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

Lu, Lijun; Ma, Xiaomian; Mohy-ud-Din, Hassan; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan

doi:10.1016/j.cmpb.2017.10.020

Cited by 2 publications

(1 citation statement)

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“…Recently, robust principal component analysis (RPCA), which takes combined regularizations of low-rank and sparse constraints for signal representation, has been applied in dynamic imaging analysis tasks, such as 4DCT, 19,20 D-MRI, 21 and dynamic PET. 22 This mechanism derives from recent studies in the natural image processing field, especially in salient motion detection for video surveillance. 23 For a temporal image sequence, each image in the sequence could be decomposed into a superposition of a low-rank matrix modeling a temporally correlated background and a sparse matrix representing dynamic components.…”

Section: Introductionmentioning

confidence: 99%

High‐quality initial image‐guided 4D CBCT reconstruction

2020

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Purpose: Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse-view CT procedure wherein only under-sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high-quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. Methods: The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase-resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA-decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac-torso (XCAT) phantoms and two sets of patient data. Several state-of-the-art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. Results: The image quality of phase-resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root-mean-square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA-4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method. Conclusions: The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase-resolved volumes guided by the initial image achieves high spatiotempora...

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Section: Introductionmentioning

confidence: 99%

High‐quality initial image‐guided 4D CBCT reconstruction

2020

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Deep learned triple-tracer multiplexed PET myocardial image separation

Pan,

Marsden,

Reader

2024

Front. Nucl. Med.

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IntroductionIn multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling method (MTCM) requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known.MethodsIn this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. Dynamic triple-tracer noisy MLEM reconstruction was used as the network input and dynamic single-tracer noisy MLEM reconstructions were used as the training labels.ResultsA simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([18F]FDG+82Rb+[94mTc]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and ROI levels.DiscussionAs compared to the MTCM separation, the proposed method uses spatiotemporal information for separation, which enhances the separation performance at both the voxel and ROI levels. The simulation study also indicates the feasibility and potential of the proposed DL-based method for the application to pre-clinical and clinical studies.

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

Cited by 2 publications

References 55 publications

High‐quality initial image‐guided 4D CBCT reconstruction

High‐quality initial image‐guided 4D CBCT reconstruction

Deep learned triple-tracer multiplexed PET myocardial image separation

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