From the cluster to the clinic: Real-time treatment planning for transcranial ultrasound therapy using deep learning (Ext.)

从集群到诊所：利用深度学习进行经颅超声治疗的实时治疗计划（Ext.）

• 批准号: EP/S026371/1
• 负责人: Bradley Treeby
• 金额: $ 121.32万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS026371%2F1
• 关键词: cluster; clinic; Real; time; treatment

This is an extension of the Early Career Fellowship: Model-Based Treatment Planning for Focused Ultrasound Surgery.Brain disorders present a huge challenge for health services across the world, with studies showing these conditions affect as many as one third of the adult population. In the UK, approximately 1 in 6 people are affected by a neurological disorder and 1 in 6 by a common psychiatric disorder. The total annual cost of these conditions is estimated to exceed £100 billion. These disorders can be devastating for patients and greatly reduce their quality of life. Today, patients are often treated by the prescription of drugs that alter the way the brain functions. For many patients, this causes a reduction in their symptoms. However, if these drugs are used for long periods of time, their effectiveness often decreases and there can be many side-effects. It can also be difficult for drugs to exit the blood-stream and enter the brain as desired because of a protective lining called the blood-brain barrier. Depending on their diagnosis, some patients may also be offered surgical procedures to remove part of the brain or implant small wires that use electricity to stimulate brain cells.One exciting alternative to drugs and surgery is the use of ultrasound. Ultrasound imaging is well known for taking pictures of developing babies during pregnancy. However, ultrasound is now also starting to be used to treat brain disorders. This is possible because ultrasound waves cause mechanical vibrations that affect the brain in different ways. For example, they can cause the tissue to heat up or generate forces that agitate the brain cells and tissue scaffolding. Several different types of treatment are possible depending on the pattern of ultrasound pulses used. This includes precisely destroying small regions of tissue, generating or suppressing electrical signals in the brain, or temporarily opening the blood-brain barrier to allow drugs to be delivered more effectively. These treatments are all completely non-invasive and have the potential to significantly improve outcomes for patients. A major challenge for ultrasound therapy is ensuring the ultrasound energy is delivered to the precise location identified by the doctor. This is difficult because the skull bone is very rigid and causes the ultrasound waves to be reflected and distorted. It is possible to predict and correct for these distortions using powerful computer models of how ultrasound waves travel through the body. However, these models can take many hours or days to run on large supercomputers, so cannot currently be used for patient treatments. The aim of this fellowship extension is to develop a new type of model that can make very fast predictions of how sound waves travel in the brain. This will be based on a special type of artificial intelligence called deep learning. The deep learning models will be trained to predict the distortion caused by the skull bone. The models will learn this using a large number of training examples generated using the powerful computer models mentioned above. As part of the project, the models will be rigorously tested using patient data from previous clinical treatments. Carefully planned laboratory experiments using phantom materials designed to mimic the skull and brain will also be conducted. The new models will allow doctors to automatically correct for distortions caused by the skull and quickly predict the treatment outcomes. This would be a major breakthrough in the treatment of brain disorders and enable the wide-spread application of ground-breaking ultrasound therapies.

这是早期职业奖学金的延伸：聚焦超声手术的基于模型的治疗计划。脑部疾病对世界各地的卫生服务提出了巨大的挑战，研究表明这些疾病影响了多达三分之一的成年人口。在英国，大约六分之一的人受到神经系统疾病的影响，六分之一的人受到常见精神疾病的影响。这些条件每年的总成本估计超过1000亿英镑。这些疾病对患者来说是毁灭性的，大大降低了他们的生活质量。今天，病人经常通过处方药物来治疗，这些药物可以改变大脑的功能。对于许多患者来说，这会减少他们的症状。然而，如果这些药物长期使用，其有效性往往会降低，并且可能会有许多副作用。药物也很难按照需要离开血流进入大脑，因为有一层叫做血脑屏障的保护膜。根据他们的诊断，一些患者也可能会接受外科手术，切除部分大脑或植入用电刺激脑细胞的小电线。一个令人兴奋的替代药物和手术的方法是使用超声波。众所周知，超声成像可以拍摄怀孕期间发育中的婴儿的照片。然而，超声波现在也开始用于治疗大脑疾病。这是可能的，因为超声波会引起机械振动，以不同的方式影响大脑。例如，它们可以导致组织升温或产生破坏脑细胞和组织支架的力。几种不同类型的治疗是可能的，这取决于所使用的超声脉冲的模式。这包括精确破坏组织的小区域，在大脑中产生或抑制电信号，或暂时打开血脑屏障以使药物更有效地输送。这些治疗都是完全无创的，有可能显着改善患者的预后。超声治疗的一个主要挑战是确保超声能量被输送到医生确定的精确位置。这是困难的，因为颅骨非常坚硬，导致超声波被反射和扭曲。利用强大的计算机模型来预测和纠正这些失真是可能的，这些模型可以解释超声波如何在人体内传播。然而，这些模型可能需要数小时或数天才能在大型超级计算机上运行，因此目前无法用于患者治疗。这项研究的目的是开发一种新型的模型，可以非常快速地预测声波如何在大脑中传播。这将基于一种特殊类型的人工智能，称为深度学习。深度学习模型将被训练来预测头骨造成的变形。这些模型将使用上面提到的强大的计算机模型生成的大量训练示例来学习这一点。作为该项目的一部分，这些模型将使用以前临床治疗的患者数据进行严格测试。还将进行精心计划的实验室实验，使用旨在模拟头骨和大脑的体模材料。新模型将允许医生自动纠正头骨造成的扭曲，并快速预测治疗结果。这将是治疗脑部疾病的一个重大突破，并使突破性的超声波疗法得到广泛应用。

项目成果

Test materials for characterising heating from HIFU devices using photoacoustic thermometry

使用光声测温法表征 HIFU 设备加热特性的测试材料

• DOI: 10.1117/12.2542429
• 发表时间: 2020
• 作者: Bakaric M

Characterisation of hydrophone sensitivity with temperature using a broadband laser-generated ultrasound source

使用宽带激光产生的超声源表征水听器灵敏度随温度的变化

• DOI: 10.1088/1681-7575/ace3c3
• 发表时间: 2023
• 期刊: Metrologia
• 影响因子: 2.4
• 作者: Bakaric M

Measurement of the temperature-dependent speed of sound and change in Grüneisen parameter of tissue-mimicking materials

测量与温度相关的声速和模拟组织材料的 Grüneisen 参数的变化

• DOI: 10.1109/ultsym.2019.8925838
• 发表时间: 2019
• 作者: Bakaric M

Measurement of the ultrasound attenuation and dispersion in 3D-printed photopolymer materials from 1 to 3.5 MHz.

• DOI: 10.1121/10.0006668
• 发表时间: 2021-10
• 期刊: The Journal of the Acoustical Society of America
• 作者: Marina Bakaric;P. Miloro;A. Javaherian;B. Cox;B. Treeby;Michael D. Brown

Modelling and measurement of laser-generated focused ultrasound: Can interventional transducers achieve therapeutic effects?

激光产生的聚焦超声的建模和测量：介入透明剂可以实现治疗效果吗？

• DOI: 10.1121/10.0004302
• 发表时间: 2021-04
• 期刊: The Journal of the Acoustical Society of America
• 作者: Aytac-Kipergil E;Desjardins AE;Treeby BE;Noimark S;Parkin IP;Alles EJ
• 通讯作者: Alles EJ