Imaging Setup Research Articles

Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients

Abstract Biophysical models of diffusion tailored to quantify gray matter microstructure are gathering increasing interest. The two-compartment Neurite EXchange Imaging (NEXI) model has been proposed recently to account for neurites, extra-cellular space, and exchange across the cell membrane. NEXI parameter estimation requires multi-shell multi-diffusion time data and has so far only been implemented experimentally on animal data collected on a preclinical magnetic resonance imaging (MRI) set-up. In this work, the translation of NEXI to the human cortex in vivo was achieved using a 3 T Connectom MRI system with 300 mT/m gradients, that enables the acquisition of a broad range of b-values (0 – 7.5 ms/µm²) with a window covering short to intermediate diffusion times (20 – 49 ms) suitable for the characteristic exchange times (10 – 50 ms). Microstructure estimates of four model variants: NEXI, NEXIdot (its extension with the addition of a dot compartment), and their respective versions that correct for the Rician noise floor (NEXIRM and NEXIdot,RM) that particularly impacts high b-value signal, were compared. The reliability of estimates in each model variant was evaluated in synthetic and human in vivo data. In the latter, the intra-subject (scan-rescan) versus between-subjects variability of microstructure estimates was compared in the cortex. The better performance of NEXIRM highlights the importance of correcting for Rician bias in the NEXI model to obtain accurate estimates of microstructure parameters in the human cortex, and the sensitivity of the NEXI framework to individual differences in cortical microstructure. This application of NEXI in humans represents a significant step, unlocking new avenues for studying neurodevelopment, aging, and various neurodegenerative disorders.

Nano-optical theory of planar x-ray waveguides.

X-ray waveguides are routinely used at synchrotron light sources in imaging setups and as a platform for experiments with quantum emitters, providing nanometer-sized confinement - even x-ray optics on a chip has been showcased. X-ray waveguides are weakly guiding and experience significant material absorption, such that the established waveguide theory is not immediately applicable. Here, a general self-contained nano-optical theory of planar waveguides is derived, which is appropriate for hard x-ray energies. Solutions of the electromagnetic fields and its Green's functions are derived in detail. Asymptotic expansions into resonant and non-resonant modes are derived, which are particularly useful in the presence of strong material absorption. A method to reliably find the resonant modes of x-ray waveguide structures is presented. Based on the general theory, certain common experimental geometries, namely evanescent coupling in grazing-incidence, front-coupling in forward-incidence and radiation from buried emitters, are discussed in more detail. Complementing the analytic discussion, numerical tools are provided and applied to quantitatively extract the main figures of merit. The theory provides an analytic foundation for the interpretation of past and future experiments and, combined with the numerical tools, will facilitate the computer-aided design of x-ray waveguides.

Automated detection of vertebral body misalignments in orthogonal kV and MV guided radiotherapy: application to a comprehensive retrospective dataset

Objective. In image-guided radiotherapy (IGRT), off-by-one vertebral body misalignments are rare but potentially catastrophic. In this study, a novel detection method for such misalignments in IGRT was investigated using densely-connected convolutional networks (DenseNets) for applications towards real-time error prevention and retrospective error auditing. Approach. A total of 4213 images acquired from 527 radiotherapy patients aligned with planar kV or MV radiographs were used to develop and test error-detection software modules. Digitally reconstructed radiographs (DRRs) and setup images were retrieved and co-registered according to the clinically applied alignment contained in the DICOM REG files. A semi-automated algorithm was developed to simulate patient positioning errors on the anterior-posterior (AP) and lateral (LAT) images shifted by one vertebral body. A DenseNet architecture was designed to classify either AP images individually or AP and LAT image pairs. Receiver-operator characteristic curves (ROC) and areas under the curves (AUC) were computed to evaluate the classifiers on test subsets. Subsequently, the algorithm was applied to the entire dataset in order to retrospectively determine the absolute off-by-one vertebral body error rate for planar radiograph guided RT at our institution from 2011–2021. Main results. The AUCs for the kV models were 0.98 for unpaired AP and 0.99 for paired AP-LAT. The AUC for the MV AP model was 0.92. For a specificity of 95%, the paired kV model achieved a sensitivity of 99%. Application of the model to the entire dataset yielded a per-fraction off-by-one vertebral body error rate of 0.044% [0.0022%, 0.21%] for paired kV IGRT including one previously unreported error. Significance. Our error detection algorithm was successful in classifying vertebral body positioning errors with sufficient accuracy for retrospective quality control and real-time error prevention. The reported positioning error rate for planar radiograph IGRT is unique in being determined independently of an error reporting system.

Monte Carlo simulation of phase-contrast X-ray imaging with a single-grid setup

Phase-contrast X-ray imaging is widely used to visualize the internal structures of samples that cannot be seen with conventional X-ray imaging. Among various phase-sensitive imaging techniques available—propagation-based, grating-interferometry, edge-illumination, and speckle-based—single-grid-based imaging, a variant of the speckle-based technique, provides benefits like single exposure and a simple setup. In this study, we developed a Monte Carlo (MC) simulator based on Geant4 for phase-contrast imaging with a single-grid setup. We then validated the MC simulations of single-grid imaging against theoretical predictions. Our results demonstrate that the phase-contrast images obtained using the simulator align well with the theoretical predictions. Moreover, we quantitatively investigated the effects of imaging parameters, such as X-ray focal spot size and grid period, on image quality using the MC simulator. This confirmed the viability of the simulator. We expect that this simulator will be useful in designing an optimal single-grid-based imaging setup.

Fourier imaging for nanophotonics

Abstract Standard optical characterization and spectroscopy techniques rely on the measurement of specular reflection, transmission, or emission at normal incidence. Although the usefulness of these methods is without question, they do not provide information on the angular dependence of the scattered light and, therefore, miss crucial insights on the physical processes governing light emission and scattering. In this Review, we explain the basics of Fourier imaging and show how it can be used to measure the angular distribution of scattered light in single-shot measurements. We then give a comprehensive panorama on recent research exploiting this technique to analyze nanostructures and detail how it unlocks fundamental understandings on the underlying physics of nanophotonic structures. We finally describe how simple additions to a Fourier imaging setup enable measuring not only the radiation pattern of an object but also the energy, polarization, and phase toward resolving all aspects of light in real time.

A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement.

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

Optical Photothermal Infrared - Fluorescence In Situ Hybridization (OPTIR-FISH).

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we present a comprehensive protocol for simultaneous cell identification and metabolic analysis with the OPTIR-FISH platform by combining rRNA-tagged FISH probes and isotope-labeled substrates. Fluorescence imaging provides cell identification by the specific binding of rRNA-tagged FISH probes, while OPTIR imaging provides metabolic activities within single cells by isotope-induced red shift on OPTIR spectra. Using bacteria cultured with 13C-glucose as a test bed, the protocol outlines microbial culture with isotopic labeling, fluorescence in situ hybridization (FISH), sample preparation, optimization of the OPTIR-FISH imaging setup, and data acquisition. We also demonstrate how to perform image analysis and interpret spectral data at the single-cell level with high throughput. This protocol's standardized and detailed nature will greatly facilitate its adoption by researchers from diverse backgrounds and disciplines within the broad single-cell metabolism research community.

A blending method for forward-looking sonar mosaicing handling intra- and inter-frame artifacts

Forward-looking sonar (FLS) has been gaining attractions in the realm of near-bottom, close-range underwater inspections, owing to its high-resolution and rapid framerate capabilities. Although automatic target recognition (ATR) algorithms have been tentatively employed for object detection tasks, the necessity for human oversight remains vital, particularly in sensitive areas. A comprehensive FLS mosaic encapsulating all relevant information is highly desired to aid experts in managing an extensive array of perception data. Yet, prior research has often presumed that FLS operates under an ideal system configuration, assuming optimal sonar imaging setups and the availability of precise positioning data. Without these assumptions, intra-frame and inter-frame artifacts would emerge, deteriorating the quality of the resultant mosaic by obscuring essential information. In this study, we propose an innovative blending method specifically for FLS mosaicing aiming preserve important information. We first develop a long–short time sliding window (LST-SW) to rectify the local statistics of raw sonar images. These statistics then serve to create a global variance map (GVM). This step helps to emphasize the useful information contained in images in the blending phase by classifying the informative and featureless pixels, thereby enhancing the quality of final mosaic. This approach is substantiated through data collected in real-world scenarios. The results show that our method can preserve more details in FLS mosaics for human inspection purposes in practice.

Microscopic Imaging on Diesel Spray and Atomization Process

Improving diesel engine performance requires a comprehensive understanding of fuel atomization and air–fuel mixing within the combustion chamber. Numerous studies have been conducted to reduce emissions and enhance diesel engines. However, further investigation is required on the detailed diesel spray process. In this study, we adopted extinction measurement to analyze the effects of a fuel injection pressure range of 300 to 700 bar on spray morphology. For the extinction imaging setup, we utilized a high-intensity continuous LED source along with a diffuser to ensure uniform light distribution. The high-speed extinction and image processing results indicate that increasing the injection pressure from 300 to 700 bar effectively produced a smaller particulate size (15% reduction) and a better air–fuel mixing process. Especially at the end of injection, our results show smaller liquid ligaments (50% reduction) and droplets around the injector tip with higher injection pressure cases.

Mueller matrix-based characterization of cervical tissue sections: a quantitative comparison of polar and differential decomposition methods.

Quantitative optical polarimetry has received considerable recent attention owing to its potential for being an efficient diagnosis and characterizing tool with potential applications in biomedical research and various other disciplines. In this regard, it is crucial to validate various Mueller matrix (MM) decomposition methods, which are utilized to extract and quantify the intrinsic individual polarization anisotropy properties of various complex optical media. To quantitatively compare the performance of both polar and differential MM decomposition methods for probing the structural and morphological changes in complex optical media through analyzing their intrinsic individual polarization parameters, which are extracted using the respective decomposition algorithms. We also intend to utilize the decomposition-derived anisotropy parameters to distinguish among the cervical tissues with different grades of cervical intraepithelial neoplasia (CIN) and to characterize the healing efficiency of an organic crystal. Polarization MM of the cervical tissues with different grades of CIN and the different stages of the self-healing crystal are recorded with a home-built MM imaging setup in the transmission detection geometry with a spatial resolution of . The measured MMs are then processed with both the polar and differential MM decomposition methods to extract the individual polarization parameters of the respective samples. The derived polarization parameters are further analyzed to validate and compare the performance of both the MM decomposition methods for probing and characterizing the structural changes in the respective investigated optical media through their decomposition-derived intrinsic individual polarization properties. Pronounced differences in the decomposed-derived polarization anisotropy parameters are observed for cervical tissue sections with different grades of CIN. While a significant increase in the depolarization parameter is obtained with the increment of CIN stages for both the polar [ for CIN grade one (CIN-I) and for CIN grade two (CIN-II))] and differential ( for CIN-I and for CIN-II) decomposition methods, a trend reversal is seen for the linear diattenuation parameter , indicating the structural distortion in the cervical morphology due to the CIN disease. More importantly, with the differential decomposition algorithm, the magnitude of the derived parameter decreases from 0.26 to 0.19 with the progression of CIN, which was not being probed by the polar decomposition method. Our results demonstrate that the differential decomposition of MM holds certain advantages over the polar decomposition method to characterize and probe the structural changes in the cervical tissues with different grades of CIN. Although the quantified individual polarization parameters obtained through both the MM decomposition methods can be used as useful metrics to characterize various optical media, in case of complex turbid media such as biological tissues, incorporation of the differential decomposition technique may yield more efficient information. Also, the study highlights the utilization of MM polarimetry with an appropriate decomposition technique as an efficient diagnostic and characterizing tool in the realm of biomedical clinical research, and various other disciplines.

Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat.

The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = [Formula: see text]) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation. This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions.

Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients.

Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-observer variability. There has been little research investigating gross failure rates and failure modes of such systems. 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI-generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid). An AI-based commercial system was applied to 500 H&N patients. AI-generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI-based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub-types (setup position, anatomy, image artefacts and unknown). There were 24 true failures of the AI-based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid). True failures of the AI-based system were predominantly associated with a non-standard element within the CT scan. It is likely that these non-standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI-based system in the H&N region for the OARs investigated was low (∼1%).

Machine-learning-based mapping of blood oxygen saturation from dual-wavelength optoacoustic measurements

We developed a novel machine-learning-based algorithm based on a gradient boosting regressor for three-dimensional pixel-by-pixel mapping of blood oxygen saturation based on dual-wavelength optoacoustic data. Algorithm training was performed on in silico data produced from Monte-Carlo-generated absorbed light energy distributions in tissue-like vascularized media for probing wavelengths of 532 and 1064 nm and the empirical instrumental function of the optoacoustic imaging setup with further validation of the independent in silico data. In vivo optoacoustic data for rabbit-ear vasculature was employed as a testing dataset. The developed algorithm allowed in vivo blood oxygen saturation mapping and showed clear differences in blood oxygen saturation values in veins at 15 °C and 43 °C due to functional arteriovenous anastomoses. These results indicated that dual-wavelength optoacoustic imaging could serve as a cost-effective alternative to complicated multiwavelength quantitative optoacoustic imaging.

Receive coil quality assurance procedure and automated analysis for ViewRay MRIdian MR-Linac.

Regular receiving coil quality assurance (QA) is required to ensure image quality of an MRIdian Linac system. The manufacturer provides a spherical phantom and positioning tube for single-slice signal-to-noise ratio (SNR) and uniformity assessments. We aimed to improve imaging setup and coverage and eliminate inter-scan variability by employing multi-slice imaging of a stable phantom. Additionally, we strived to expedite analysis by developing objective, automated analysis software. A 5300mL cylindrical plastic bottle placed in plastic bins was scanned at isocenter using a spin-echo sequence with NEMA-recommended parameters and 18 axial slices, avoiding phantom repositioning. Acquisition was repeated with and without prescan normalization filtering and by saving uncombined element images. Obtained data were analyzed using custom open-source MATLAB code. Signal and noise images were automatically assigned, and ROIs for SNR and uniformity calculations were defined using image thresholding. SNR and uniformity pass/fail decisions were made using baseline comparisons. The proposed method was successfully implemented as monthly coil QA for 3.5 years. Setup and scanning took 41 min on average for a coil set. Automated image analysis was completed in a few minutes. Signal intensity peaked around +90 or -90mm for Torso or Head/Neck coil unfiltered images. Noise peaked and minimized SNR inside±30mm from isocenter, while maximizing it around±130mm. Prescan normalization smoothed signal response, reduced SNR and increased uniformity. Individual coil element image analysis identified their position, signal or noise response and SNR. SNR and uniformity pass/fail thresholds were set for already tested and new coils. Conspicuous and subtle Torso coil malfunctions were detected considering baseline deviations of combined and individual element results. Our QA method eliminated observer bias and provided insights into coil function, image filtering performance and coil element location. It provided SNR and uniformity thresholds and identified faulty coil elements.

EFFECT OF MAGNESIUM ON IGNITION AND COMBUSTION OF BORON-LOADED GELLED JET A-1 FUEL

Boron (B) is a desirable fuel for explosives and propellants due to its high-energy density. However, the combustion of B particles is difficult because its native oxide layers serve as inhibitors and cover the particles. This oxide layer makes the combustion process more difficult. However, using B as an additive in gelled fuel has significant potential use in ramjets. The present study used magnesium (Mg) with B-loaded gel-based fuel as a burning promotor. Different combustion parameters were evaluated for four different fuel compositions. The present investigation employed a droplet combustion setup and high-speed imaging technique to examine the impact of Mg on B particles in gel fuel. The influences of Mg addition on the droplet combustion flame, B ignition delay time, boron oxide (BO&lt;sub&gt;2&lt;/sub&gt;) intensity, and post-combustion particles were studied. The addition of Mg significantly influenced B in reducing the ignition delay time, where the reduction was almost 70.58&amp;#37; at higher Mg loadings (GBMg30). The combustion of B-based samples resulted in the emission of BO&lt;sub&gt;2&lt;/sub&gt;, which was characterized by a greenish appearance. The findings were corroborated by spectroscopic analysis. The analysis of the burning events was conducted utilizing a combination of color and high-speed camera techniques in conjunction with a 546 nm BO&lt;sub&gt;2&lt;/sub&gt; filter. Post-combustion particles were analyzed with the help of scanning electron microscopy images and thermogravimetric analysis.

A Magnetic Pincher for the Dynamic Measurement of the Actin Cortex Thickness in Live Cells.

The actin cortex is an essential element of the cytoskeleton allowing cells to control and modify their shape. It is involved in cell division and migration. However, probing precisely the physical properties of the actin cortex has proved to be challenging: it is a thin and dynamic material, and its location in the cell-directly under the plasma membrane-makes it difficult to study with standard light microscopy and cell mechanics techniques. In this chapter, we present a novel protocol to probe dynamically the thickness of the cortex and its fluctuations using superparamagnetic microbeads in a uniform magnetic field. A bead ingested by the cell and another outside the cell attract each other due to dipolar forces. By tracking their position with nanometer precision, one can measure the thickness of the cortex pinched between two beads and monitor its evolution in time. We first present the set of elements necessary to realize this protocol: a magnetic field generator adapted to a specific imaging setup and the aforementioned superparamagnetic microbeads. Then we detail the different steps of a protocol that can be used on diverse cell types, adherent or not.

Super resolution label-free dark-field microscopy by deep learning.

Dark-field microscopy (DFM) is a powerful label-free and high-contrast imaging technique due to its ability to reveal features of transparent specimens with inhomogeneities. However, owing to the Abbe's diffraction limit, fine structures at sub-wavelength scale are difficult to resolve. In this work, we report a single image super resolution DFM scheme using a convolutional neural network (CNN). A U-net based CNN is trained with a dataset which is numerically simulated based on the forward physical model of the DFM. The forward physical model described by the parameters of the imaging setup connects the object ground truths and dark field images. With the trained network, we demonstrate super resolution dark field imaging of various test samples with twice resolution improvement. Our technique illustrates a promising deep learning approach to double the resolution of DFM without any hardware modification.

MosaicMVS: Mosaic-Based Omnidirectional Multi-View Stereo for Indoor Scenes

We present MosaicMVS, a novel learning-based depth estimation framework for a mosaic-based omnidirectional multi-view stereo (MVS) camera setup. It uses a regular field of view (FOV) MVS network for an omnidirectional imaging setup with explicit consideration of hypothetical voxel-wise FOV overlaps. The resulting depth predictions are accurate and agree on the omnidirectional multi-view geometry. Unlike existing MVS setups, MosaicMVS camera setup can be easily applied to omnidirectional indoor scenes without having to account for con-straints such as intricate epipolar constraints and the distortion of omnidirectional cameras. We validate the effectiveness of our framework on a new challenging indoor dataset in terms of depth estimation, reconstruction, and view synthesis. We also present new evaluation metric to check reconstruction performance using post-processed masks for accurate evaluation without any ground truth depth map or laser-scanned reconstructions. Experimental results show that our framework outperforms the state-of-the-art MVS methods in a large margin in all test scenes.

Lensless multi-core-fiber-based imaging from speckle autocorrelation under highly coherent illumination

Flexible fiber-optic lensless endoscopic imaging plays a significant role in medical and biological applications. Lensless imaging based on far-field speckle of multi-core fibers under spatially incoherent light source illumination relied on the angular optical memory effect, resulting in limited field of view and susceptibility to interference effects. Image reconstruction from fiber speckle intensity under the highly coherent light illumination has been investigated using classical coherent diffraction theory, but it imposes limitations on imaging distance and detection positions. However, existing alternative solutions involve reducing coherence level to satisfy the angular optical memory effect, which increases system complexity and sacrifices the advantages of single-frame imaging. The reconstruction of intensity-based images remains a significant challenge under highly coherent illumination, primarily due to the interference effect. In this study, an imaging algorithm that reconstructs the original image solely from the intensity of far-field fiber speckle under coherent illumination conditions is proposed. The proposed method is based on statistical optics theory and aims to extract the relevant information within coherent speckle patterns to reconstruct the image. Our approach expands the imaging field of view, simplifies the imaging setup, and broadens the range of viable light sources for speckle correlation imaging. The effectiveness of the algorithm is verified by simulated experiments and a real imaging system.

Assessing spontaneous sensory neuron activity using in vivo calcium imaging.

Heightened spontaneous activity in sensory neurons is often reported in individuals living with chronic pain. It is possible to study this activity in rodents using electrophysiology, but these experiments require great skill and can be prone to bias. Here, we have examined whether in vivo calcium imaging with GCaMP6s can be used as an alternative approach. We show that spontaneously active calcium transients can be visualised in the fourth lumbar dorsal root ganglion (L4 DRG) through in vivo imaging in a mouse model of inflammatory pain. Application of lidocaine to the nerve, between the inflamed site and the DRG, silenced spontaneous firing and revealed the true baseline level of calcium for spontaneously active neurons. We used these data to train a machine learning algorithm to predict when a neuron is spontaneously active. We show that our algorithm is accurate in 2 different models of pain: intraplantar complete Freund adjuvant and antigen-induced arthritis, with accuracies of 90.0% ±1.2 and 85.9% ±2.1, respectively, assessed against visual inspection by an experienced observer. The algorithm can also detect neuronal activity in imaging experiments generated in a different laboratory using a different microscope configuration (accuracy = 94.0% ±2.2). We conclude that in vivo calcium imaging can be used to assess spontaneous activity in sensory neurons and provide a Google Colaboratory Notebook to allow anyone easy access to our novel analysis tool, for the assessment of spontaneous neuronal activity in their own imaging setups.