MRI-based, patient-specific 3D bone imaging for orthopaedic surgery planning

为了计划手术以修复断裂或患病的骨骼，外科医生依靠2D图像，计算机断层扫描（CT）扫描，以可视化患者的3D骨骼解剖结构。即使是最有经验的外科医生，这也是一项艰巨的任务，导致许多手术计划错误。使用从患者CT扫描中获得的患者特定3D骨骼图像可以提高手术计划的准确性。因此，医疗保健专业人员强烈要求利用这些3D图像来治疗病变和骨折。医学成像公司提供基本的软件，可以生成患者骨骼解剖结构的3D可视化。然而，该软件仅提供2D成像数据的3D表示，称为3D体积渲染，显示扫描内的所有解剖结构。因此，它不能为医疗保健专业人员提供感兴趣的特定解剖结构的患者特定可视化。AXIAL MEDICAL PRINTING LIMITED（Axial 3D）目前提供基于云的软件平台Axial 3D-Insight，它可以从CT扫描中自动生成患者特定的骨骼3D图像，可以在任何设备上随时查看。与3D容积渲染软件不同，使用了一种称为分割的过程，其中机器学习（ML）算法自动标记扫描中不同类型的解剖结构（骨骼或软组织）。这就为特定的解剖结构类型创建了准确的、患者特定的3D可视化，而无需手动输入。医学成像公司正在积极寻求扩展其成像模式的功能，以减少患者在CT扫描期间暴露于电离辐射。传统上，CT对骨成像，磁共振成像（MRI）对软组织成像。GE Healthcare开发了一种新的MRI成像协议oZTEo，可以在同一次扫描中有效地对骨骼和软组织解剖结构进行成像。在这个项目中，我们将通过开发新的机器学习软件来扩展我们目前的产品，该软件可以自动分割单个感兴趣的骨骼解剖区域，如膝盖和该骨骼解剖区域的部分，如胫骨，在这些新的MRI骨骼扫描中，股骨和膝盖骨（语义分割）可以生成精确的、患者特定的3D图像，用于手术规划。

To plan a surgery to fix broken or diseased bone, surgeons rely on a 2D image, a computed tomography (CT) scan, to visualise the patient's 3D bony anatomy. This is a difficult task for even the most experienced surgeon, leading to many misplanned surgeries.The use of patient-specific 3D bone images derived from a patient's CT scan improves surgical planning accuracy. Therefore, there is a strong demand from healthcare professionals to utilise these 3D images in treating diseased and broken bones.Medical imaging companies provide basic software with their CT scanners that can generate a 3D visualisation of the patient's bony anatomy. However, this software only provides a 3D representation of the 2D imaging data, known as a 3D volumetric render, showing all the anatomy within the scan. Therefore, it can't provide healthcare professionals with a patient-specific visualisation of the specific anatomy of interest. Furthermore, the 3D visual has to be manually generated and can only be viewed on a specific hospital computer.AXIAL MEDICAL PRINTING LIMITED (Axial3D) currently provides the cloud-based software platform, Axial3D-Insight, which can automatically generate patient-specific 3D images of bone from CT scans that can be viewed on the go, on any device. Unlike the 3D volumetric rendering software, a process called segmentation is used where machine learning (ML) algorithms automatically label different types of anatomy (bone or soft tissue) within the scan. This creates an accurate, patient-specific, 3D visualisation of the specific anatomy type of interest without the need for manual input.Medical imaging companies are actively looking to expand the capabilities of their imaging modalities to reduce patient exposure to ionising radiation during CT scanning. Conventionally, CT images bone and magnetic resonance imaging (MRI) images soft tissue. GE Healthcare has developed a new MRI imaging protocol, oZTEo, that can effectively image bone and soft tissue anatomy within the same scan. CT-like imaging of the bone is achieved without the need for ionising radiation and also the patient only needs to be scanned once, saving time and healthcare costs.In this project, we will extend our current offering by developing new ML software that automatically segments a single bony anatomical region of interest such as the knee and the parts of this bony anatomical region such as the shinbone, thigh bone and kneecap (semantic segmentation) within these new MRI bone scans to generate a precise, patient-specific 3D image of the same for surgical planning use.