Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections—achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds.

[1]  Jie Tian,et al.  In-vivo Optical Tomography of Small Scattering Specimens: time-lapse 3D imaging of the head eversion process in Drosophila melanogaster , 2014, Scientific Reports.

[2]  Masahiko Sugimoto,et al.  A novel transgenic zebrafish model for blood-brain and blood-retinal barrier development , 2010, BMC Developmental Biology.

[3]  G T Herman,et al.  Image reconstruction from a small number of projections , 2008, Inverse problems.

[4]  Ulf Ahlgren,et al.  Differential regulation of myosin heavy chains defines new muscle domains in zebrafish , 2014, Molecular biology of the cell.

[5]  Jorge Ripoll,et al.  Microscopic Optical Projection Tomography In Vivo , 2011, PloS one.

[6]  Cosimo D'Andrea,et al.  In vivo label-free three-dimensional imaging of zebrafish vasculature with optical projection tomography. , 2011, Journal of biomedical optics.

[7]  James Sharpe,et al.  Fluorescence lifetime optical projection tomography , 2008, Journal of biophotonics.

[8]  ANTONIN CHAMBOLLE,et al.  An Algorithm for Total Variation Minimization and Applications , 2004, Journal of Mathematical Imaging and Vision.

[9]  J. Vane,et al.  Optical Projection Tomography as a Tool for 3D Microscopy and Gene Expression Studies , 2002 .

[10]  Chao Liu,et al.  Control of Angiogenesis by AIBP-mediated Cholesterol Efflux , 2013, Nature.

[11]  Jeffrey A. Fessler,et al.  Parallel MR Image Reconstruction Using Augmented Lagrangian Methods , 2011, IEEE Transactions on Medical Imaging.

[12]  Ajay Limaye,et al.  Drishti: a volume exploration and presentation tool , 2012, Optics & Photonics - Optical Engineering + Applications.

[13]  Paul M. W. French,et al.  In vivo fluorescence lifetime optical projection tomography , 2011, Biomedical optics express.

[14]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[15]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[16]  Wotao Yin,et al.  An Iterative Regularization Method for Total Variation-Based Image Restoration , 2005, Multiscale Model. Simul..

[17]  I. Cuthill,et al.  Reporting : The ARRIVE Guidelines for Reporting Animal Research , 2010 .

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[19]  Mehmet Fatih Yanik,et al.  High-throughput hyperdimensional vertebrate phenotyping , 2013, Nature Communications.

[20]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[21]  P. Lions,et al.  Image recovery via total variation minimization and related problems , 1997 .

[22]  J. Mumm,et al.  Multicolor Time-lapse Imaging of Transgenic Zebrafish: Visualizing Retinal Stem Cells Activated by Targeted Neuronal Cell Ablation , 2010, Journal of visualized experiments : JoVE.

[23]  P. Currie,et al.  Animal models of human disease: zebrafish swim into view , 2007, Nature Reviews Genetics.

[24]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[25]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[26]  B. Weinstein,et al.  The vascular anatomy of the developing zebrafish: an atlas of embryonic and early larval development. , 2001, Developmental biology.

[27]  D. Moher,et al.  CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials , 2010, Journal of pharmacology & pharmacotherapeutics.

[28]  L. Zon,et al.  Transparent adult zebrafish as a tool for in vivo transplantation analysis. , 2008, Cell stem cell.

[29]  Martin Stevens,et al.  Repeated targeting of the same hosts by a brood parasite compromises host egg rejection , 2013, Nature Communications.

[30]  Giulia De Sena,et al.  Zebrafish embryo, a tool to study tumor angiogenesis. , 2011, The International journal of developmental biology.

[31]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[32]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[33]  Xu Wang,et al.  High dynamic range optical projection tomography (HDR-OPT). , 2012, Optics express.

[34]  José M. Bioucas-Dias,et al.  Fast Image Recovery Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.

[35]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[36]  Jeffrey A. Fessler,et al.  A Splitting-Based Iterative Algorithm for Accelerated Statistical X-Ray CT Reconstruction , 2012, IEEE Transactions on Medical Imaging.

[37]  G. Serbedzija,et al.  Zebrafish angiogenesis: A new model for drug screening , 2004, Angiogenesis.

[38]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.