Kuang Gong
Harvard University
Deep learningCorrection for attenuationIterative reconstructionMagnetic resonance imagingArtificial intelligenceNoise reductionGaussianPattern recognitionAttenuationImage resolutionPositron emission tomographyScannerMedical imagingComputer visionComputer scienceArtificial neural networkImage qualityConvolutional neural networkDetectorNoise (video)
61Publications
15H-index
770Citations
Publications 60
Newest
#1Kuang Gong (Harvard University)H-Index: 15
#2Ciprian Catana (Harvard University)H-Index: 43
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 4 authors...
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number o...
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#1Kuang Gong (Harvard University)H-Index: 15
#2Kyungsang Kim (Harvard University)H-Index: 17
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 5 authors...
PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-time limit of system components leads to the loss of the count rate; the scattered and random events received by the detector introduce additional noise; the characteristics of the detector limit the spatial resolution; and the low signal-to-noise rat...
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#1Jianan Cui (ZJU: Zhejiang University)H-Index: 4
#2Kuang Gong (Harvard University)H-Index: 15
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 7 authors...
Our study aims to improve the signal-to-noise ratio (SNR) of PET imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily applied to existing PET/CT and PET/MR datasets. This method includes two steps: populational training and individual fine-tuning. As for populational training, a network was first pre-trained by a group of patients' noisy PET images and the corresponding anatomical prior images f...
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#1Nuobei Xie (Harvard University)H-Index: 1
#2Kuang Gong (Harvard University)H-Index: 15
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 7 authors...
Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by...
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#1Kuang Gong (Harvard University)H-Index: 15
#2Paul Kyu Han (Harvard University)H-Index: 7
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 6 authors...
PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer’s disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging. Thirty-five subjects that...
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Attenuation correction (AC) is important for the quantitative merits of positron emission tomography (PET). However, attenuation coefficients cannot be derived from magnetic resonance (MR) images directly for PET/MR systems. In this work, we aimed to derive continuous AC maps from Dixon MR images without the requirement of MR and computed tomography (CT) image registration. To achieve this, a 3-D generative adversarial network with both discriminative and cycle-consistency loss (Cycle-GAN) was d...
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#1Kuang Gong (Harvard University)H-Index: 15
#2Dufan Wu (Harvard University)H-Index: 13
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 27 authors...
Abstract Purpose As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients’ electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severit...
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#1Nuobei XieH-Index: 2
#2Kuang GongH-Index: 15
Last. Quanzheng LiH-Index: 33
view all 7 authors...
Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors into PET image reconstruction, especially with the development of hybrid PET/CT and PET/MRI systems. In this work, we proposed a cube-based 3D structural convolutional sparse coding (CSC) concept for penalized-likelihood PET image reconstruction, named 3D PET-CSC. The proposed 3D PET-CSC takes advantage of the convoluti...
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#1Nuobei Xie (Harvard University)H-Index: 1
#2Kuang Gong (Harvard University)H-Index: 15
Last. Huafeng Liu (ZJU: Zhejiang University)H-Index: 13
view all 0 authors...
Patlak model is constantly considered as a critical topic in the application of dynamic positron emission tomography (PET). Traditionally, direct reconstruction methods were preferred over indirect methods, for their complete noise modelling and comprehensive information exploited from raw data. Nevertheless, the development of direct methods was inevitably constrained by the scarcity for raw dynamic PET and the requirement for computation cost. In addition, the increasing demand for low-dose PE...
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#1Dufan WuH-Index: 13
#2Kuang GongH-Index: 15
Last. Pengcheng XuH-Index: 1
view all 28 authors...
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish...
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