Dr. Quanzheng Li Profile

Dr. Quanzheng Li

Associate Professor at Massachusetts General Hospital

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 1 April 2024 Presentation + Paper

Motion-compensated 4DCT reconstruction from single-beat cardiac CT scans using convolutional networks

Zhenyao Yan, Zhennong Chen, Li Zhang, Quanzheng Li, Dufan Wu

Proceedings Volume 12925, 129251D (2024) https://doi.org/10.1117/12.3005368

KEYWORDS: Medical image reconstruction, Education and training, Reconstruction algorithms, Image restoration, CT reconstruction, Deformation, Computed tomography, 3D projection, Temporal resolution, Image registration

Read Abstract +

Proceedings Article | 28 May 2019 Paper

Parametric image estimation using Residual simplified reference tissue model

Kyungsang Kim, Young Don Son, Jong-Hoon Kim, Quanzheng Li

Proceedings Volume 11072, 1107237 (2019) https://doi.org/10.1117/12.2534958

KEYWORDS: Image quality, Image analysis, Signal to noise ratio, Image resolution, Brain, Blood

Read Abstract +

Proceedings Article | 28 May 2019 Paper

Direct patlak reconstruction from dynamic PET using unsupervised deep learning

Kuang Gong, Ciprian Catana, Jinyi Qi, Quanzheng Li

Proceedings Volume 11072, 110720R (2019) https://doi.org/10.1117/12.2534902

KEYWORDS: Data modeling, Neural networks, Convolution, Blood, Expectation maximization algorithms, Medical imaging, Positron emission tomography, Magnetic resonance imaging, Reconstruction algorithms

Read Abstract +

Proceedings Article | 28 May 2019 Paper

Population and individual information guided PET image denoising using deep neural network

Jianan Cui, Kuang Gong, Ning Guo, Chenxi Wu, Kyungsang Kim, Huafeng Liu, Quanzheng Li

Proceedings Volume 11072, 110721E (2019) https://doi.org/10.1117/12.2534901

KEYWORDS: Positron emission tomography, Image denoising, Neural networks

Read Abstract +

Proceedings Article | 28 May 2019 Paper

Learned primal-dual reconstruction for dual energy computed tomography with reduced dose

Dufan Wu, Kyungsang Kim, Mannudeep Kalra, Bruno De Man, Quanzheng Li

Proceedings Volume 11072, 1107206 (2019) https://doi.org/10.1117/12.2534943

KEYWORDS: Iodine, Computed tomography, Dual energy imaging, Image quality, Liver, Blood, Chest, Medical research, Neural networks

Read Abstract +

Proceedings Article | 28 May 2019 Paper

MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction

Kuang Gong, Dufan Wu, Kyungsang Kim, Jaewon Yang, Tao Sun, Georges El Fakhri, Youngho Seo, Quanzheng Li

Proceedings Volume 11072, 110720O (2019) https://doi.org/10.1117/12.2534904

KEYWORDS: Image restoration, Neural networks, Positron emission tomography, Denoising, 3D image processing, Computer simulations, Reconstruction algorithms, 3D modeling, Medical imaging

Read Abstract +

Proceedings Article | 13 March 2019 Presentation + Paper

Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net

Zhe Guo, Ning Guo, Kuang Gong, Quanzheng Li

Proceedings Volume 10950, 1095009 (2019) https://doi.org/10.1117/12.2513229

KEYWORDS: Image segmentation, Positron emission tomography, Cancer, Head, Neck, Radiotherapy, Computed tomography, Tumors, Network architectures, Convolution

Read Abstract +

Proceedings Article | 1 March 2019 Presentation + Paper

CT-guided PET parametric image reconstruction using deep neural network without prior training data

Jianan Cui, Kuang Gong, Ning Guo, Kyungsang Kim, Huafeng Liu, Quanzheng Li

Proceedings Volume 10948, 109480Z (2019) https://doi.org/10.1117/12.2513077

KEYWORDS: Positron emission tomography, Neural networks, Computed tomography, Image restoration, Image quality, Tissues, Medical imaging, Optimization (mathematics), Image analysis, Image processing

Read Abstract +

Proceedings Article | 1 March 2019 Paper

EMnet: an unrolled deep neural network for PET image reconstruction

Kuang Gong, Dufan Wu, Kyungsang Kim, Jaewon Yang, Georges El Fakhri, Youngho Seo, Quanzheng Li

Proceedings Volume 10948, 1094853 (2019) https://doi.org/10.1117/12.2513096

KEYWORDS: Neural networks, Positron emission tomography, Image restoration, Denoising, Expectation maximization algorithms, Medical imaging, Image quality, Brain, Neuroimaging, Medical image reconstruction

Read Abstract +

Proceedings Article | 1 March 2019 Paper

Computational-efficient cascaded neural network for CT image reconstruction

Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li

Proceedings Volume 10948, 109485Z (2019) https://doi.org/10.1117/12.2511526

KEYWORDS: Neural networks, CT reconstruction, Computed tomography, Reconstruction algorithms, 3D image processing, Medical imaging, Image restoration

Read Abstract +

Proceedings Article | 2 March 2018 Paper

Subject-specific brain tumor growth modelling via an efficient Bayesian inference framework

Yongjin Chang, Gregory Sharp, Quanzheng Li, Helen Shih, Georges El Fakhri, Jong Beom Ra, Jonghye Woo

Proceedings Volume 10574, 105742I (2018) https://doi.org/10.1117/12.2293145

KEYWORDS: Tumors, Brain, Image segmentation, Diffusion, Neuroimaging, Modeling, Bayesian inference, Magnetic resonance imaging, In vivo imaging, Medical imaging

Read Abstract +

SPIE Journal Paper | 26 April 2017

Joint estimation of activity image and attenuation sinogram using time-of-flight positron emission tomography data consistency condition filtering

Quanzheng Li, Hao Li, Kyungsang Kim, Georges El Fakhri

JMI, Vol. 4, Issue 02, 023502, (April 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.023502

KEYWORDS: Signal attenuation, Positron emission tomography, Image analysis, Image filtering, Lithium, Medical imaging, Scanners, Computed tomography, Image quality, Electroluminescence

Read Abstract +

Proceedings Article | 13 March 2017 Presentation + Paper

Image deblurring using a joint entropy prior in x-ray luminescence computed tomography

Chang Su, Joyita Dutta, Hui Zhang, Georges El Fakhri, Quanzheng Li

Proceedings Volume 10137, 1013706 (2017) https://doi.org/10.1117/12.2254649

KEYWORDS: X-ray computed tomography, Image processing, Point spread functions, X-ray imaging, Luminescence, Image quality, CCD cameras, Lung, Tissues, Bone

Read Abstract +

SPIE Journal Paper | 12 July 2016

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

Auranuch Lorsakul, Georges El Fakhri, William Worstell, Jinsong Ouyang, Yothin Rakvongthai, Andrew Laine, Quanzheng Li

JMI, Vol. 3, Issue 03, 035501, (July 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.3.035501

KEYWORDS: Computed tomography, Signal attenuation, Signal to noise ratio, Sensors, Image filtering, X-ray computed tomography, Arteries, Imaging systems, X-rays, Medical imaging

Read Abstract +

Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre–Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p<0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p<0.01) for FBP-Ramp images and 53.2% to 67.7% (all p<0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.

Showing 5 of 14 publications

Conference Committee Involvement (7)

Physics of Medical Imaging

16 February 2025 | San Diego, California, United States

Physics of Medical Imaging

19 February 2024 | San Diego, California, United States

Physics of Medical Imaging

20 February 2023 | San Diego, California, United States

Physics of Medical Imaging

20 February 2022 | San Diego, California, United States

Physics of Medical Imaging

15 February 2021 | Online Only, California, United States

Showing 5 of 7 Conference Committees

Keywords/Phrases

Search In:

Publication Years