Transforming Tissue Differentiation via Quantum Digital Tomosynthesis

通过量子数字断层合成改变组织分化

• 批准号: 106175
• 金额: $ 175.42万
• 依托单位: ADAPTIX LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=106175
• 关键词: Transforming; Tissue; Differentiation; via; Quantum

This application is about improving an existing medical imaging technique which is used during cancer surgery to distinguish between healthy and non-healthy tissue. The improvements will rely on the application of 'quantum technology'.Pathology is the study and diagnosis of disease through examination of surgically removed organs, tissues (biopsy samples) and fluids. When a cancerous tumour is excised (taken out) the surgeon needs to be certain that all the diseased tissue has been removed, and therefore they also remove some surrounding tissue around the edge of the tumour (the 'margins'). The surgeon needs to be sure these margins are free of cancer and can be described as 'clear or negative'. Clear margins suggest all the cancer has been removed and is not able to spread, giving the best outcome for the patient.So, a highly sensitive method of differentiating between healthy and unhealthy soft tissue is vital, and also between soft and hard tissues (bones). The establishment of these 'clear tissue margins' is best done whilst surgery is ongoing -- so the technique also needs to give accurate 3D images quickly and not take up much room in a busy operating theatre.Currently this is done via 'pathology cabinets' which give 2D or 3D images - but are often are slow (several minutes) and bulky (similar to a filing cabinet). The need is for more accurate differentiation of the boundaries between the tumour and healthy tissue, enabling surgeons to make confident real-time decisions during operations. The equipment also needs to be cost-effective, have a small footprint in the operating theatre and give accurate, easily understandable images.This grant would be used to build a prototype of a new type of pathology cabinet -- using quantum technology applied to both key parts of the system (the X-ray source & detector), plus new software to produce high-resolution material discriminating images (which are also better suited for the training of machine learning and application of Artificial Intelligence).The resulting images would give better differentiation between cancerous and healthy tissue, enabling surgeons to confidently remove the minimum amount of healthy tissue whilst being sure of clear margins. This will benefit healthcare providers in terms of better patient care, reduced workflow and costs, and most importantly, improve outcomes for patients in terms of reduced risk of more than one operation and a reduced chance of cancer spreading from positive margins left after initial surgery.

该应用程序旨在改进现有的医学成像技术，该技术在癌症手术期间用于区分健康组织和非健康组织。这些改进将依赖于“量子技术”的应用。病理学是通过检查手术切除的器官、组织（活检样本）和体液来研究和诊断疾病。当癌性肿瘤被切除（取出）时，外科医生需要确定所有患病组织都已被切除，因此他们还会切除肿瘤边缘（“边缘”）周围的一些周围组织。外科医生需要确保这些边缘没有癌症，并且可以被描述为“清晰或阴性”。清晰的边缘表明所有癌症已被切除并且无法扩散，从而为患者提供最佳结果。因此，区分健康和不健康软组织以及软组织和硬组织（骨骼）的高度灵敏的方法至关重要。这些“清晰的组织边缘”的建立最好在手术进行时完成，因此该技术还需要快速提供准确的 3D 图像，并且不会在繁忙的手术室中占用太多空间。目前，这是通过提供 2D 或 3D 图像的“病理柜”完成的，但通常速度慢（几分钟）且笨重（类似于文件柜）。需要更准确地区分肿瘤和健康组织之间的边界，使外科医生能够在手术期间做出自信的实时决策。该设备还需要具有成本效益，在手术室中占用空间小，并提供准确、易于理解的图像。这笔赠款将用于建造新型病理柜的原型——使用应用于系统关键部分（X射线源和探测器）的量子技术，加上新软件来生成高分辨率材料辨别图像（这也更适合机器学习的培训和人工智能的应用）。由此产生的图像 可以更好地区分癌组织和健康组织，使外科医生能够自信地切除最少量的健康组织，同时确保边缘清晰。这将使医疗保健提供者受益于更好的患者护理、减少工作流程和成本，最重要的是，改善患者的治疗结果，降低多次手术的风险，并减少癌症从初次手术后留下的阳性切缘扩散的机会。