Mongioj V, Brusa A, Loi G, Pignoli E, Gramaglia A, Scorsetti M, Bombardieri E, Marchesini R
Division of Health Physics, Istituto Nazionale Tumori, Milan, Italy.
Int J Radiat Oncol Biol Phys. 1999 Jan 1;43(1):227-34. doi: 10.1016/s0360-3016(98)00363-0.
A problem for clinicians is to mentally integrate information from multiple diagnostic sources, such as computed tomography (CT), magnetic resonance (MR), and single photon emission computed tomography (SPECT), whose images give anatomic and metabolic information.
To combine this different imaging procedure information, and to overlay correspondent slices, we used commercially available software packages (SRS PLATO and IFS). The algorithms utilize a fiducial-based coordinate system (or frame) with 3 N-shaped markers, which allows coordinate transformation of a clinical examination data set (9 spots for each transaxial section) to a stereotactic coordinate system. The N-shaped markers were filled with fluids visible in each modality (gadolinium for MR, calcium chloride for CT, and 99mTc for SPECT). The frame is relocatable, in the different acquisition modalities, by means of a head holder to which a face mask is fixed so as to immobilize the patient. Position errors due to the algorithms were obtained by evaluating the stereotactic coordinates of five sources detectable in each modality.
SPECT and MR position errors due to the algorithms were evaluated with respect to CT: deltax was < or = 0.9 mm for MR and < or = 1.4 mm for SPECT, deltay was < or = 1 mm and < or = 3 mm for MR and SPECT, respectively. Maximal differences in distance between estimated and actual fiducial centers (geometric mismatch) were in the order of the pixel size (0.8 mm for CT, 1.4 mm for MR, and 1.8 mm for SPECT). In an attempt to distinguish necrosis from residual disease, the image fusion protocol was studied in 35 primary or metastatic brain tumor patients.
The image fusion technique has a good degree of accuracy as well as the potential to improve the specificity of tissue identification and the precision of the subsequent treatment planning.
临床医生面临的一个问题是如何在脑海中整合来自多种诊断源的信息，比如计算机断层扫描（CT）、磁共振成像（MR）和单光子发射计算机断层扫描（SPECT），这些检查的图像能提供解剖和代谢信息。
为了整合这些不同成像程序的信息并叠加相应层面，我们使用了商用软件包（SRS PLATO和IFS）。该算法利用基于基准的坐标系（或框架），带有3个N形标记物，这使得临床检查数据集（每个横断面有9个点）的坐标能转换为立体定向坐标系。N形标记物填充有在每种模态下可见的液体（MR用钆，CT用氯化钙，SPECT用99mTc）。通过固定面罩的头架，该框架在不同采集模态下可重新定位，从而固定患者。通过评估每种模态下可检测到的5个源的立体定向坐标，得出算法导致的位置误差。
相对于CT评估了算法导致的SPECT和MR位置误差：MR的deltax≤0.9毫米，SPECT的deltax≤1.4毫米；MR的deltay≤1毫米，SPECT的deltay≤3毫米。估计的和实际基准中心之间距离的最大差异（几何不匹配）约为像素大小（CT为0.8毫米，MR为1.4毫米，SPECT为1.8毫米）。为了区分坏死与残留病灶，对35例原发性或转移性脑肿瘤患者研究了图像融合方案。
图像融合技术具有良好的准确性，并且有潜力提高组织识别的特异性以及后续治疗计划的精确性。
