency operations and regularization implemented by recurrent convolutions.
It can be shown that unrolling by means of a recurrent network produced better results than using a weight-shared network or a cascade of proximal networks.
arXiv Detail & Related papers (2021-05-19T20:00:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.