DIFFERENCE: DIFFusion magnetic resonance imaging with Enhanced Resolution ENCoding - Precision Imaging in Cancer

差异:具有增强分辨率编码的扩散磁共振成像 - 癌症精密成像

基本信息

  • 批准号:
    MR/S031995/1
  • 负责人:
  • 金额:
    $ 144.51万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Fellowship
  • 财政年份:
    2021
  • 资助国家:
    英国
  • 起止时间:
    2021 至 无数据
  • 项目状态:
    已结题

项目摘要

Cancer causes a third of all UK deaths and its management remains a challenge. For example, 1 in 7 men will be diagnosed with prostate cancer in their lifetime; 8 in 10 men will survive for >10 years, yet, it is difficult to tell at the beginning who is likely to respond well or poorly to treatment and indeed, who may not need treatment at all. Our current way of diagnosing cancer is to sample the prostate using cutting needles (a 'biopsy'). This can be painful, is invasive, and importantly, biopsies may miss prostate cancer because they are not aimed at the right area or yield a false diagnosis because they miss the most aggressive part of the cancer in up to a third of patients.Imaging already plays a key role in cancer diagnostics. Diffusion imaging using a magnetic resonance scanner is particularly good for imaging cancer. It uses magnetic fields to scan the motion of water molecules at a microscopic level. As this water motion is reduced in cancers, they stand out against the background and are easily detected. However, the problem with diffusion imaging is that the images can appear blurred and misshapen ('low quality') as it takes time to scan during which the body organs move: These images are also only two dimensional, 2D not three-dimensional, 3D, and lack fine detail ('low resolution').If we had 3D images with fine detail and more accurate numerical measurements instead, we could use diffusion imaging to detect even small cancers, to plan new treatments that require an exact picture of the cancer, and with new computing techniques ('artificial intelligence') unravel currently hidden information about the cancer that may forecast its future behaviour. However, the robustness and sensitivity of diffusion imaging data needs to be improved significantly for these sophisticated analyses to work.My aim is to turn diffusion imaging into a precise diagnostic tool, focussing first on prostate cancer. Specifically, I will tackle the technical challenges that currently prevent us from having high-quality, high-resolution 3D diffusion images.Challenge 1: Diffusion imaging takes too long: movement from breathing, bowel contraction and heart motion blurs the microscopic water movement we are trying to see when we scan.Challenge 2: The quality of data from images is not detailed enough for precise treatments to specific cancer areas.Challenge 3: Current analysis only achieves a very simple measurement of 'diffusion length '. Deeper insight into cancer microstructure, and how the cancer may behave, could be gained by developing more sophisticated analysis combining diffusion imaging with artificial intelligence (deep learning).I will develop a new method to acquire diffusion images that will use detailed magnetic resonance signal 'simulations' and a novel 3D 'phase navigator signal' to track, model and get rid of the movement that interferes with the diffusion signal we wish to capture. I will also develop more sophisticated analysis methods to better quantify and show this information. I will combine the new imaging method with 'artificial intelligence' computing to see if together they can help forecast the aggressiveness of an individual patient's cancer.My research will take place at King's College London, where there is a unique interdisciplinary environment with artificial intelligence researchers, clinicians, and industry partners. At the end of this fellowship, I believe the new high-resolution diffusion technique together with new analysis methods will improve the early detection of clinically significant prostate cancer, avoid unnecessary biopsy and guide biopsy when needed, and help determine whether treatment is required or not. Combined with artificial intelligence methods, my work will open up new opportunities to transform patient outcomes by enabling personalised precision treatment such as MR-guided radiotherapy treatment.
癌症占英国死亡人数的三分之一,其管理仍然是一个挑战。例如,七分之一的男性在其一生中会被诊断出患有前列腺癌;十分之八的男性将存活>10年,然而,很难在开始时判断谁可能对治疗反应良好或较差,实际上,谁可能根本不需要治疗。我们目前诊断癌症的方法是使用切割针(“活检”)对前列腺进行采样。这可能是痛苦的,是侵入性的,重要的是,活检可能会错过前列腺癌,因为他们没有针对正确的区域或产生错误的诊断,因为他们错过了最具侵略性的部分癌症在多达三分之一的患者。成像已经在癌症诊断中发挥了关键作用。使用磁共振扫描仪的扩散成像特别适合于癌症成像。它利用磁场在微观水平上扫描水分子的运动。由于这种水运动在癌症中减少,它们在背景中脱颖而出,很容易被检测到。然而,扩散成像的问题是,图像可能会出现模糊和畸形(“低质量”),因为扫描需要时间,在此期间身体器官移动:这些图像也只是二维的,2D不是三维的,3D的,并且缺乏精细的细节(“低分辨率”)。如果我们有3D图像与精细的细节和更准确的数值测量,我们可以使用扩散成像来检测甚至是小的癌症,以计划需要癌症的精确图像的新治疗,并利用新的计算技术(“人工智能”)解开当前隐藏的关于癌症的信息,这些信息可能预测其未来的行为。然而,扩散成像数据的鲁棒性和灵敏度需要显着提高这些复杂的分析工作。我的目标是把扩散成像成为一个精确的诊断工具,首先集中在前列腺癌。具体来说,我将解决目前阻碍我们获得高质量、高分辨率3D扩散图像的技术挑战。挑战1:扩散成像耗时太长:呼吸、肠道收缩和心脏运动的运动模糊了我们扫描时试图看到的微观水运动。挑战2:来自图像的数据质量不够详细,无法对特定癌症区域进行精确治疗。挑战3:目前的分析只实现了非常简单的“扩散长度”测量。通过将扩散成像与人工智能相结合,可以开发更复杂的分析,从而更深入地了解癌症的微观结构以及癌症的行为方式(深度学习)。我将开发一种新的方法来获取扩散图像,该方法将使用详细的磁共振信号“模拟”和新颖的3D“相位导航信号”来跟踪,建模并消除干扰我们希望捕获的扩散信号的运动。我还将开发更复杂的分析方法,以更好地量化和显示这些信息。我将联合收割机与“人工智能”计算相结合,看看它们是否能共同帮助预测个体患者癌症的侵袭性。我的研究将在伦敦国王学院进行,那里有一个独特的跨学科环境,有人工智能研究人员,临床医生和行业合作伙伴。在本次研究结束时,我相信新的高分辨率扩散技术以及新的分析方法将改善临床上重要的前列腺癌的早期检测,避免不必要的活检并在需要时引导活检,并帮助确定是否需要治疗。不。结合人工智能方法,我的工作将通过实现个性化的精确治疗(如MR引导的放射治疗),为改变患者预后开辟新的机会。

项目成果

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Isabel Dregely其他文献

FIRST IN MAN OBSERVATIONS WITH SIMULTANEOUS 18F-FDG PET AND MR IMAGING IN PERIPHERAL ARTERY DISEASE USING A WHOLE-BODY INTEGRATED SCANNER
  • DOI:
    10.1016/s0735-1097(15)61078-6
  • 发表时间:
    2015-03-17
  • 期刊:
  • 影响因子:
  • 作者:
    Tobias Koppara;Isabel Dregely;Kristin Kuhs;Stephan Nekolla;Karl-Ludwig Laugwitz;Markus Schwaiger;Renu Virmani;Tareq Ibrahim
  • 通讯作者:
    Tareq Ibrahim
Analysis of Student Behaviour on Large Learning Management Systems
大型学习管理系统上的学生行为分析

Isabel Dregely的其他文献

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