Pacific Parkinson's Research Centre, The University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada.
Department of Physics and Astronomy, The University of British Columbia, 6224 Agricultural Road, Vancouver, BC, V6T 1Z1, Canada.
Med Phys. 2021 May;48(5):2230-2244. doi: 10.1002/mp.14751. Epub 2021 Mar 22.
Reconstructed PET images are typically noisy, especially in dynamic imaging where the acquired data are divided into several short temporal frames. High noise in the reconstructed images translates to poor precision/reproducibility of image features. One important role of "denoising" is therefore to improve the precision of image features. However, typical denoising methods achieve noise reduction at the expense of accuracy. In this work, we present a novel four-dimensional (4D) denoised image reconstruction framework, which we validate using 4D simulations, experimental phantom, and clinical patient data, to achieve 4D noise reduction while preserving spatiotemporal patterns/minimizing error introduced by denoising.
Our proposed 4D denoising operator/kernel is based on HighlY constrained backPRojection (HYPR), which is applied either after each update of OSEM reconstruction of dynamic 4D PET data or within the recently proposed kernelized reconstruction framework inspired by kernel methods in machine learning. Our HYPR4D kernel makes use of the spatiotemporal high frequency features extracted from a 4D composite, generated within the reconstruction, to preserve the spatiotemporal patterns and constrain the 4D noise increment of the image estimate.
Results from simulations, experimental phantom, and patient data showed that the HYPR4D kernel with our proposed 4D composite outperformed other denoising methods, such as the standard OSEM with spatial filter, OSEM with 4D filter, and HYPR kernel method with the conventional 3D composite in conjunction with recently proposed High Temporal Resolution kernel (HYPRC3D-HTR), in terms of 4D noise reduction while preserving the spatiotemporal patterns or 4D resolution within the 4D image estimate. Consequently, the error in outcome measures obtained from the HYPR4D method was less dependent on the region size, contrast, and uniformity/functional patterns within the target structures compared to the other methods. For outcome measures that depend on spatiotemporal tracer uptake patterns such as the nondisplaceable Binding Potential (BP ), the root mean squared error in regional mean of voxel BP values was reduced from 8% (OSEM with spatial or 4D filter) to ~3% using HYPRC3D-HTR and was further reduced to ~2% using our proposed HYPR4D method for relatively small target structures (10 mm in diameter). At the voxel level, HYPR4D produced two to four times lower mean absolute error in BP relative to HYPRC3D-HTR.
As compared to conventional methods, our proposed HYPR4D method can produce more robust and accurate image features without requiring any prior information.
重建后的 PET 图像通常存在噪声，尤其是在动态成像中，所采集的数据被分为几个短的时间帧。重建图像中的高噪声会导致图像特征的精度和可重复性变差。因此，“去噪”的一个重要作用是提高图像特征的精度。然而，典型的去噪方法在降低噪声的同时牺牲了准确性。在这项工作中，我们提出了一种新的四维（4D）去噪图像重建框架，该框架通过 4D 模拟、实验体模和临床患者数据进行验证，以实现 4D 降噪，同时保留时空模式/最小化去噪引入的误差。
我们提出的 4D 去噪算子/核基于 HighlY 约束反向投影（HYPR），该算子/核应用于动态 4D PET 数据的 OSEM 重建每次更新之后，或者应用于最近提出的基于机器学习核方法的核化重建框架内。我们的 HYPR4D 核利用从重建过程中生成的 4D 复合体内提取的时空高频特征来保留时空模式，并约束图像估计的 4D 噪声增量。
模拟、实验体模和患者数据的结果表明，在 4D 降噪的同时保留时空模式或 4D 图像估计内的 4D 分辨率方面，我们提出的 4D 复合体内的 HYPR4D 核优于其他去噪方法，如带有空间滤波器的标准 OSEM、带有 4D 滤波器的 OSEM 和与最近提出的 High Temporal Resolution 核（HYPRC3D-HTR）结合使用的 HYPR 核方法，与其他方法相比，HYPR4D 方法获得的结果测量中的误差对目标结构内的区域大小、对比度和均匀性/功能模式的依赖性较小。对于依赖于时空示踪剂摄取模式的结果测量，例如不可置换结合势（BP），使用 HYPRC3D-HTR 将区域均值体素 BP 值的均方根误差从约 8%（带有空间或 4D 滤波器的 OSEM）降低至约 3%，使用我们提出的 HYPR4D 方法进一步降低至约 2%，用于相对较小的目标结构（直径约 10 毫米）。在体素水平上，HYPR4D 产生的 BP 平均绝对误差比 HYPRC3D-HTR 低两到四倍。
与传统方法相比，我们提出的 HYPR4D 方法可以在不依赖任何先验信息的情况下生成更稳健和准确的图像特征。
