EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging

EPSRC 多模态临床影像数学和统计分析中心

• 批准号: EP/N014588/1
• 负责人: Carola-Bibiane Schönlieb
• 金额: $ 245.03万
• 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: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN014588%2F1
• 关键词: EPSRC; Centre; Mathematical; Statistical; Analysis

Applied Mathematics and Statistics are routinely seen as separate disciplines. However, many of the methodological challenges in image analysis, particularly from the types of multiscale multimodal images available from Neurological, Cardiovascular and Oncology imaging, illustrate that a combined approach, dissolving intradisciplinary mathematical boundaries, is the only possible way forward if a step-change in image analysis of combined data is to occur. This Centre will foster links between applied maths and statistics, particularly high dimensional and functional statistical analysis with applied and computational numerical analysis, through the focus on multimodal imaging data. The Centre proposes to provide a research focus on bringing state-of-the-art mathematical tools to clinical end users through collaborations both within mathematics and between mathematics and healthcare professionals, particularly those in oncology, cardiovascular medicine and neurology. This will ultimately lead to new mathematical frontiers, joining statistics and computational mathematics, as well as a move away from individual image analysis to a holistic approach to all available imaging, from the cellular to systems scale, for clinical diagnosis, prognosis and treatment planning.

应用数学和统计学通常被视为独立的学科。然而，图像分析中的许多方法学挑战，特别是来自神经病学、心血管和肿瘤学成像的多尺度多模态图像类型的挑战，说明了如果要发生组合数据图像分析的阶跃变化，那么溶解学科内数学边界的组合方法是唯一可能的前进方向。该中心将通过关注多模态成像数据，促进应用数学和统计学之间的联系，特别是高维和功能统计分析与应用和计算数值分析。该中心建议提供一个研究重点，通过数学内部以及数学和医疗保健专业人员之间的合作，为临床最终用户提供最先进的数学工具，特别是肿瘤学，心血管医学和神经学。这将最终导致新的数学前沿，加入统计学和计算数学，以及从单个图像分析转向从细胞到系统规模的所有可用成像的整体方法，用于临床诊断，预后和治疗计划。

项目成果

CS+M: A Simultaneous Reconstruction and Motion Estimation Approach for Improving Undersampled MRI Reconstruction.

CS M：一种用于改进欠采样 MRI 重建的同步重建和运动估计方法。

• 发表时间: 2018
• 作者: A.I Aviles-Rivero, G. Williams, M. Graves;C.B Schönlieb
• 通讯作者: C.B Schönlieb

Task adapted reconstruction for inverse problems

• DOI: 10.1088/1361-6420/ac28ec
• 发表时间: 2022-07-01
• 期刊: INVERSE PROBLEMS
• 影响因子: 2.1
• 作者: Adler, Jonas;Lunz, Sebastian;Oktem, Ozan
• 通讯作者: Oktem, Ozan

Computing reconstructions from nonuniform Fourier samples: Universality of stability barriers and stable sampling rates

从非均匀傅立叶样本计算重建：稳定壁垒和稳定采样率的普遍性

• DOI: 10.1016/j.acha.2017.05.004
• 发表时间: 2019
• 期刊: Applied and Computational Harmonic Analysis
• 影响因子: 2.5
• 作者: Adcock B

Breaking the coherence barrier: A new theory for compressed sensing

打破相干障碍：压缩感知的新理论

• DOI: 10.48550/arxiv.1302.0561
• 发表时间: 2013
• 期刊: arXiv e-prints
• 作者: Adcock Ben

Density Theorems for Nonuniform Sampling of Bandlimited Functions Using Derivatives or Bunched Measurements

• DOI: 10.1007/s00041-016-9504-8
• 发表时间: 2017-12-01
• 期刊: JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS
• 影响因子: 1.2
• 作者: Adcock, Ben;Gataric, Milana;Hansen, Anders C.
• 通讯作者: Hansen, Anders C.