Image-guided interventions conducted under a 2D modality benefit from the overlay of relevant 3D information from the preoperative stage. The enabling technology for this overlay is 3D-2D registration: the process of finding the spatial pose of a 3D preoperative image in relation to 2D intraoperative images. The successful integration of a registration solution to a surgery has the potential for significant positive impact in terms of likelihood of treatment success and intervention duration. However, many surgeries are routinely done without the assistance of registration because no current solution is practical in their clinical context. In order to remedy these issues, we focus on producing practical, targeted registration solutions to assist image-guided interventions.;The first surgery we address is catheter ablation for atrial fibrillation (CA for AF), an electrophysiology procedure to treat heart arrhythmia conducted under X-ray fluoroscopy. In this surgery, a 3D image, either magnetic resonance (MR) or computed tomography (CT), is taken preoperatively to define the anatomy of the left atrium (LA) and pulmonary veins (PV)s. A mesh, segmented from the 3D image, is subsequently used to help positioning the ablation catheter via its overlay on the intraoperative fluoroscopic images. Current clinical registration solutions for CA for AF are slow and often require extensive manual manipulations such as the identification of fiducial points, which is problematic when intraoperative updates of the 3D image's pose are required because of patient movement. The automatic solutions are currently not precise enough to be used clinically. Also, the solutions which do not involve electroanatomic mapping are not suitable for MR/fluoroscopy registration. This is problematic since we target CA for AF interventions where the 3D modality is MR and electroanatomic mapping is not used.;There are two principal challenges to overcome in order to provide a clinically useful registration algorithm. First, solving the notoriously hard MR to X-ray fluoroscopy registration problem which is further complicated in cases of CA for AF because of the partial match between modalities at the level of the PVs. Second, solving the registration quickly enough to allow for intraoperative updates required due to the patient's movement. We introduce a new registration methodology based on mesh-derived image partition (MDIP) which uses projections of a mesh segmented from the 3D image in order to infer a segmentation of the 2D X-ray fluoroscopy images. This is orders of magnitude faster than producing volumetric projections and, since the mesh can be segmented from either MR or CT, the same procedure is valid for both modalities. The fitness of the registration is evaluated by custom-built similarity measures that compare the statistical properties of the segmented zones and incorporates mask-depth information to account for the partial match at the level of the PVs.;We validate the MDIP algorithm on 7 cases of patients undergoing CA for AF surgery. Four MDIP-based similarity measures are introduced; each one is validated on 1400 biplane registrations. The precision, range, speed and robustness of the solution is assessed by calculating the distribution of projection distance error in function of the correctness of the initial pose for all 5600 biplane registrations. The precision is also evaluated visually by overlaying the ground-truths with results from the registration algorithm. To give a fair appraisal of the expected behavior, the examples are taken from cases exemplifying the average error measured as well as one standard deviation above and under.;The registration algorithm is also applied to cases of sclerotherapy for venous malformation (SfVM) in order to assess its portability to other type of surgeries. SfVM are especially challenging because the malformation can be present on any body part, which offers little predictability on the properties of the medical images from one patient to another. Our dataset is sampled from monoplane surgeries and did not come with metadata allowing a geometrical calibration of the system. We demonstrate that MDIP-based registration is applicable to cases of monoplane SfVM, but that modifications are required in order to account for the wide variety of body parts where VMs are common. The protocol developed for CA for AF surgeries can be used for embolizations or when the interior/exterior border of the organ is prominent, but gradient information has to be taken into account by the similarity measures in order to properly register cases where bones are predominant.
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机译：在2D方式下进行的图像引导干预受益于术前阶段相关3D信息的覆盖。此覆盖图的启用技术是3D-2D配准：查找3D术前图像相对于2D术中图像的空间姿势的过程。成功地将配准解决方案集成到手术中，就治疗成功的可能性和干预时间而言，可能会产生重大的积极影响。但是，由于没有最新的解决方案在其临床环境中可行，因此许多外科手术通常无需注册的协助。为了解决这些问题，我们专注于提供实用的，有针对性的配准解决方案，以协助图像指导的干预。我们要解决的第一项手术是心房纤颤的导管消融术（CA for AF），这是一种电生理学方法，用于治疗心律失常。 X射线透视。在该手术中，术前会采集3D图像（磁共振（MR）或计算机断层扫描（CT））以定义左心房（LA）和肺静脉（PV）的解剖结构。从3D图像中分割出的网格随后用于通过消融导管在术中透视图像上的覆盖来帮助定位消融导管。用于AF的CA的当前临床注册解决方案是缓慢的，并且通常需要大量的手动操作，例如基准点的识别，当由于患者的运动而需要术中更新3D图像的姿势时，这是有问题的。目前，自动解决方案不够精确，无法在临床上使用。同样，不涉及电解剖图的解决方案也不适合MR /荧光检查配准。这是有问题的，因为我们将CA定位于3D方式为MR且未使用电解剖标测的AF干预。为了提供临床上有用的注册算法，有两个主要的挑战需要克服。首先，要解决众所周知的很难进行MR到X射线透视的配准问题，这在CA for AF的情况下更加复杂，因为在PV级别的模态之间存在部分匹配。第二，足够快地解决配准，以允许由于患者的运动而需要术中更新。我们介绍了一种基于网格衍生图像分区（MDIP）的新配准方法，该算法使用从3D图像分割而来的网格投影来推断2D X射线荧光透视图像的分割。这比产生体积投影要快几个数量级，并且由于可以从MR或CT分割网格，因此对于两种模态而言，相同的过程均有效。注册的适用性通过定制的相似性度量进行评估，该相似性度量比较了分段区域的统计属性，并结合了遮罩深度信息以说明PV级别的部分匹配。；我们在7种情况下验证了MDIP算法接受CA进行AF手术的患者数量。引入了四种基于MDIP的相似性度量；每架飞机都经过1400架双翼飞机注册的验证。通过计算所有5600个双平面配准的初始姿态正确性的函数计算投影距离误差的分布，可以评估解决方案的精度，范围，速度和鲁棒性。通过将地面真相与配准算法的结果相叠加，还可以直观地评估精度。为了对预期的行为进行公正的评估，这些示例均来自示例，这些示例例示了所测得的平均误差以及上下的一个标准偏差。;配准算法也按顺序应用于硬化性静脉畸形（SfVM）的病例评估其对其他类型手术的可移植性。 SfVM尤其具有挑战性，因为畸形可能存在于任何身体部位，这对于从一个患者到另一个患者的医学图像特性几乎没有可预测性。我们的数据集是从单翼飞机手术中取样的，并且没有附带允许对系统进行几何校准的元数据。我们证明了基于MDIP的注册适用于单平面SfVM的情况，但是需要进行修改才能解决VM常见的多种身体部位。为房颤手术的CA开发的协议可用于栓塞术或器官的内部/外部边界突出时使用，但相似性措施必须考虑到梯度信息，以便正确记录骨骼占主导地位的病例。
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