Ms. Jiahong Ouyang Profile

Jiahong Ouyang

at Carnegie Mellon Univ

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Proceedings Article | 10 March 2020 Presentation + Paper

Ultra-low-dose 18F-FDG brain PET/MR denoising using deep learning and multi-contrast information

Junshen Xu, Enhao Gong, Jiahong Ouyang, John Pauly, Greg Zaharchuk

Proceedings Volume 11313, 113131P (2020) https://doi.org/10.1117/12.2548350

KEYWORDS: Positron emission tomography, Magnetic resonance imaging, Denoising, Image quality, Diagnostics, Brain

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Positron emission tomography (PET) is a widely used molecular imaging modality for various clinical applications. With Magnetic Resonance Imaging (MRI) providing anatomical information, simultaneous PET/MR reduces the radiation risk. Both improved hardware and algorithms have been developed to further reduce the amount of radiotracer dosage, but these methods are not yet applied to very low dose. Here, we propose a Deep Learning based method to enable ultra-low-dose PET denoising with multi-contrast information from simultaneous MRI. Methods:The method is implemented to denoise 18F-fluorodeoxyglucose (FDG) brain PET images from low-dose images with 200-fold dose reduction through undersampling, and evaluated for glioblastoma (GBM) patients. Comprehensive quantitative and qualitative evaluations were conducted to verify the performance and clinical applicability of the proposed method, including quantitative accuracy evaluation, visual quality evaluation, reader study with manual tumor segmentation to evaluate the diagnostic quality. Results:The results demonstrate that the proposed method achieves superior results in performance and efficiency comparing with the state-of-art denoising methods. Conclusion:Though reconstructed from scans with only 0.5% of the standard dose, the denoised ultra-low-dose PET images deliver similar visual quality and diagnostic information as the standard-dose PET images. By combining PET and MR information, the proposed Deep Learning based method improves image quality of ultra-low-dose PET, preserves diagnostic quality, and potentially enables much safer, faster, and more cost-effective PET/MR studies.

Proceedings Article | 14 March 2019 Presentation

Ultra-low dose PET reconstruction using generative adversarial network with feature matching (Conference Presentation)

Jiahong Ouyang

Proceedings Volume 10948, 1094812 (2019) https://doi.org/10.1117/12.2512946

KEYWORDS: Positron emission tomography, Image quality, Gallium nitride, Signal to noise ratio, Data acquisition, Visualization, Electron beam melting

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Purpose: Our goal is to synthesize high quality and accurate Amyloid PET images with only ultra-low-dose PET images as input by using Generative Adversarial Network (GAN). Methods: 40 patients’ PET data was acquired with the injection of 330±30 MBq amyloid radiotracer 18F-florbetaben. The raw list mode PET data was reconstructed as the standard-dose ground truth and was randomly undersampled by a factor of 100 to reconstruct 1% low-dose PET scans. 32 volumes were used for training and the other 8 for testing. A 2D encoder-decoder network was implemented as the generator to synthesize a standard-dose image and a CNN based discriminator network was used to evaluate them. The two networks contested with each other to achieve accurate synthesis of standard-dose PET images with high visual quality from ultra-low-dose PET. Multi-slice input is used to reduce noise by providing the network with 2.5D information. Feature matching was applied to reduce the hallucinate structure. The image quality was evaluated by peak signal-to-noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE), frequency domain blur measure (FBM) and edge blur measure (EBM) metrics. Results: The synthesized PET images showed remarkable improvement on all quality metric compared with the low-dose images. Comparing with the state-of-art method, adversarial learning is essential to ensure image quality and mitigate the blurring in the generated image. Multi-slice input reduced random noise and feature matching suppressed the hallucinate structure. Conclusion: Standard-dose Amyloid PET images can be synthesized from ultra-low-dose image by GAN. Applying adversarial learning, multi-slice input and feature matching technique are essential to ensure image quality.

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