Nagpal Simrin, Abolmaesumi Purang, Rasoulian Abtin, Hacihaliloglu Ilker, Ungi Tamas, Osborn Jill, Lessoway Victoria A, Rudan John, Jaeger Melanie, Rohling Robert N, Borschneck Dan P, Mousavi Parvin
School of Computing, Queen's University, Kingston, ON, Canada.
Int J Comput Assist Radiol Surg. 2015 Sep;10(9):1371-81. doi: 10.1007/s11548-015-1247-5. Epub 2015 Jul 15.
Spinal needle injections are widely applied to alleviate back pain and for anesthesia. Current treatment is performed either blindly with palpation or using fluoroscopy or computed tomography (CT). Both fluoroscopy and CT guidance expose patients to ionizing radiation. Ultrasound (US) guidance for spinal needle procedures is becoming more prevalent as an alternative. It is challenging to use US as the sole imaging modality for intraoperative guidance of spine needle injections due to the acoustic shadows created by the bony structures of the vertebra that limit visibility of the target areas for injection. We propose registration of CT and the US images to augment anatomical visualization for the clinician during spinal interventions guided by US.
The proposed method involves automatic global and multi-vertebrae registration to find the closest alignment between CT and US data. This is performed by maximizing the similarity between the two modalities using voxel intensity information as well as features extracted from the input volumes. In our method, the lumbar spine is first globally aligned between the CT and US data using intensity-based registration followed by point-based registration. To account for possible curvature change of the spine between the CT and US volumes, a multi-vertebrae registration step is also performed. Springs are used to constrain the movement of the individually transformed vertebrae to ensure the optimal alignment is a pose of the lumbar spine that is physically possible.
Evaluation of the algorithm is performed on 10 clinical patient datasets. The registration approach was able to align CT and US datasets from initial misalignments of up to 25 mm, with a mean TRE of 1.37 mm. These results suggest that the proposed approach has the potential to offer a sufficiently accurate registration between clinical CT and US data.
脊髓穿刺注射广泛应用于缓解背痛和麻醉。目前的治疗方法要么是通过触诊盲目进行，要么使用荧光透视或计算机断层扫描（CT）。荧光透视和CT引导都会使患者暴露于电离辐射中。超声（US）引导下的脊髓穿刺操作正越来越普遍地作为一种替代方法。由于椎骨的骨结构产生的声影限制了注射目标区域的可视性，将超声作为脊髓穿刺注射术中唯一的成像方式用于引导具有挑战性。我们建议对CT和超声图像进行配准，以在超声引导的脊柱介入手术中增强临床医生对解剖结构的可视化。
所提出的方法涉及自动全局和多椎体配准，以找到CT和超声数据之间的最紧密对齐。这是通过使用体素强度信息以及从输入体积中提取的特征来最大化两种模式之间的相似性来实现的。在我们的方法中，首先使用基于强度的配准在CT和超声数据之间对腰椎进行全局对齐，然后进行基于点的配准。为了考虑CT和超声体积之间脊柱可能的曲率变化，还执行了多椎体配准步骤。使用弹簧来约束各个变换后的椎体的运动，以确保最佳对齐是腰椎在物理上可行的姿势。
在10个临床患者数据集上对该算法进行了评估。配准方法能够将CT和超声数据集从初始高达25毫米的错位对齐，平均配准误差（TRE）为1.37毫米。这些结果表明，所提出的方法有可能在临床CT和超声数据之间提供足够准确的配准。
