Dr. Mengzhou Li Profile

Dr. Mengzhou Li

at Rensselaer Polytechnic Institute

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Author

Publications (12)

Proceedings Article | 27 March 2023 Poster

End-to-end reconstruction for mesoscopic fluorescence molecular tomography via deep learning

Shan Gao, Mengzhou Li, Navid Ibtehaj Nizam, Intes Xavier

Proceedings Volume PC12371, PC123710H (2023) https://doi.org/10.1117/12.2650648

KEYWORDS: Luminescence, Tomography, Fluorescence tomography, Inverse problems, 3D modeling, Spatial resolution, Monte Carlo methods, Biomedical optics, 3D image processing, 3D acquisition

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Proceedings Article | 18 October 2022 Open Access

Motion correction via locally linear embedding for helical photon-counting CT

Mengzhou Li, Chiara Lowe, Anthony Butler, Phil Butler, Ge Wang

Proceedings Volume 12304, 123042I (2022) https://doi.org/10.1117/12.2646714

KEYWORDS: Motion estimation, Sensors, X-ray computed tomography, Reconstruction algorithms, Motion measurement, Photodetectors, Image quality, CT reconstruction, Image restoration, Computed tomography

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Proceedings Article | 14 October 2022 Presentation + Paper

Metal pin artifact reduction with masked iterative nano-CT reconstruction

Mengzhou Li, Victoria Cooley, Viktor Nikitin, Stuart Stock, Ge Wang

Proceedings Volume 12242, 122421H (2022) https://doi.org/10.1117/12.2635261

KEYWORDS: Metals, Image segmentation, Teeth, Tomography, Lawrencium, Image quality, X-rays, Synchrotron radiation, Signal attenuation, X-ray microscopy, X-ray computed tomography

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Proceedings Article | 14 October 2022 Presentation + Paper

Motion correction for robot-based x-ray photon-counting CT at ultrahigh resolution

Mengzhou Li, Jana Bohacova, Josef Uher, Wenxiang Cong, Jay Rubinstein, Ge Wang

Proceedings Volume 12242, 122420Y (2022) https://doi.org/10.1117/12.2635263

KEYWORDS: Sensors, Robotics, X-ray computed tomography, X-rays, Scanners, Calibration, Image resolution, Imaging systems, X-ray sources, Reconstruction algorithms

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Ultrahigh-resolution CT is highly desirable in many clinical applications, such as cochlear implantation and coronary stenosis assessment. Despite the wide accessibility to micro-CT for preclinical research, there are no such micro-CT scanners for clinical use, due to several issues including high radiation dose, patient movement, limited X-ray source power and long image time. To meet the challenge, here we design a robotic-arm-based clinical photon-counting micro-CT system in the interior tomography mode for temporal bone imaging at a 50 µm resolution. We adopt twin collaborative robotic arms. These robotic arms can be operated near humans without danger of injury due to collision. One robot steers the source, the other the detector. The six-axis robot pair defines a volume of interest (VOI) more flexibly than the traditional rotation gantries and C-arm systems. The robotic system allows arbitrary imaging geometry. The required spatial resolution is on the same order of the robot mechanical precision, therefore the geometric errors due to robotic coordination together with patient motion, and system misalignment must be addressed. In this paper, we propose a locally linear embedding-based motion correction method to solve this problem to correct all these geometric errors in an unified framework with a nine-degree-of-freedom model. Different from the conventional motion correction methods that rely on gradient-based parametric optimization, our method utilizes a parametric searching mechanism through iterative reconstruction and without assuming any smoothness of the errors as a function of the view angle. The effectiveness of our method is first demonstrated with numerical simulation and further verified on a set of limited-angle cone-beam scans of a sacrificed mouse using our robotic-arm-based imaging system. Sharper images with clearer anatomical details in the absence of misalignment artifacts are obtained after our data-driven geometric calibration, suggesting a great potential of our method in biomedical and industrial applications.

Proceedings Article | 14 October 2022 Presentation + Paper

Self-supervised dual-domain network for low-dose CT denoising

Chuang Niu, Mengzhou Li, Xiaodong Guo, Ge Wang

Proceedings Volume 12242, 122420H (2022) https://doi.org/10.1117/12.2633197

KEYWORDS: Denoising, Image processing, X-ray computed tomography, Reconstruction algorithms, Signal attenuation, Data modeling, Computed tomography, Image quality, Radiation effects, Machine learning

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Proceedings Article | 4 April 2022 Poster + Paper

Effects of image denoising on quantitative material decomposition in photon-counting spectral computed tomography

Connor Evans, Mengzhou Li, Chuang Niu, Ge Wang, Ryan Roeder

Proceedings Volume 12031, 120312Y (2022) https://doi.org/10.1117/12.2611828

KEYWORDS: Gadolinium, Image denoising, Calcium, Iodine, Spatial resolution, Tissues, Image quality, Image classification, Spectral computed tomography, X-ray computed tomography, X-ray medical imaging

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Material decomposition algorithms enable discrimination and quantification of multiple contrast agent and tissue compositions in spectral image datasets acquired by photon-counting computed tomography (PCCT). Image denoising has been shown to improve PCCT image reconstruction quality and feature recognition while preserving fine image detail. Reduction of image artifacts and noise could also improve the accuracy of material decomposition but the effects of denoising on material decomposition have not been investigated. In particular, deep learning methods can reduce inherent PCCT image noise without using a system-based or assumed prior noise model. Therefore, the objective of this study was to investigate the effects of image denoising on quantitative material decomposition in the absence of an influence of spatial resolution on feature recognition. Phantoms comprising multiple pure and spatially uniform contrast agent (gadolinium, iodine) and tissue (calcium, water) compositions were imaged by PCCT with four energy thresholds chosen to normalize photon counts and leverage contrast agent k-edges. Image denoising was performed by the established blockmatching and 3D-filtering (BM3D) algorithm or deep learning using convolutional neural networks. Material decomposition was performed on as-acquired, BM3D-denoised, and deep-learning-denoised datasets using constrained maximum likelihood estimation and compared to known material concentrations in the phantom. Image denoising by BM3D and deep learning improved the quantitative accuracy of material concentrations determined by material decomposition compared to ground truth, as measured by the root-mean-squared error. Material classification was not improved by image denoising compared with as-acquired images, suggesting that material decomposition was robust against inherent acquisition noise when feature recognition was not challenged by the system spatial resolution. Deeplearning-denoised images balanced preservation of local detail compared to more aggressive smoothing with BM3D, as measured by line profiles across features.

Proceedings Article | 9 September 2021 Presentation + Paper

Multiple window learning for CT metal artifact reduction

Chuang Niu, Mengzhou Li, Ge Wang

Proceedings Volume 11840, 1184015 (2021) https://doi.org/10.1117/12.2596239

KEYWORDS: Metals, Computed tomography, X-ray computed tomography, Spine, Computer simulations, Visualization, Tissues, Inspection, Image processing, Germanium

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Proceedings Article | 9 September 2021 Presentation + Paper

High-resolution interior tomography with a deep neural network trained on a low-resolution dataset

Mengzhou Li, Wenxiang Cong, Ge Wang

Proceedings Volume 11840, 1184018 (2021) https://doi.org/10.1117/12.2594286

KEYWORDS: Lawrencium, Bone, Tomography, Image filtering, Computed tomography, Sensors, Neural networks, Image restoration, CT reconstruction, Signal attenuation

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SPIE Journal Paper | 14 February 2020

Deep adversarial network for super stimulated emission depletion imaging

Mengzhou Li, Hongming Shan, Sergey Pryshchep, Maria Lopez, Ge Wang

JNP, Vol. 14, Issue 01, 016009, (February 2020) https://doi.org/10.1117/12.10.1117/1.JNP.14.016009

KEYWORDS: Stimulated emission depletion microscopy, Image resolution, Point spread functions, Super resolution, Data modeling, Confocal microscopy, Deconvolution, Microscopes, Image enhancement, Microscopy

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Proceedings Article | 10 September 2019 Paper

Deep learning based CT thermometry for thermal tumor ablation

Nathan Wang, Mengzhou Li, Hongming Shan, Pingkun Yan

Proceedings Volume 11113, 111131T (2019) https://doi.org/10.1117/12.2530771

KEYWORDS: Thermometry, Computed tomography, Ultrasonography, Tissues, Neural networks, Ultrasonics, Robotics, Tomography, Surgery, Temperature metrology

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Proceedings Article | 10 September 2019 Paper

Deformable and reconfigurable mouse phantoms with 3D printing and LTS technologies

Hannah Wang, Mengzhou Li, Matthew Getzin, Brigitte Arduini, Nathaniel Covert, Deepak Vashishth, Paul Fitzgerald

Proceedings Volume 11113, 111131S (2019) https://doi.org/10.1117/12.2530770

KEYWORDS: Tissues, Signal attenuation, 3D printing, Skin, 3D modeling, X-rays, Liver, Polymethylmethacrylate, Polyurethane, Scanners

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Proceedings Article | 28 May 2019 Paper

Non-uniformity correction for MARS photon-counting detectors

Matthew Getzin, Mengzhou Li, David Rundle, Anthony Butler, Ge Wang

Proceedings Volume 11072, 110722W (2019) https://doi.org/10.1117/12.2534914

KEYWORDS: Sensors, Photodetectors, Nonuniformity corrections, Mars, Data corrections, X-ray detectors, X-ray imaging, Time correlated photon counting, Photon counting

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Showing 5 of 12 publications

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