Anatomically Constrained Tractography of the Fetal Brain

• URL: http://arxiv.org/abs/2403.02444v1
• Date: Mon, 4 Mar 2024 19:56:19 GMT
• Title: Anatomically Constrained Tractography of the Fetal Brain
• Authors: Camilo Calixto, Camilo Jaimes, Matheus D. Soldatelli, Simon K. Warfield, Ali Gholipour, Davood Karimi
• Abstract summary: We advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve tractography results.
• Score: 6.112565873653592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can lead to significant improvements in the accuracy and reproducibility of quantitative assessment of the fetal brain with dMRI.

Related papers

• Streamline tractography of the fetal brain in utero with machine learning [7.164734676863147]
  This work presents the first machine learning model for fetal tractography. We trained the model on manually-refined whole-brain fetal tractograms and validated the trained model on an independent set of 11 test scans with gestational ages between 23 and 36 weeks.
  arXiv  Detail & Related papers  (2024-08-26T14:54:14Z)
• Detailed delineation of the fetal brain in diffusion MRI via multi-task learning [5.261252388770444]
  We developed and validated a unified computational framework to segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations.
  arXiv  Detail & Related papers  (2024-08-26T13:51:04Z)
• HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI [5.393543723150301]
  This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HaITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction. HaITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling.
  arXiv  Detail & Related papers  (2024-06-28T16:40:57Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• Fetal-BET: Brain Extraction Tool for Fetal MRI [4.214523989654048]
  We build a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures. Our approach leverages the rich information from multi-contrast (multi-sequence) fetal MRI data, enabling precise delineation of the fetal brain structures.
  arXiv  Detail & Related papers  (2023-10-02T18:14:23Z)
• Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes [52.242103848335354]
  We propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans.
  arXiv  Detail & Related papers  (2023-08-13T12:51:15Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification [0.6117371161379209]
  We have developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40%. Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.
  arXiv  Detail & Related papers  (2021-06-14T12:19:27Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)

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.