Fast Imaging Technique Research Articles

Adaptive focusing optical coherence tomography for corneal imaging.

Optical coherence tomography (OCT) plays a crucial role in diagnosing corneal diseases due to its capacity to provide high-resolution three-dimensional imaging. However, the convex shape of the cornea and the inherent trade-off between depth of field (DOF) and lateral resolution in OCT systems often result in defocusing issues, leading to reduced lateral resolution and sensitivity in single-shot high-resolution imaging. Traditional methods typically involve multiple focusing at different depths followed by image stitching to achieve full-depth high-resolution imaging of the cornea, which increases imaging times and introduces potential stitching artifacts. To address these limitations, we propose a novel adaptive focusing OCT system. By leveraging the symmetry of the corneal structure and the periodic focusing stability of an electrically tunable lens, our system can achieve full-depth high-resolution imaging of the entire cornea in a single scan, covering an 11 mm imaging range and 1.5 mm depth variation with a lateral resolution of 10 µm. This approach not only halves the imaging time but also eliminates the need for image stitching. Imaging of curved tape samples and excised porcine corneas demonstrates the potential of this novel technique for high-resolution and fast corneal imaging.

New fast imaging techniques for electrical source transient electromagnetic data: Approaches and application

The rapid imaging of electrical source transient electromagnetic (TEM) data involves two essential processes: the calculation of apparent resistivity and the conversion of time to depth. Traditionally, the definition of full-time apparent resistivity is defined by considering solely the vertical magnetic field, which is predicated on the monotonic relationship between the resistivity and the electromagnetic field response. Based on the concept of peak time, we have developed distinct methodologies for calculating the apparent resistivity for both the horizontal electric field (ex) and the vertical induced voltage (vz), which demonstrated accuracy across the entire time range examined. We also introduced a formula to address discrepancies in apparent resistivity arising from the non-dipole size effect of the source, thereby ensuring that the algorithm can adapt to any transmitting and receiving configuration. Furthermore, we provided straightforward and precise time-depth conversion equations applicable to both ex and vz, which facilitate the rapid imaging of observational data. Multiple numerical examples were employed to illustrate the effectiveness and robustness of this approach. Finally, we applied this imaging technique to the data processing of actual measured data from a survey area conducted in Ningxia Province, and the imaging results accurately reflected the distribution of the electrical structure of the subsurface strata. The innovative imaging technique presented in this study holds considerable potential for the expedited processing and analysis of ground-based and semi-aerial electrical source transient electromagnetic survey data, which are widely employed in contemporary applications.

Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model

Abstract Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) is becoming the most widely explored technique in clinical practice for tumor assessment. In acquiring DCE-MRI, a contrast agent (CA), also called tracer, is injected into the blood flow before or during the acquisition of a time series of T 1 {T_{1}} -weighted images with fast imaging techniques. When the CA goes through the tissue, MR signal intensity measurements in voxels of the region of interest (ROI) are registered and used to calculate the CA concentration in each voxel. The Tofts models have become standard for the analysis of DCE-MRI and which express tissue CA concentration C ⁢ ( t ) {C(t)} as function of time t. The analysis of quantitative parameters in DCE-MRI provides the quantitative criterion as a reference rather than relying only on the shape of the DCE-curve, as it is used for diagnosis of prostate cancer (PCa). This study aim to provide a new thinking in quantitative analysis which may therefore improve diagnostic accuracy for detection of prostate cancer and could be used in patient baseline prediction and guide management. A hierarchical Bayesian model was built to estimate the values of the four pharmacokinetic parameters ( K trans {K_{\mathrm{trans}}} , k ep {k_{\mathrm{ep}}} , υ p {\upsilon_{\mathrm{p}}} , υ e {\upsilon_{\mathrm{e}}} ) for both prostate healthy and lesion tissues in the peripheral zone. This estimation is important because it help to understand the behavior of the CA in the body and how this latter reacts to the CA in order to emphasize the expectation or the absence of prostate lesion during the diagnosis step.

The effect of non-perpendicular incidence angles on discharge characteristics in an argon atmospheric plasma jet impinging on metal, water and glass substrates

The spatial–temporal discharge behavior of an AC argon plasma jet tilted at non-perpendicular incidence angles (60°, 45°, and 30°) interacting with an ungrounded metal, water, and glass plate placed on the jet propagation track was studied by the fast-imaging technique. The conductivity of surface and incidence angles plays an essential role in the discharge current and dynamic process of the plasma jet. The nearly consistent time delay between subsequent breakdowns occurred four times for metal and two times for glass treatments. The mean luminous intensity of the plasma in one discharge cycle at the discharge area between ground electrode and target surface region for the water and glass case decreased by 39.5% and 20.5% when the incidence angle decreased from 60° to 30°, respectively. In particular, the incidence angle and gas flow rate notably impacted the spatial extension behavior created on the glass surface but had no significant difference in discharge characteristic of plasma jet with metal case. In addition, two equivalent circuit models were developed based on the simulation of the micro-discharges and the geometry of the “plasma jet–substrate” system, respectively. These results will obtain further insight into the underlying mechanisms of plasma-target interaction and facilitate the designing of appropriate jet for environmental and biomedical applications.

Single-Shot MRI in parahydrogen hyperpolarized samples

The site-specific signal enhancement provided by parahydrogen induced polarization (PHIP) may be combined with magnetic resonance imaging (MRI) to study chemical and biomolecular processes. However, imaging of hydrogen nuclei (1H) is hampered by background signals arising from the presence of thermally polarized nuclei. Additionally, fast imaging sequences are commonly based on multiple radio-frequency pulses, where the signals resulting from PHIP oscillate due to the evolution with a J-coupling Hamiltonian. In this article, an innovative imaging scheme for single-scan MRI is presented that effectively detects hyperpolarized components while simultaneously canceling out thermal contributions. This method is based on the quenching of inherent oscillations of PHIP-originated signals due to J-couplings during the multipulse sequence and the suppression of thermal signals by spin dynamics and a tailored restructuring of the k-space. A series of numerical simulations on specific two- and three-spin systems serve to support the feasibility of the approach. Furthermore, this theoretical study demonstrates the potential of combining hyperpolarization and long-lived states (PHIP and LLS) in the selected molecules, which could be seen as a preliminary step towards the development of fast imaging techniques, for example in the field of biomolecular research.

Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy.

Holotomography (HT) is a cutting-edge fast live-cell quantitative label-free imaging technique. Based on the principle of quantitative phase imaging, it combines holography and tomography to record a three-dimensional map of the refractive index, used as intrinsic optical and quantitative imaging contrast parameter of biological samples, at a sub-micrometer spatial resolution. In this study HT has been employed for the first time to analyze the changes of fibroblasts differentiating towards myofibroblasts - recognized as the main cell player of fibrosis - when cultured in vitro with the pro-fibrotic factor, namely transforming growth factor-β1. In parallel, F-actin, vinculin, α-smooth muscle actin, phospho-myosin light chain 2, type-1 collagen, peroxisome proliferator-activated receptor-gamma coactivator-1α expression and mitochondria were evaluated by confocal laser scanning microscopy. Plasmamembrane passive properties and transient receptor potential canonical channels' currents were also recorded by whole-cell patch-clamp. The fluorescence images and electrophysiological results have been compared to the data obtained by HT and their congruence has been discussed. HT turned out to be a valid approach to morphologically distinguish fibroblasts from well differentiated myofibroblasts while obtaining objective measures concerning volume, surface area, projection area, surface index and dry mass (i.e., the mass of the non-aqueous content inside the cell including proteins and subcellular organelles) of the entire cell, nuclei and nucleoli with the major advantage to monitor outer and inner features in living cells in a non-invasive, rapid and label-free approach. HT might open up new research opportunities in the field of fibrotic diseases. RESEARCH HIGHLIGHTS: Holotomography (HT) is a label-free laser interferometric imaging technology exploiting the intrinsic optical property of cells namely refractive index (RI) to enable a direct imaging and analysis of whole cells or intracellular organelles. HT turned out a valid approach to distinguish morphological features of living unlabeled fibroblasts from differentiated myofibroblasts. HT provided quantitative information concerning volume, surface area, projection area, surface index and dry mass of the entire fibroblasts/myofibroblasts, nuclei and nucleoli.

Body MRI in pediatrics: where we are and what the future holds.

Body magnetic resonance imaging (MRI) is increasingly used for disease diagnosis, characterization, and monitoring in children of all ages. MRI has numerous advantages when compared to other imaging modalities, including a lack of ionizing radiation, superior soft tissue image contrast, and ability to provide objective, quantitative assessments. As MRI continues to evolve, pediatric body MRI examinations of the future will certainly be different than our current and past protocols. In this review article, we will discuss the present and likely future states of pediatric body MRI, including the increasing application of quantitative MRI methods, faster imaging techniques and implementation of abbreviated targeted protocols, and the growing use of artificial intelligence methods.

Assessment of chemotherapeutic effects on cancer cells using adhesion noise spectroscopy.

With cancer as one of the leading causes of death worldwide, there is a need for the development of accurate, cost-effective, easy-to-use, and fast drug-testing assays. While the NCI 60 cell-line screening as the gold standard is based on a colorimetric assay, monitoring cells electrically constitutes a label-free and non-invasive tool to assess the cytotoxic effects of a chemotherapeutic treatment on cancer cells. For decades, impedance-based cellular assays extensively investigated various cell characteristics affected by drug treatment but lack spatiotemporal resolution. With progress in microelectrode fabrication, high-density Complementary Metal Oxide Semiconductor (CMOS)-based microelectrode arrays (MEAs) with subcellular resolution and time-continuous recording capability emerged as a potent alternative. In this article, we present a new cell adhesion noise (CAN)-based electrical imaging technique to expand CMOS MEA cell-biology applications: CAN spectroscopy enables drug screening quantification with single-cell spatial resolution. The chemotherapeutic agent 5-Fluorouracil exerts a cytotoxic effect on colorectal cancer (CRC) cells hampering cell proliferation and lowering cell viability. For proof-of-concept, we found sufficient accuracy and reproducibility for CAN spectroscopy compared to a commercially available standard colorimetric biological assay. This label-free, non-invasive, and fast electrical imaging technique complements standardized cancer screening methods with significant advances over established impedance-based approaches.

Cardiac dimensions and hemodynamics in healthy juvenile Landrace swine

BackgroundSwine are frequently used as animal model for cardiovascular research, especially in terms of representativity of human anatomy and physiology. Reference values for the most common species used in research are important for planning and execution of animal testing. Transesophageal echocardiography is the gold standard for intraoperative imaging, but can be technically challenging in swine. Its predecessor, epicardial echocardiography (EE), is a simple and fast intraoperative imaging technique, which allows comprehensive and goal-directed assessment. However, there are few echocardiographic studies describing echocardiographic parameters in juvenile swine, none of them using EE. Therefore, in this study, we provide a comprehensive dataset on multiple geometric and functional echocardiographic parameters, as well as basic hemodynamic parameters in swine using EE.MethodsThe data collection was performed during animal testing in ten female swine (German Landrace, 104.4 ± 13.0 kg) before left ventricular assist device implantation. Hemodynamic data was recorded continuously, before and during EE. The herein described echocardiographic measurements were acquired according to a standardized protocol, encompassing apical, left ventricular short axis and long axis as well as epiaortic windows. In total, 50 echocardiographic parameters and 10 hemodynamic parameters were assessed.ResultsEpicardial echocardiography was successfully performed in all animals, with a median screening time of 14 min (interquartile range 11–18 min). Referring to left ventricular function, ejection fraction was 51.6 ± 5.9% and 51.2 ± 6.2% using the Teichholz and Simpson methods, respectively. Calculated ventricular mass was 301.1 ± 64.0 g, as the left ventricular end-systolic and end-diastolic diameters were 35.3 ± 2.5 mm and 48.2 ± 3.5 mm, respectively. The mean heart rate was 103 ± 28 bpm, mean arterial pressure was 101 ± 20 mmHg and mean flow at the common carotid artery was 627 ± 203 mL/min.ConclusionEpicardial echocardiography allows comprehensive assessment of most common echocardiographic parameters. Compared to humans, there are important differences in swine with respect to ventricular mass, size and wall thickness, especially in the right heart. Most hemodynamic parameters were comparable between swine and humans. This data supports study planning, animal and device selection, reinforcing the three R principles in animal research.Graphical

Wavefront folding interferometer for single-shot lensless imaging

We have developed a fast, high-resolution, and single-shot lensless imaging technique based on the wavefront folding interferometer (WFI). Considering a stationary, quasimonochromatic, and quasihomoge-ncous source of light we studied the effect of spatial coherence on the retrieved image quality. Moreover, a thorough investigation of the resolution and the semi-infinity depth of focus of the imaging system was performed and demonstrated experimentally.

Axial super-resolution optical coherence tomography via complex-valued network

Optical coherence tomography (OCT) is a fast and non-invasive optical interferometric imaging technique that can provide high-resolution cross-sectional images of biological tissues. OCT’s key strength is its depth resolving capability which remains invariant along the imaging depth and is determined by the axial resolution. The axial resolution is inversely proportional to the bandwidth of the OCT light source. Thus, the use of broadband light sources can effectively improve the axial resolution and however leads to an increased cost. In recent years, real-valued deep learning technique has been introduced to obtain super-resolution optical imaging. In this study, we proposed a complex-valued super-resolution network (CVSR-Net) to achieve an axial super-resolution for OCT by fully utilizing the amplitude and phase of OCT signal. The method was evaluated on three OCT datasets. The results show that the CVSR-Net outperforms its real-valued counterpart with a better depth resolving capability. Furthermore, comparisons were made between our network, six prevailing real-valued networks and their complex-valued counterparts. The results demonstrate that the complex-valued network exhibited a better super-resolution performance than its real-valued counterpart and our proposed CVSR-Net achieved the best performance. In addition, the CVSR-Net was tested on out-of-distribution domain datasets and its super-resolution performance was well maintained as compared to that on source domain datasets, indicating a good generalization capability.

3-D Millimeter Wave Fast Imaging Technique Based on 2-D SISO/MIMO Array

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, its computational complexity and computational error are analyzed. Compared to the classical Fourier algorithm, such as the range migration algorithm (RMA) and the phase shift migration (PSM), the proposed algorithm can replace the time-consuming interpolation and accumulation operations with reasonable approximations and transformations offering a more efficient approach, while maintaining the image quality. In addition, a method based on RDA which is applicable to the transformation between MIMO and SISO, is proposed to further enhance the processing efficiency. Proof-of-principle simulation using echo data collected from a large number of antennas, verifies that the proposed algorithm has higher efficiency. In order to better verify the feasibility of the proposed algorithm, a scanning prototype located in the millimeter wave band is designed. The experimental results of different targets demonstrate that the proposed algorithm achieves significantly higher reconstruction efficiency when compared to the traditional algorithms.

Dynamics of laser-induced plasma and cavitation bubble at high pressures and the impacts on underwater LIBS signals

Characteristics of laser-induced plasma depends strongly on its ambient pressure, and a deeper understanding of the high pressure impact on the underwater plasma emission is essential for the application of LIBS in the deep-sea. In this work, we investigated the temporal evolution of underwater plasma and cavitation bubble at the high pressures up to 50 MPa, by using fast imaging and shadowgraph techniques. It showed that as the ambient pressure increases, the plasma expansion is much suppressed with a stronger emission intensity and a slower emission decay, but it quenches much earlier and ends with a sharp drop at the higher pressures. From the shadowgraph imaging results, it showed that the increase in pressure has a great impact on the bubble evolution. Both the bubble maximum radius and the bubble lifetime decrease dramatically with the pressure as a consequence of faster dynamics. By comparing between the plasma and bubble results, we demonstrated the critical role of bubble dynamics on the characterization of plasma emission at high pressures. At relatively low pressures, the bubble expansion time is long enough to finish the plasma radiation. While at high pressures, strong bubble-plasma interaction occurs that will guide the plasma evolution behavior at the late stage. The plasma can gain energy from the confinement effect caused by the bubble shrinking process which leads to an increase in the emission intensity, but it will quench immediately after the bubble collapse. Such effects caused by bubble dynamics at high pressures explain well the evolution behaviors of underwater LIBS signals of OH, Ca II, Ca I, and CaOH, considering the different lifetimes of these emitting species in the plasma. This work reveals the complex impacts of high pressure on underwater plasma emissions and underwater LIBS signals.

Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks.

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

Electrical impedance tomography with deep Calderón method

Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject. Calderón's method is a relatively recent EIT imaging algorithm that is non-iterative, fast, and capable of reconstructing complex-valued electric impedances. However, due to the regularization via low-pass filtering and linearization, the reconstructed images suffer from severe blurring and under-estimation of the exact conductivity values. In this work, we develop an enhanced version of Calderón's method, using deep convolution neural networks (i.e., U-net) as an effective targeted post-processing step, and term the resulting method by deep Calderón's method. Specifically, we learn a U-net to postprocess the EIT images generated by Calderón's method so as to have better resolutions and more accurate estimates of conductivity values. We simulate chest configurations with which we generate the current-density/voltage boundary measurements and the corresponding reconstructed images by Calderón's method. With the paired training data, we learn the deep neural network and evaluate its performance on real tank measurement data. The experimental results indicate that the proposed approach indeed provides a fast and direct (complex-valued) impedance tomography imaging technique, and substantially improves the capability of the standard Calderón's method.

Breast ultrasound image classification with hard sample generation and semi-supervised learning

Ultrasound is a fast and non-invasive imaging technique widely used in the early-stage diagnosis of breast cancer. Ultrasound images are also commonly used in deep learning, but traditional computer algorithms and even ultrasound diagnosticians often struggle to accurately distinguish between benign and malignant cases, as some ultrasound images exhibit mixed characteristics of both. To address the above problems, we defined hard sample mining rules firstly. To address the above problems, we defined rules for hard sample mining and utilized shape descriptors (Concavity, Growth Direction, Compactness, Circle Variance, and Elliptic Variance) to describe the samples. For those samples with overlapped features of both categories, i.e., benign and malignant, we identify them as hard samples. In order to make the network pay more attention to hard samples, we propose a Phased GAN (PGAN) that can generate hard samples to increase the number of such samples in the dataset. However, since the generated hard samples lack labels, they cannot be used in supervised training. To overcome this limitation, we propose a new semi-supervised network (SSN) that trains them with labeled samples. We also add a hard sample loss function to the SSN to regularize the features of the hard samples, which helps the network learn these features better. In summary, we propose a breast ultrasound image classification using PGAN and SSN, which achieves a classification accuracy of 97.7% on a public breast dataset (445 cases of benign and 210 cases of malignant).

Early dynamics of laser-induced plasma and cavitation bubble in water

The knowledge of early laser-induced plasma and cavitation bubble dynamics are of great importance for a better understanding of underwater laser-induced breakdown spectroscopy. In this work, we investigated the temporal evolution of the plasma and bubble at an early stage before 2 μs, by using fast imaging and shadowgraph techniques. It showed that the initial plasma forms at the laser focal point and then grows up toward the incident laser. As time evolves, the plasma expands simultaneously forward and backward to its peripheral region and a sub-plasma structure can be observed at low laser energies. The plasma has a good pulse-to-pulse repeatability during the laser pulse, while soon after the laser pulse (from 20 to 50 ns), it undergoes a severe pulse-to-pulse fluctuation with an inhomogeneous emission distribution. At longer times, the plasma becomes much stable again. To clarify the interrelationship between the bubble dynamics and the plasma characteristics, shadowgraph images of the bubble were shown and compared with the plasma images. It showed that the morphology of the bubble and its evolution have a good consistency with the morphology of plasma. The bubble radius as a function of time can be well expressed by the equation R=at0.4, and a is a constant related to the laser energy. By inspecting the evolution curve of the bubble wall and the bubble pressure calculated based on the Gilmore model, a transition phase from 20 to 50 ns can be identified between the moving breakdown phase and the thermal expansion phase. In this transition phase, both the bubble expansion speed and the bubble pressure drop drastically that may account for the severe plasma fluctuations as observed in this period. These results of early bubble dynamics provide insights into the critical role of bubble-plasma interaction on the characterization of laser-induced plasma in water.

Deep-learning-based 3D blood flow reconstruction in transmissive laser speckle imaging.

Transmissive laser speckle imaging (LSI) is useful for monitoring large field-of-view (FOV) blood flow in thick tissues. However, after longer transmissions, the contrast of the transmitted speckle images is more likely to be blurred by multiple scattering, resulting in decreased accuracy and spatial resolution of deep vessels. This study proposes a deep-learning-based strategy for high spatiotemporal resolution three-dimensional (3D) reconstruction from a single transilluminated laser speckle contrast image, providing more structural and functional details without multifocus two-dimensional (2D) imaging or 3D optical imaging with point/line scanning. Based on the correlation transfer equation, a large training dataset is generated by convolving vessel masks with depth-dependent point spread functions (PSF). The UNet and ResNet are used for deblurring and depth estimation. The blood flow in the reconstructed 3D vessels is estimated by a depth-dependent contrast model. The proposed method is evaluated with simulated data and phantom experiments, achieving high-fidelity structural reconstruction with a depth-independent estimation of blood flow. This fast 3D blood flow imaging technique is suitable for real-time monitoring of thick tissue and the diagnosis of vascular diseases.

Differential synthetic illumination based on multi-line detection for resolution and contrast enhancement of line confocal microscopy.

Line confocal (LC) microscopy is a fast 3D imaging technique, but its asymmetric detection slit limits resolution and optical sectioning. To address this, we propose the differential synthetic illumination (DSI) method based on multi-line detection to enhance the spatial resolution and optical sectioning capability of the LC system. The DSI method allows the imaging process to simultaneously accomplish on a single camera, which ensures the rapidity and stability of the imaging process. DSI-LC improves X- and Z-axis resolution by 1.28 and 1.26 times, respectively, and optical sectioning by 2.6 times compared to LC. Furthermore, the spatially resolved power and contrast are also demonstrated by imaging pollen, microtubule, and the fiber of the GFP fluorescence-labeled mouse brain. Finally, Video-rate imaging of zebrafish larval heart beating in a 665.6 × 332.8 µm2 field-of-view is achieved. DSI-LC provides a promising approach for 3D large-scale and functional imaging in vivo with improved resolution, contrast, and robustness.

A convergence analysis for projected fast iterative soft-thresholding algorithm under radial sampling MRI

Radial sampling is a fast magnetic resonance imaging technique. Further imaging acceleration can be achieved with undersampling but how to reconstruct a clear image with fast algorithm is still challenging. Previous work has shown the advantage of removing undersampling image artifacts using the tight-frame sparse reconstruction model. This model was further solved with a projected fast iterative soft-thresholding algorithm (pFISTA). However, the convergence of this algorithm under radial sampling has not been clearly set up. In this work, the authors derived a theoretical convergence condition for this algorithm. This condition was approximated by estimating the maximal eigenvalue of reconstruction operators through the power iteration. Based on the condition, an optimal step size was further suggested to allow the fastest convergence. Verifications were made on the prospective in vivo data of static brain imaging and dynamic contrast-enhanced liver imaging, demonstrating that the recommended parameter allowed fast convergence in radial MRI.