Image Sequences Research Articles

A Multi‐Fusion Residual Attention U‐Net Using Temporal Information for Segmentation of Left Ventricular Structures in 2D Echocardiographic Videos

ABSTRACTThe interpretation of cardiac function using echocardiography requires a high level of diagnostic proficiency and years of experience. This study proposes a multi‐fusion residual attention U‐Net, MURAU‐Net, to construct automatic segmentation for evaluating cardiac function from echocardiographic video. MURAU‐Net has two benefits: (1) Multi‐fusion network to strengthen the links between spatial features. (2) Inter‐frame links can be established to augment the temporal coherence of sequential image data, thereby enhancing its continuity. To evaluate the effectiveness of the proposed method, we performed nine‐fold cross‐validation using CAMUS dataset. Among state‐of‐the‐art methods, MURAU‐Net achieves highly competitive score, for example, Dice similarity of 0.952 (ED phase) and 0.931 (ES phase) in , 0.966 (ED phase) and 0.957 (ES phase) in , and 0.901 (ED phase) and 0.917 (ES phase) in , respectively. It also achieved the Dice similarity of 0.9313 in the EchoNet‐Dynamic dataset for the overall left ventricle segmentation. In addition, we show MURAU‐Net can accurately segment multiclass cardiac ultrasound videos and output the animation of segmentation results using the original two‐chamber cardiac ultrasound dataset MUCO.

Quantitative analysis of malignant brain tumor on MRI images

Longitudinal relaxation time (T1)- weighted, Transverse relaxation time (T2)- weighted and flair Magetic Resonance Imaging (MRI) sequences are used for the study of high grade malignant brain tumors and their surrounding edemas. This study aimed to examine the characteristic of MRI on malignant brain tumor and to evaluate the benefits of Flair and T2 (Transverse relaxation time) -weighted over the T1(Longitudinal relaxation time) -weighted sequence. A total of 25 patients of malignant brain tumors were selected for analysis including 16 women aged 1 to 75 (mean age, 38.31 years) and 9 men aged 1 to 75 (mean age, 19.5 years). All patients had the T1- weighted, T2- weighted and Flair MRI sequences. A Region based segmentation of MATLAB software was performed on each type of tumor and 3D volume was calculated by spheroid volume tool of MATLAB. For all the patients tumor volume was calculated in cubic millimeter and compared with other sequences.The Flair and T2- weighted images volumes were significantly larger than the T1 – weighted image volume. There was also statistical difference of volume occurred in MRI sequences. T2- weighted and Flair MRI sequences showed 100% more volume than T1- weighted in the age group between 51 to 75 years. Similarly, both T2-weighted and Flair MRI sequence showed 61% volume more than T1-weighted in the age groups betrwen 26 to 75 years. Our findings suggest that the volume produced by these techniques are distinct and not interchangeable. Flair and T2-weighted images proved efficient for the measurement of abnormal tissues and CSF fluid in the brain. On the other hand T1-weighted images efficiently locate the tumor in fat and white matter of the brain.

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

Interaction between shallow and deep structures: Forward-modeling kinematics sequence of a fold-and-thrust belt with double detachment

It is crucial to determine the sequence of deformation and the interaction between shallow and deep structures within the multiple detachment systems to comprehend geological processes fully in fold-and-thrust belts (FTBs). Here, we implement forward modeling to investigate the geometric, kinematic, and syntectonic sedimentary characteristics of shallow and deep structural interactions. We design four combinative patterns of geometric structures, three kinematic sequence scenarios, and two settings with different ratios of the sedimentation rate to the uplift rate to examine the influence of such settings on growth strata geometry. The most effective results of 24 groups of shallow and deep structural interactions were obtained for various combinations of fault-bend fold, detachment fold, and tectonic wedge. This study provides a sequential kinematic image of FTBs, illustrating the interaction of two sets of detachments and demonstrating how different styles of pre- and growth strata can be utilized to identify shallow and deep structure deformation processes. The geometry of pre- and growth strata observed in the models is similar to that observed in natural structures. These forward models facilitate a better understanding of the complex geometry and kinematics of the interaction between shallow and deep fault-related folds.

Reach-Scale Mapping of Surface Flow Velocities from Thermal Images Acquired by an Uncrewed Aircraft System along the Sacramento River, California, USA

An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with R2 values ranging from 0.59–0.97 across eight transects. Velocity maps derived from the thermal image sequences acquired on consecutive days during a period of steady flow were compared. These maps showed consistent spatial patterns of velocity vector magnitude and orientation, indicating that the technique is repeatable and robust. PIV of thermal imagery can yield velocity estimates in situations where natural water-surface textures or tracers are either insufficient or absent in visible imagery. Future work could be directed toward defining optimal environmental conditions, as well as limitations for mapping flow velocities based on thermal images acquired via UAS.

Thermal behaviors of coal particles in an impinging entrained-flow gasifier: Char oxidation

The thermal behaviors of high-temperature particle (HTP) and low-temperature particle (LTP) are investigated based on the bench-scale impinging entrained-flow coal-water slurry (CWS) gasification experimental platform with a modified visualization system. The size and velocity distribution of both particles, and the evolution of HTP are analyzed through the algorithmic and precise processing of the image sequences. In addition, in-situ temperature diagnosis of the particles during the reaction process were realized. The typical evolution process and the temperature of HTP in char oxidation stage are obtained. The results show that the concentration of HTP in the gasifier is greater than LTP, but the particle size is relatively small. Particles moving at the low speed (0–2 m/s) account for the largest proportion of both HTP and LTP. The char oxidation process lasts over 300 ms and can be divided into three reaction stages. During the reaction, the peak temperature at the center of the particle can reach more than 2000 K. The average temperature of the particles gradually increased, reaching a peak in reaction stage II (1500 K) followed by a gradual decrease. The particle temperature is affected by O/C and is prone to experience swelling and bubbling phenomena during char oxidation.

Reliable object tracking by multimodal hybrid feature extraction and transformer-based fusion

Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, incorporating the advantages of multiple visual modalities is a promising solution for achieving reliable object tracking. However, the existing approaches usually integrate multimodal inputs through adaptive local feature interactions, which cannot leverage the full potential of visual cues, thus resulting in insufficient feature modeling. In this study, we propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking. The MMHT model employs a hybrid backbone consisting of an artificial neural network (ANN) and a spiking neural network (SNN) to extract dominant features from different visual modalities and then uses a unified encoder to align the features across different domains. Moreover, we propose an enhanced transformer-based module to fuse multimodal features using attention mechanisms. With these methods, the MMHT model can effectively construct a multiscale and multidimensional visual feature space and achieve discriminative feature modeling. Extensive experiments demonstrate that the MMHT model exhibits competitive performance in comparison with that of other state-of-the-art methods. Overall, our results highlight the effectiveness of the MMHT model in terms of addressing the challenges faced in visual object tracking tasks.

Kalman Filter for a Particular Class of Dynamic Object Images

We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.

Model PEM water electrolyzer cell for studies of periodically alternating drainage/imbibition cycles

Investigating the counter-current two-phase flow within the anodic porous transport layer (PTL) of polymer electrolyte membrane water electrolyzers (PEMWEs) is a complex yet intriguing challenge. Until now, the gas-liquid invasion processes inside PTLs are only to a limited extent accessible under operation conditions, usually using sophisticated and expensive optical approaches, i.e., neutron imaging. We propose a model-supported experimental method with a fully operating microfluidic PEMWE, that allows to examine pore-scale oxygen-water distributions at the anode with high spatial and temporal resolution. The microfluidic cell is made of transparent Poly-Methyl-Methacrylate (PMMA), and the PTL is simplified by a quasi-2D pore network (PN) with a uniform pore-throat structure in the first preliminary study. The proposed setup is a significant advancement over previous studies, where gas was only injected at a constant flow rate from a single point. Test cases with current densities of 0.1, 1, and 2 A/cm2 and water flow rates of 1, 3, 5, and 10 ml/min were realized in the novel setup. We found periodically alternating invasion of oxygen (drainage) and water (imbibition), which were analyzed based on image sequences as well as voltage measurements. The experimental data is additionally supported by pore-scale Lattice Boltzmann (LB) and PN simulations. The preliminary results with the simplified PN structure are used to study the dominating transport mechanisms, revealing that drainage and imbibition occur simultaneously and are affected by evaporation and wetting liquid films formed in sharp pore corners. These phenomena are also expected to occur in more complex PTL structures. The preliminary results can, therefore, be regarded as an important basis for PTL studies, which are structurally more complex.

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer.

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.

Three-Dimensional Feature Tracking Study of Healthy Chinese Ventricle by Cardiac Magnetic Resonance.

Myocardial strain, as a crucial quantitative indicator of myocardial deformation, can detect the changes of cardiac function earlier than parameters such as ejection fraction (EF). It has reported that cardiac magnetic resonance(CMR) and post-processing software possess the ability to obtain the stability and repeatability strain values. Recently, the normal strain values range of people are debatable, especially in the Chinese population. Therefore, we aim to explore the ventricular characteristics and the myocardial strain values of the Chinese people by using the cardiac magnetic resonance feature tracking (CMR-FT). Additionally, we attempted to use the myocardial and chordae tendineae contours to calculate the ventricular volumes by the CMR-FT. This study may provide valuable insights into the application of CMR-FT in tracking the ventricular characteristics and myocardial strain for Chinese population, especially in suggesting an referable myocardial strain parameters of the Chinese. A total of 109 healthy Chinese individuals (age range: 18 to 58 years; 52 males and 57 females) underwent 3.0T CMR to acquire the cardiac images. The commercial post-processing software was employed to analyse the image sequence by semi-automatic processing, then the biventricular morphology (End-Diastolic Volume, EDV; EDV/Body Surface Area, EDV/BSA), function(EF; Cardiac Output, CO; Cardiac Index, CI) and strain(Radial Strain, RS; Circumferential Strain, CS; Longitudinal Strain, LS) values were obtained.The biventricular myocardial strain values were stratified according to the age and gender. The Left Ventricular( LV base, mid, apex) and myocardial strain values of three coronary artery areas were calculated based on the the strain value of LV American Heart Association(AHA) 16 segments. It was shown that the females had larger LV globe strain values compared with the males (LVGPRS: 42.0 ± 8.5 versus 33.6 ± 6.2%, P < 0.001; LVGPCS: -21.2 ± 2.1 versus - 19.7 ± 2.3%, P < 0.001; LVGPLS: -16.4 ± 2.6 versus - 14.6 ± 2.2%, P < 0.001;). Moreover, the differences in RS, CS, and LS among the LV myocardium 16 segments were obvious. However, the right ventricle (RV) strain values showed non-normal distribution in the volunteers of this research. Here, we successfully tracked the characteristics of bilateral ventricles in healthy Chinese populations through using the 3.0T CMR. We confirmed that there was a gender difference in LV Globe Strain values. In addition, we obtained strain values for each myocardial segment of the LV and different coronary artery regions based on the AHA 16 segments method, Our results also showed that the RV strain values with a non-normal distribution, and RV global strain values were not related to the gender and age. Furthermore, LVGPRS, LVGPLS, and RVGPRS were significantly correlated with BMI, CO, CI, and EDV in the Chinese population.

Ceramic Aneurysm Clip with Titanium Spring

ObjectiveTo design and evaluate ceramic aneurysm clips with integrated titanium springs, focusing on ergonomic application and precision in neurosurgical procedures. MethodsThe clip design was executed with precision using Creo Parametric 3D CAD software. It comprises a zirconia body and a titanium spring for durability and consistent tension and features a four-coil hairpin titanium spring for enhanced closing force and a ball-type head for versatile maneuverability during surgery. To assess durability, closing forces were rigorously measured using a force gauge system, comparing the ceramic clip with the standard Mizuho permanent clip over 30 open-close cycles. For MR artifact assessment, both ceramic and Yasargil clips were evaluated using a 3 Tesla magnetic resonance imaging (MRI) scanner with specific imaging sequences. ResultsThe straight type ceramic clip’s initial closing force was 1.70 N, dropping to 1.22 N after 30 cycles, indicating a retention of 72% of its initial force. In MRI, the ceramic clip displayed significantly lower measurement discrepancies compared to the titanium alloy Yasargil clip, particularly in high resolution T1 weighted images. The lowest variance was at measurement point L2, where the ceramic clip showed 3% discrepancy. Further, the ceramic clip yielded clearer images than the titanium alloy clip, particularly at the clip’s end. ConclusionsCeramic clips with titanium springs demonstrated satisfactory closing force and superior MRI compatibility, promising enhancements in surgical application and postoperative assessment.

Multiplexed sequential imaging in living cells with orthogonal fluorogenic RNA aptamer/dye pairs.

Detecting multiple targets in living cells is important in cell biology. However, multiplexed fluorescence imaging beyond two-to-three targets remains a technical challenge. Herein, we introduce a multiplexed imaging strategy, 'sequential Fluorogenic RNA Imaging-Enabled Sensor' (seqFRIES), which enables live-cell target detection via sequential rounds of imaging-and-stripping. In seqFRIES, multiple orthogonal fluorogenic RNA aptamers are genetically encoded inside cells, and then the corresponding cell membrane permeable dye molecules are added, imaged, and rapidly removed in consecutive detection cycles. As a proof-of-concept, we have identified in this study four fluorogenic RNA aptamer/dye pairs that can be used for highly orthogonal and multiplexed imaging in living bacterial and mammalian cells. After further optimizing the cellular fluorescence activation and deactivation kinetics of these RNA/dye pairs, the whole four-color semi-quantitative seqFRIES process can be completed in ∼20 min. Meanwhile, seqFRIES-mediated simultaneous detection of critical signalling molecules and mRNA targets was also achieved within individual living cells. We expect our validation of this new seqFRIES concept here will facilitate the further development and potential broad usage of these orthogonal fluorogenic RNA/dye pairs for multiplexed and dynamic live-cell imaging and cell biology studies.

Advanced image stitching method and evaluation for underwater structure inspection utilizing planar array cameras

The inspection of underwater structural health is crucial for comprehensive bridge health assessments. In underwater structure imaging, traditional methods include single-camera and binocular camera inspection. However, due to water turbidity and long working distances with a small field-of-view, obtaining clear and high-quality detection images with these methods takes much work. To address this problem, this paper presents a method for planar array image stitching based on Harris corner point extraction, utilizing the advantages of planar array cameras characterized by short working distances and wide field-of-view. The core contribution of this paper is the introduction of an innovative image sequence stitching algorithm utilizing Harris corner point extraction and the combination of the first proposed planar array cameras with the image sequence stitching algorithm, which solves the problem of long distance and small field-of-view during the underwater inspection. The image stitching method involves calibrating camera parameters with a checkerboard and stitching underwater images from planar array cameras to reveal underwater structural features. Furthermore, five quantitative evaluation metrics and the method for calculating the field-of-view loss rate are presented to evaluate and analyze the stitched images. A series of experiments were performed on concrete surfaces, aquatic and underwater, with a total field-of-view of the underwater image after stitching of 358.86 mm × 319.24 mm at a working distance of 160 mm. Five evaluation methods were used to quantitatively evaluate the quality of the stitched images and calculate the field-of-view loss rate of the images. The results indicate that the proposed method improves the ability to inspect underwater. The stitched images achieve notable metrics: an entropy of approximately 6.7, an average gradient of about 1.7, a spatial frequency of around 3.5, an edge strength of about 17, mutual information of approximately 1.2, and a field-of-view loss rate of &lt;0.1, facilitating more effective underwater structure inspection.

Monocular Depth Estimation via Self-Supervised Self-Distillation.

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.

Monitoring of industrial crystallization processes through image sequence segmentation and characterization

Monitoring of industrial crystallization processes through image sequence segmentation and characterization

Epifluorescence microscopy study of a quadruple node of triple junctions of grain boundaries in a Eu2+-decorated highly textured composite of (Cl, Br)(K, Rb) and I(K, Rb) solid solutions.

The structural nature and geometry, as well as the lattice-relative orientation, of an arrangement of crystal defects in a highly textured Eu2+-doped composite of two alkali-halide solid solutions was studied by epifluorescence microscopy (EFM) using the doping ion as a fluorochrome. A three-dimensional reconstruction and a skeleton type model, as built from a sequence of EFM images of different optical cross-sections of this arrangement, are presented. Structurally, this arrangement is a quadruple node (QN) of triple junctions of grain boundaries. The QN core geometry is that of a tetragonal tristetrahedron (TTTH), centred at the QN site, whose tetrahedron vertices and edges are on the QN triple junctions and grain boundaries, respectively, whereas the tristetrahedron tetragonal axis is nearly parallel to the lattice [001]-axis. The measured values of the angles between triple junctions and between the grain boundaries forming them are reported. The distinct chemical compositions of the composite solid solutions are discussed to be responsible, in last instance, for the tristetrahedron departure from a cubic configuration. Collaterally, certain families of translationally periodic almost-parallel (TPAP)-wall-like regions which consist of TPAP-columns of TPAP-spindle-like singularities, as well as certain zigzag arrays of columns of this like, existing into the QN grains, are reported to be observed. Three-dimensional reconstructions of typical individuals of these families and arrays as well as of their constituent parts are presented and geometrically analysed. These families and arrays are discussed to be families of tilt subboundaries, whose constituent dislocations are decorated by cylindrical second-phase europium di-halide precipitates, and regularly faceted tilt subboundaries, respectively. Crystal growing and sample preparation, composite structural characterisation by powder and single-slab X-ray diffraction (PXRD and SSXRD, respectively), microscopy and fluorescence-cube unit optics, image processing, electronic three-dimensional reconstruction and measuring methodologies, are all described in detail.

Automatic classification of normal and abnormal cell division using deep learning

In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives like the Cell Tracking Challenge driving progress in the field. Most studies focus on regular cell population videos in which cells are segmented and followed, and parental relationships annotated. However, DNA damage induced by genotoxic drugs or ionizing radiation produces additional abnormal events since it leads to behaviors like abnormal cell divisions (resulting in a number of daughters different from two) and cell death. With this in mind, we developed an automatic mitosis classifier to categorize small mitosis image sequences centered around one cell as “Normal” or “Abnormal.” These mitosis sequences were extracted from videos of cell populations exposed to varying levels of radiation that affect the cell cycle’s development. We explored several deep-learning architectures and found that a network with a ResNet50 backbone and including a Long Short-Term Memory (LSTM) layer produced the best results (mean F1-score: 0.93 ± 0.06). In the future, we plan to integrate this classifier with cell segmentation and tracking to build phylogenetic trees of the population after genomic stress.

Comparison of a Whole-Brain Contrast-Enhanced 3D TSE T1WI versus Orbits Contrast-Enhanced 2D Coronal T1WI at 3T MRI for the Detection of Optic Nerve Enhancement in Patients with Acute Loss of Visual Acuity.

MR imaging is the technique of choice for patients presenting with acute loss of visual acuity with no obvious ophthalmologic cause. The goal of our study was to compare orbits contrast-enhanced 2D coronal T1WI with a whole-brain contrast-enhanced 3D (WBCE-3D) TSE T1WI at 3T for the detection of optic nerve enhancement. This institutional review board-approved retrospective single-center study included patients presenting with acute loss of vision who underwent 3T MR imaging from November 2014 to February 2020. Two radiologists, blinded to all data, individually assessed the presence of enhancement of the optic nerve on orbits contrast-enhanced 2D T1WI and WBCE-3D T1WI separately and in random order. A McNemar test and a Cohen κ method were used for comparing the 2 MR imaging sequences. One thousand twenty-three patients (638 women and 385 men; mean age, 42 [SD, 18.3] years) were included. There was a strong concordance between WBCE-3D T1WI and orbits contrast-enhanced 2D T1WI when detecting enhancement of the optic nerve: κ = 0.87 (95% CI, 0.84-0.90). WBCE-3D T1WI was significantly more likely to detect canalicular enhancement compared with orbits contrast-enhanced 2D T1WI: 178/1023 (17.4%) versus 138/1023 (13.5%) (P < .001) and 108/1023 (10.6%) versus 90/1023 (8.8%) (P = .04), respectively. The WBCE-3D T1WI sequence detected 27/1023 (3%) instances of optic disc enhancement versus 0/1023 (0%) on orbits contrast-enhanced 2D T1WI. There were significantly fewer severe artifacts on WBCE-3D T1WI compared with orbits contrast-enhanced 2D T1WI: 68/1023 (6.6%) versus 101/1023 (9.8%) (P < .001). The median reader-reported confidence was significantly higher with coronal T1WI compared with 3D TSE T1WI: 5 (95% CI, 4-5) versus 3 (95% CI, 1-4; P < .001). Our study showed that there was a strong concordance between WBCE-3D T1WI and orbits contrast-enhanced 2D T1WI when detecting enhancement of the optic nerve in patients with acute loss of visual acuity with no obvious ophthalmologic cause. WBCE-3D T1WI demonstrated higher sensitivity and specificity in diagnosing optic neuritis, particularly in cases involving the canalicular segments.

IMG-21. HOW CAN WE BEST UTILISE HISTORICAL IMAGING DATASETS FOR RESEARCH IN PAEDIATRIC NEURO-ONCOLOGY? A REVIEW OF HISTORICAL PRE-OPERATIVE IMAGING PERFORMED FOR 92 PAEDIATRIC BRAIN TUMOUR PATIENTS WITH COMPARISON TO 2021 SIOPE GUIDELINES

Abstract BACKGROUND Utilisation of historical imaging is important for studying long-term outcomes in paediatric brain tumours often pooling data across multiple centres retrospectively over many years. Disparities in MRI sequence parameters, imaging planes, fields of view and resolution limit quantitative radiomic analysis and segmentation algorithms. We review historical imaging datasets, analysing the MRI scans performed assessing suitability for modern research techniques. METHODS Retrospective review of presenting MRI scans was performed in 92 paediatric brain tumour patients between 2006-2020. Whether imaging was performed internally at our paediatric neurosciences centre or externally at their referring hospitals was recorded. Image sequences, planes and slice thickness were compared with 2021 SIOPE guidelines for paediatric brain tumour imaging. T2 signal intensities for tumour, white and grey matter were compared for scans completed in different centres. Statistical testing was performed with T-Test and Paired T-Test. RESULTS 75% of patients had all SIOPE essential sequences and 60.9% were in the recommended planes. Only 1 patient had the desired slice thickness in all sequences recommended for 1.5T scanners. 38.5% had T1 and 27.8% had post-contrast T1 in the ≤1mm slice thickness recommended for 3T scanners. Comparison between 62 internal and 30 external scans revealed statistically significant differences in T2 signal intensities for regions of interest (ROI) within tumour, white and grey matter (p&amp;lt;0.01). Comparison of matched ROI for 12 patients imaged internally and externally demonstrated statistically significant differences in T2 signal intensities (p&amp;lt;0.05). Differences persisted despite normalisation of T2 signal intensity as ratios between tumour and the weighted mean of white and grey matter (p&amp;lt;0.01). CONCLUSIONS This study highlights the challenges of non-standardisation of imaging resolution and acquisition parameters in historical imaging. One approach to utilise these datasets is to use qualitative classifications where human experts label imaging features and encode data for future statistical or machine learning analysis.