A Monte Carlo study for optimizing the detector of SPECT imaging using a XCAT human phantom

Khoshakhlagh, Mohammad; Islamian, Jalil Pirayesh; Abedi, Seyyed Mohammad; Moeini, Babak; Azar, Masoud Shayesteh

doi:10.5603/nmr.2017.0001

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…As shown in Figure 3G,3H, to further optimize the generalizability of the network in specific fields, the extended cardiac-torso (XCAT) phantoms were validated (40). In total, 40 sets of XCAT phantoms were applied, and the CT images were converted to corresponding SPECT images based on tissue segmentation (41). In this study, the liver was chosen as the target organ.…”

Section: D F H B E G Amentioning

confidence: 99%

Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study

Cheng¹,

Wen²,

Zhang³

et al. 2022

Ann Transl Med

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Background: Single-photon emission computed tomography (SPECT) is widely used in the early diagnosis of major diseases such as cardiovascular disease and cancer. High-resolution (HR) imaging requires HR projection data, which typically comes with high costs. This study aimed to obtain HR SPECT images based on a deep learning algorithm using low-resolution (LR) detectors.Methods: A super-resolution (SR) reconstruction network based on deep learning and transfer learning for parallel-beam SPECT was proposed. LR SPECT sinograms were converted into HR sinograms.Training data were designed and generated, including digital phantoms (128×128 pixels), HR sinograms (128×128 pixels), and LR sinograms (128×64 pixels). A series of random phantoms was first used for pretraining, and then the extended cardiac-torso (XCAT) phantom was used to fine-tune the parameters. The effectiveness of the method was evaluated using an unknown cardiac phantom. To simulate a wide range of noise levels, the total count levels of the projection were normalized to 1e7 (100%), 1e6 (10%), and 1e5 (5%). Finally, the training sets for different count levels were generated. Transfer learning was employed to accelerate the training. Results:The proposed network was validated on the simulation data sets using different Poisson noise levels. The quantitative values of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) indicators of the reconstructed images were improved compared to those recorded using the benchmark methods. Using the proposed method, an image resolution comparable to that of images reconstructed from the HR projection could be achieved.Conclusions: Based on deep learning and transfer learning, an SR reconstruction network in the projection domain of the parallel-beam SPECT was developed. The simulation results under a wide range of noise levels evidenced the potential of the proposed network to improve SPECT resolution for LR detector scanners.

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Section: D F H B E G Amentioning

confidence: 99%