MRC Clinical Sciences Centre, Hammersmith Hospital Campus, Imperial College, London, UK.
Med Phys. 2011 Sep;38(9):4920-3. doi: 10.1118/1.3608907.
Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details.
A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method.
Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy.
A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.
部分容积效应（PVE）是发射断层成像中空间分辨率有限的结果，导致对与扫描仪的点扩散函数（PSF）大小相似的组织中的摄取量的低估，以及相邻结构之间的活性溢出。在 PVE 校正方法中，最近引入了一种基于体素的互多分辨率分析（MMA）。MMA 基于从解剖图像（MR/CT）中提取和转换高分辨率细节，并使用小波分解将其随后合并到低分辨率 PET 图像中。尽管该方法允许创建 PVE 校正图像，但它基于二维全局相关模型，这可能会在解剖学和功能细节之间不存在显著相关性的区域中引入伪影。
为了克服这两个问题（二维和全局相关性），设计了一种新模型，该模型使用 3D 小波分解过程与局部分析相结合。该算法在合成、模拟和患者图像上进行了评估，并将其性能与原始方法和几何传输矩阵（GTM）方法进行了比较。
在相关情况下，定量性能与二维全局模型和 GTM 相似。在解剖学和功能信息不匹配的情况下，新模型优于二维全局方法，避免了伪影，并显著提高了校正图像的质量及其定量准确性。
提出了一种新的基于原始互多分辨率分析方法的基于体素的 PVE 校正的 3D 局部模型。其评估表明，与原始 MMA 方法相比，该模型在定性和定量准确性方面均有改进和更稳健，特别是在解剖学和功能信息之间没有完全相关的情况下。
