General purpose abnormality detection in whole body PET

全身 PET 通用异常检测

• 批准号: 2424288
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2424288
• 关键词: General; purpose; abnormality; detection; whole

Aim of the PhD Project:Develop AI methods that can predict healthy PET radiotracer uptake from anatomical data.Model the uncertainty of such predictions in a heteroscedastic (spatially varying) manner using Bayesian deep learning.Translate these algorithms to clinical practice and integrate them within the clinical workflow.Project Description / Background:Cancers can have a highly heterogeneous pattern of anomalies in positron emission tomography (PET). These specific patterns of anomaly are important to detect, stage and predict the evolution of disease. In a research context, images are often analysed by performing group comparisons. This approach does not correspond to the clinic scenario, where the analysis has to be performed at the individual level to detect subject-specific patterns. In clinical practice, PET images are mostly analysed visually. The sensitivity and specificity of this approach greatly depends on the observer's experience and is not in favour of centres where advanced expertise in image reading is unavailable [Perani et al., 2014]. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings.PET uptake can be quantitatively evaluated either regionally or on a voxel-by-voxel basis. In regional analysis, the regional uptake is compared with the regional uptake expected in a normal control population. This analysis usually requires prior knowledge to select the appropriate atlas and relevant discriminant regions, which should be adapted to a specific pathology, limiting its use (Signorini et al., 2019).In voxel-wise analysis, a subject's PET image is usually aligned to a standardised group space to compare the metabolic activity of the spatially normalised scan to a distribution obtained from normal control scans, on a voxel-by-voxel basis. The approach implemented in software such as Neurostat consists of registering the PET image of the subject under investigation to a standard space and comparing it to a population of controls by means of a Z-score. The Z-score map is then projected onto different surfaces resulting in three-dimensional stereotactic surface projections that are used for image interpretation. Other software tools implementing a similar technique have been used for the analysis of PET data, such as NeuroGam by GE Healthcare. These packages have been mostly developed for brain imaging, limiting their applicability for other bodily diseases, but also have several non-optimal assumptions (noise model, parametric distribution, etc) about the statistical models of abnormality.The PhD candidate will work towards creating patient- and tracer-specific models of healthy PET tracer distribution in an optimal way for full body data using artificial intelligence semantic regression models (Klaser et al. 2019). Due to the non-Gaussian nature of the expected tracer distribution, these models will use deep learning based uncertainty estimation using Dropout (Gal et al. 2015), jointly with multiple hypothesis output predictions (M-heads) to create a robust statistical model of tracer distribution. Due to the sampling nature of such model, a non-parametric statistical test can then be used to estimate a per-pixel degree of abnormality of the PET signal (Burgos et al. 2017), making the application of this technique safe in a clinical setting. Further to this, as PET imaging has both local and global uptake patterns, architectures will have to be optimised to take the full body semantics into account as to appropriately model PET at multiple spatial scales. These models will be applied to a large cohort of cancer patients with full body PET imaging, either in a PET-CT or PET-MRI setting.

博士项目的目的：开发人工智能方法，可以从解剖数据预测健康的PET放射性示踪剂摄取。使用贝叶斯深度学习以异方差（空间变化）的方式对这种预测的不确定性进行建模。将这些算法转化为临床实践，并将其整合到临床工作流程中。项目描述/背景：癌症可能具有高度异质的正电子发射断层扫描（PET）异常模式。这些特定的异常模式对于检测、分期和预测疾病的演变非常重要。在研究背景下，图像通常通过执行组比较进行分析。这种方法不符合临床场景，其中必须在个体水平上进行分析以检测受试者特异性模式。在临床实践中，PET图像主要是视觉分析。这种方法的灵敏度和特异性在很大程度上取决于观察者的经验，并且不适合那些无法获得图像阅读方面的高级专业知识的中心[Perani等人，2014年]。PET图像的定量分析可以通过帮助定义正常和病理结果之间的客观界限来缓解这个问题。PET摄取可以在区域或逐体素的基础上进行定量评估。在区域分析中，将区域摄取与正常对照人群中预期的区域摄取进行比较。这种分析通常需要先验知识来选择适当的图谱和相关的判别区域，其应适应于特定的病理学，从而限制了其使用（Signorini等人，在逐体素分析中，受试者的PET图像通常与标准化的组空间对齐，以在逐体素的基础上将空间标准化扫描的代谢活动与从正常对照扫描获得的分布进行比较。在诸如Neuostat的软件中实施的方法包括将受调查对象的PET图像配准到标准空间，并通过Z分数将其与对照群体进行比较。然后将Z评分图投影到不同表面上，得到用于图像判读的三维立体定向表面投影。实现类似技术的其他软件工具已用于PET数据的分析，例如GE Healthcare的NeuroGam。这些软件包主要是为大脑成像而开发的，限制了它们对其他身体疾病的适用性，但也有一些非最佳假设（噪声模型，参数分布，等）关于异常的统计模型。博士候选人将致力于使用人工智能语义回归模型，以全身数据的最佳方式创建健康PET示踪剂分布的患者和示踪剂特定模型（Klaser等人，2019）。由于预期示踪剂分布的非高斯性质，这些模型将使用基于深度学习的不确定性估计，使用Dropout（Gal等人，2015），结合多个假设输出预测（M-heads），以创建示踪剂分布的稳健统计模型。由于这种模型的采样性质，然后可以使用非参数统计检验来估计PET信号的每像素异常程度（Burgos等人，2017），从而使该技术在临床环境中的应用安全。此外，由于PET成像具有局部和全局摄取模式，因此必须优化架构以考虑全身语义，从而在多个空间尺度上适当地对PET进行建模。这些模型将应用于在PET-CT或PET-MRI环境中进行全身PET成像的大型癌症患者队列。