Tran Minh Quan

     Ms-PhD. Student
     E-mail: quantm (at) unist.ac.kr

 

 

 

Research Interests

  • GPU computing
  • Compressive Sensing MRI

Compressive Sensing has drawn many attractions from researchers in diverse fields not only in signal processing area but also bioclinical department. In Magnetic Resonance Imaging (MRI), it provides a mathematical description to quickly perform the scanning process but still preserve the good quality of the images. Taking into account the large-scaled and high-dimensional data keep growing up from the scanner, we focus on developing a multi-GPU heterogeneous system that can speed up the reconstruction process. With the modified Split-Bregman method, we propose the new CSMRI scheme which is running fast, scalable, and arise the images as good as conventional diagnosis.

Research topic: Compressive Sensing MRI on a multi-GPU system including
  • Fast Inverse Problems
  • Fast Wavelet Transform
  • High-performance Computing on Heterogeneous Parallel Systems

Education

  • B.S. in Electrical Engineering, KAIST, Daejeon, Korea (2012)

Work Experience

  • Smart Sensor Architecture Laboratory, National Nano Fabrication Center (Daejeon, Korea), research assistant, 2011.
  • Photonic Energy and Signal Processing Laboratory (Daejeon, Korea),  winter intern, 2011.
  • L&Y Vision Technology Corporation (Daejeon, Korea),  summer intern, 2011.
  • VLSI Systems Laboratory, National Nano Fabrication Center (Daejeon, Korea), summer intern, 2010.

Publications

  • [PDF] [DOI] T. M. Quan, J. Choi, H. Jeong, and W. Jeong, “An intelligent system approach for robust volume rendering using hierarchical 3d convolutional sparse coding,” IEEE transactions on visualization and computer graphics (TVCG), vol. 24, iss. 1, pp. 964-973, 2017.
    [Bibtex]
    @article{quan_jun_haejin_vis_2017,
    title = {An Intelligent System Approach for Robust Volume Rendering using Hierarchical 3D Convolutional Sparse Coding},
    year={2017},
    volume={24},
    number={1},
    pages={964-973},
    author = {Tran Minh Quan and JunYoung Choi and Haejin Jeong and Won-Ki Jeong},
    journal = {{IEEE} transactions on visualization and computer graphics ({TVCG})},
    doi={10.1109/TVCG.2017.2744078},
    ISSN={1077-2626},
    }

  • [DOI] D. G. C. Hildebrand, M. Cicconet, R. M. Torres, W. Choi, T. M. Quan, J. Moon, A. W. Wetzel, A. Scott Champion, B. J. Graham, O. Randlett, G. S. Plummer, R. Portugues, I. H. Bianco, S. Saalfeld, A. D. Baden, K. Lillaney, R. Burns, J. T. Vogelstein, A. F. Schier, W. A. Lee, W. Jeong, J. W. Lichtman, and F. Engert, “Whole-brain serial-section electron microscopy in larval zebrafish,” Nature, vol. 545, iss. 7654, pp. 345-349, 2017.
    [Bibtex]
    @Article{hildebrand_nature_2017,
    author={Hildebrand, David Grant Colburn
    and Cicconet, Marcelo
    and Torres, Russel Miguel
    and Choi, Woohyuk
    and Quan, Tran Minh
    and Moon, Jungmin
    and Wetzel, Arthur Willis
    and Scott Champion, Andrew
    and Graham, Brett Jesse
    and Randlett, Owen
    and Plummer, George Scott
    and Portugues, Ruben
    and Bianco, Isaac Henry
    and Saalfeld, Stephan
    and Baden, Alexander David
    and Lillaney, Kunal
    and Burns, Randal
    and Vogelstein, Joshua Tzvi
    and Schier, Alexander Franz
    and Lee, Wei-Chung Allen
    and Jeong, Won-Ki
    and Lichtman, Jeff William
    and Engert, Florian},
    title={Whole-brain serial-section electron microscopy in larval zebrafish},
    journal={Nature},
    year={2017},
    month={May},
    day={18},
    publisher={Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
    volume={545},
    number={7654},
    pages={345-349},
    note={Letter},
    issn={0028-0836},
    url={http://dx.doi.org/10.1038/nature22356},
    doi = {10.1038/nature22356}
    }

  • [PDF] [DOI] T. M. Quan and W. Jeong, “Compressed sensing dynamic MRI reconstruction using GPU-accelerated 3D convolutional sparse coding,” in Proceedings of the 19th international conference on medical image computing and computer-assisted intervention (MICCAI), Springer International Publishing, 2016, pp. 484-492.
    [Bibtex]
    @incollection{quan_compressed3d_2016,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Compressed Sensing Dynamic {MRI} Reconstruction Using {GPU}-accelerated {3D} Convolutional Sparse Coding},
    copyright = {©2016 Springer International Publishing Switzerland},
    isbn = {978-3-319-46726-9},
    url = {http://dx.doi.org/10.1007/978-3-319-46726-9_56},
    language = {en},
    number = {9351},
    booktitle = {Proceedings of the 19th international conference on Medical image computing and computer-assisted intervention ({MICCAI})},
    publisher = {Springer International Publishing},
    author = {Quan, Tran Minh and Jeong, Won-Ki},
    year = {2016},
    doi = {10.1007/978-3-319-46726-9_56},
    keywords = {Artificial Intelligence (incl. Robotics), computer graphics, Health Informatics, Image Processing and Computer Vision, Imaging / Radiology, Pattern Recognition},
    pages = {484--492},
    }

 

  • [PDF] [DOI] T. M. Quan and W. K. Jeong, “A fast discrete wavelet transform using hybrid parallelism on GPUs,” IEEE transactions on parallel and distributed systems, vol. 27, iss. 11, pp. 3088-3100, 2016.
    [Bibtex]
    @ARTICLE{quan_fast_2016,
    author={T. M. Quan and W. K. Jeong},
    journal={{IEEE} Transactions on Parallel and Distributed Systems},
    title={A Fast Discrete Wavelet Transform Using Hybrid Parallelism on {GPU}s},
    year={2016},
    volume={27},
    number={11},
    pages={3088-3100},
    keywords={discrete wavelet transforms;graphics processing units;optimisation;parallel processing;CPU;{GPU} DWT methods;{GPU} optimization strategies;{GPU}-based discrete wavelet transform;Haar DWT;ILP maximization;acceleration techniques;computationally-intensive problem acceleration;fast discrete wavelet transform;graphics processing unit;hybrid parallelism;mixed-band memory layout;multilevel transform;single fused kernel launch;time-critical applications;Acceleration;Discrete wavelet transforms;Graphics processing units;Parallel processing;Registers;{GPU} computing;Wavelet transform;bit rotation;hybrid parallelism;lifting scheme},
    doi={10.1109/TPDS.2016.2536028},
    ISSN={1045-9219},
    month={Nov},}

 

  • [PDF] [DOI] T. M. Quan and W. K. Jeong, “Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding,” in 2016 IEEE 13th international symposium on biomedical imaging (ISBI), 2016, pp. 518-521.
    [Bibtex]
    @INPROCEEDINGS{quan_compressed_2016,
    author={T. M. Quan and W. K. Jeong},
    booktitle={2016 {IEEE} 13th International Symposium on Biomedical Imaging ({ISBI})},
    title={Compressed sensing reconstruction of dynamic contrast enhanced {MRI} using {GPU}-accelerated convolutional sparse coding},
    year={2016},
    pages={518-521},
    keywords={Convolution;Convolutional codes;Dictionaries;Encoding;Fourier transforms;Image reconstruction;Magnetic resonance imaging;Compressed Sensing;Convolutional Sparse Coding;{GPU};{MRI}},
    doi={10.1109/ISBI.2016.7493321},
    month={April},}

 

  • [PDF] [DOI] T. M. Quan, S. Han, H. Cho, and W. Jeong, “Multi-GPU Reconstruction of Dynamic Compressed Sensing MRI,” in Proceedings of the 18th international conference on medical image computing and computer-assisted intervention (MICCAI), Springer International Publishing, 2015, pp. 484-492.
    [Bibtex]
    @incollection{quan_multi_2015,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Multi-{GPU} {Reconstruction} of {Dynamic} {Compressed} {Sensing} {MRI}},
    copyright = {©2015 Springer International Publishing Switzerland},
    isbn = {978-3-319-24573-7 978-3-319-24574-4},
    url = {http://link.springer.com/chapter/10.1007/978-3-319-24574-4_58},
    abstract = {Magnetic resonance imaging (MRI) is a widely used in-vivo imaging technique that is essential to the diagnosis of disease, but its longer acquisition time hinders its wide adaptation in time-critical applications, such as emergency diagnosis. Recent advances in compressed sensing (CS) research have provided promising theoretical insights to accelerate the MRI acquisition process, but CS reconstruction also poses computational challenges that make MRI less practical. In this paper, we introduce a fast, scalable parallel CS-MRI reconstruction method that runs on graphics processing unit ({GPU}) cluster systems for dynamic contrast-enhanced (DCE) MRI. We propose a modified Split-Bregman iteration using a variable splitting method for CS-based DCE-MRI. We also propose a parallel {GPU} Split-Bregman solver that scales well across multiple {GPU}s to handle large data size. We demonstrate the validity of the proposed method on several synthetic and real DCE-MRI datasets and compare with existing methods.},
    language = {en},
    number = {9351},
    urldate = {2015-10-12},
    booktitle = {Proceedings of the 18th international conference on Medical image computing and computer-assisted intervention ({MICCAI})},
    publisher = {Springer International Publishing},
    author = {Quan, Tran Minh and Han, Sohyun and Cho, Hyungjoon and Jeong, Won-Ki},
    year = {2015},
    doi = {10.1007/978-3-319-24574-4\_58},
    keywords = {Artificial Intelligence (incl. Robotics), computer graphics, Health Informatics, Image Processing and Computer Vision, Imaging / Radiology, Pattern Recognition},
    pages = {484--492},
    file = {Full Text PDF:files/227/Quan et al. - 2015 - Multi-{GPU} Reconstruction of Dynamic Compressed Sen.pdf:application/pdf;Snapshot:files/234/978-3-319-24574-4_58.html:text/html}
    }

 

  • [PDF] [DOI] H. Choi, W. Choi, T. M. Quan, D. G. C. Hildebrand, H. Pfister, and W. Jeong, “Vivaldi: a domain-specific language for volume processing and visualization on distributed heterogeneous systems,” IEEE transactions on visualization and computer graphics, vol. 20, iss. 12, pp. 2407-2416, 2014.
    [Bibtex]
    @article{choi_vivaldi_2014,
    title = {Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems},
    volume = {20},
    issn = {1077-2626},
    shorttitle = {Vivaldi},
    doi = {10.1109/TVCG.2014.2346322},
    abstract = {As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and {GPU} accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing {GPU} clusters is difficult for novice programmers, and even experienced programmers often fail to fully leverage the computing power of new parallel architectures due to their steep learning curve and programming complexity. In this paper, we propose Vivaldi, a new domain-specific language for volume processing and visualization on distributed heterogeneous computing systems. Vivaldi's Python-like grammar and parallel processing abstractions provide flexible programming tools for non-experts to easily write high-performance parallel computing code. Vivaldi provides commonly used functions and numerical operators for customized visualization and high-throughput image processing applications. We demonstrate the performance and usability of Vivaldi on several examples ranging from volume rendering to image segmentation.},
    number = {12},
    journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Choi, H. and Choi, W. and Quan, T.M. and Hildebrand, D.G.C. and Pfister, H. and Jeong, W.},
    month = dec,
    year = {2014},
    keywords = {computational modeling, Data models, Data visualization, distributed heterogeneous systems, Domain-specific language, {GPU} computing, graphics processing units, image classification, parallel processing, rendering (computer graphics), volume rendering},
    pages = {2407--2416},
    file = {{IEEE} Xplore Abstract Record:H\:\\Zotero\\storage\\ED5RN7ME\\articleDetails.html:text/html;{IEEE} Xplore Full Text PDF:H\:\\Zotero\\storage\\XKKZSRWV\\Choi et al. - 2014 - Vivaldi A Domain-Specific Language for Volume Pro.pdf:application/pdf}
    }

 

  • [PDF] [DOI] T. M. Quan and W. Jeong, “A fast Mixed-Band lifting wavelet transform on the GPU,” in IEEE International Conference on Image Processing, 2014, pp. 1238-1242.
    [Bibtex]
    @INPROCEEDINGS{quan_fast_2013,
    AUTHOR="Tran Minh Quan and Won-Ki Jeong",
    TITLE="A Fast {Mixed-Band} Lifting Wavelet Transform on the {GPU}",
    BOOKTITLE="{{IEEE} International Conference on Image Processing}",
    PAGES="1238-1242",
    DAYS=27,
    MONTH=oct,
    YEAR=2014,
    KEYWORDS="Mixed-band, Wavelet, Denoising, {GPU}, Parallel Computing, Compressive
    Sensing, MRI",
    DOI = {10.1109/ICIP.2014.7025247},
    ABSTRACT="Discrete wavelet transform (DWT) has been widely used in many image
    compression applications, such as JPEG2000 and compressive sensing MRI.
    Even though a lifting scheme has been widely adopted to accelerate DWT,
    only a handful of research has been done on its efficient implementation on
    many-core accelerators, such as graphics processing units ({GPU}s). Moreover,
    we observe that rearranging the spatial locations of wavelet coefficients
    at every level of DWT significantly impairs the performance of memory
    transaction on the {GPU}. To address these problems, we propose a mixed-band
    lifting wavelet transform that reduces uncoalesced global memory access on
    the {GPU} and maximizes on-chip memory bandwidth by implementing in-place
    operations using registers. We assess the performance of the proposed
    method by comparing with the state-of-the-art DWT libraries, and show its
    usability in a compressive sensing (CS) MRI application."
    }

 

Talks

  • Various optimization strategies for implementing fast discrete wavelet transforms on GPUs
    Tran Minh Quan, Won-Ki Jeong
    NVIDIA GPU Technology Conference Korea (GTCx)  at Seoul, South Korea, 2016
  • S3308 – Fast Compressive Sensing MRI Reconstruction on a Multi-GPU System
    Tran Minh Quan, Won-Ki Jeong
    NVIDIA GPU Technology Conference (GTC) at San Jose, California, USA, 2013

Honor / Award

  • 2016 Aug: NAVER PhD Fellowship awarded.
  • 2016 Apr: MICCAI Student Travel Grant awarded.
  • 2015 Apr: MICCAI Student Travel Grant awarded.
  • 2014 Jun: IEEE SPS Student Travel Grant awarded.
  • 2013 Aug: Statement of Accomplishment (Distinction)
    of Interactive 3D Graphics,
    by Eric Haines, Senior Principal Engineer, Autodesk.
  • 2013 Jul: Statement of Accomplishment (Distinction)
    of Introduction to Parallel Programming: Using CUDA to Harness the Power of GPUs,
    by David Luebke, PhD., Senior Director of Research, Visual Computing, NVIDIA
    and John Owens, PhD., Professor of Electrical and Computer Eng., UC Davis.
  • 2013 Mar: Statement of Accomplishment (Distinction)
    of Image and Video Processing: From Mars to Hollywood with a stop at the hospital,
    by G. Sapiro, PhD., Professor of Electrical and Computer Eng., Duke University.
  • 2013 Feb: Statement of Accomplishment (Distinction)
    of Heterogeneous Parallel  Programming,
    by Wen-Mei Hwu, PhD., Professor of College of Eng., University of Illinois.
  • 2012 Feb:  Full Graduate Scholarship from UNIST
  • 2008 Feb:  Full Undergraduate Scholarship from KAIST
  • 2005 Jul: Campaign Medal in Australian National Chemistry Quiz – Award of Excellence,
    by The Royal Australian Chemical Institute.

Teaching Assistant

Spring 2014 Heterogeneous Parallel Programming, Massive Open Online Course, Coursera
Fall       2013 ECE519 – Massively Parallel Programming, Graduate Course, UNIST
Spring 2013 ITP107 – Engineering Programming 1, Undergraduate Course, UNIST
Fall       2012 CSE231 – Data Structure, Undergraduate Course, UNIST
Spring 2012 CSE431 – Introduction to Computer Graphics, Undergraduate Course, UNIST