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QUANTIMA: Quantitative imaging platform for the diagnosis, subtyping, staging and outcome prognosis in dementia

QUANTIMA：用于痴呆症诊断、分型、分期和结果预后的定量成像平台

基本信息

批准号：
MR/W011980/1
负责人：
Hojjat Azadbakht
金额：
$ 93.72万
依托单位：
AINOSTICS Limited
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FW011980%2F1
关键词：
QUANTIMA Quantitative imaging platform diagnosis

项目摘要

Analysis of conditions altering microstructural tissue integrity & cellular arrangement, such as dementia, is vital to personalise patient treatment & improved outcomes. Presently, it is often the case that these subjects are only identified after the disease is at a grossly advanced stage, making prognosis poor.In this fellowship I aim to develop an innovative AI-enabled magnetic resonance (MR) image analysis platform, known as QUANTIMA, capable of generating new non-invasive biomarkers of brain microstructure & providing non-invasive tools for improved diagnostic information as powerful as invasive and/or expensive techniques such as CSF lumbar puncture & PET. Understanding the design (morphology) & arrangement (tissue microstructure) of the brain's individual components is the key to deciphering both its structure & function, and more importantly its degeneration/dysregulation in diseases. The platform will make use of advanced diffusion-weighted magnetic resonance imaging (DW-MRI) based microstructure modelling techniques, providing an indirect but non-invasive probe of the tissue microstructure at the micrometre scale. However, tissue microstructure is highly complex while the DW-MRI signal is quite simple, so the mapping from signal to microstructure is ill-posed. Current computational modelling techniques aimed at overcoming this challenge use mathematical models, mapping the DW-MRI signal to underlying tissue properties, to estimate those properties by fitting the models per voxel of the DW-MRI data. Nevertheless, these methods suffer from a number of limitations that have restricted their diagnostic power & clinical adoption, such as poor sensitivity to features of complex cellular morphologies, requirements for long MRI scan times & state-of-the-art hardware not commonly available in clinical settings, reliance on predefined models of cellular arrangement & biology trying to mimic healthy tissue, therefore limiting the methods' applicability to disease processes (especially unobserved), & unquantified ambiguities.To overcome these limitations, the proposed platform will use advanced AI-based optimisations & accelerations, capable of generating quantitative estimates of tissue microstructure, comparable with the state-of-the-art in microstructure computational modelling, whilst overcoming their limitations by: i) relying on clinically achievable MR acquisition protocols applicable on commonly available MR hardware (1.5T & 3T scanners), ii) providing a model free approach that solely relies on the observations made using the diffusion MR signal, iii) is capable of estimating uncertainty, quantifying ambiguity & the significance of the results.When diagnosing these conditions, aside from imaging, doctors rely on information gathered from the patient's medical history, physical examination, laboratory tests, and the characteristic changes in thinking, day-to-day function, & behaviour. Offering a unified solution, the platform will also be able to perform multimodal & multi parametric data fusion, utilising information from such data sources (e.g.,cognitive tests), allowing for earlier & accurate dementia diagnosis, subtyping, staging, and disease-trajectory prediction; therefore, enabling personalised treatment selection & improved outcomes.The project will also lead to the development of a multiparametric dementia-specific optimised (for clinical use) MRI scan protocol, maximising information obtained over short scans & satisfying the requirements of the novel AI-enabled microstructure modelling technique that I intend to develop.In collaborations with the London AI Centre for Value Based Healthcare, University of Manchester, Greater Manchester Mental Health NHS Foundation Trust, GSK, and Salford Royal NHS Foundation Trust, the platform will then be validated using a multifaceted approach: using both retrospective & prospective patients data, and through a clinical pilot study.

分析改变微观结构组织完整性和细胞排列的状况（例如痴呆症）对于个性化患者治疗和改善结果至关重要。目前，通常情况下，这些受试者只有在疾病处于严重晚期阶段后才能被识别，从而导致预后不佳。在这项奖学金中，我的目标是开发一种创新的人工智能磁共振 (MR) 图像分析平台，称为 QUANTIMA，能够生成新的脑微结构非侵入性生物标志物，并提供非侵入性工具来改进诊断信息，其功能与侵入性和/或昂贵的技术一样强大。如脑脊液腰椎穿刺和 PET。了解大脑各个组成部分的设计（形态学）和排列（组织微观结构）是破译其结构和功能的关键，更重要的是破译其在疾病中的退化/失调。该平台将利用先进的基于扩散加权磁共振成像（DW-MRI）的微观结构建模技术，提供微米级组织微观结构的间接但非侵入性探测。然而，组织微观结构非常复杂，而 DW-MRI 信号却相当简单，因此从信号到微观结构的映射是不适定的。当前旨在克服这一挑战的计算建模技术使用数学模型，将 DW-MRI 信号映射到底层组织属性，通过拟合 DW-MRI 数据的每个体素模型来估计这些属性。然而，这些方法受到许多限制，限制了它们的诊断能力和临床应用，例如对复杂细胞形态特征的敏感性差、对长MRI扫描时间的要求和临床环境中不常见的最先进的硬件、依赖于试图模仿健康组织的细胞排列和生物学的预定义模型，因此限制了这些方法对疾病过程（尤其是未观察到的）的适用性，以及为了克服这些限制，所提出的平台将使用先进的基于人工智能的优化和加速，能够生成组织微观结构的定量估计，与最先进的微观结构计算模型相当，同时通过以下方式克服其局限性：i）依靠适用于常用 MR 硬件（1.5T 和 3T）的临床上可实现的 MR 采集协议扫描仪），ii）提供一种无模型方法，仅依赖于使用扩散 MR 信号进行的观察，iii）能够估计不确定性、量化模糊性和结果的重要性。在诊断这些病症时，除了成像之外，医生还依赖于从患者的病史、体格检查、实验室测试以及思维、日常功能和行为的特征变化中收集的信息。该平台提供统一的解决方案，还能够利用来自此类数据源（例如认知测试）的信息进行多模式和多参数数据融合，从而实现早期和准确的痴呆症诊断、分型、分期和疾病轨迹预测；因此，实现个性化治疗选择并改善结果。该项目还将导致开发多参数痴呆症特异性优化（用于临床）MRI 扫描协议，最大限度地利用短扫描获得的信息，并满足我打算开发的新型人工智能微结构建模技术的要求。与伦敦人工智能价值医疗中心、曼彻斯特大学、大曼彻斯特心理健康中心合作 NHS 基金会信托基金、葛兰素史克和索尔福德皇家 NHS 基金会信托基金随后将使用多方面的方法对该平台进行验证：使用回顾性和前瞻性患者数据，并通过临床试点研究。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Resolving quantitative MRI model degeneracy with machine learning via training data distribution optimisation

通过训练数据分布优化，利用机器学习解决定量 MRI 模型简并性

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Michele Guerreri
通讯作者：
Michele Guerreri

Altered Amygdala Volumes and Microstructure in Focal Epilepsy Patients with Tonic-Clonic Seizures, Ictal and Post-Ictal Central Apnea

伴有强直阵挛发作、发作期和发作后中枢性呼吸暂停的局灶性癫痫患者杏仁核体积和微观结构的改变

DOI：
10.1101/2023.03.16.23287369
发表时间：
2023
期刊：
影响因子：
0
作者：
Zeicu C
通讯作者：
Zeicu C

Microstructural dynamics of motor learning and sleep-dependent consolidation: A diffusion imaging study.

DOI：
10.1016/j.isci.2023.108426
发表时间：
2023-12-15
期刊：
ISCIENCE
影响因子：
5.8
作者：
Stee, Whitney;Legouhy, Antoine;Guerreri, Michele;Villemonteix, Thomas;Zhang, Hui;Peigneux, Philippe
通讯作者：
Peigneux, Philippe

Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk.

局灶性癫痫中杏仁核的体积和微观结构异常，具有不同程度的 SUDEP 风险。