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

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:
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
项目状态:
已结题
起止时间:
2019 至 --

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项目成果

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中文摘要
翻译
正电子发射断层扫描(PET)是现代诊断成像的支柱,允许非侵入性,敏感和特异性检测几种疾病类型的功能变化。在内分泌学中,垂体或肾上腺小功能肿瘤的精确定位对于计划治疗性手术或放疗至关重要。虽然PET成像在这项任务中显示出良好的前景,但初步研究表明,改进的PET成像和随后更准确的定位为更适应的治疗开辟了可能性,这表明有很大的改进空间。在痴呆症中,准确量化PET图像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早期发现痴呆,并且一旦找到合适的药物,就可以提高对评估和监测治疗的敏感性。在这个项目中,数学家们与来自剑桥大学阿登布鲁克医院、英国痴呆症平台(DPUK)、GE医疗保健和伦敦大学学院(UCL)的研究人员和临床医生合作,改进内分泌学肿瘤的诊断和定位,并通过改进的PET成像技术早期诊断痴呆症。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图像分辨率和对比度,同时保持较低的计算复杂度,从而直接有利于临床工作流程。
英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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
DOI: 10.1137/20m1357500
发表时间: 2017-12
期刊: SIAM J. Imaging Sci.
影响因子: --
作者: [Martin Benning;M. Betcke;Matthias Joachim Ehrhardt;C. Schonlieb]
通讯作者: 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
期刊:
影响因子: --
作者: [Bai X]
通讯作者: Bai X
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
Research Exchanges in the Mathematics of Deep Learning with Applications
  • 批准号:
    EP/Y037308/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $24.32万
  • 财政年份:
    2024
  • 负责人:
    Carola-Bibiane Schönlieb
  • 依托单位:
Combining Knowledge And Data Driven Approaches to Inverse Imaging Problems
  • 批准号:
    EP/V029428/1
  • 项目类别:
    Fellowship
  • 资助金额:
    $158.04万
  • 财政年份:
    2021
  • 负责人:
    Carola-Bibiane Schönlieb
  • 依托单位:
Cambridge Mathematics of Information in Healthcare (CMIH)
  • 批准号:
    EP/T017961/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $165.11万
  • 财政年份:
    2020
  • 负责人:
    Carola-Bibiane Schönlieb
  • 依托单位:
Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India
  • 批准号:
    EP/T003553/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $70.41万
  • 财政年份:
    2019
  • 负责人:
    Carola-Bibiane Schönlieb
  • 依托单位:
国内基金
海外基金
Improving modelling of compact binary evolution.
  • 批准号:
    10903001
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    20.0万元
  • 批准年份:
    2009
  • 负责人:
    史蒂芬
  • 依托单位: