Dr. Lu Jiang Profile

Dr. Lu Jiang

Lead Reviewer at Johns Hopkins Univ

SPIE Involvement:

Author

Area of Expertise:

multimodal molecular imaging , statistical image processing , Mass Spectrometric Imaging , Magnetic Resonance Imaging , Optical Imaging

My research interest is in molecular imaging and statistical image processing of cancer. My research focuses on applying multimodal imaging techniques, such as Magnetic Resonance Imaging, Optical Imaging and Mass Spectrometric Imaging, and statistical image processing and machine learning technology to study the breast tumor microenvironment and breast tumor treatment response.

Proceedings Article | 19 March 2007 Paper

Sinogram restoration for ultra-low-dose x-ray multi-slice helical CT by nonparametric regression

Lu Jiang, Khan Siddiqui, Bin Zhu, Yang Tao, Eliot Siegel

Proceedings Volume 6510, 65105L (2007) https://doi.org/10.1117/12.711631

KEYWORDS: X-ray computed tomography, Computed tomography, X-rays, Reconstruction algorithms, Image filtering, Data acquisition, Denoising, Lung cancer, Data modeling, Sensors

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During the last decade, x-ray computed tomography (CT) has been applied to screen large asymptomatic smoking and nonsmoking populations for early lung cancer detection. Because a larger population will be involved in such screening exams, more and more attention has been paid to studying low-dose, even ultra-low-dose x-ray CT. However, reducing CT radiation exposure will increase noise level in the sinogram, thereby degrading the quality of reconstructed CT images as well as causing more streak artifacts near the apices of the lung. Thus, how to reduce the noise levels and streak artifacts in the low-dose CT images is becoming a meaningful topic. Since multi-slice helical CT has replaced conventional stop-and-shoot CT in many clinical applications, this research mainly focused on the noise reduction issue in multi-slice helical CT. The experiment data were provided by Siemens SOMATOM Sensation 16-Slice helical CT. It included both conventional CT data acquired under 120 kvp voltage and 119 mA current and ultra-low-dose CT data acquired under 120 kvp and 10 mA protocols. All other settings are the same as that of conventional CT. In this paper, a nonparametric smoothing method with thin plate smoothing splines and the roughness penalty was proposed to restore the ultra-low-dose CT raw data. Each projection frame was firstly divided into blocks, and then the 2D data in each block was fitted to a thin-plate smoothing splines' surface via minimizing a roughness-penalized least squares objective function. By doing so, the noise in each ultra-low-dose CT projection was reduced by leveraging the information contained not only within each individual projection profile, but also among nearby profiles. Finally the restored ultra-low-dose projection data were fed into standard filtered back projection (FBP) algorithm to reconstruct CT images. The rebuilt results as well as the comparison between proposed approach and traditional method were given in the results and discussions section, and showed effectiveness of proposed thin-plate based nonparametric regression method.

SPIE Journal Paper | 1 January 2007

Three-dimensional shape enhanced transform for automatic apple stem-end/calyx identification

Bin Zhu, Lu Jiang, Yang Tao

OE, Vol. 46, Issue 01, 017201, (January 2007) https://doi.org/10.1117/12.10.1117/1.2434382

KEYWORDS: Near infrared, Cameras, 3D image processing, Image enhancement, Imaging systems, 3D modeling, Light sources, Lutetium, 3D image enhancement, Optical engineering

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Proceedings Article | 7 November 2005 Paper

3D surface reconstruction of apples from 2D NIR images

Bin Zhu, Lu Jiang, Xuemei Cheng, Yang Tao

Proceedings Volume 6000, 60000R (2005) https://doi.org/10.1117/12.630030

KEYWORDS: Near infrared, 3D image processing, 3D modeling, Image segmentation, Cameras, Fourier transforms, 3D image reconstruction, Light sources, Machine vision, Reflectivity

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