Optical Projection Tomography

Optical Projection Tomography: 3D Imaging Beyond the Microscopic Scale

Optical Projection Tomography (OPT) is a non-invasive imaging technique that has gained prominence in recent years for its ability to provide three-dimensional reconstructions of biological specimens. Originally developed for imaging small, transparent samples, OPT has found applications in various fields, including developmental biology, neuroscience, and preclinical research. In this article by Academic Block, we will explore the principles, instrumentation, applications, and challenges associated with Optical Projection Tomography.

  1. Principles of Optical Projection Tomography

Basics of Tomography: Tomography is a technique used to create cross-sectional images of an object by collecting data from multiple angles. The fundamental concept behind tomography involves capturing projections of an object and then reconstructing a three-dimensional representation from these projections.

Optical Tomography: In the context of OPT, the imaging is based on the interaction of light with biological tissues. OPT employs the principles of optical sectioning, where the specimen is illuminated with light, and the transmitted or fluorescent light is detected. The use of optical techniques allows for non-destructive imaging of delicate biological samples.

Optical Projection Tomography Setup: The OPT setup typically includes a light source, a sample chamber, and a detector. The sample is placed between the light source and the detector, and a series of images are acquired as the sample is rotated. These 2D projections are then used to reconstruct a 3D image of the specimen.

  1. Instrumentation

Light Sources: Various light sources can be used in OPT, including white light sources for transmission imaging and specific wavelengths for fluorescence imaging. The choice of light source depends on the optical properties of the sample and the desired imaging contrast.

Sample Containers: The sample container is crucial in OPT to hold the specimen in a stable position during imaging. Specialized sample containers are designed to minimize distortions and artifacts in the reconstructed images.

Detectors: Detectors in OPT systems capture the transmitted or emitted light from the sample. Photodiodes, CCD cameras, and other sensitive detectors are employed to achieve high-resolution imaging.

Rotational Stage: To capture images from different angles, a rotational stage is incorporated into the OPT setup. The sample is rotated incrementally, and images are acquired at each rotation angle.

  1. Applications of Optical Projection Tomography

Developmental Biology: OPT has proven to be particularly valuable in developmental biology, allowing researchers to visualize and analyze the internal structures of embryos. It facilitates the study of organogenesis and morphogenesis in various organisms.

Neuroscience: In neuroscience, OPT has been employed for imaging brain structures in small animal models. It aids in understanding neural development, connectivity, and pathology, providing insights into neurological disorders.

Plant Biology: OPT has applications in plant biology, enabling researchers to study the internal structures of plant specimens. This includes root system architecture, vascular networks, and other aspects crucial for plant development.

Preclinical Imaging: In preclinical research, OPT is used for imaging small animal models to assess the efficacy of drugs, monitor disease progression, and investigate the effects of genetic modifications.

  1. Advantages of Optical Projection Tomography

Non-destructive Imaging: One of the key advantages of OPT is its non-destructive nature. Biological specimens can be imaged without the need for sectioning or staining, preserving the integrity of the sample.

High Spatial Resolution: OPT can achieve high spatial resolution, allowing for detailed imaging of fine structures within biological specimens. This is essential for capturing intricate details in developmental and anatomical studies.

3D Visualization: The ability to create 3D reconstructions from 2D projections provides a comprehensive view of the internal structures of the sample. This is a significant advantage over traditional 2D imaging techniques.

Versatility: OPT is a versatile technique that can be adapted for various types of samples, including embryos, tissues, and small animals. Its adaptability makes it a valuable tool in different scientific disciplines.

  1. Mathematical equations behind the Optical Projection Tomography

The mathematical principles behind Optical Projection Tomography (OPT) involve concepts from tomographic reconstruction, specifically the Radon transform, and the application of the Beer-Lambert law for optical attenuation. Let’s explore the mathematical equations that underlie the OPT imaging process:

Radon Transform:

The Radon transform is fundamental to tomographic imaging, including OPT. It represents the integral of a function over all possible lines in a specific direction. The formula for the Radon transform Rf of a function f(x,y,z) is expressed as follows:

Rf(θ,s) = −∞ -∞ f(x,y,z) δ(xcos⁡θ + ysin⁡θ − s) dx dy ;

Here, θ is the rotation angle, s is the distance parameter, and δ is the Dirac delta function. This equation describes the projection of the function f along a line at an angle θ and distance s.

Filtered Back Projection (FBP):

Once the Radon transform is obtained, the filtered back projection algorithm is commonly used to reconstruct the three-dimensional image. The filtered back projection equation is given by:

f(x,y,z) = (1 / 2π) 0π −∞ Rf(θ,s) Q ds dθ ;

Q = (∂ /∂s) [1 / sqrt(s2 − x2 − y2 )] ;

In this equation:

  • f(x,y,z) represents the reconstructed image.
  • Rf(θ,s) is the Radon transform data acquired at angle θ and distance s.
  • The integral is performed over all projection angles θ and distances s.
  • The term Q is the derivative of the back projection kernel, representing the contribution of each point in the projection to the reconstructed volume.

This equation describes the process of back projecting the Radon transform data after applying a filtering step. The integration is performed over all projection angles and distances, leading to the reconstruction of the three-dimensional image f(x,y,z).

Optical Attenuation and Beer-Lambert Law:

For optical imaging, the Beer-Lambert law is often applied to describe the attenuation of light as it passes through a medium. The equation is given by:

I(x,y,z) = I0 exp⁡( − 0d μ(x,y,z,s) ds) ;

Here, I(x,y,z) is the intensity of light at a point in the specimen, I0 is the initial intensity, d is the distance traveled, and μ(x,y,z,s) is the linear attenuation coefficient, which depends on the optical properties of the specimen. This equation represents how the intensity of light decreases exponentially as it traverses the specimen.

These mathematical expressions form the basis for understanding the principles of Optical Projection Tomography and the reconstruction of three-dimensional images from two-dimensional projections. The actual implementation may involve additional considerations, such as noise correction, system calibration, and the specific characteristics of the imaging setup.

  1. Challenges and Limitations

Light Scattering: Light scattering within biological tissues can pose challenges in achieving high image quality. Strategies such as optical clearing agents are employed to minimize scattering effects.

Sample Size Limitations: OPT is primarily suitable for imaging small, transparent samples. Larger and more opaque samples may present challenges in obtaining clear and detailed images.

Image Processing Complexity: The reconstruction of 3D images from 2D projections involves complex algorithms and computational methods. Image processing can be time-consuming, and the quality of the reconstruction depends on the accuracy of the algorithms used.

Limited Depth Penetration: The depth penetration of light in biological tissues is limited, affecting the imaging of deeper structures. This limitation can be addressed to some extent by using longer wavelengths of light.

  1. Future Developments and Trends

Improved Image Processing Algorithms: Advancements in image processing algorithms will likely enhance the efficiency and accuracy of 3D reconstructions in OPT. Machine learning techniques may play a role in automating and optimizing image analysis.

Enhanced Light Sources: Innovations in light sources, including the development of more advanced laser systems, may improve the quality and depth of imaging in OPT. This could expand the range of samples that can be effectively imaged.

Integration with Other Imaging Modalities: Integrating OPT with other imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), could provide complementary information and overcome some of the limitations associated with individual techniques.

Application in Clinical Imaging: While OPT is currently more prevalent in research settings, ongoing developments may lead to its application in clinical imaging, offering new possibilities for non-invasive diagnostics and treatment monitoring.

Final Words

In this article by Academic Block we have seen that, Optical Projection Tomography has emerged as a powerful imaging technique with wide-ranging applications in the biological and medical sciences. Its ability to provide high-resolution, non-destructive, and three-dimensional reconstructions has made it an invaluable tool for researchers studying developmental processes, neurobiology, and preclinical models. Despite its current challenges, ongoing advancements in technology and methodology are likely to further enhance the capabilities of OPT, opening new avenues for exploration and discovery in the world of biological imaging. Please provide your comments below, it will help us in improving this article. Thanks for Reading!

Academic References on Optical Projection Tomography

  1. Sharpe, J. (2004). Optical projection tomography as a new tool for studying embryo anatomy. Journal of Anatomy, 204(3), 203–208.

  2. Arridge, S. R., Lionheart, W. R., & Hurrell, J. P. (1995). Optical tomography in medical imaging. Inverse Problems, 11(2), 429.

  3. McGinty, J., Taylor, H. B., Chen, L., Bugeon, L., Lamb, J. R., & Dallman, M. J. (2011). In vivo fluorescence lifetime optical projection tomography. Biomedical Optics Express, 2(5), 1340–1350.

  4. Sharpe, J., Ahlgren, U., Perry, P., Hill, B., Ross, A., Hecksher-Sørensen, J., … & Davidson, D. (2002). Optical projection tomography as a tool for 3D microscopy and gene expression studies. Science, 296(5567), 541–545.

  5. Siedentopf, H., & Zsigmondy, R. (1902). Über Sichtbarmachung und Grössenbestimmung ultramikroskopischer Teilchen, mit besonderer Anwendung auf Goldrubingläser. Annalen der Physik, 315(1), 1–39.

  6. Alanentalo, T., Asayesh, A., Morrison, H., Loren, C. E., Holmberg, D., Sharpe, J., … & Ahlgren, U. (2007). Tomographic molecular imaging and 3D quantification within adult mouse organs. Nature Methods, 4(1), 31–33.

  7. Krenkel, M., Schedensack, M., Breitbarth, K., & Ittrich, H. (2013). Light sheet-based fluorescence microscopic imaging: a tutorial. Journal of Microscopy, 259(1), 2–10.

  8. Sharpe, J., & Wong, R. O. (2011). Optical projection tomography. The Journal of Neuroscience, 31(45), 16125–16126.

  9. McSheehy, P. M., & Hellriegel, B. (2007). Light sheet fluorescence imaging to localize cardiac lineage and protein distribution. Nature Methods, 4(5), 413.

  10. Laufer, J., Zhang, E. Z., Raivich, G., & Beard, P. (2009). Three-dimensional noninvasive imaging of the vasculature in the mouse brain using a high resolution photoacoustic scanner. Applied Optics, 48(10), D299–D306.

  11. Huisken, J., Swoger, J., Del Bene, F., Wittbrodt, J., & Stelzer, E. H. (2004). Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science, 305(5686), 1007–1009.

  12. Chu, S. W., Chen, I. H., Liu, T. M., Sun, C. K., & Lin, B. L. (2007). Fiber-delivered, 1–nJ femtosecond laser pulses for nonlinear optical imaging of subcellular structures. Optics Express, 15(5), 2452–2462.

  13. Huisken, J., & Stainier, D. Y. (2007). Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Optics Letters, 32(17), 2608–2610.

  14. Dodt, H. U., Leischner, U., Schierloh, A., Jährling, N., Mauch, C. P., Deininger, K., … & Becker, K. (2007). Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nature Methods, 4(4), 331–336.

Optical Projection Tomography

Hardware and software required for Optical Projection Tomography


  1. Light Source:

    • White light sources for transmission imaging.
    • Specific wavelengths or lasers for fluorescence imaging.
  2. Sample Container:

    • Transparent sample containers designed to hold the specimen in a stable position during imaging.
    • The material should minimize distortions and artifacts in the reconstructed images.
  3. Detector:

    • Photodiodes, CCD (Charge-Coupled Device) cameras, or other sensitive detectors.
    • The choice depends on the desired imaging resolution and sensitivity.
  4. Rotational Stage:

    • A rotational stage to rotate the sample incrementally during image acquisition.
    • This allows for capturing images from different angles.
  5. Optical System:

    • Lenses, mirrors, and filters to control the light path and optimize imaging conditions.
    • The optical system may include components such as collimators or beam expanders.
  6. Imaging System:

    • Microscopes or other imaging systems compatible with the sample size and type.
    • Objective lenses with various magnifications for different applications.
  7. Computer:

    • A high-performance computer for data acquisition and processing.
    • Adequate storage for storing large datasets generated during imaging.


  1. Image Acquisition Software:

    • Software to control the imaging system, including the light source, detector, and rotational stage.
    • Allows for capturing 2D projections at different angles.
  2. Reconstruction Software:

    • Algorithms and software for reconstructing 3D images from the acquired 2D projections.
    • Filtered back projection or other reconstruction algorithms are commonly used.
  3. Image Processing Software:

    • Software for pre-processing and post-processing of images.
    • Includes tasks such as noise reduction, image enhancement, and artifact correction.
  4. Analysis Software:

    • Tools for quantitative analysis of the reconstructed 3D images.
    • Segmentation, measurement, and visualization functionalities may be included.
  5. Calibration Software:

    • Software to calibrate the system, correcting for distortions and ensuring accurate reconstructions.
    • Calibration may involve parameters such as pixel size, optical properties, and system geometry.
  6. Data Visualization Software:

    • Software for visualizing and interacting with 3D image datasets.
    • Enables researchers to explore and analyze the reconstructed structures.
  7. Automation and Scripting Tools:

    • Tools for automating repetitive tasks and scripting to customize the imaging and analysis workflow.

  8. System Control Software:

    • Software for overall system control and coordination, ensuring synchronization between different components during the imaging process.

Facts on Optical Projection Tomography

  1. Development and Introduction:

    • OPT was introduced by James Sharpe in 2002, providing a method for three-dimensional imaging of small, transparent specimens.
    • Sharpe’s work laid the foundation for OPT, and the technique has since evolved with advancements in technology.
  2. Principles of Imaging: OPT relies on the principles of tomographic reconstruction, involving the acquisition of 2D projections of a sample from different angles and the subsequent reconstruction of a 3D image.
  1. Optical Sectioning: OPT achieves optical sectioning by illuminating the specimen with light and detecting either transmitted or fluorescent light. This allows for non-destructive imaging of delicate biological samples.

  2. Non-Destructive Imaging: One of the major advantages of OPT is its non-destructive nature. It allows for imaging biological specimens without the need for physical sectioning or staining, preserving the integrity of the sample.

  3. Sample Size and Transparency: OPT is particularly suitable for small, transparent samples. The technique is well-suited for imaging embryos, tissues, and small animals, where transparency is essential for capturing detailed internal structures.

  4. High Spatial Resolution: OPT can achieve high spatial resolution, enabling researchers to visualize fine structures within biological specimens. This is crucial for detailed imaging in developmental biology and anatomical studies.

  5. Rotational Imaging: The sample is typically placed on a rotational stage, and a series of 2D projections are acquired as the sample is rotated incrementally. This rotation allows for capturing images from different angles.

  6. Filtered Back Projection Reconstruction: The reconstruction of 3D images in OPT often involves the use of filtered back projection algorithms. These algorithms convert the acquired 2D projections into a three-dimensional representation of the sample.

  7. Applications in Developmental Biology: OPT has become a valuable tool in developmental biology, allowing researchers to study embryonic development, organogenesis, and morphogenesis. It provides insights into the dynamic processes shaping biological structures.

  8. Neuroscience and Brain Imaging: In neuroscience, OPT is employed for imaging brain structures in small animal models. It aids in understanding neural development, connectivity, and neurological disorders.

  9. Plant Biology: OPT has applications in plant biology, facilitating the imaging of internal structures such as root systems and vascular networks. It contributes to the study of plant development and growth.

  10. Preclinical Imaging: In preclinical research, OPT is used for imaging small animal models to assess drug efficacy, monitor disease progression, and investigate genetic modifications.

  11. Challenges: Challenges in OPT include addressing light scattering in biological tissues, limitations in imaging larger or opaque samples, and the complexity of image processing algorithms.

Father of Optical Projection Tomography

Optical Projection Tomography (OPT) was introduced by James Sharpe, a developmental biologist. His work on OPT was first published in 2002, establishing the foundations of this imaging technique. James Sharpe’s contributions to the field have been significant, and he is often credited as the “father” or one of the key pioneers of Optical Projection Tomography.

List Key Discoveries Where Optical Projection Tomography is used

  1. Embryonic Development:

    • Discovery: OPT has been crucial in studying embryonic development in various organisms. Researchers have used OPT to visualize and analyze the development of organs and tissues, leading to a deeper understanding of embryogenesis.
    • Significance: OPT has allowed for non-destructive, high-resolution imaging of embryos, enabling researchers to track and quantify changes in structures during development.
  2. Neuroanatomy and Brain Development:

    • Discovery: In neuroscience, OPT has been employed to study the intricate structures of the brain and nervous system in small animal models. It has provided insights into neuroanatomy, connectivity, and developmental processes.
    • Significance: OPT has contributed to unraveling the complexity of neural circuits, aiding in the understanding of brain development, and providing valuable information for neuroscience research.
  3. Vascular Imaging in Plants:

    • Discovery: OPT has been used to investigate the vascular systems of plants, including roots and stems. Researchers have gained insights into the organization and development of plant vasculature.
    • Significance: This application of OPT has advanced our understanding of how plants transport water, nutrients, and signaling molecules, impacting research in plant biology and agriculture.
  4. Tumor Microenvironment Studies:

    • Discovery: OPT has been applied to study the 3D architecture of tumor microenvironments in preclinical models. This includes the visualization of blood vessels, immune cell infiltration, and the distribution of cancer cells.
    • Significance: OPT has contributed to a better understanding of the spatial relationships within tumors, aiding in the development of targeted therapies and enhancing the evaluation of treatment responses.
  5. Dental and Craniofacial Research:

    • Discovery: OPT has been utilized in dental and craniofacial research to study tooth development, jaw morphology, and related structures.
    • Significance: Researchers have used OPT to investigate dental anomalies, craniofacial disorders, and the impact of genetic factors on oral and facial structures, contributing to insights in developmental biology and clinical dentistry.
  6. Functional Imaging in Small Animals:

    • Discovery: OPT has been employed for functional imaging studies in small animal models. This includes dynamic imaging of biological processes, such as cardiac function, in vivo.
    • Significance: OPT’s ability to capture functional information complements structural imaging, allowing researchers to study physiological processes in a non-invasive manner, impacting fields like cardiology and physiology.
  7. Whole-Organ Imaging:

    • Discovery: OPT has been instrumental in imaging entire organs in 3D, providing a comprehensive view of internal structures.
    • Significance: This application has facilitated detailed anatomical studies, aiding researchers in understanding organ architecture and function, especially in the context of developmental and pathological processes.
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