KEYWORDS: Image restoration, Signal to noise ratio, Point spread functions, Simulation of CCA and DLA aggregates, Microscopy, 3D image processing, Image processing, Computer simulations, Focus stacking, Imaging systems
Effectiveness of extended depth of field microscopy (EDFM) implementation with wavefront encoding methods is reduced by depth-induced spherical aberration (SA) due to reliance of this approach on a defined point spread function (PSF). Evaluation of the engineered PSF’s robustness to SA, when a specific phase mask design is used, is presented in terms of the final restored image quality. Synthetic intermediate images were generated using selected generalized cubic and cubic phase mask designs. Experimental intermediate images were acquired using the same phase mask designs projected from a liquid crystal spatial light modulator. Intermediate images were restored using the penalized space-invariant expectation maximization and the regularized linear least squares algorithms. In the presence of depth-induced SA, systems characterized by radially symmetric PSFs, coupled with model-based computational methods, achieve microscope imaging performance with fewer deviations in structural fidelity (e.g., artifacts) in simulation and experiment and 50% more accurate positioning of 1-μm beads at 10-μm depth in simulation than those with radially asymmetric PSFs. Despite a drop in the signal-to-noise ratio after processing, EDFM is shown to achieve the conventional resolution limit when a model-based reconstruction algorithm with appropriate regularization is used. These trends are also found in images of fixed fluorescently labeled brine shrimp, not adjacent to the coverslip, and fluorescently labeled mitochondria in live cells.
KEYWORDS: Signal to noise ratio, Principal component analysis, Image analysis, Expectation maximization algorithms, Point spread functions, Photons, Algorithm development, Microscopy, Computer simulations, 3D image processing
In 3D wide-field computational microscopy, the image formation process is depth variant due to the refractive index
mismatch between the imaging layers. In a previous study, an image estimation method based on a principle component
analysis (PCA) model for the representation of the depth varying point spread function (DV-PSF) was presented and
demonstrated with noiseless simulations. In this study, the performance of the PCA-based DV expectation
maximization algorithm (PCA-DVEM) was further evaluated with noisy simulations. Different levels of Poisson noise
were used in simulated forward images of a synthetic object computed using theoretically-determined DV-PSFs
approximated by the PCA approach. The noise influence on the reconstructed images obtained with PCA-DVEM was
evaluated. We found that without regularization, the algorithm performs well when the signal-to-noise ratio (SNR) is 14
dB or higher. The relationship of the number of PCA components, B, to the image reconstruction performance was also
investigated on both noiseless and noisy simulated data. In both cases, we found that the number of PCA components
has limited effect on the image reconstruction performance for B > 1. To reduce computational complexity while
maintaining image estimation performance, B = 2 is suggested for processing experimental data.
KEYWORDS: Point spread functions, 3D image processing, Microscopy, Simulation of CCA and DLA aggregates, Image processing, 3D modeling, Computer simulations, Microscopes, Computing systems, Wavefronts
In this study, we evaluated a point-spread function (PSF) engineered using wave front encoding (WFE) and a
generalized cubic phase mask (GCPM) design selected to reduce the impact of depth-induced spherical aberration (SA)
on extended depth-of-field (EDOF) microscopy with high NA lenses. Mean-square-error based metrics computed from
three-dimensional (3D) depth-variant WFE-PSFs with increasing amounts of SA were used to quantify the engineered
PSF's sensitivity to SA and to compare it to the sensitivity of the cubic-phase mask (CPM) PSF traditionally used for
EDOF microscopy. The potential performance of the engineered PSF with resilience to SA was further evaluated with
simulations in which EDOF images of a 3D object were obtained by processing WFE images with and without SA. A
qualitative and quantitative comparison of the EDOF images with the true object show that the WFE-PSF engineering
with the selected GCPM design provides better performance in reducing the impact of SA. In addition, the GCPM-based
EDOF images do not suffer from the known lateral shift of object features located away from the plane of focus
encountered in traditional CPM-based EDOF images.
In three-dimensional (3D) computational imaging for wide-field microscopy, estimation methods that solve the inverse
imaging problem play an important role. The accuracy of the forward model has a significant impact on the complexity
of the estimation method and consequently on the accuracy of the estimated intensity. Previous studies have shown that a
forward model based on a depth-varying point-spread function (DV-PSF) leads to a substantial improvement in the
resulting images because it accounts for depth-induced aberrations present in the imaging system. In this depth-varying
(DV) model, the depth-dependent imaging effects are handled using a stratum-based interpolation method defined on
discrete, non-overlapping layers or strata along the Z axis. Recently, a new approximation method based on a principle
component analysis (PCA) was developed to predict DV-PSFs1 with improved accuracy over the DV-PSFs predicted by
the strata interpolation method of Ref. [11]. In this study, we implemented the PCA-based forward model for DV
imaging to further compare the two approaches. DV-PSFs and forward models were computed using both the strata-based
and the new PCA-based approximation schemes. Differences are quantified as a function of the approximation,
i.e. the number of bases or strata used in each case respectively. A new PCA-based image estimation method was also
developed based on the DV expectation maximization (DV-EM) algorithm of Ref. [11]. Preliminary evaluation of the
performance of the PCA-based estimation shows promising results and consistency with previous results obtained in
previous studies.
Laser speckle contrast imaging (LSCI) is becoming an established method for full-field imaging of blood flow dynamics
in animal models. Blood flow pulsation originated from heart beat affects blood flow measurement results of LSCI and it
is considered as major physiology noise source for most biomedical applications. But in some biomedical applications,
the details of the pulsation process might provide useful information for disease diagnostics. In this study, we
investigated the ability as well as the limitation of LSCI in monitoring flow pulsation in phantom study. Both intralipid
(2% - 5%) and human whole blood samples are used in phantom study. A syringe pump is controlled by a computer-programmable
motor controller and liquid phantom is pushed through a 400 μm ID capillary tube by the pump at
different pulsation patterns, varied in frequency (1-7 Hz),
valley-to-peak ratio (10%-50%), acceleration/deceleration rate,
etc. Speckle contrast images are acquired at 15-30
frames-per-seconds. Our results show: (1) it is very hard for LSCI to
pick up signals from high frequency pulsation (5-7 Hz), which is close to the heart back frequency of rats. This might be
caused by the nature of fluid dynamics of blood during pulsation. LSCI might not work well for animal models in
detecting pulsation. (2) With low frequency pulsation (1 Hz, close to human normal pulsation rate), our experimental
results shows from most pulsation patterns, LSCI could catch the fine details of the blood flow change in a cycle. LSCI
might be used for studying human blood flow pulsation.
Laser speckle contrast imaging (LSCI) is becoming an established method for full-field imaging of blood flow dynamics
in animal models. A reliable quantitative model with comprehensive noise analysis is necessary to fully utilize this
technique in biomedical applications and clinical trials. In this study, we investigated several major noise sources in
LSCI: periodic physiology noise, shot noise and statistical noise. (1) We observed periodic physiology noise in our
experiments and found that its sources consist principally of motions induced by heart beats and/or ventilation. (2) We
found that shot noise caused an offset of speckle contrast (SC) values, and this offset is directly related to the incident
light intensity. (3) A mathematical model of statistical noise was also developed. The model indicated that statistical
noise in speckle contrast imaging is related to the SC values and the total number of pixels used in the SC calculation.
Our experimental results are consistent with theoretical predications, as well as with other published works.
Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. There is great interest in
studying the relationship among microstructures and molecular processes of colorectal cancer during its progression at
early stages. In this study, we use our multi-modality optical system that could obtain co-registered optical coherence
tomography (OCT) and fluorescence molecular imaging (FMI) images simultaneously to study CRC. The overexpressed
carbohydrate α-L-fucose on the surfaces of polyps facilitates the bond of adenomatous polyps with UEA-1
and is used as biomarker. Tissue scattering coefficient derived from OCT axial scan is used as quantitative value of
structural information. Both structural images from OCT and molecular images show spatial heterogeneity of tumors.
Correlations between those values are analyzed and demonstrate that scattering coefficients are positively correlated
with FMI signals in conjugated. In UEA-1 conjugated samples (8 polyps and 8 control regions), the correlation
coefficient is ranged from 0.45 to 0.99. These findings indicate that the microstructure of polyps is changed gradually
during cancer progression and the change is well correlated with certain molecular process. Our study demonstrated that
multi-parametric imaging is able to simultaneously detect morphology and molecular information and it can enable
spatially and temporally correlated studies of structure-function relationships during tumor progression.
Optical coherence tomography (OCT) provides high-resolution,
cross-sectional imaging of tissue microstructure in situ
and in real-time, while fluorescence molecular imaging (FMI) enables the visualization of basic molecular processes.
There are great interests in combining these two modalities so that the tissue's structural and molecular information can
be obtained simultaneously. This could greatly benefit biomedical applications such as detecting early diseases and
monitoring therapeutic interventions. In this research, a new optical system that combines OCT and FMI was developed.
The system demonstrated that it could co-register en face OCT and FMI images with a 2.4 x 2.4 mm field of view. The
transverse resolutions of OCT and FMI of the system are both 10 μm. Capillary tubes filled with Cy 5.5 fluorescent dye
in different concentrations (750nM to 24μM) under a scattering medium (1% - 2% intralipid) are used as the phantom.
En face OCT images of the phantoms were obtained and successfully co-registered with FMI images that were acquired
simultaneously. A linear relationship between FMI intensity and dye concentration was observed. The relationship between FMI intensity and target fluorescence tube depth measured by OCT images was also observed and compared with theoretical modeling. This relationship could help in correcting reconstructed dye concentration. Imaging of colon polyps of APCmin mouse model is presented as an example of biological applications of this co-registered OCT/FMI system. In conclusion, a co-registering OCT and FMI imaging system has been demonstrated. The system enables simultaneous visualization of tissue morphology and molecular information at high resolutions over a 2-3 mm field-of-view.
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