来自用户 Ricardo 的文献。
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21.
Ricardo
(2022-06-01 00:45):
#paper https://doi.org/10.1016/j.neuroimage.2022.119097 A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort. 2022年发表于neuroimage。由于人类大脑在出生后的头两年处于快速发育的过程,随着年龄的增长,其MRI影像的图像appearance和contrast呈现动态的变化。因此,为婴幼儿早期发育研究构建高精度的时空脑图谱是一件非常重要的事情。这篇研究从240名26月龄以前的婴幼儿被试中采集了542例T1和T2的纵向影像数据用于图谱的构建。出乎我意料的是,他们没有采用他们实验室之前开发的一系列针对于婴幼儿脑影像数据特点的配准技术,而是通过结合强度图像和分割图像并利用基于成人大脑开发的配准算法构建的图谱。他们对0-24个月的婴幼儿分年龄段的构建了17个时间点的图谱,其中前12个月每一个月构建一个图谱,后12个月每3个月构建一个图谱。当然这篇文章存在一些技术问题,我的博士课题也正在考虑做相似的工作,可能会根据里面出现的问题做一些改进。
Abstract:
Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain …
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Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.
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22.
Ricardo
(2022-05-30 23:39):
#paper https://arxiv.org/abs/2102.04159v3 Deep Residual Learning in Spiking Neural Networks. 2021年发表于NIPS。基于人工神经网络的现代深度学习技术在各个领域上都取得了相当大的进展,但是由于其数学上的黑箱不可解释性、功耗高的问题,有一部分研究开始关注于基于生物脉冲神经元的脉冲神经网络上(SNN)。SNN有较高的生物解释性、事件驱动性和低功耗等特点,被视为人工神经网络的潜在竞争对手。但是SNN仍然面临许多理论和工程问题,在一些复杂任务上的表现仍然比ANN差。基于残差学习在ANN上取得的巨大成功,自然会去研究如何利用残差学习去训练SNN。之前的一些研究仿照ANN中标准的残差模块,简单地将relu激活函数替换成脉冲神经元,但是这样的网络伴随着深度的增加会出现退化问题,从而难以实现残差学习。在这篇论文里,作者证明了之前在SNN上的残差学习方法会导致梯度爆炸/消失问题,从而难以实现identity mapping。因此,他们提出了一个方法用来解决这么一个梯度爆炸/消失问题。实验结果也挺漂亮的,在多个数据集上都比之前的snn方法更好,当然不如ann的结果啦。并且能够通过加深网络深度提高snn的performance。而且,也首次实现了能够直接训练超过100层的snn。
arXiv,
2022.
DOI: 10.48550/arXiv.2102.04159
Abstract:
Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it …
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Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning. In this paper, we propose the spike-element-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet. We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps. Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible.
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23.
Ricardo
(2022-04-30 21:13):
#paper DOI: 10.1109/WACV51458.2022.00162. Uncertainty Learning towards Unsupervised Deformable Medical Image Registration. WACV(2022) 这篇文章没啥新意,感觉有点灌水。总而言之,在前列腺MRI图像中的配准工作,加入了分割标签作为形变场的约束,同时提出了一种基于laplace分布的模型不确定度估计的方法。嗯,没了。
Abstract:
Uncertainty estimation in medical image registration enables surgeons to evaluate the operative risk based on the trustworthiness of the registered image data thus of paramount importance for practical clinical applications. …
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Uncertainty estimation in medical image registration enables surgeons to evaluate the operative risk based on the trustworthiness of the registered image data thus of paramount importance for practical clinical applications. Despite the recent promising results obtained with deep unsupervised learning-based registration methods, reasoning about uncertainty of unsupervised registration models remains largely unexplored. In this work, we propose a predictive module to learn the registration and uncertainty in correspondence simultaneously. Our framework introduces empirical randomness and registration error based uncertainty prediction. We systematically assess the performances on two MRI datasets with different ensemble paradigms. Experimental results highlight that our proposed framework significantly improves the registration accuracy and uncertainty compared with the baseline.
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24.
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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25.
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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26.
Ricardo
(2022-04-30 20:39):
#paper https://doi.org/10.48550/arXiv.1806.09055 DARTS: differentiable architecture search ICLR(2019) Neural Architectural Search (NAS) 这个问题是出了名的消耗算力,动不动就需要消耗上千个gpu hour,基本也只能在顶级的研究机构做这类研究。这篇文章没有使用类似于进化算法或者强化学习这样的方法在离散和不可微的空间中搜索网络架构, 而是通过对神经网络的架构表征进行松弛,将NAS问题转化为一个可微分的形式,从而能够使用梯度下降法在连续空间中搜索神经网络架构。作者将这个问题建模成一个bilevel的优化问题,然后提出了一个类似于EM算法的优化方法,通过交替优化模型架构参数\alpha和模型权重w来找到较优的模型架构\alpha 。由于优化过程中涉及二阶导的计算,作者进一步对二阶导的计算做了松弛,将其转化为形式为一阶导的估计,从而进一步降低了方法的复杂度。结果也都很漂亮,相比于之前那些动辄需要上千个gpu day的计算量,darts方法只需要几个gpu day的计算,而且也能达到差不多的效果。
arXiv,
2019.
DOI: 10.48550/arXiv.1806.09055
Abstract:
This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and …
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This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms.
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27.
Ricardo
(2022-04-01 00:05):
#paper https://doi.org/10.1016/j.neuroimage.2020.117161
NeuroImage, 2020 Cortical surface registration using unsupervised learning
由于我们经常需要在不同被试间或者同一个被试的不同时间点的脑图像上建立空间映射关系,因此非线性配准是脑影像分析中非常重要的一步。这几年时间里,大家开始使用深度学习开发新的脑图像配准算法,但是大都关注于基于volume空间下的配准算法的研究,鲜少有研究关注于脑皮层的点云空间下的配准。这篇文章通过将卷积操作拓展到极坐标空间下,实现了在球面空间上的卷积操作,从而开发了针对于脑皮层的配准算法。
IF:4.700Q1
NeuroImage,
2020-11-01.
DOI: 10.1016/j.neuroimage.2020.117161
PMID: 32702486
PMCID:PMC7784120
Abstract:
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to …
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Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
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28.
Ricardo
(2022-02-27 22:12):
#paper doi:https://doi.org/10.1038/s41592-020-01008-z nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation 介绍这一篇2020年发表在nature methods上的文章,做医学图像算法的同学估计都知道这个非常牛逼的工作,用一套自己设计的图像分割的pipeline,没有对神经网络结构做什么改进,在23个公开的医学影像数据集上大都获得了非常好的结果。细看文章和源码,可以看到作者在数据集的预处理上、超参数的选择上、模型调优和集成以及后处理等步骤上做了相当多的工作。
Abstract:
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable …
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Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.
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29.
Ricardo
(2022-01-31 21:02):
#paper doi:https://doi.org/10.1523/JNEUROSCI.3479-08.2008 A Structural MRI Study of Human Brain Development from Birth to 2 Years. 读一篇08年发表在The Journal of Neuroscience上的一篇关于婴幼儿脑结构发育的文章。之前介绍过几篇婴幼儿大脑发育相关的文章,也提到了在出生后的两年时间里,婴幼儿大脑处于快速的动态发育过程,并且这一时期的发育在一些神经发育疾病中(自闭症或精神分裂症)有着重要的影响。这个工作采集了包括98名健康被试从出生到2岁时期的脑结构磁共振影像,并使用北卡罗来纳大学开发的自动分割方法划分脑组织,并测定了侧脑室、尾状核和海马的体积。
分析结果表明:
1. 出生后的第一年全脑容量增加了101%;第二年增加了15%。灰质体积的增长占据了全脑体积增长量的主要部分,在第一年增长了149%,而白质体积仅增加了11%;
2. 小脑容量在第一年增加了240%,侧脑室体积在第一年则增加了280%,在第二年略有下降;
3. 从1岁到2碎,尾状核增长了19%,海马增长了13%。
人类大脑在出生后的两年快速发育,这主要是受到灰质生长的驱动(也就是大脑皮层的增长非常快速)。相比之下,白质的增长要慢得多。
Abstract:
Brain development in the first 2 years after birth is extremely dynamic and likely plays an important role in neurodevelopmental disorders, including autism and schizophrenia. Knowledge regarding this period is …
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Brain development in the first 2 years after birth is extremely dynamic and likely plays an important role in neurodevelopmental disorders, including autism and schizophrenia. Knowledge regarding this period is currently quite limited. We studied structural brain development in healthy subjects from birth to 2. Ninety-eight children received structural MRI scans on a Siemens head-only 3T scanner with magnetization prepared rapid gradient echo T1-weighted, and turbo spin echo, dual-echo (proton density and T2 weighted) sequences: 84 children at 2-4 weeks, 35 at 1 year and 26 at 2 years of age. Tissue segmentation was accomplished using a novel automated approach. Lateral ventricle, caudate, and hippocampal volumes were also determined. Total brain volume increased 101% in the first year, with a 15% increase in the second. The majority of hemispheric growth was accounted for by gray matter, which increased 149% in the first year; hemispheric white matter volume increased by only 11%. Cerebellum volume increased 240% in the first year. Lateral ventricle volume increased 280% in the first year, with a small decrease in the second. The caudate increased 19% and the hippocampus 13% from age 1 to age 2. There was robust growth of the human brain in the first two years of life, driven mainly by gray matter growth. In contrast, white matter growth was much slower. Cerebellum volume also increased substantially in the first year of life. These results suggest the structural underpinnings of cognitive and motor development in early childhood, as well as the potential pathogenesis of neurodevelopmental disorders.
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30.
Ricardo
(2022-01-22 17:06):
#paper doi:10.1109/TMI.2021.3137280 Recurrent Tissue-Aware Network for Deformable
Registration of Infant Brain MR Images. 介绍一篇21年末发表在TMI上的文章。众所周知,非线性配准技术是一种用于纵向发育分析和群体分析的基础技术。然而由于出生后的两年时间里,婴幼儿大脑处于快速发育过程中,对同一个被试的不同发育时间点的婴儿脑图像或者对不同被试的婴幼儿脑图像进行精细的配准是一件非常困难的事。主要有几点原因:1.婴幼儿大脑处于持续的髓鞘化进程,脑图像体素强度呈现出区域间的不一致性;2.0~2岁这个阶段婴幼儿大脑图像的信号强度会出现反转的变化,这使得纵向图像的配准变得更加困难;3.婴幼儿大脑非常小,而大脑组织结构又相对比较复杂,并且还存在许多图像噪声及伪影。所以这篇文章干脆不对MRI强度信号图像进行配准,而是使用基于T1/T2图像的脑组织分割图进行配准,这样就规避了出生后的头两年大脑组织信号强度的快速变化的问题。这篇文章主要有两个创新点:1.只对模型做一次训练,但是在测试阶段进行多次配准,一步步对脑图像的形变场进行finetune;2.作者将速度场建模成一个多元高斯场,每个体素都服从高斯分布。并给予速度场的方差建模形变场的不确定度(uncertainty),并基于这样的uncertainty对形变场进行动态平滑(adaptive smoothing),而非以往的全局平滑。具体结果当然要比其他方法更好啦,这个没啥说的。不过这个方法的局限在于需要精细分割后的脑组织图像,而对婴幼儿的脑图像进行组织分割又是一个非常困难的事啊(就是又缺数据又难打label)。
IF:8.900Q1
IEEE transactions on medical imaging,
2022-05.
DOI: 10.1109/TMI.2021.3137280
PMID: 34932474
Abstract:
Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional …
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Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net.
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31.
Ricardo
(2022-01-22 16:40):
#paper doi:https://doi.org/10.1016/j.neuroimage.2014.11.042 DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions. 2015年发表在neuroimage。介绍一篇和我目前做的工作比较相关的一篇paper。弥散磁共振成像(dMRI)可以定量地测量活体脑白质结构,是一种研究人脑白质微观结构特性或脑区间通路的一种重要的神经成像技术。在过去的几十年里,由于回波平面成像(EPI)技术可以很快地对全脑进行成像,所以大部分dMRI都是基于EPI序列进行采集的。但是由于不同人脑组织(如骨、脑脊液)的磁化率不同,因此使得MRI腔体中的磁场呈现一定程度的不均匀性,从而影响磁共振图像体素的空间编码,并导致解剖结构上的畸变和磁共振信号的畸变。这种畸变也被称为磁敏感伪影(susceptibility artifact,SA)。03年的时候Oxford大学有一个大佬开发了用于消除这种畸变的影像算法(Topup),并且广泛应用于各种大型神经影像数据项目中。不过这篇文章的作者认为,topup算法仅仅使用了b0图像对不均匀场进行估计,并没有充分利用结构像和弥散加权图像的信息对不均匀场的求解空间进行约束。这篇工作从以下几个方面对SA矫正算法进行改进:1.使用一种对称的(symmetric)、微分同胚的(diffeomorphic)以及基于变换的速度场的配准模型构建优化模型;2.作者不仅仅使用一个constant的不均匀场,而是两个相互依赖的不均匀场来矫正成对图像间的扭曲;3.引入T2加权结构像引导图像畸变的恢复;4.引入弥散加权图像约束模型求解空间。结果表明DR-BUDDI算法在多个指标上均比目前广泛使用Topup算法表现更佳。
我最近做的工作也是类似的工作,在多个数据集上进行了验证测试,等文章发表出来我再做一些介绍。
Abstract:
We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up …
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We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. Imposing the theoretical "blip-up blip-down distortion symmetry" may not be appropriate in the presence of common clinical scanning artifacts such as motion, ghosting, Gibbs ringing, vibrations, and low signal-to-noise. The performance of DR-BUDDI is evaluated with several data sets and compared to other existing blip-up blip-down correction approaches. The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways.
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32.
Ricardo
(2022-01-20 19:09):
#paper doi:10.1158/1078-0432.CCR-17-1038 Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. 于2017年发表于clinical cancer research。这篇文章算是跟我方向没啥关系,为啥会看这篇文章主要是为了应付老板给安排的一个医院的项目。简单来说,这篇文章就是开发了一个放射组学的模型,用于评估局部晚期直肠癌(LARC)患者对新辅助放化疗的病理完全缓解(pCR,pathological complete response,不知道怎么翻译好)。这篇文章纳入了222名LARC患者(152例primary cohort,70例属于validation cohort),在术前都接受了放化疗。所有患者在放化疗前后均采集了T2像和弥散像。
模型构建流程:1. 由两名放射科医生对放疗前后的T2w图像和弥散像手动提取肿瘤的ROI区域;2.分别从这4个图像中提取3组影像学特征:4个统计特征,43个体素强度计算特征和516个小波特征。总计每个病人有(516+43+4)*4=2252个影像组学特征。3.首先用2-sample t-test在primary cohort中pCR组和non-pCR组中有差异的最佳特征;其次用LASSO进一步筛选特征。4.然后使用SVM来区分患者是否achieve pCR,并使用基于所选特征的线性核训练的SVM模型计算每个患者的放射组学评分。5.最后在多个临床信息数据上使用多变量logistic回归分析。
结果:放射性组学特征包括30个选定的特征,在primary cohort和validation cohort中均表现出良好的鉴别性能。个体化放射组学模型融合了放射组学特征和肿瘤长度,具有良好的辨别性,在validation cohort中roc曲线面积为0.9756(95%置信区间为0.9185-0.9711)。
IF:10.000Q1
Clinical cancer research : an official journal of the American Association for Cancer Research,
2017-Dec-01.
DOI: 10.1158/1078-0432.CCR-17-1038
PMID: 28939744
Abstract:
To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC). We enrolled 222 patients (152 in …
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To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC). We enrolled 222 patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation. The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model. Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy. .
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33.
Ricardo
(2022-01-20 18:02):
#paper doi:https://doi.org/10.1073/pnas.1821523116 Developmental topography of cortical thickness during infancy. 这篇文章于2019年发表在pnas上。在出生后的两年时间里,人类大脑经历了快速的动态发育,这表现在行为和认知能力上的快速发展。而绘制健康婴幼儿大脑皮层厚度的发育模式对于理解一些神经发育疾病来说有着重要价值。虽然利用磁共振成像技术研究人类大脑的发育老化规律已经有几十年的时间了,但是对于两岁以前这样非常早期的研究其实还非常少。这主要是因为婴幼儿大脑的核磁成像数据非常难以获取(需要婴幼儿保持几十分钟的相对静止)以及婴幼儿大脑磁共振图像相对于成年人来说非常难处理(所以需要开发特定的影像处理算法)。这篇文章利用了一个被称为Baby Connectome Project的脑影像数据库,并利用作者所在研究组开发的一系列图像处理算法对婴幼儿脑影像数据进行预处理。他们还利用非负矩阵分解这一经典的分析技术建模婴幼儿大脑皮层厚度的时空发育轨迹。
这篇文章主要有两个发现:1.在出生后的两年,婴幼儿大脑的平均皮层厚度先快速增加,然后大约在14个月的时候达到峰值点,之后在以缓慢的速度减少;2.作者根据皮层厚度的发育模式将婴幼儿大脑分成若干个区域,他们发现不同脑区都有不同的皮层厚度的发育特点,有的脑区在不同时间点达到皮层厚度的峰值点,有的区域则在这两年时间里保持持续的增长。
IF:9.400Q1
Proceedings of the National Academy of Sciences of the United States of America,
2019-08-06.
DOI: 10.1073/pnas.1821523116
PMID: 31332010
Abstract:
During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development …
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During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development renders this period critical for later cognitive outcomes and vulnerable to early neurodevelopmental disorders. However, due to the difficulties in longitudinal infant brain MRI acquisition and processing, our knowledge still remains limited on the dynamic changes, peak age, and spatial heterogeneities of cortical thickness during infancy. To fill this knowledge gap, in this study, we discover the developmental regionalization of cortical thickness, i.e., developmentally distinct regions, each of which is composed of a set of codeveloping cortical vertices, for better understanding of the spatiotemporal heterogeneities of cortical thickness development. We leverage an infant-dedicated computational pipeline, an advanced multivariate analysis method (i.e., nonnegative matrix factorization), and a densely sampled longitudinal dataset with 210 serial MRI scans from 43 healthy infants, with each infant being scheduled to have 7 longitudinal scans at around 1, 3, 6, 9, 12, 18, and 24 mo of age. Our results suggest that, during the first 2 y, the whole-brain average cortical thickness increases rapidly and reaches a plateau at about 14 mo of age and then decreases at a slow pace thereafter. More importantly, each discovered region is structurally and functionally meaningful and exhibits a distinctive developmental pattern, with several regions peaking at varied ages while others keep increasing in the first 2 postnatal years. Our findings provide valuable references and insights for early brain development.
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