Myocardial Perfusion Single-Photon Emission Computed Tomography (SPECT) Image Denoising: A Comparative Study

Purpose The problem of image denoising in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image processing techniques have been presented, they may degrade the contrast of denoised images. The proposed idea in this study is to use a deep neural network as the denoising procedure during the iterative reconstruction process rather than the post-reconstruction phase. This method could decrease the background coefficient of variation (COV_bkg) of the final reconstructed image, which represents the amount of random noise, while improving the contrast-to-noise ratio (CNR). Methods In this study, a generative adversarial network is used, where its generator is trained by a two-phase approach. In the first phase, the network is trained by a confined image region around the heart in transverse view. The second phase improves the network’s generalization by tuning the network weights with the full image size as the input. The network was trained and tested by a dataset of 247 patients who underwent two immediate serially high- and low-noise SPECT-MPI. Results Quantitative results show that compared to post-reconstruction low pass filtering and post-reconstruction deep denoising methods, our proposed method can decline the COV_bkg of the images by up to 10.28% and 12.52% and enhance the CNR by up to 54.54% and 45.82%, respectively. Conclusion The iterative deep denoising method outperforms 2D low-pass Gaussian filtering with an 8.4-mm FWHM and post-reconstruction deep denoising approaches. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-024-00687-3.

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A few-shot target detection method for wildfires: Research example of Fujian Province, China

Yang,

Wang

2024

IFS

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Unmanned aerial vehicle (UAV) remote-sensing images have a wide range of applications in wildfire monitoring, providing invaluable data for early detection and effective management. This paper proposes an improved few-shot target detection algorithm tailored specifically for wildfire detection. The quality of UAV remote-sensing images is significantly improved by utilizing image enhancement techniques such as Gamma change and Wiener filter, thereby enhancing the accuracy of the detection model. Additionally, ConvNeXt-ECA is used to focus on valid information within the images, which is an improvement of ConvNeXt with the addition of the ECANet attention mechanism. Furthermore, multi-scale feature fusion is performed by adding a feature pyramid network (FPN) to optimize the extracted small target features. The experimental results demonstrate that the improved algorithm achieves a detection accuracy of 93.2%, surpassing Faster R-CNN by 6.6%. Moreover, the improved algorithm outperforms other target detection algorithms YOLOv8, RT-DETR, YoloX, and SSD by 3.4%, 6.4%, 7.6% and 21.1% respectively. This highlights its superior recognition accuracy and robustness in wildfire detection tasks.

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Myocardial Perfusion Single-Photon Emission Computed Tomography (SPECT) Image Denoising: A Comparative Study

Cited by 3 publications

References 26 publications

Potential of [99mTc] Tc-IONPs in SPECT: a systematic review on efficiency and accumulation rates

Potential of [99mTc] Tc-IONPs in SPECT: a systematic review on efficiency and accumulation rates

SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network

A few-shot target detection method for wildfires: Research example of Fujian Province, China

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