Abstract：
Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution.Approach. Blind deconvolution is a method of estimating the hypothetical \'deblurred\' image along with the blur kernel (related to the point spread function) simultaneously. Traditionalmaximum a posterioriblind deconvolution methods require stringent assumptions and suffer from convergence to a trivial solution. A method of modelling the deblurred image and kernel with independent neural networks, called \'neural blind deconvolution\' had demonstrated success for deblurring 2D natural images in 2020. In this work, we adapt neural blind deconvolution to deblur PSMA PET images while simultaneous supersampling to double the original resolution. We compare this methodology with several interpolation methods in terms of resultant blind image quality metrics and test the model\'s ability to predict accurate kernels by re-running the model after applying artificial \'pseudokernels\' to deblurred images. The methodology was tested on a retrospective set of 30 prostate patients as well as phantom images containing spherical lesions of various volumes.Main results. Neural blind deconvolution led to improvements in image quality over other interpolation methods in terms of blind image quality metrics, recovery coefficients, and visual assessment. Predicted kernels were similar between patients, and the model accurately predicted several artificially-applied pseudokernels. Localization of activity in phantom spheres was improved after deblurring, allowing small lesions to be more accurately defined.Significance. The intrinsically low spatial resolution of PSMA PET leads to partial volume effects (PVEs) which negatively impact uptake quantification in small regions. The proposed method can be used to mitigate this issue, and can be straightforwardly adapted for other imaging modalities.
摘要：
目的：使用神经盲解卷积同时对前列腺特异性膜抗原（PSMA）正电子发射断层扫描（PET）图像进行模糊和超采样。方法：盲解卷积是一种同时估计假设的“去模糊”图像以及模糊核（与点扩散函数相关）的方法。传统的最大后验盲反褶积方法需要严格的假设，并且会收敛到琐碎的解决方案。一种用独立神经网络对去模糊图像和核建模的方法，被称为“神经盲去卷积”的人在2020年证明了对2D自然图像进行去模糊的成功。在这项工作中,我们采用神经盲反卷积对PSMAPET图像进行PVE校正，同时进行超采样。我们将这种方法与几种插值方法进行比较，使用盲图像质量度量，并通过在将人工“伪内核”应用于去模糊图像后重新运行模型来测试模型预测内核的能力。该方法在30名前列腺患者的回顾性集合以及包含各种体积的球形病变的体模图像上进行了测试。主要结果：神经盲反卷积在盲图像质量度量方面比其他插值方法提高了图像质量，恢复系数，视觉评估。患者之间预测的内核相似，该模型准确地预测了几个人工应用的伪核。去模糊后，幻像球中活动的定位得到了改善，允许更准确地定义小病变。意义:PSMAPET的固有低空间分辨率导致PVE,其负面影响小区域中的摄取定量。所提出的方法可以用来缓解这个问题，并且可以直接适用于其他成像模式。
