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Advancing Optical Coherence Microscopy: Speckle-Modulation Unit Development for Enhanced Brain Imaging
The unique combination of hardware-based speckle-noise reduction and extended-focus optical coherence microscopy has not yet been demonstrated but is expected to facilitate label-free structural brain imaging in vivo, for example to quantify myelinated axons.
Optical coherence tomography (OCT) and microscopy (OCM) enable fast and label-free imaging in 3D, deep into biological tissue in vivo. Due to this unique combination, it has reached clinical relevance in ophthalmology. However, many other fields that could benefit from OCT have been held back by its affliction by strong speckle noise, often masking relevant content in structural OCT image volumes. Many software-based approaches are available in the literature, but these can only partially mitigate the speckle as they do not counteract the physical speckle noise process. Recently, a hardware-based solution has been proposed that aims to directly reduce speckle noise by creating multiple imaging volumes with uncorrelated speckle patterns [1, 2].
The goal of this project is to implement a similar speckle-modulation module in our extended-focus OCM system, optimized for brain imaging [3]. This will likely include optical simulations, the necessary hardware and software implementation, as well as performance characterization. One possible application might be large-scale quantification of myelinated axons in the mouse brain. Potentially, the resulting speckle-reduced images may be used as ground truth data to assess if a deep learning approach can obtain similar performance.
[1] Liba, Orly, et al. "Speckle-modulating optical coherence tomography in living mice and humans." Nature communications 8.1 (2017): 1-13.
[2] Yecies, Derek, et al. "Speckle modulation enables high-resolution wide-field human brain tumor margin detection and in vivo murine neuroimaging." Scientific reports 9.1 (2019): 1-9.
[3] Marchand, Paul James, et al. "Imaging of cortical structures and microvasculature using extended-focus optical coherence tomography at 1.3 μm." Optics letters 43.8 (2018): 1782-1785.
**What you will learn**
- Acquire hands-on experience in the full cycle of microscope design from theory, over hardware implementation to software post-processing
- Exposure to the multidisciplinary field of biomedical imaging
- Engage in collaborative work with a diverse team in a scientific setting
- Gain experience in planning and executing a scientific project, including the freedom to pitch and implement your own ideas
- Develop an understanding of the complexities of biomedical imaging
- Experience in comparing, analyzing, and validating different methodologies
**What you bring**
- CAD design experience
- Solid programming skills (Matlab and/or Python preferred but not required)
- Prior experience in designing optical systems is advantageous but not required
- Teamwork spirit and the ability to work independently
- Excellent problem-solving abilities, critical for navigating the complexities of biomedical imaging
- Strong written and verbal communication skills, to articulate your findings and ideas effectively
Optical coherence tomography (OCT) and microscopy (OCM) enable fast and label-free imaging in 3D, deep into biological tissue in vivo. Due to this unique combination, it has reached clinical relevance in ophthalmology. However, many other fields that could benefit from OCT have been held back by its affliction by strong speckle noise, often masking relevant content in structural OCT image volumes. Many software-based approaches are available in the literature, but these can only partially mitigate the speckle as they do not counteract the physical speckle noise process. Recently, a hardware-based solution has been proposed that aims to directly reduce speckle noise by creating multiple imaging volumes with uncorrelated speckle patterns [1, 2].
The goal of this project is to implement a similar speckle-modulation module in our extended-focus OCM system, optimized for brain imaging [3]. This will likely include optical simulations, the necessary hardware and software implementation, as well as performance characterization. One possible application might be large-scale quantification of myelinated axons in the mouse brain. Potentially, the resulting speckle-reduced images may be used as ground truth data to assess if a deep learning approach can obtain similar performance.
[1] Liba, Orly, et al. "Speckle-modulating optical coherence tomography in living mice and humans." Nature communications 8.1 (2017): 1-13.
[2] Yecies, Derek, et al. "Speckle modulation enables high-resolution wide-field human brain tumor margin detection and in vivo murine neuroimaging." Scientific reports 9.1 (2019): 1-9.
[3] Marchand, Paul James, et al. "Imaging of cortical structures and microvasculature using extended-focus optical coherence tomography at 1.3 μm." Optics letters 43.8 (2018): 1782-1785.
**What you will learn**
- Acquire hands-on experience in the full cycle of microscope design from theory, over hardware implementation to software post-processing
- Exposure to the multidisciplinary field of biomedical imaging
- Engage in collaborative work with a diverse team in a scientific setting
- Gain experience in planning and executing a scientific project, including the freedom to pitch and implement your own ideas
- Develop an understanding of the complexities of biomedical imaging
- Experience in comparing, analyzing, and validating different methodologies
**What you bring**
- CAD design experience
- Solid programming skills (Matlab and/or Python preferred but not required)
- Prior experience in designing optical systems is advantageous but not required
- Teamwork spirit and the ability to work independently
- Excellent problem-solving abilities, critical for navigating the complexities of biomedical imaging
- Strong written and verbal communication skills, to articulate your findings and ideas effectively
a) Plan and simulate a hardware-based speckle-modulation (SM) module, ensuring compatibility with the existing xf-irOCM system
b) Implement the SM module into the xf-irOCM, developing a mechanism for easy switching between imaging modes
c) Characterize the performance of the SM module through tests in phantoms and tissue
And depending on the scope and type of thesis:
d) Use the speckle-reduced images as ground-truth data to train a deep learning model and assess its performance on data without the SM module
and/or
Evaluate the capability of imaging myelinated axons using speckle-modulated xf-irOCM volumes.
a) Plan and simulate a hardware-based speckle-modulation (SM) module, ensuring compatibility with the existing xf-irOCM system
b) Implement the SM module into the xf-irOCM, developing a mechanism for easy switching between imaging modes
c) Characterize the performance of the SM module through tests in phantoms and tissue
And depending on the scope and type of thesis:
d) Use the speckle-reduced images as ground-truth data to train a deep learning model and assess its performance on data without the SM module
and/or
Evaluate the capability of imaging myelinated axons using speckle-modulated xf-irOCM volumes.