Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies

• URL: http://arxiv.org/abs/2305.01827v1
• Date: Tue, 2 May 2023 23:36:06 GMT
• Title: Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies
• Authors: Karthik Gopinath, Douglas N. Greve, Sudeshna Das, Steve Arnold, Colin Magdamo, and Juan Eugenio Iglesias
• Abstract summary: Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence.
• Score: 2.930354460501359
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. The convoluted cortical geometry requires isotropic scans (e.g., 1mm MPRAGEs) and good gray-white matter contrast for 3D reconstruction. This precludes the analysis of most brain MRI scans acquired for clinical purposes. Analyzing such scans would enable neuroimaging studies with sample sizes that cannot be achieved with current research datasets, particularly for underrepresented populations and rare diseases. Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence. The methods has a learning component and a classical optimization module. The former uses domain randomization to train a CNN that predicts an implicit representation of the white matter and pial surfaces (a signed distance function) at 1mm isotropic resolution, independently of the pulse sequence and resolution of the input. The latter uses geometry processing to place the surfaces while accurately satisfying topological and geometric constraints, thus enabling subsequent parcellation and thickness estimation with existing methods. We present results on 5mm axial FLAIR scans from ADNI and on a highly heterogeneous clinical dataset with 5,000 scans. Code and data are publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical

Related papers

• 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)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases [33.191270998887326]
  We present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$3$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w FLASH sequence at 7T.
  arXiv  Detail & Related papers  (2023-03-21T23:44:02Z)
• Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets [1.0499611180329802]
  We present SynthSeg+, an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. Specifically, in addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations. We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality.
  arXiv  Detail & Related papers  (2022-09-05T16:09:24Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining [1.0499611180329802]
  Retrospective analysis of brain MRI scans acquired in the clinic has the potential to enable neuroimaging studies with sample sizes much larger than those found in research datasets. Recent advances in convolutional neural networks (CNNs) and domain randomisation for image segmentation may enable morphometry of clinical MRI at scale. We show that SynthSeg is generally robust, but frequently falters on scans with low signal-to-noise ratio or poor tissue contrast. We propose SynthSeg+, a novel method that greatly mitigates these problems using a hierarchy of conditional segmentation and denoising CNNs.
  arXiv  Detail & Related papers  (2022-03-03T19:18:28Z)
• 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)
• Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast [4.987889348212769]
  We present SynthSR, a method to train a CNN that receives one or more thick-slice scans with different contrast, resolution and orientation. The presented method does not require any preprocessing, e.g., stripping or bias field correction.
  arXiv  Detail & Related papers  (2020-12-24T17:29:53Z)
• Surface Agnostic Metrics for Cortical Volume Segmentation and Regression [3.1543820811374483]
  We propose a machine learning solution to predict cortical thickness and curvature metrics from T2 MRI images. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.
  arXiv  Detail & Related papers  (2020-10-04T19:46:04Z)
• Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset [81.02949933048332]
  This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of Representational Similarity Analysis (RSA) DRSL is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects.
  arXiv  Detail & Related papers  (2020-09-28T18:30:14Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.