Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be miti gated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point -scanning super-resolution (PSSR) imaging. Oversampled ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were used to generate semi-synthetictrain ing data for PSSR models that were then used to restore undersampled images. Remarkably, our EM PSSR model was able to restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs . PSSR enabled previously unattainable xy resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan-equivalent spati al resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity.
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Super-resolution reconstruction in ultrahigh-field MRI
Magnetic resonance imaging (MRI) is a highly significant imaging platform for a variety of medical and research applications. However, the low spatiotemporal resolution of conventional MRI limits its applicability toward rapid acquisition of ultrahigh-resolution scans. Current aims at high-resolution MRI focus on increasing the accuracy of tissue delineation, as- sessments of structural integrity, and early identification of malignancies. Unfortunately, high-resolution imaging often leads to decreased signal/noise (SNR) and contrast/noise (CNR) ratios and increased time cost, which are unfeasible in many clinical and academic settings, offsetting any potential benefits. In this study, we apply and assess the efficacy of super-res- olution reconstruction (SRR) through iterative back-projection utilizing through-plane voxel offsets. SRR allows for high-res- olution imaging in condensed time frames. Rat skulls and archerfish samples, typical models in academic settings, were used to demonstrate the impact of SRR on varying sample sizes and applicability for translational and comparative neuroscience. The SNR and CNR increased in samples that did not fully occupy the imaging probe and in instances where the low-resolution data were acquired in three dimensions, while the CNR was found to increase with both 3D and 2D low-resolution data recon- structions when compared with directly acquired high-resolution images. Limitations to the applied SRR algorithm were inves- tigated to determine the maximum ratios between low-resolution inputs and high-resolution reconstructions and the overall cost effectivity of the strategy. Overall, the study revealed that SRR could be used to decrease image acquisition time, in- crease the CNR in nearly all instances, and increase the SNR in small samples.
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- Award ID(s):
- 2129617
- PAR ID:
- 10482357
- Publisher / Repository:
- Biophysical Reports
- Date Published:
- Journal Name:
- Biophysical Reports
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2667-0747
- Page Range / eLocation ID:
- 100107
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Stokes polarimeter based endoscopes are emerging as an area of technology where polarization imaging can greatly impact clinical care by improving diagnostic tools without the use of exogenous contrast. Image acquisition in minimally invasive surgical settings is often beset by inherently limited illumination. A comprehensive analysis of how signal-to-noise (SNR) propagates through Stokes polarimetric outcomes such as degree of linear polarization (DoLP) and angle of polarization (AoP) in low light is important for future interpretation of data acquired in low-light conditions. A previously developed theoretical model of quantitative polarized light imaging (QPLI) analysis described SNR as a function of both incident light intensity and DoLP. When polarized light interacts with biological tissues, the resultant DoLP of exiting light is dependent on the underlying tissue microstructure. Therefore, in this study we explore how low light impacts SNR of QPLI outcomes of DoLP and AoP differently in tissue phantoms of varying microstructures. Data are compared to theoretical solutions of SNR of DoLP and AoP. Tissues were additionally loaded to varying magnitudes of strain to investigate how variable SNR affects the ability to discern dynamic realignment in biological tissues. We observed a high degree of congruency between experimental and theoretical data, with SNR depending on both light intensity and DoLP. Additionally, we found that AoP may have a greater resilience to noise overall than DoLP and, as such, may be particularly useful in conditions where light is inherently limited.
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Abstract Background Dual‐energy CT (DECT) systems provide valuable material‐specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast‐to‐noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x‐ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality.
Purpose The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose.
Methods The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms‐to‐DECT measurements map.
Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize the CNR.
Results It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions.
Instances of non‐uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non‐uniqueness can lead to singular solutions during the inversion of sinograms‐to‐DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection.
Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x‐ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53 keV.
Conclusions Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposed framework.
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in vivo .
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.bpr.2023.100107
