My research interests are mathematical
innovations in biomedical imaging and image processing, with x-ray computed
tomography, optical tomography and multi-modality biomedical imaging as the
major applications. I am honored to work with colleagues from mathematics,
statistics, physics, computing technology, biology and clinics to develop
theories, methods and software and hardware implementations for biomedical
applications. I have been the PI/MPI/Co-PI of large-scale projects funded by the
National Science Foundation of China, Ministry
of Science and Technology of China, Ministry of Education of China, and
Sino-German Center. My publications profile can be found at ResearchID or Google Scholars.
[Iterative Image Reconstruction
Algorithms] With the increasing complexity of imaging modalities,
iterative reconstruction methods become more and more useful because a closed
form solution is hardly available. With collaborators,
we proved the convergence of SART (simultaneous algebraic reconstructive
technique), which is one of the most popular iterative image reconstruction
algorithms, and established its convergence dependence on initial value. This
work was extended to a unified framework for block-iterative Landweber algorithms. Representative publications in this
work are
·
Ming Jiang, Ge Wang, Convergence of the simultaneous algebraic
reconstruction technique (SART), IEEE Transactions on Imaging Processing,
2003.
·
Ming Jiang, Ge Wang, Convergence studies on iterative algorithms for image
reconstruction,
IEEE Transactions on Medical Imaging, 2003.
[Bioluminescence
Tomography] Bioluminescence tomography (BLT) is an optical tomography
technique developed since 2004 to image in
vivo 3D distributions of bioluminescent probes for preclinical molecular
imaging of small animals. With collaborators, we published the first journal paper on the theoretical
aspects of BLT. There are ~20 groups in
this area, and many bioluminescence imagers for studies on animal models of
almost all human diseases. The representative publication in this work is
·
Ge
Wang, Yi Li, Ming Jiang, Uniqueness theorems in bioluminescence tomography, Medical Physics, 2004.
[Regularization
Techniques] Imaging and image processing problems are typical ill-posed
inverse problems, for which the regularization technique with priors or other
regularization approaches within the general Bayesian inference are necessary. With
collaborators, we developed the theory and algorithms of higher order total
variations, which is a non-trivial extension of the widely used total
variations regularization, and has been applied successfully to interior
tomography of x-ray CT and SPECT. Our
recent work is the regularization properties of the Mumford-Shah functional,
which can used to simultaneously reconstruct image and its segmentation. The edge prior can help improve the
reconstructed image quality, which is missed in other conventional priors. The
representative publication in this work is
·
Ming Jiang,
Peter Maaß, Thomas Page, Regularizing properties of the Mumford-Shah functional for
imaging applications,
Inverse Problems, 2014.
[Hardware Implementation] We have established an energy-efficient and
memory-optimized implementation with FPGA for our asynchronous parallel
iterative reconstruction algorithm for the regularization of Mumford-Shah
functional. We have evaluated the performance in terms speed and image quality
for low dose spiral x-ray CT in clinics and electron transmission tomography.
For low dose spiral x-ray CT, our implementation can reach the same image
quality under the same low dose and with a reconstruction speed for clinical
applications. Representative
publications in this work are
·
Wentai Zhang, Linjun Qiao,
William Hsu, Yong Cui, Ming Jiang, Guojie Luo, FPGA
Acceleration for 3D Low-Dose Tomographic Reconstruction, IEEE Transactions on Computer-Aided Design of Integrated
Circuits and Systems, 2021.
·
Linjun Qiao, Guojie Luo, Wentai Zhang and Ming
Jiang, FPGA-accelerated Iterative
Reconstruction for Transmission Electron Tomography, IEEE 29th
Annual International Symposium on Field-Programmable Custom Computing Machines
(FCCM), 2021.