Image Sequences Research Articles

A novel transcranial MR Guided focused ultrasound method based on the ultrashort echo time skull acoustic model and phase retrieval techniques

Transcranial ultrasound stimulation (TUS) has been clinically applied as a neuromodulation tool. Particularly, the phase array ultrasound can be applied in TUS to non-invasively focus on the cortex or deep brain. However, the vital phase distortion of the ultrasound induced by the skull limits its clinical application. In the current study, we aimed to develop a hybrid method that combines the ultrashort echo time (UTE) magnetic resonance imaging (MRI) sequences with the prDeep technique to achieve focusing ventral intermediate thalamic nucleus (VIM). The time-reversal (TR) approach of the UTE numerical acoustic model of the skull combined with the prDeep algorithm was used to reduce the number of iterations. The skull acoustic model simulation therapy process was establish to valid this method’s prediction and focus performance, and the classical TR method were considered as the gold standard (GS). Our approach could restore 75% of the GS intensity in 25 iteration steps, with a superior the noise immunity. Our findings demonstrate that the phase aberration caused by the skull can be estimated using phase retrieval techniques to achieve a fast and accurate transcranial focus. The method has excellent adaptability and anti-noise capacity for satisfying complex and changeable scenarios.

A Phase 0 Study to Assess the Biodistribution and Pharmacokinetics of a Radiolabeled Antibody Targeting Human Kallikrein 2 in Participants with Metastatic Castration-Resistant Prostate Cancer.

Despite the inclusion of multiple agents within the prostate cancer treatment landscape, new treatment options are needed to address the unmet need for patients with metastatic castration-resistant prostate cancer (mCRPC). Although prostate-specific membrane antigen is the only cell-surface target to yield clinical benefit in men with advanced prostate cancer, additional targets may further advance targeted immune, cytotoxic, radiopharmaceutical, and other tumor-directed therapies for these patients. Human kallikrein 2 (hK2) is a novel prostate-specific target with little to no expression in nonprostate tissues. This first-in-human phase 0 trial uses an 111In-radiolabeled anti-hK2 monoclonal antibody, [111In]-DOTA-h11B6, to credential hK2 as a potential target for prostate cancer treatment. Methods: Participants with progressive mCRPC received a single infusion of 2 mg of [111In]-DOTA-h11B6 (185 MBq of 111In), with or without 8 mg of unlabeled h11B6 to assess antibody mass effects. Sequential imaging and serial blood samples were collected to determine [111In]-DOTA-h11B6 biodistribution, dosimetry, serum radioactivity, and pharmacokinetics. Safety was assessed within a 2-wk follow-up period from the time of [111In]-DOTA-h11B6 administration. Results: Twenty-two participants received [111In]-DOTA-h11B6 and are included in this analysis. Within 6-8 d of administration, [111In]-DOTA-h11B6 visibly accumulated in known mCRPC lesions, with limited uptake in other organs. Two treatment-emergent adverse events unrelated to treatment occurred, including tumor-related bleeding in 1 patient, which led to early study discontinuation. Serum clearance, biodistribution, and tumor targeting were independent of total antibody mass (2 or 10 mg). Conclusion: This first-in-human study demonstrates that tumor-associated hK2 can be identified and targeted using h11B6 as a platform as the h11B6 antibody selectively accumulated in mCRPC metastases with mass-independent clearance kinetics. These data support the feasibility of hK2 as a target for imaging and hK2-directed agents as potential therapies in patients with mCRPC.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

A Technique for In-Situ Displacement and Strain Measurement with Laboratory-Scale X-Ray Computed Tomography

Abstract Purpose: Establish a technique for simultaneous interrupted volumetric imaging of internal structure and time-resolved full-field surface strain measurements during in-situ X-ray micro-computed tomography (XCT) experiments. This enables in-situ testing of stiff materials with large forces relative to the compliance of the in-situ load frame, which might exhibit localization (e.g., necking, compaction banding) and other inhomogeneous behaviors.Methods: The system utilizes a combination of in-situ XCT, 2D X-ray imaging, and particle tracking to conduct volumetric imaging of the internal structure of a specimen with interrupted loading and surface strain mapping during loading. Critically, prior to the laboratory-scale XCT experiments, specimens are speckled with a high-X-ray-contrast powder that is bonded the surface. During in-situ loading, the XCT system is programmed to capture sequential 2D X-ray images orthogonal to the speckled specimen surface. A single particle tracking (SPT) or digital image correlation (DIC) algorithm is used to measure full-field surface strain evolution throughout the time-sequence of images. At specified crosshead displacements, the motion and 2D image sequence is paused for volumetric XCT image collection. Results: We show example results on a micro-tensile demonstration specimen additive manufactured from Inconel 718 nickel-chrome alloy. Results include XCT volume reconstructions, crosshead-based engineering stress, and full-field strain maps. Conclusion: We demonstrate an in-situ technique to obtain surface strain evolution during laboratory-scale XCT testing and interrupted volumetric imaging. This allows closer investigation of, for example, the effect of micro-pores on the strain localization behavior of additive manufactured metal alloys. In addition to describing the method using a representative test piece, the dataset and code are published as open-source resources for the community. Graphical abstract

Estimating Fire Radiative Energy Density with Repeat-Pass Aerial Thermal-Infrared Imaging of Actively Progressing Wildfires

Studies on estimating cumulative fire intensity from spreading wildland fires based on fire radiative energy density (FRED) have primarily been conducted through controlled experiments. The objective of this study was to assess the potential for estimating FRED for freely-burning wildfires at landscape scales. Airborne thermal infrared image sequences collected 8 and 9 December 2017 during the Thomas Fire were used for surface temperature derivation and FRED estimation. Sensitivity of varying ambient temperatures, and a newly developed method that adjusts for ash radiances on fire radiative flux density (FRFD) and FRED estimates were tested. Pixel-level image classification was run to identify FRFD time sequences that were complete or incomplete because of cloud obscuration and provided the basis for an obscuration gap filling technique. Variations in estimated ambient temperature used to estimate FRFD had little impact on FRED estimates, while our ash adjustment led to notable differences. An exponential decay model characterized FRFD time sequences well, providing a basis for gap filling irregular sequences caused by atmospheric obscuration. FRED estimates were regressed on rate of spread (ROS) magnitudes and found to be positively and significantly correlated. FRED magnitudes were higher on 9 December when the Thomas Fire burned under higher wind speeds and lower relative humidity levels (Santa Ana weather conditions) than on 8 December.

Three-dimensional EPI with shot-selective CAIPIRIHANA for rapid high-resolution quantitative susceptibility mapping at 3 T.

QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging(3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring. To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.

Auditory Effects of Acoustic Noise From 3-T Brain MRI in Neonates With Hearing Protection.

Neonates with immature auditory function (eg, weak/absent middle ear muscle reflex) could conceivably be vulnerable to noise-induced hearing loss; however, it is unclear if neonates show evidence of hearing loss following MRI acoustic noise exposure. To explore the auditory effects of MRI acoustic noise in neonates. Prospective. Two independent cohorts of neonates (N = 19 and N = 18; mean gestational-age, 38.75 ± 2.18 and 39.01 ± 1.83 weeks). T1-weighted three-dimensional gradient-echo sequence, T2-weighted fast spin-echo sequence, single-shot echo-planar imaging-based diffusion-tensor imaging, single-shot echo-planar imaging-based diffusion-kurtosis imaging and T2-weighted fluid-attenuated inversion recovery sequence at 3.0 T. All neonates wore ear protection during scan protocols lasted ~40 minutes. Equivalent sound pressure levels (SPLs) were measured for both cohorts. In cohort1, left- and right-ear auditory brainstem response (ABR) was measured before (baseline) and after (follow-up) MRI, included assessment of ABR threshold, wave I, III and V latencies and interpeak interval to determine the functional status of auditory nerve and brainstem. In cohort2, baseline and follow-up left- and right-ear distortion product otoacoustic emission (DPOAE) amplitudes were assessed at 1.2 to 7.0 kHz to determine cochlear function. Wilcoxon signed-rank or paired t-tests with Bonferroni's correction were used to compare the differences between baseline and follow-up ABR and DPOAE measures. Equivalent SPLs ranged from 103.5 to 113.6 dBA. No significant differences between baseline and follow-up were detected in left- or right-ear ABR measures (P > 0.999, Bonferroni corrected) in cohort1, or in DPOAE levels at 1.2 to 7.0 kHz in cohort2 (all P > 0.999 Bonferroni corrected except for left-ear levels at 3.5 and 7.0 kHz with corrected P = 0.138 and P = 0.533). A single 40-minute 3-T MRI with equivalent SPLs of 103.5-113.6 dBA did not result in significant transient disruption of auditory function, as measured by ABR and DPOAE, in neonates with adequate hearing protection. 2. Stage 5.

Real-time image denoising corrupted by shot-noise

In this paper the new model for the image noise is proposed. The video data is modelled as a sequence of images corrupted by the shot-noise. Shot-noise may occur due to the defect in the device’s hardware or in the camera’s sensor. This kind of noise effects not only the current image of the video sequence but also the subsequent images, the effect decays as time passes and disappears totally after a certain time period. The shot-noise process consists of the sequence of jumps that decay gradually as time passes. The times at which the jumps occur are Poisson distributed and the hight of the jumps is normally distributed. It can be proved that under certain conditions shot-noise process tends to Gaussian process. Therefore, it was proposed to apply Kalman filter to remove the noise and to restore the corrupted sequence of images. The numerical experiments that were carried out that show the effectiveness of the proposed approach for denoising the shot-noise corrupted videos.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images

Objective. Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning networks as convolutional neural network and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption. Approach. In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume. Main results. Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% versus 76.5%), smaller needle tip position (1.28 mm versus 1.87 mm) and length (1.78 mm versus 2.19 mm) errors on the kidney dataset as well as DSC (78.5% versus 76.9%), needle tip position (0.86 mm versus 1.12 mm) and length (1.01 mm versus 1.26 mm) errors on the prostate dataset. Significance. The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study

ObjectivesMagnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence.MethodsA retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort’s support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set.ResultsFor contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set.ConclusionsThis study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans.Clinical relevance statementWe propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.

Changes in the central nervous system in football players: an MRI study.

Football (soccer) is the world's most popular team sport. To comprehensively examine the brain in football (soccer) players, with the use of magnetic resonance imaging (MRI) techniques. The study involved 65 football players and 62 controls. The MR examinations were performed using MR 1.5-T system (Optima MR 360; GE Medical Systems). The examinations were carried out in the 3D Bravo, CUBE, FSEpropeller, and diffusion-weighted imaging (DWI) sequences. The 1HMRS signal was obtained from the volume of interest in the frontal and occipital lobes on both sides. The present study, based on structural MRI, shows some changes in the brains of the group of football players. The findings show asymmetry of the ventricular system in four football players, arachnoid cysts in the parieto-occipital region, and pineal cysts. NAA/Cr concentration in the right frontal lobe was lower in the football players than in the controls, and the Glx/Cr concentration in the right occipital lobe was higher. The apparent diffusion coefficient value is lower in football players in the occipital lobes. Playing football can cause measurable changes in the brain, known to occur in patients diagnosed with traumatic brain injury. The present findings fill the gap in the literature by contributing evidence showing that playing football may lead to changes in the brain, without clinical symptoms of concussion.

Three-dimensional reconstruction of wear particle surfaces using multi-focus image sequence

Wear particles hold morphological traits that are linked to wear rate and mechanisms. Wear particle analysis is vital in the context of wear monitoring. Current methods can be used in three-dimensional (3D) reconstruction for particle type recognition and wear mechanism identification. A 3D reconstruction method based on microscopic focusing and image processing is here proposed. A fixed-focus quasi-planar strategy is built using the defocusing principle. The stationary wavelet transform is employed to assess the degree of focus for the purpose of depth mapping, facilitating the construction of 3D particle models. The model is then optimized for surface morphology to address noise and unevenness. The reconstruction results of the proposed approach were compared with that of laser scanning confocal microscope with average errors of arithmetic mean height (Sa), root-mean-square height (Sq), and kurtosis (Sku) at 2.81%, 2.96%, and 10.06%, respectively. The algorithm is non-contact and adaptable, offering value across various fields.

The Use of Dixon Magnetic Resonance Imaging Methods for the Quantification of Rotator Cuff Fatty Infiltration: A Systematic Review

Fatty infiltration of the rotator cuff muscles is very common following rotator cuff tears and is one of the most important factors in determining treatment. Current clinical practice relies on subjective evaluation of fatty infiltration through categorical scoring based on the Goutallier classification system. The Dixon magnetic resonance imaging (MRI) sequence provides flexibility in selecting echo times for water–fat separation. The Dixon method, therefore, has the potential to provide robust and high-quality fat quantification that allows for more accurate calculation of fat fraction (%Fat) of the rotator cuff muscles than the Goutallier classification system. However, significant variance exists in sequencing and post-processing methodology within the recent application of Dixon sequences to quantify rotator cuff fatty infiltration. In this paper, we conducted a systematic review to synthesize the relevant literature utilizing Dixon sequencing for the quantification of rotator cuff fatty infiltration. The literature search was extracted from 1094 articles, with 12 studies included in the final review. Regardless of the varying sequencing pattern and post-processing techniques among studies, the findings suggest the Dixon method is reliable for quantitatively calculating the fat fraction of the rotator cuff muscles, even at very low levels of fatty infiltration. In addition, a quantitative difference in fat fraction was observed between participants with different degrees of tear vs. those without any shoulder pathologies. Multi-point Dixon imaging has the potential to be utilized clinically to objectively quantify fatty infiltration and may lead to improved clinical decision making for patients with rotator cuff tears.

The Effect of Battery Configuration on Dendritic Growth: A Magnetic Resonance Microscopy Study on Symmetric Lithium Cells

The potential of metallic lithium to become the anode material for next-generation batteries is hampered by significant challenges, chief among which is dendrite growth during battery charging. These dendritic structures not only impair battery performance but also pose safety risks. Among the non-destructive analytical techniques in battery research, Magnetic Resonance Imaging (MRI) stands out as a promising tool. However, the direct imaging of lithium by 7Li MRI is limited by its low sensitivity and spatial resolution, making it a less effective way of imaging dendrite growth. Instead, a recently introduced indirect imaging approach which is based on 1H MRI of the electrolyte was used in this study. This method was used to sequentially 3D image and thus monitor the charging process of lithium metal symmetric cells in three different electrical circuits, namely those composed of a single cell, four cells in parallel, and four cells in series. The measured sequential images allowed for the measurement of dendrite growth in each cell using volumetric analysis. The growth results confirmed the theoretical prediction that the growth across cells is uneven in a parallel circuit, and even in a series circuit. The methods presented in this study can also be applied to analyze many other dendrite-related issues in batteries.

A multi-vision monitoring framework for simultaneous real-time unmanned aerial monitoring of farmer activity and crop health

Current remote sensing technologies employing Unmanned Aerial Vehicles (UAVs) for farm monitoring have shown promise in characterizing the environment through diverse sensor systems, including hyperspectral cameras, LiDAR, thermal cameras, and RGB sensors. However, these solutions often specialize in either activity recognition or crop monitoring, but not both. To address this limitation and enhance efficacy, we propose a multi-vision monitoring (MVM) framework capable of simultaneously recognizing farm activities and assessing crop health. Our approach involves computer vision techniques that transform aerial videos into sequential images to extract essential environmental features. Central to our framework are two pivotal components: the Farmer Activity Recognition (FAR) algorithm and the Crop Image Analysis (CIA). The FAR algorithm introduces a novel feature extraction method capturing motion across various maps, enabling distinct feature sets for each activity. Meanwhile, the CIA component utilizes the normalized Triangular Greenness Index (nTGI) to estimate leave chlorophyll levels, an important indicator for crop health. By unifying these components, we achieve dual functionality—activity recognition and crop health estimation—using identical input data, thereby enhancing efficiency and versatility in farm monitoring. Our framework employs a diverse range of machine learning models, demonstrating the potential of our extracted features to address the defined problem effectively in unison.

Self-supervised learning for interventional image analytics: toward robust device trackers.

The accurate detection and tracking of devices, such as guiding catheters in live X-ray image acquisitions, are essential prerequisites for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness/no failures during tracking. To achieve this, one needs to efficiently tackle challenges, such as device obscuration by the contrast agent or other external devices or wires and changes in the field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion. To overcome the aforementioned challenges, we propose an approach to learn spatio-temporal features from a very large data cohort of over 16million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation-based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream in a light-weight model. Our approach achieves state-of-the-art performance, in particular for robustness, compared to ultra optimized reference solutions (that use multi-stage feature fusion or multi-task and flow regularization). The experiments show that our method achieves a 66.31% reduction in the maximum tracking error against the reference solutions (23.20% when flow regularization is used), achieving a success score of 97.95% at a faster inference speed of 42 frames-per-second (on GPU). In addition, we achieve a 20% reduction in the standard deviation of errors, which indicates a much more stable tracking performance. The proposed data-driven approach achieves superior performance, particularly in robustness and speed compared with the frequently used multi-modular approaches for device tracking. The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.

Multi-Object Tracking Model Based on Detection Tracking Paradigm in Panoramic Scenes

Multi-Object Tracking (MOT) technology is dedicated to continuously tracking multiple targets of interest in a sequence of images and accurately identifying their specific positions at different times. This technology is crucial in key application areas such as autonomous driving and security surveillance. However, the application process often requires the coordination of cameras from multiple angles for tracking. Directly studying Multi-Object Tracking algorithms in panoramic scenes is an effective way to address this issue. The uniqueness of panoramic scenes causes target position changes at the boundaries and tracking difficulties due to continuous changes in target scales. To ensure the accuracy of target tracking, this study explores a detection-based tracking method using the newly improved YOLOx detector and the adjusted DeepSORT algorithm. Firstly, YOLOx_s was chosen as the detector because its simple network structure ensures a fast computational speed. During the feature extraction stage, we used the Polarized Self-Attention (PSA) mechanism to capture more feature information, thereby improving the tracking performance on small-scale targets. Secondly, the tracker was improved by adding a camera motion compensation module before predicting the target’s position to mitigate the impact of camera shake on tracking. Finally, to address the difficulty of continuously tracking targets in specific areas of panoramic scenes, this study proposes specific tracking strategies. These strategies effectively resolve the problem of tracking failure caused by target position changes at the boundaries. Experimental results show that improved algorithms have a superior performance on multiple evaluation metrics compared to other algorithms in the field. Compared to the original algorithm, the improved algorithm exhibits a 6% increase in the quantitative metric MOTA, a 7% increase in IDF1, and a 40% decrease in IDSWs, demonstrating its leading performance.

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

The Current Diagnostic Performance of MRI-Based Radiomics for Glioma Grading: A Meta-Analysis.

Multiple radiomics models have been proposed for grading glioma using different algorithms, features, and sequences of magnetic resonance imaging. The research seeks to assess the present overall performance of radiomics for grading glioma. A systematic literature review of the databases Ovid MEDLINE PubMed, and Ovid EMBASE for publications published on radiomics for glioma grading between 2012 and 2023 was performed. The systematic review was carried out following the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analysis. In the meta-analysis, a total of 7654 patients from 40 articles, were assessed. R-package mada was used for modeling the joint estimates of specificity (SPE) and sensitivity (SEN). Pooled event rates across studies were performed with a random-effects meta-analysis. The heterogeneity of SPE and SEN were based on the χ2 test. Overall values for SPE and SEN in the differentiation between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were 84% and 91%, respectively. With regards to the discrimination between World Health Organization (WHO) grade 4 and WHO grade 3, the overall SPE was 81% and the SEN was 89%. The modern non-linear classifiers showed a better trend, whereas textural features tend to be the best-performing (29%) and the most used. Our findings confirm that present radiomics' diagnostic performance for glioma grading is superior in terms of SEN and SPE for the HGGs vs. LGGs discrimination task when compared to the WHO grade 4 vs. 3 task.

A two‐dimensional, reach‐scale implementation of space‐time image velocimetry (STIV) and comparison to particle image velocimetry (PIV)

AbstractImage‐based algorithms have become a powerful tool for estimating flow velocities in rivers. In this study, we generalize the space‐time image velocimetry (STIV) framework for reach‐scale application rather than along a cross section. The new algorithm provides information on both the magnitude and orientation of velocity vectors, and we refer to the algorithm as two‐dimensional STIV, or 2D‐STIV. The workflow involves setting up a grid, using centreline tangent vectors as initial estimates of flow direction, and then extracting space‐time images (STIs) along search lines radiating from each grid node. The autocorrelation function is used to infer the inclination of streak lines present in STIs, which represents the advection of water surface features. Information on flow direction is obtained by evaluating various candidate search lines and identifying that which yields the highest velocity. This search can be performed exhaustively or via optimization. We applied the new 2D‐STIV algorithm to three test cases, one simulated data set and two natural channels, and compared image‐derived velocities to modelled or measured values. We also applied two established particle image velocimetry (PIV) algorithms to the same data sets. 2D‐STIV performed as well as the two PIV algorithms for simulated images. For a natural river with distinct water surface features, 2D‐STIV was effective for much of the channel but also led to a more patchy, irregular velocity field than the two PIV algorithms. For a site lacking obvious surface features, exhaustive 2D‐STIV led to velocity estimates uncorrelated with field data while the optimization‐based version produced erratic flow directions. 2D‐STIV also required greater image sequence durations, higher frame rates, and generally longer computational run times. Overall, ensemble PIV was the most reliable algorithm.