PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation

PET：通过随机优化改善临床和医学 PET 成像的定位、诊断和量化

• 批准号: EP/S026045/1
• 负责人: Carola-Bibiane Schönlieb
• 金额: $ 104.67万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS026045%2F1
• 关键词: PET++; Improving; Localisation; Diagnosis; Quantification

Positron Emission Tomography (PET) is a pillar of modern diagnostic imaging, allowing non-invasive, sensitive and specific detection of functional changes in several disease types. In endocrinology, the precise localisation of small functioning tumours of the pituitary or adrenal glands is crucial for planning curative surgery or radiotherapy. While PET imaging shows good promise for this task, initial studies suggest significant room for improvement, with improved PET imaging and subsequent more accurate localisation opening up the possibility for more adapted therapies. In dementia, the accurate quantification of PET images is key for the early detection of disease. Improved PET imaging may allow for earlier detection of dementia while asymptomatic and increased sensitivity to assess and monitor treatment once appropriate drugs have been found. In this project mathematicians team up with researchers and clinicians from Addenbrooke's Hospital Cambridge, Dementias Platform UK (DPUK), GE Healthcare and University College London (UCL) for improved diagnosis and localization for tumours in endocrinology and earlier diagnosis of dementia with improved PET imaging. In particular, we investigate modern PET reconstruction approaches based on advanced mathematical methods to increase the PET image resolution and contrast, while keeping computational complexity low, thereby directly benefiting clinical workflow.

正电子发射断层扫描(PET)是现代诊断成像的支柱，可以对多种疾病类型的功能变化进行非侵入性、灵敏和特异性的检测。在内分泌学中，对垂体或肾上腺的小的功能性肿瘤的精确定位对于计划根治性手术或放射治疗是至关重要的。虽然PET成像显示了这项任务的良好前景，但初步研究表明还有很大的改进空间，改进的PET成像和随后更准确的定位为更适应的治疗打开了可能性。在痴呆症中，PET图像的准确量化是早期发现疾病的关键。改进的PET成像可以在无症状的情况下更早地发现痴呆症，并提高敏感度，以便在找到合适的药物后评估和监测治疗。在这个项目中，数学家与来自剑桥Addenbrooke医院、Dementias Platform UK(DPUK)、GE Healthcare和伦敦大学学院(UCL)的研究人员和临床医生合作，通过改进的PET成像改进内分泌学肿瘤的诊断和定位，以及痴呆症的早期诊断。特别是，我们研究了基于先进数学方法的现代PET重建方法，以提高PET图像的分辨率和对比度，同时保持较低的计算复杂度，从而直接受益于临床工作流程。

项目成果

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays.

• DOI: 10.1016/j.patcog.2021.108274
• 发表时间: 2022-03
• 期刊: Pattern recognition
• 影响因子: 8
• 作者: Aviles-Rivero AI;Sellars P;Schönlieb CB;Papadakis N
• 通讯作者: Papadakis N

Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework

• DOI: 10.1137/20m1357500
• 发表时间: 2017-12
• 期刊: SIAM J. Imaging Sci.
• 作者: Martin Benning;M. Betcke;Matthias Joachim Ehrhardt;C. Schonlieb

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

通过人工智能中的隐私保护协作推进 COVID-19 诊断。

• DOI: 10.17863/cam.79503
• 发表时间: 2021
• 作者: Bai X

Solving inverse problems using data-driven models

• DOI: 10.1017/s0962492919000059
• 发表时间: 2019-01-01
• 期刊: ACTA NUMERICA
• 影响因子: 14.2
• 作者: Arridge, Simon;Maass, Peter;Schonlieb, Carola-Bibiane
• 通讯作者: Schonlieb, Carola-Bibiane