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Extracting brain PET-MRI joint structures for statistical analysis
Many applications of joint PET-MRI image require statistical analysis of these images, across subjects for group studies in clinical research, and longitudinally on a single subject in clinical practice. This statistical analysis might be challenging due to the number of parameters to investigate and associated issues: the small number of available samples compared to the large size of the multi-modal images (when it is not possible to define ROIs a priori) and the possibly low Signal-to-Noise of the functional images (be they MR or PET images).
To optimize the sensitivity under an accurate control of the specificity when performing inference, it is thus essential to account for the structure of multi-modal images: not only do the two modalities share information, but also this information is best captured by considering image patterns that match the underlying physiopathology rather than voxels. Indeed, a voxel-level statistical analysis is plagued by fundamental limitations such as multiple comparisons or the curse of dimensionality. Taking into account the image structure in the statistical analysis is typically performed by means of a dictionary learning procedure: several components are identified based on a linear mixing model together with a prior on their statistical structure.
We will design some procedures dedicated to the joint modeling of PET and MRI that present good convergence properties and computational efficiency.
Partners:
Project Leader: Gaël Varoquaux (CEA NeuroSpin)
U1000
Fig.1 Dictionary learning can be used to learn a decomposition of brain image volume so that the extracted dictionary components represent some key features of the brain's anatomo-functional organization that are reflected in activation images (fMRI or PET).
Fig.2 Dictionary models learned from the data (called parcellations in the above figure) can be integrated into an inferential framework as a means to regularize spatially the statistical inference done to compare brain images with external (behavioral or genetic) data. This framework has been tested successfully on group functional MRI studies and will be extended to PET imaging within PIM.
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