Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition

• Published in: Computer methods and programs in biomedicine Vol. 154; pp. 57 - 69
• Main Authors: Lu, Lijun, Ma, Xiaomian, Mohy-ud-Din, Hassan, Ma, Jianhua, Feng, Qianjin, Rahmim, Arman, Chen, Wufan
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-02-2018
• Subjects: Algorithms; Humans; Image Enhancement; Low-rank; Models, Theoretical; Multimodal Imaging; Myocardial perfusion; Myocardial Perfusion Imaging - methods; Pet imaging; Positron Emission Tomography Computed Tomography - methods; Positron-Emission Tomography - methods; Principal Component Analysis; Rubidium Radioisotopes; Signal-To-Noise Ratio; Sparse; Pet imaging; Low-rank; Sparse; Myocardial perfusion
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2017.10.020

•Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition was proposed.•The model utilized the low-rank and sparse properties owned by the background and dynamic components.•The polar map was developed and used to quantify the MP perfusion imaging.
Background and objective: The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images.
Methods: We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82Rb MP PET scan data, we optimized and compared its performance with other restoration methods.
Results: The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms.
Conclusions: The proposed method is effective for restoration and enhancement of dynamic PET images.