Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-24T06:30:06.106Z Has data issue: false hasContentIssue false
  • English
  • Français

Total Variation-Based Reduction of Streak Artifacts, Ring Artifacts and Noise in 3D Reconstruction from Optical Projection Tomography

Published online by Cambridge University Press:  13 October 2015

Jan Michálek
Jan Michálek*
Affiliation:
Department of Biomathematics, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic
*
*Corresponding author.[email protected]
Get access

Abstract

Optical projection tomography (OPT) is a computed tomography technique at optical frequencies for samples of 0.5–15 mm in size, which fills an important “imaging gap” between confocal microscopy (for smaller samples) and large-sample methods such as fluorescence molecular tomography or micro magnetic resonance imaging. OPT operates in either fluorescence or transmission mode. Two-dimensional (2D) projections are taken over 360° with a fixed rotational increment around the vertical axis. Standard 3D reconstruction from 2D OPT uses the filtered backprojection (FBP) algorithm based on the Radon transform. FBP approximates the inverse Radon transform using a ramp filter that spreads reconstructed pixels to neighbor pixels thus producing streak and other types of artifacts, as well as noise. Artifacts increase the variation of grayscale values in the reconstructed images. We present an algorithm that improves the quality of reconstruction even for a low number of projections by simultaneously minimizing the sum of absolute brightness changes in the reconstructed volume (the total variation) and the error between measured and reconstructed data. We demonstrate the efficiency of the method on real biological data acquired on a dedicated OPT device.

Keywords

optical projection tomographymicroscopyartifactstotal variationdata mismatch
Type
Equipment and Techniques Development
Information
Microscopy and Microanalysis , Volume 21 , Issue 6 , December 2015 , pp. 1602 - 1615
Copyright
© Microscopy Society of America 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bassi, A., Fieramonti, L., D’Andrea, C., Mione, M. & Valentini, G. ( 2011). In vivo label-free three-dimensional imaging of zebrafish vasculature with optical projection tomography. J Biomed Opt 16, 100502.Google Scholar
Boyd, S., Parikh, N., Chu, E., Peleato, B. & Eckstein, J. ( 2010). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations Trends Mach Lear 3, 1–122.Google Scholar
Bruyant, P.P., Sau, J. & Mallet, J.J. ( 2000). Streak artifact reduction in filtered backprojection using a level line-based interpolation method. J Nucl Med 41, 1913–1919.Google Scholar
Candès, E., Romberg, J. & Tao, T. ( 2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory 52, 489–509.Google Scholar
Candès, E.J. & Wakin, M.B. ( 2008). An introduction to compressive sampling. IEEE Sig Proc Magazine 25, 21–30.CrossRefGoogle Scholar
Dodt, H.-U., Leischner, U., Schierloh, A., Jährling, N., Mauch, C.P., Deininger, K., Deussing, J.M., Eder, M., Zieglgänsberger, W. & Becker, K. (2007). Ultramicroscopy: Three-dimensional visualization of neuronal networks in the whole mouse brain. Nat Methods 4, 331–336.CrossRefGoogle ScholarPubMed
Eckstein, J. & Bertsekas, D.P. ( 1992). On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math Program 55, 293–318.Google Scholar
Hama, H., Kurokawa, H., Kawano, H., Ando, R., Shimogori, T., Noda, H., Fukami, K., Sakaue-Sawano, A. & Miyawaki, A. (2011). Scale: A chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat Neurosci 14, 1481–1488.CrossRefGoogle Scholar
Han, X., Bian, J., Eaker, D.R., Kline, T.L., Sidky, E.Y., Ritman, E.L. & Pan, X. ( 2011). Algorithm-enabled low-dose micro-CT imaging. IEEE Trans Med Imaging 30, 606–620.Google Scholar
Leary, R., Saghi, Z., Midgley, P.A. & Holland, D.J. (2013). Compressed sensing electron tomography. Ultramicroscopy 131, 70–91.Google Scholar
Li, C.H. (2009). An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing. Master Thesis. Computational and Applied Mathematics, Rice University, Houston, Texas.Google Scholar
Niu, T., Ye, X., Fruhauf, Q., Petrongolo, M. & Lei Zhu, L. (2014). Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence. Phys Med Biol 59, 1801–1814.Google Scholar
Park, J.C., Song, B., Kim, J.S., Park, S.H., Kim, H.K., Liu, Z., Suh, T.S. & Song, W.Y. ( 2012). Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT. Med Phys 39, 1207–1217.Google Scholar
Radon, J. ( 1917). Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Berichte über die Verhandlungen der Königlich-Sächsischen Akademie der Wissenschaften zu Leipzig, Mathematisch-Physische Klasse 69, 262–277.Google Scholar
Romberg, J. ( 2008). Imaging via compressive sampling. IEEE Sig Proc Mag 25, 14–20.Google Scholar
Sharpe, J., Ahlgren, U., Perry, P., Hill, B., Ross, A., Hecksher-Sørensen, J., Baldock, R. & Davidson, D. (2002). Optical projection tomography as a tool for 3D microscopy and gene expression studies. Science 296, 541–545.Google Scholar
Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. (2014). Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat Protoc 9, 1682–1697.Google Scholar
Yu, Z., Thibault, J.B., Bouman, C.A., Sauer, K.D. & Hsieh, J. ( 2011). Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization. IEEE Trans Image Process 20(1), 161–175.Google Scholar
Wang, Y., Yang, J., Yin, W., & Zhang, Y. ( 2008). A New Alternating Minimization Algorithm for Total Variation Image Reconstruction. SIAM J. Imaging Sci. 1(3), 248–272.Google Scholar