Acquisition Speed Research Articles

Imaging lung tumor motion using integrated‐mode proton radiography—A phantom study towards tumor tracking in proton radiotherapy

AbstractBackgroundMotion of lung tumors during radiotherapy leads to decreased accuracy of the delivered dose distribution. This is especially true for proton radiotherapy due to the finite range of the proton beam. Methods for mitigating motion rely on knowing the position of the tumor during treatment.PurposeProton radiography uses the treatment beam, at an energy high enough to traverse the patient, to produce a radiograph. This work shows the first results of using an integrated‐mode proton radiography system to track the position of moving objects in an experimental phantom study; demonstrating the potential of using this method for measuring tumor motion.MethodsProton radiographs of an anthropomorphic lung phantom, with a motor‐driven tumor insert, were acquired approximately every 1 s, using tumor inserts of 10, 20, and 30 mm undergoing a known periodic motion. The proton radiography system used a monolithic scintillator block and digital cameras to capture the residual range of each pencil beam passing through the phantom. These ranges were then used to produce a water equivalent thickness map of the phantom. The centroid of the tumor insert in the radiographs was used to determine its position. This measured position was then compared to the known motion of the phantom to determine the accuracy.ResultsSubmillimeter accuracy on the measurement of the tumor insert was achieved when using a 30 mm tumor insert with a period of 24 s and was found to be improved for decreasing motion amplitudes with a mean absolute error (MAE) of 1.0, 0.9, and 0.7 mm for 20, 15, and 10 mm respectively. Using smaller tumor inserts reduced the accuracy with a MAE of 1.8 and 1.9 mm for a 20 and 10 mm insert respectively undergoing a periodic motion with an amplitude of 20 mm and a period of 24 s. Using a shorter period resulted in significant motion artifacts reducing the accuracy to a MAE of 2.2 mm for a 12 s period and 3.1 mm for a 6 s period for the 30 mm insert with an amplitude of 20 mm.ConclusionsThis work demonstrates that the position of a lung tumor insert in a realistic anthropomorphic phantom can be measured with high accuracy using proton radiographs. Results show that the accuracy of the position measurement is the highest for slower tumor motions due to a reduction in motion artifacts. This indicates that the primary obstacle to accurate measurement is the speed of the radiograph acquisition. Although the slower tumor motions used in this study are not clinically realistic, this work demonstrates the potential for using proton radiography for measuring tumor motion with an increased scanning speed that results in a decreased acquisition time.

LC dynamic signal modeling and analysis of resonant frequency measurement error under rapid eye movements interference

Abstract LC resonant wireless passive intraocular pressure (IOP) sensors measure IOPs by mapping them to LC resonance frequencies. However, during the sweeping acquisition process of each LC signal frame, possible rapid eye movement (REM) can interfere with the wireless mutual inductance coupling, leading to the signal distortion and resulting in the error of resonance frequency measuring. Currently, there is a lack of modelling analysis of the errors generated by REM effect, resulting in an absence of guidance for reducing the impact. Therefore, here, we start from the perspective of the signal model and establish the LC dynamic signal model for sweeping acquisition. By means of the limit state analysis and Monte Carlo simulation, we analyze the influence of external REM parameters (including REM range and velocity) and internal parameters of the LC sensing system (including the quality coefficient of LC sensor, diameter, and number of turns of the reader coil, signal sweep acquisition speed and period) on the errors. We demonstrated theoretical relationship between the extreme errors with these parameters and verify it through a computer simulation. Based on the results, we propose to optimize the internal parameters of the LC sensing system to reduce the REMs effect on errors, safeguarding the quality of signal acquisition and improving the measurement accuracy.

Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning.


This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity CT images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent MRI-to-CT image conversion. 
Approach. 
Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net Generative Adversarial Network (NUGAN) and Attention U-Net Generative Adversarial Network (AUGAN), were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets.
Main Results.
Our approach successfully generated high-quality CT images (0.142 mm²) from both lower-resolution (0.282 mm²) and higher-resolution (0.142 mm²) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs.
 Significance. 
This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy.&#xD.

Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography.

Under-sampled scans with iterative reconstruction and high-frequency preserving transform (Us-IRHF) can increase the acquisition speed without degrading the image quality by recovering image information from under-sampled data. We investigate the clinical applicability of high spatial resolution magnetic resonance cholangiopancreatography (MRCP) images without extending the scanning time using Us-IRHF. A slit phantom was scanned with conventional- (without Us-IRHF), Us-IR- (without HF), and Us-IRHF scanning. The matrix size was 320 × 320 for Us-IR- and Us-IRHF- and 288 × 208 for conventional scanning. Modulation transfer function (MTF) focused on the 1.0 lp/cm gauge for each scanning was calculated. For clinical study we acquired respiratory-triggered 3D MRCP scans with and without Us-IRHF (U+-, U-MRCP) in 41 patients. The matrix size was 320 × 320 for U+- and 288 × 208 for U-MRCP. The acquisition time and the relative duct-to-periductal contrast ratios (RCs) for the right- and left intrahepatic bile-, the common bile-, and the main pancreatic duct were recorded. Visualization of each duct and overall image quality was scored on 5-point confidence scales. For visualization of each duct the score ranged from 1 (not visible) to 5 (visible with excellent details), for the image quality, it ranged from 1 (undiagnostic) to 5 (excellent). Superiority for the qualitative visualization score and non-inferiority for the RC values with prespecified margins were assessed. Phantom study showed that compared to the conventional- and Us-IR (without HF) images, the MTF for the Us-IRHF image revealed the highest response. For clinical study, the mean acquisition time was 161s for U+- and 165s for U-MRCP. For all ducts, the RC value of U+MRCP was non-inferior to U-MRCP and the qualitative visualization score assigned to U+MRCP was superior to U-MRCP. Us-IRHF improved the image quality of high spatial resolution MRCP without extending the scanning time.

FEFA: Frequency Enhanced Multi-Modal MRI Reconstruction With Deep Feature Alignment.

Integrating complementary information from multiple magnetic resonance imaging (MRI) modalities is often necessary to make accurate and reliable diagnostic decisions. However, the different acquisition speeds of these modalities mean that obtaining information can be time consuming and require significant effort. Reference-based MRI reconstruction aims to accelerate slower, under-sampled imaging modalities, such as T2-modality, by utilizing redundant information from faster, fully sampled modalities, such as T1-modality. Unfortunately, spatial misalignment between different modalities often negatively impacts the final results. To address this issue, we propose FEFA, which consists of cascading FEFA blocks. The FEFA block first aligns and fuses the two modalities at the feature level. The combined features are then filtered in the frequency domain to enhance the important features while simultaneously suppressing the less essential ones, thereby ensuring accurate reconstruction. Furthermore, we emphasize the advantages of combining the reconstruction results from multiple cascaded blocks, which also contributes to stabilizing the training process. Compared to existing registration-then-reconstruction and cross-attention-based approaches, our method is end-to-end trainable without requiring additional supervision, extensive parameters, or heavy computation. Experiments on the public fastMRI, IXI and in-house datasets demonstrate that our approach is effective across various under-sampling patterns and ratios.

Correlation Hyperspectral Imaging

Hyperspectral imaging aims at providing information on both the spatial and the spectral distribution of light, with high resolution. However, state-of-the-art protocols are characterized by an intrinsic trade-off imposing to sacrifice either resolution or image acquisition speed. We address this limitation by exploiting light intensity correlations, which are shown to enable overcoming the typical downsides of traditional hyperspectral imaging techniques, both scanning and snapshot. The proposed approach also opens possibilities that are not otherwise achievable, such as sharper imaging and natural filtering of broadband spectral components that would otherwise hide the spectrum of interest. The enabled combination of high spatial and spectral resolution, high speed, and insensitivity to undesired spectral features shall lead to a paradigm change in hyperspectral imaging devices and open up new application scenarios. Published by the American Physical Society 2024

How changes in the mean latency, jitter amplitude, and jitter frequency impact target acquisition performance

Interactive technologies require the user to frequently generate purposeful movements, where actions are guided towards targets. While delays between the movement onset and the corresponding update within the virtual space have been shown to impair target acquisition, the relative contribution of the individual latency parameters remains unclear. This paper investigates how changes in latency, the moment-by-moment variability in the latency (jitter amplitude) and the rate of variability (jitter frequency), affect the speed and accuracy of target acquisition at different mean latencies. Experiment 1 explored changes in the mean latency and jitter amplitude. As the mean latency increased, we observed substantial impairments in completion time and accuracy, with increased variability in performance. There was also an interaction between the mean latency and jitter amplitude: although large jitter amplitudes caused a small completion time impairment, this effect decreased as the mean latency increased. Experiment 2 isolated the effect of the jitter by investigating changes in the jitter amplitude and jitter frequency at a fixed mean latency. Here, we observed completion time impairments from 67ms amplitude and accuracy impairments from 134ms amplitude. Like Experiment 1, the effect of the amplitude was small. Notably, we found no evidence that changes in the jitter frequency significantly influenced performance. Overall, increases in the mean latency contributed most to the impairment, disrupting acquisition speed and accuracy as it increased. Large jitter amplitudes also disrupted speed and accuracy, but this was a comparatively small effect that was mediated by the mean latency.

Real-time four-dimensional scanning transmission electron microscopy through sparse sampling

Abstract Four-dimensional scanning transmission electron microscopy (4-D STEM) is a state-of-theart image acquisition mode used to reveal high and low mass elements at atomic resolution. The acquisition of the electron momenta at each real space probe location allows for various analyses to be performed from a single dataset, including virtual imaging, electric field analysis, as well as analytical or iterative extraction of the object induced phase shift. However, the limiting factor in 4-D STEM is the speed of acquisition which is bottlenecked by the read-out speed of the camera, which must capture a convergent beam electron diffraction (CBED) pattern at each probe position in the scan. Recent developments in sparse sampling and image inpainting (a branch of compressive sensing) for STEM have allowed for real-time recovery of sparsely acquired data from fixed monolithic detectors, Further developments in compressive sensing for 4-D STEM have also demonstrated that acquisition speeds can be increased i.e. live video rate 4D imaging is now possible. In this work, we demonstrate the first practical implementations of compressive 4-D STEM for real-time inference on two different scanning transmission electron microscopes.

Overcoming photon and spatiotemporal sparsity in fluorescence lifetime imaging with SparseFLIM.

Fluorescence lifetime imaging microscopy (FLIM) provides quantitative readouts of biochemical microenvironments, holding great promise for biomedical imaging. However, conventional FLIM relies on slow photon counting routines to accumulate sufficient photon statistics, restricting acquisition speeds. Here we demonstrate SparseFLIM, an intelligent paradigm for achieving high-fidelity FLIM reconstruction from sparse photon measurements. We develop a coupled bidirectional propagation network that enriches photon counts and recovers hidden spatial-temporal information. Quantitative analysis shows over tenfold photon enrichment, dramatically improving signal-to-noise ratio, lifetime accuracy, and correlation compared to the original sparse data. SparseFLIM enables reconstructing spatially and temporally undersampled FLIM at full resolution and channel count. The model exhibits strong generalization across experimental modalities including multispectral FLIM and in vivo endoscopic FLIM. This work establishes deep learning as a promising approach to enhance fluorescence lifetime imaging and transcend limitations imposed by the inherent codependence between measurement duration and information content.

Intravital Microscopy With an Airy Beam Light Sheet Microscope Improves Temporal Resolution and Reduces Surgical Trauma.

Intravital microscopy has emerged as a powerful imaging tool, which allows the visualization and precise understanding of rapid physiological processes at sites of inflammation in vivo, such as vascular permeability and leukocyte migration. Leukocyte interactions with the vascular endothelium can be characterized in the living organism in the murine cremaster muscle. Here, we present a microscopy technique using an Airy Beam Light Sheet microscope that has significant advantages over our previously used confocal microscopy systems. In comparison, the light sheet microscope offers near isotropic optical resolution and faster acquisition speed, while imaging a larger field of view. With less invasive surgery we can significantly reduce side effects such as bleeding, muscle twitching, and surgical inflammation. However, the increased acquisition speed requires exceptional tissue stability to avoid imaging artefacts. Since respiratory motion is transmitted to the tissue under investigation, we have developed a relocation algorithm that removes motion artefacts from our intravital microscopy images. Using these techniques, we are now able to obtain more detailed 3D time-lapse images of the cremaster vascular microcirculation, which allow us to observe the process of leukocyte emigration into the surrounding tissue with increased temporal resolution in comparison to our previous confocal approach.

Quantitative Analysis of Acquisition Speed of High-Precision FLIM Technologies via Simulation and Modeling

In practical applications such as cancer diagnosis and industrial detection, there is a critical demand for fast fluorescence lifetime imaging (Fast-FLIM). The Fast-FLIM systems suitable for complex environments are typically achieved by enhancing the hardware performance of time-correlated single-photon counting (TCSPC), with an acquisition speed of about a few frames per second (fps). However, due to the limitation of single-photon acquisition, the imaging speed is still far from the demand of practical application. The synchroscan streak camera (SC) maps signals from the temporal dimension to the spatial dimension, effectively overcoming the long acquisition time caused by single-photon acquisition. This paper constructs a method to calculate the acquisition time for the TCSPC-FLIM and SC-FLIM systems, and it quantitatively compares the speed. The research demonstrates that the main factors limiting the acquisition speed of the FLIM systems are the photon emission rate, the photon counting rate, the required SNR, the dwell time, and the number of parallel channels. In high-quality and large-scale lifetime imaging, the acquisition speed of the SC-FLIM is at least 104 times faster than that of the TCSPC-FLIM. Therefore, the synchroscan streak camera has more significant potential to promote Fast-FLIM.

Accelerating float current measurement with temperature ramps revealing entropy insights

This research examines the behavior of three types of lithium-ion cells during a voltage hold, precisely measuring the necessary float current IFloat during stepwise changes and temperature ramps from 5 to 50 °C to increase data acquisition rate and speed compared to stepwise approaches. The float current is thereby associated with calendar aging and superimposed by entropy effects in case of temperature ramps. By combining upward and downward ramps and different ramp speeds, the shares of entropy and aging can be separated. Thereby, the entropy-induced current IE across all tested cells is not constant but increases linearly with temperature. This linearity, coupled with direct proportionality between IE and the temperature ramp speed, underscores the predictability of the entropy effect in these cells under varying temperature conditions. With the knowledge of the entropy current, the aging current IAging can be derived from temperature ramps and is compared with the state-of-the-art measurements during temperature steps, employing two evaluation methods. While 9 out of 12 cells show high agreement. Significant discrepancies are observed at lower voltages, particularly for high capacity NMC/NCA-cells. Kinetic effects and measurement limitations at low aging currents are expected to be the root cause, especially for upward ramps starting from cold temperatures.

State ownership and acquisition speed: evidence from China

ABSTRACT Speed is a key driver for the success of acquisitions. Although state ownership is widely believed to affect the management efficiency and action speed of firms, the relationship between state ownership and acquisition speed remains unknown. This paper investigates how firms’ acquisition speed varies with state ownership. Our results suggest that state ownership is associated with slower acquisition speed. Higher administrative hierarchy of the state-owned shareholder is correlation with slower acquisition speed. Reducing the shareholding ratio of state owner and hiring a younger CEO are likely to mitigate the negative correlation between state ownership and acquisition speed.

Non-invasive brain-machine interface control with artificial intelligence copilots.

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3 × in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.

Flocculation of fiber suspensions studied by Rheo-OCT

When dealing with papermaking fiber suspensions, particle flocculation takes place even before the paper web is formed. The particle flocculation depends on several aspects, including particle mass concentration (consistency), particle collisions, electrochemical interactions promoted by chemical additives, etc. Due to its impor-tance, fiber suspension flocculation has been studied for a long time in papermaking, and several methods have been developed for this purpose. The traditional techniques include, for example, focused beam reflectance micros-copy (FBRM) and high-speed video imaging (HSVI). Recently, a new optical method, optical coherence tomography (OCT), has emerged for flocculation analysis. The advantages of OCT are the possibility to study opaque suspensions, its micron-level resolution, and its high data acquisition speed. The OCT measurements can be combined with rheological (Rheo) measurements, allowing simul-taneous measurement of both the time evolution of the floc size and the suspension viscosity. In this work, we used this approach, Rheo-OCT, to study the flocculation of suspensions of various papermaking furnishes. We analyzed the time evolution of the floc size and the fiber suspension viscosity when the studied paper-making suspensions were treated with highly refined furnish (HRF) — a furnish that contained a significant amount of micofibrillated cellulose (MFC)-type fibrils — and/or chemical additives. Such studies can lead to a better under-standing of the impact of flocculation on the produced paper web in terms of qualities like formation, drainage potential, and strength behavior.

Application of deep learning reconstruction in abdominal magnetic resonance cholangiopancreatography for image quality improvement and acquisition time reduction

PurposeTo compare deep learning (DL)-based and conventional reconstruction through subjective and objective analysis and ascertain whether DL-based reconstruction improves the quality and acquisition speed of clinical abdominal magnetic resonance imaging (MRI). MethodsThe 124 patients who underwent abdominal MRI between January and July 2021 were retrospectively studied. For each patient, two-dimensional axial T2-weighted single-shot fast spin-echo MRI images with or without fat saturation were reconstructed using DL-based and conventional methods. The subjective image quality scores and objective metrics, including signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were analysed. An explorative analysis was performed to compare 20 patients’ MRI images with site routine settings, high-resolution settings and high-speed settings. Paired t tests and Wilcoxon signed-rank tests were used for subjective and objective comparisons. ResultsA total of 144 patients were evaluated (mean age, 62.2 ± 14.1 years; 83 men). The MRI images reconstructed using DL-based methods had higher SNRs and CNRs than did those reconstructed using conventional methods (all p < 0.01). The subjective scores of the images reconstructed using DL-based methods were higher than those of the images reconstructed using conventional methods (p < 0.01), with significantly lower variation (p < 0.01). Exploratory analysis revealed that the DL-based reconstructions with thin slice thickness and higher temporal resolution had the highest image quality and were associated with the shortest scan times. ConclusionsDL-based reconstruction methods can be used to improve the quality with higher stability and accelerate the acquisition of abdominal MRI.

THE USE OF VIRTUAL REALITY TECHNOLOGIES IN MILITARY EDUCATION

The article examines the topical topic of introducing virtual reality technology into military education to increase the level of effectiveness and safety of training military personnel. In the context of the latest challenges to global security and rapid technological progress, the need to improve the quality of military personnel training has become extremely urgent. The purpose(s) of the article is to analyze the state, problems and prospects of introducing virtual reality into military education in order to increase the level of efficiency and safety of training military personnel. The advantages of using virtual reality technology in the field of military education are outlined, in particular, reproduction of close to reality combat scenarios, immersive simulations and optimization of team interaction. Having analyzed in the article modern approaches to military training and the shortcomings of classical methods, we singled out the prospects of using virtual reality to increase the effectiveness of the educational process, balancing the realism of the experience and the safety of military training. The introduction of virtual reality into the educational process of military education will require investments, the development of specialized software and the training of qualified instructors, although over time these efforts can increase the level of combat readiness of the armed forces. The conclusion of the article emphasizes that the introduction of virtual reality into military education should become one of the key decisions in the framework of the reform of military personnel training. The use of this technology helps to increase the level and speed of knowledge acquisition, the safety of military training and exercises, to develop key skills and readiness to act in a real combat environment. Investments in innovative technologies and the latest training tools are critical to maintaining the combat capability of the armed forces and the successful execution of combat missions. The results of the article will have important implications for commanders, military instructors, and researchers trying to improve the effectiveness and safety of military personnel training. They should be able to use the latest virtual reality technology for greater flexibility and efficiency of the educational process, which will help strengthen the combat capability of the military personnel of the armed forces.

The effect of individual differences on Pavlovian conditioning in specific Internet-use disorders

The I-PACE model suggests that Internet-use disorders result from the interplay of individual vulnerabilities and cognitive and affective processes. As in substance use disorders, Pavlovian conditioning processes are attributed a key role. However, and despite progress in identifying individual vulnerabilities, factors influencing appetitive conditioning remain poorly understood. We therefore conducted a Pavlovian conditioning experiment in which individuals with risky as well as non-problematic use of either gaming or buying-shopping applications learned to associate different abstract stimuli with either gaming or buying-shopping. Regression analyses were used to identify individual characteristics influencing awareness of the experimental contingencies, speed of acquisition of awareness and the magnitude of the conditioned emotional responses regarding pleasantness and arousal ratings of the stimuli. Results demonstrated successful Pavlovian conditioning and an attentional bias towards reward-predicting cues. Awareness of the experimental contingencies was linked solely to cognitive abilities, while the speed of acquisition of awareness and the magnitude of conditioned responses was influenced by specific personality characteristics, experiences of compensation from using the application and severity of problematic use. Importantly, certain characteristics specifically predicted the magnitude of the conditioned response towards gaming, while others specifically predicted the response towards buying-shopping, highlighting differing vulnerabilities. These findings underscore the importance of targeted interventions and prevention strategies tailored to these specific vulnerability factors. Further implications and limitations are discussed.

Exploring the structure, metabolism, and biochemistry of the neuronal microenvironment label-free using fast simultaneous multimodal optical microscopy

The technologies to examine the neuronal microenvironment label free remain critically underexplored. There is a gap in our knowledge of underlying metabolic, biochemical, and electrophysiological mechanisms behind several neurological processes at a cellular level, which can be traced to the lack of versatile and high-throughput tools to investigate neural networks. In this paper, four label-free contrasts were explored as mechanisms to study neuronal activity, namely, scattering, birefringence, autofluorescence from metabolic cofactors and molecules, and local biochemistry. To overcome challenges of observing neuronal activity spanning three orders of magnitude in space and time, microscopes had to be developed to simultaneously capture these contrasts quickly, with high resolution, and over a large FOV. We developed versatile autofluorescence lifetime, multiharmonic generation, polarization-sensitive interferometry, and Raman imaging in epi-detection (VAMPIRE) microscopy to simultaneously observe multiple facets of neuronal structure and dynamics. The accelerated computational-imaging-driven acquisition speeds, the utilization of a single light source to evoke all contrasts, the simultaneous acquisition that provides an otherwise impossible multimodal dynamic imaging capability, and the real-time processing of the data enable VAMPIRE microscopy as a powerful imaging platform for neurophotonics and beyond.

Arkitekt: streaming analysis and real-time workflows for microscopy.

Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making 'smart microscopy' a reality.