来自杂志 Medical image analysis 的文献。
当前共找到 3 篇文献分享。
1.
Ricardo
(2022-12-31 23:50):
#paper http://dx.doi.org/10.1016/j.media.2015.04.005
Construction of 4D high-definition cortical surface atlases of infants: Methods and applications 在神经影像学中,皮层表面图谱在空间归一化、分析、可视化以及个体和不同研究结果的比较中发挥着重要作用。然而,现有的为成人创建的皮层表面图谱并不适合出生后头两年的婴儿大脑,这是出生后高度折叠的大脑皮层结构和功能发育最活跃的时期。因此非常需要婴儿时期的大脑皮层表面的时空图谱集,但目前仍缺乏精细的早期动态脑发育图谱。为了弥补这一重大差距,作者利用团队开发的婴儿皮层表面分析计算管道和自己获得的纵向MRI数据集,基于35名健康婴儿的202个系列MRI扫描,构建了第一个时空(4D)高清皮层表面地图集,用于七个时间点的动态发育研究,包括1、3、6、9、12、18和24个月龄。
Abstract:
In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for …
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In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two postnatal years, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at seven time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development.
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2.
Ricardo
(2022-04-30 21:07):
#paper https://doi.org/10.1016/j.media.2020.101939 Image registration: Maximum likelihood, minimum entropy and deep learning. MIA(2021) 作者在这篇文章里给pair-wise和group-wise的配准任务提出了一个基于maximum profile likelihood (MPL)的理论框架,并利用渐进分析方法证明了基于MPL的配准过程实际上是最小化生成联合图像数据分布的联合熵(minimizes an upper bound on the joint entropy of the distribution that generates the joint image data)。通过优化闭合形式的profile likelihood,作者推导出了groupwise配准的congealing 方法。这篇文章很多看不懂的地方,后面还得慢慢读。
IF:10.700Q1
Medical image analysis,
2021-04.
DOI: 10.1016/j.media.2020.101939
PMID: 33388458
PMCID:PMC8046343
Abstract:
In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes …
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In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.
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3.
Ricardo
(2022-04-30 20:52):
#paper https://doi.org/10.1016/j.media.2021.102292 Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance. MIA(2022) 微创颅内神经外科手术的精度可能会受到变形的脑组织结构的影响,例如,在神经内窥镜路径中,由于脑脊液的流出导致脑组织变形达10毫米。这篇文章提出了一种基于深度学习的无监督配准方法,用于术前MR和术中CT之间的配准。MR和CT之间的配准属于跨模态配准问题,由于难以衡量不同模态图像之间的相似性, 跨模态配准问题一直以来都比较难做。这篇文章的主要思路就是利用cyclegan将不同模态的图像转换成同模态图像,从而进行模态内的配准。另一方面,与其使用determistic cyclegan, 作者使用了probabilitic cyclegan,这样就可以输出模型对于预测的形变场的不准确度的估计,这种不准确度的估计可以进一步拿来作为形变场的约束。
Abstract:
PURPOSE: The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic …
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PURPOSE: The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance.METHOD: The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks.RESULTS: The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s.CONCLUSION: The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.
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