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Пишет bioRxiv Subject Collection: Neuroscience (![[info]](http://lj.rossia.org/img/syndicated.gif){kind=small} syn_bx_neuro)@ 2024-07-09 23:34:00 |
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Advancing Thalamic Nuclei Segmentation: The Impact of Compressed Sensing and FastSurfer on MRI Processing
The thalamus is a collection of gray matter nuclei that play a crucial role in sensorimotor processing and modulation of cortical activity. Characterizing thalamic nuclei is particularly relevant for patient populations with Parkinsons disease, epilepsy, dementia, and schizophrenia. However, severe head motion in these populations poses a significant challenge for in vivo mapping of thalamic nuclei. Recent advancements have leveraged the compressed sensing (CS) framework to accelerate acquisition times in MPRAGE sequence variants, while fast segmentation tools like FastSurfer have reduced processing times in MRI research.
In this study, we evaluated thalamic nuclei segmentations derived from six different MPRAGE variants with varying degrees of CS acceleration (from about 9 to about 1 minute acquisitions), using both FreeSurfer and FastSurfer for segmentation. Our findings show minimal sequence effects with no systematic bias, and low volume variability across sequences for the whole thalamus and major thalamic nuclei. Notably, CS-accelerated sequences produced less variable volumes compared to non-CS sequences. Additionally, segmentations of thalamic nuclei by FreeSurfer and FastSurfer were highly comparable.
We provide first evidence supporting that a good segmentation quality of thalamic nuclei with compressed sensing T1-weighted image acceleration in a clinical 3T MRI system is possible. Our findings encourage future applications of fast T1-weighted MRI to study deep gray matter. CS-accelerated sequences and rapid segmentation methods are promising tools for future studies aiming to characterize thalamic nuclei in vivo at 3T in both healthy individuals and clinical populations.
Key points1) MPRAGE variants can be accelerated with Compressed Sensing without increasing volume variability in thalamus and major thalamic nuclei; 2) Thalamic nuclei segmentations on FastSurfer whole-brain data provide comparable results to FreeSurfer.
The thalamus is a collection of gray matter nuclei that play a crucial role in sensorimotor processing and modulation of cortical activity. Characterizing thalamic nuclei is particularly relevant for patient populations with Parkinsons disease, epilepsy, dementia, and schizophrenia. However, severe head motion in these populations poses a significant challenge for in vivo mapping of thalamic nuclei. Recent advancements have leveraged the compressed sensing (CS) framework to accelerate acquisition times in MPRAGE sequence variants, while fast segmentation tools like FastSurfer have reduced processing times in MRI research.
In this study, we evaluated thalamic nuclei segmentations derived from six different MPRAGE variants with varying degrees of CS acceleration (from about 9 to about 1 minute acquisitions), using both FreeSurfer and FastSurfer for segmentation. Our findings show minimal sequence effects with no systematic bias, and low volume variability across sequences for the whole thalamus and major thalamic nuclei. Notably, CS-accelerated sequences produced less variable volumes compared to non-CS sequences. Additionally, segmentations of thalamic nuclei by FreeSurfer and FastSurfer were highly comparable.
We provide first evidence supporting that a good segmentation quality of thalamic nuclei with compressed sensing T1-weighted image acceleration in a clinical 3T MRI system is possible. Our findings encourage future applications of fast T1-weighted MRI to study deep gray matter. CS-accelerated sequences and rapid segmentation methods are promising tools for future studies aiming to characterize thalamic nuclei in vivo at 3T in both healthy individuals and clinical populations.
Key points1) MPRAGE variants can be accelerated with Compressed Sensing without increasing volume variability in thalamus and major thalamic nuclei; 2) Thalamic nuclei segmentations on FastSurfer whole-brain data provide comparable results to FreeSurfer.
