Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia.
Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia.
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DOI：
10.1016/j.media.2021.102257
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发表时间：
2022-01
影响因子：
10.9
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Ravi D;Blumberg SB;Ingala S;Barkhof F;Alexander DC;Oxtoby NP;Alzheimer’s Disease Neuroimaging Initiative
我们实现了一个新的深度学习框架，能够在衰老和阿尔茨海默病中合成逼真和准确的4D大脑MRI。我们提出了一系列的内存效率的技术，旨在提高模型的稳定性，减少文物，并提高个性化。合成的T1 w MRI扫描仅包含与真实的数据的微小结构差异，并且具有最小的噪声/纹理伪影。合成的MRI扫描与真实的扫描在诊断上无法区分。合成MRI可用于：i）数据增强，ii）模型验证和iii）理解大脑中的生物/疾病机制。准确逼真地模拟高维医学图像已成为许多AI医疗应用的重要研究领域。然而，当前最先进的方法缺乏产生令人满意的高分辨率和准确的对象特定图像的能力。在这项工作中，我们提出了一个深度学习框架，即4D-DANI-Net（4D-DANI-Net），以生成高分辨率的纵向MRI扫描，模拟衰老和痴呆症中受试者特定的神经退行性变。4D-DANI-Net是一个基于对抗训练和一组新颖的时空生物信息约束的模块化框架。为了确保有效的训练并克服影响这些高维问题的记忆限制，我们依赖于三项关键技术进步：i）一种新的3D训练一致性机制，称为轮廓权重函数（PWF），ii）3D超分辨率模块和iii）迁移学习策略，以针对给定的个体微调系统。为了评估我们的方法，我们在阿尔茨海默病神经成像倡议数据集中的876名参与者的9852次T1加权MRI扫描上训练了框架，并从170名参与者中进行了1283次MRI扫描的单独测试集，用于对合成图像的个性化时间序列进行定量和定性评估。我们进行了三项评估：i）图像质量评估; ii）量化超过和高于基准模型的局部脑体积的准确性;以及iii）量化医学专家对合成图像的视觉感知。总体而言，定量和定性结果都表明，4D-DANI-Net产生的合成T1 MRI的真实，低伪影，个性化时间序列优于基准模型。
We implemented a new deep learning framework capable of synthesising realistic and accurate 4D brain MRI in ageing and Alzheimer’s disease. We proposed a sequence of memory-efficient techniques designed to improve model stability, reduce artefacts, and improve individualization. Synthesised T1w MRI scans contain only minor structural differences with real data, and have minimal noise/texture artefacts. Synthesised MRI scans were diagnostically indistinguishable from real scans. Synthetic MRI can be used for: i) data augmentation, ii) model validation and iii) understanding biological/disease mechanisms in the brain. Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer’s Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models.
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影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
13.7
作者：
Chang, Jooyoung;Lee, Jinho;Park, Sang Min
通讯作者：
Park, Sang Min
影响因子：
4.2
作者：
Lorenzi, Marco;Pennec, Xavier;Ayache, Nicholas
通讯作者：
Ayache, Nicholas
DOI：
10.1016/j.jalz.2013.10.003
发表时间：
2014-10
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Donohue MC;Jacqmin-Gadda H;Le Goff M;Thomas RG;Raman R;Gamst AC;Beckett LA;Jack CR Jr;Weiner MW;Dartigues JF;Aisen PS;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
38.1
作者：
Frisoni, Giovanni B.;Fox, Nick C.;Jack, Clifford R., Jr.;Scheltens, Philip;Thompson, Paul M.
通讯作者：
Thompson, Paul M.