Dynamic Sequences Research Articles

MRI Tomoelastography to Assess the Combined Status of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma.

Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking. To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC. Retrospective. One hundred sixty-eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative-VM and positive-VM groups with mild or severe-VMs. Of them, 127 patients completed the follow-up (median: 26.1 months). 3D multifrequency tomoelastography with a single-shot spin-echo echo-planar imaging sequence, and liver MRI including T1-weighted in-phase and opposed-phase gradient echo (GRE), T2-weighted turbo spin echo, diffusion-weighted imaging and dynamic contrast-enhanced T1-weighted GRE sequences at 3.0 T. Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features. Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log-rank test. P < 0.05 was considered statistically significant. Tumor-to-liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive-VM and severe-VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive-VM (0.84 [95% CI: 0.72-0.93] vs. 0.77 [95% CI: 0.64-0.88]) and severe-VM (0.86 [95% CI: 0.68-0.96] vs. 0.75 [95% CI: 0.55-0.89]). Patients with estimated positive-VM (9.3 months)/severe-VM (9.2 months) based on nomograms had shorter median recurrence-free survival than those with estimated negative-VM (>20.0 months)/mild-VM (18.0 months) in validation cohort. Tomoelastography based-nomograms showed good performance for noninvasively assessing VM status in patients with HCC. 3 TECHNICAL EFFICACY: Stage 2.

DmTGS: Precise Targeted Enrichment Long-Read Sequencing Panel for Tandem Repeat Detection.

Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility. We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods. For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization. dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.

Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images

PurposeTotal-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\ge\\:$$\\end{document}60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time.MethodsOn the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1–6 and frames 25–30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{1}$$\\end{document}, 68Ga-PSMA: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{i}$$\\end{document}) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model.ResultsBoth the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the 68Ga-PSMA group and 33.779 ± 0.760 dB for the 68Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the 68Ga-FAPI group and 0.922 ± 0.009 for the 68Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (68Ga-PSMA, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{1}$$\\end{document}) and 43.150 ± 4.102 dB (68Ga-FAPI, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{i}$$\\end{document}). The obtained SSIM values were 0.932 ± 0.041 (68Ga-PSMA) and 0.980 ± 0.011 (68Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth.ConclusionIn this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET.

A Study of Recommendation Methods Based on Graph Hybrid Neural Networks and Deep Crossing

In the face of complex user behavior patterns and massive data, improving the performance of recommender system models is an urgent challenge. Traditional methods often struggle to effectively handle feature interactions and complex user-item relationships. Combining the advantages of graph neural networks and the Deep Crossing network, this paper proposes a recommendation method based on hybrid neural networks with Deep Crossing (Deep Crossing with Graph Convolution and GRU, DCGCN-GRU). First, by constructing the graph structure of users and items, higher-order feature representations are extracted, and node features are updated using a multilayer graph convolution operation. Then, the higher-order features learned by the graph convolution network are spliced and weighted with the original features to form new feature inputs. Next, a Gated Recurrent Unit (GRU) is introduced to capture the inter-feature temporal dynamic relationships and sequence information. Finally, the Deep Crossing model is utilized to learn the interactions between the fused features at multiple levels and enhance the interactions between the features. Comparative experiments on three public datasets, MovieLens-ml-25m, Book-Crossings, and Amazon Reviews’23, show that the model achieves significant improvements in accuracy, mean square error (MSE), and mean absolute error (MAE).

Measurement variability of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify the blood-brain barrier (BBB) permeability-surface area product. Serial measurements can indicate changes in BBB health, of interest to the study of normal physiology, neurological disease, and the effect of therapeutics. We performed a scan-rescan study to inform both sample size calculation for future studies and an appropriate reference change value for patient care. The final dataset included 28 healthy individuals (mean age 53.0 years, 82% female) scanned twice with mean interval 9.9 weeks. DCE-MRI was performed at 3T using a 3D gradient echo sequence with whole brain coverage, T1 mapping using variable flip angles, and a 16-min dynamic sequence with a 3.2-s time resolution. Segmentation of white and grey matter (WM/GM) was performed using a 3D magnetization-prepared gradient echo image. The influx constant Ki was calculated using the Patlak method. The primary outcome was the within-subject coefficient of variation (CV) of Ki in both WM and GM. Ki values followed biological expectations in relation to known GM/WM differences in cerebral blood volume (CBV) and consequently vascular surface area. Subject-derived arterial input functions showed marked within-subject variability which were significantly reduced by using a venous input function (CV of area under the curve 46 vs. 12%, p < 0.001). Use of the venous input function significantly improved the CV of Ki in both WM (30 vs. 59%, p < 0.001) and GM (21 vs. 53%, p < 0.001). Further improvement was obtained using motion correction, scaling the venous input function by the artery, and using the median rather than the mean of individual voxel data. The final method gave CV of 27% and 17% in WM and GM, respectively. No further improvement was obtained by replacing the subject-derived input function by one standard population input function. CV of Ki was shown to be highly sensitive to dynamic sequence duration, with shorter measurement periods giving marked deterioration especially in WM. In conclusion, measurement variability of 3D brain DCE-MRI is sensitive to analysis method and a large precision improvement is obtained using a venous input function.

Associations between Dynamic-Contrast enhanced MRI with histopathological features in cholangiocarcinoma.

The associations between histopathology and imaging remain elusive and investigating underlying reasons of tumor microstructure resulting in an imaging phenotype is of clinical importance. The present study used cross-sectional guided biopsy specimen to employ the exact spatial biopsy localization to correlate the prebioptic magnetic resonance imaging (MRI) with immunohistochemical stainings of the histopathological specimen. Twenty-seven patients with mass-forming cholangiocarcinoma (CCA) were included in the present analysis. All patients were imaged by a 1.5 T clinical scanner at last one month before the biopsy. The contrast enhanced dynamic sequences were analyzed with quantified signal intensities. The bioptic specimen were obtained by cross-sectional guided biopsy and was further analyzed with cell density, proliferation index (Ki67), tumor-infiltrating lymphocytes, tumor-stroma ratio as well as Collagen. There were no statistically significant correlations between the signal intensities of the MRI with cell count, tumor-stroma ratio, Ki67 index and CD45 count. Only a moderate correlation was identified between relative signal intensities of the venous phase with the Collagen-stained area (r=0.40, p=0.04). DCE MRI is not associated with histopathology features in CCA. The complex interactions of the tumor and the tumor micromilieu are not reflected by the MRI phenotype.

Gravity-guided snapping sequence in 3D modular multistable metamaterials

The snapping sequence of multistable metamaterials is critical for their applications in elastic wave control and energy release. Despite being a fundamental property, the effect of gravity on the snapping sequence has never been studied. This paper investigates the mechanical mechanism how structural gravity affects the snapping sequence of multistable metamaterials to construct deterministic static and dynamic snapping sequences. A biaxial snap-through availability three-dimensional (3D) modular multistable metamaterial (MMM) is developed. The 3D MMM is assembled from unit cells consisting of a dismountable middle bar (M-bar) and a fixed frame containing two bistable curved beams. Except experimental tests and numerical simulations, analytical analyses are also conducted to verify the snapping sequence induced by gravity in the 3D MMM. In addition, given that the M-bar is dismountable, the effects of its length on the mechanical properties and the impact resistance of the 3D MMM are discussed in detail. It is found that gravity can guide both static and dynamic deterministic snapping sequences of the 3D MMM to optimize the process of elastic wave propagation and energy release, and the proposed 3D MMM can enhance structural impact resistance through elastic deformations.

Visual SLAM system based on dynamic semantic features

Aiming at the problem that dynamic objects (such as pedestrians, vehicles, and animals) in real scenes affect the positioning and mapping accuracy of visual SLAM (Simultaneous Localization and Mapping), a YOLOv3-ORB-SLAM3 algorithm is proposed based on ORB-SLAM3 (Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3). The algorithm adds a semantic thread on the basis of ORB-SLAM3, and adopts a dual-thread mechanism for dynamic and static scene feature extraction: the semantic thread uses YOLOv3 to perform semantic recognition target detection on dynamic objects in the scene, and removes outliers from the extracted dynamic area feature points; the tracking thread extracts scene area features through ORB features, and obtains static scene features combined with semantic information and sends them to the backend, thereby eliminating the interference of dynamic scenes on the system and improving the positioning accuracy of the visual SLAM algorithm. Verified using the TUM (Technical University of Munich) dataset, the results show that the ATE (Average Treatment Effect) index of the YOLOv3-ORB-SLAM3 algorithm in the monocular mode is about 30% lower than that of the ORB-SLAM3 algorithm, and the ATE index of the dynamic sequence in the RGB-D (Red, Green and Blue-Depth) mode is reduced 10%, while the static sequence has no significant decrease.

Wind Tunnel Tests of a Two-Element Wingsail with Focus on Near-Stall Aerodynamics

_ Modern wind propulsion systems on ocean-going cargo vessels require automated trimming. Rigid wingsail designs often incorporate more than one degree of freedom enabling to trim for optimal performance in all sailing conditions. A profound understanding of the aerodynamic behavior is the basis for any trimming strategy. The region around stall is of particular importance as this is where the largest lift but also potential hysteresis effects are expected. In the present research, a two-element wingsail (main wing and flap) was tested in wind tunnel experiments investigating the two available degrees of freedom: the orientation of the main wing to the inflow and the deflection of the flap. Aerodynamic loads were measured in dynamic actuation sequences over both degrees of freedom capturing stall and reattachment. The consistent data reveals two stall stages. In the region between the first and the second stall, when changing the direction of rotation, the flow is found to be reversible. The hysteresis loop is in this case smaller and reattachment occurs at larger angles compared to when both stall stages occur. This smaller hysteresis loop could be exploited in trimming. The second stall stage, however, is identified as detrimental both in the forces and the amount of required actuation to allow the flow to reattach. A considerate flap trim, i.e. the adjustment of the wingsail curvature, is suggested to avoid the second stall. Keywords Wind propulsion; Wind tunnel tests; Stall hysteresis; Two-element wing; Rigid wingsail; Trimming; Automation

Golden-angle radial sparse parallel (GRASP) magnetic resonance angiography (MRA) for endoleak evaluation after endovascular repair of the aorta: a prospective comparison to conventional time-resolved MRA.

Time-resolved angiography with interleaved stochastic trajectories (TWIST) magnetic resonance angiography (MRA) may obscure smaller vessels and is highly susceptibility to motion artifacts, potentially reducing endoleak detection accuracy after endovascular aortic repair (EVAR). The novel golden-angle radial sparse parallel (GRASP) sequence enhances spatial and temporal resolution with continuous, motion-robust datasets, showing promise for accurate endoleak detection post-EVAR. This study aimed to compare the diagnostic effectiveness of contrast-enhanced compressed-sensing radial GRASP-volume interpolated breath-hold examination (VIBE) sequence with standard contrast-enhanced dynamic TWIST-VIBE sequence in patients with inconclusive computed tomography angiography (CTA) findings regarding endoleak after EVAR. This single-center prospective study consecutively enrolled adults with inconclusive findings regarding the presence or type of endoleak in multiphasic CTA following EVAR for abdominal aortic aneurysms. Participants underwent contrast-enhanced MRA, acquiring dynamic TWIST-VIBE and GRASP-VIBE sequences. Two independent radiologists assessed the datasets for image quality, diagnostic confidence, and the presence and type of endoleak. Additionally, quantitative assessments with signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were performed. Statistical analyses included interrater and intermethod agreement, and diagnostic performance testing. Twenty participants (mean age, 72±9 years; 13 males) were included. GRASP-VIBE demonstrated superior image quality over TWIST-VIBE sequence with predominantly absent motion artifacts and increased diagnostic confidence (all P<0.001). Diagnostic performance significantly improved for detecting type II endoleaks in GRASP-VIBE compared to TWIST-VIBE scans [area under the curve (AUC): 0.96 vs. 0.73; P=0.04]. Diagnostic accuracy improved with GRASP-VIBE for overall (AUC: 0.94 vs. 0.79) and endoleak type I detection (AUC: 1.0 vs. 0.90), however, not significantly (P≥0.05). TWIST-VIBE sequences demonstrated significantly higher SNR for measurements in the clotted aneurysm sac (P=0.01). No significant differences were observed in CNR for the aorta and any aneurysm sacs across the compared imaging sequences. Compressed-sensing dynamic GRASP-VIBE sequence, with its superior image quality, diagnostic confidence, and performance, may be preferred over standard TWIST-VIBE sequence in inconclusive endoleak cases.

Stabilized distributed online mirror descent for multi-agent optimization

In the domain of multi-agent networks, distributed online mirror descent (DOMD) and distributed online dual averaging (DODA) play pivotal roles as fundamental algorithms for distributed online convex optimization. However, in contrast to DODA, DOMD fails when employed with a dynamic learning rate sequence. To bridge this gap, we introduce two novel variants of DOMD by incorporating a distributed stabilization step in primal space and dual space, respectively. We demonstrate that our stabilized DOMD algorithms achieve a sublinear bound with a sequence of dynamic learning rates. We further evolve our dual-stabilized DOMD by integrating a lazy communicated subgradient descent step, resulting in a re-indexed DODA. This establishes a connection between the two types of distributed algorithms, which enhances our understandings of distributed optimization. Moreover, we extend our proposed algorithms to handle the case of exponentiated gradient, where the iterate is constrained within a simplex probability. Finally, we conduct extensive numerical simulations to validate our theoretical analysis.

Fetal body organ T2* relaxometry at low field strength (FOREST)

Fetal Magnetic Resonance Imaging (MRI) at low field strengths is an exciting new field in both clinical and research settings. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artifacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we present the Fetal body Organ T2* RElaxometry at low field STrength (FOREST) pipeline that analyzes ten major fetal body organs. Dynamic multi-echo multi-gradient sequences were acquired and automatically reoriented to a standard plane, reconstructed into a high-resolution volume using deformable slice-to-volume reconstruction, and then automatically segmented into ten major fetal organs. We extensively validated FOREST using an inter-rater quality analysis. We then present fetal T2* body organ growth curves made from 100 control subjects from a wide gestational age range (17–40 gestational weeks) in order to investigate the relationship of T2* with gestational age. The T2* values for all organs except the stomach and spleen were found to have a relationship with gestational age (p<0.05). FOREST is robust to fetal motion, and can be used for both normal and fetuses with pathologies. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.

Temporal Graph Network for continuous-time dynamic event sequence

Continuous-Time Dynamic Graph (CTDG) methods have shown their superior ability in learning representations for dynamic graph-structured data, the methods split the sequential updating process into discrete batches to reduce the computation costs, as a result, the message constructor in existing CTDG methods cannot be optimized by gradient descent and is designed to be parameter-free. In particular, this layer fails to embed complex event subgraphs and ignores the structure information, while most real-world events are structured and complex. For example, a paper publication event in an academic graph contains different relations like authorship and citations. Furthermore, the corresponding nodes could not receive position-wise messages to make precise representation updates. To tackle this issue, we propose a new method called Temporal Graph Network for continuous-time dynamic Event sequence (TGNE) with a structure-aware message constructor to update node representation with complex event subgraph, by treating message construction and delivery as a message-passing process, in this way, the message constructor can be formalized as a graph neural network layer. TGNE extends the input of CTDG methods to subgraphs with complex structures and preserves more information in message delivery. Extensive experiments demonstrate that the proposed method can achieve competitive performance on traditional tasks on bipartite graphs and event sequence learning tasks on heterogeneous graphs.

Observe finer to select better: Learning key frame extraction via semantic coherence for dynamic facial expression recognition in the wild

In dynamic facial expression sequences captured in natural environments, the number and distribution of expression peak frames (key frames) show significant variation within video sequences. Efficient extraction and utilization of these key expression frames are crucial for improving the accuracy of expression recognition. To address the above challenges, this paper proposed the Key Frame Extraction via Semantic Coherence (KFE-SC) method. The method explores the spatial-temporal emotional semantic information within sequences, employing a coarse-to-fine approach to extract key expression frames, thus facilitating classification tasks. By introduce a Spatial Semantic Coherence (SSC) module and designing a Global-Local Feature Aggregation (GLFA) unit, the method explores the spatial semantic correlations at global-local levels in images. Through calculating weight proportions, the method achieves a coarse-grained extraction of key frames in the spatial dimension. Considering that video sequences contain both spatial and temporal semantic relevance, KFE-SC further introduces a Temporal Semantic Coherence (TSC) module, which include the Temporal Alignment Aggregation (TA2) unit to align short and long-term temporal information and explore temporal semantic relevance within the sequences, enabling fine-grained extraction of key frames in the temporal dimension through weight calculations.

Updating and calibrating the Real-World Progression In Diabetes (RAPIDS) model in a non-Veterans Affairs population.

To present the Real-World Progression In Diabetes (RAPIDS) 2.0 Risk Engine, the only simulation model to study the long-term trajectories of outcomes arising from dynamic sequences of glucose-lowering treatments in type 2 diabetes (T2DM). The RAPIDS model's risk equations were re-estimated using a Least Absolute Shrinkage and Selection Operator (LASSO)-based regularization of features that spanned baseline data from the last two quarters of current time and interactions with age. These equations were supplemented with estimates for the impact of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitor classes of drugs as monotherapies and their combinations with metformin based on newer trial data and comprehensive meta-analyses. The probabilistic RAPIDS 2.0 model was calibrated (N = 25 000) and validated (N = 263 816) using electronic medical records (EMR) data between 2008 and 2021 from a national network of US healthcare organizations. The EMR-based cohort had a mean age of 61 years at baseline, with 50% women, 70% non-Hispanic White individuals and 20% non-Hispanic Black individuals, and was followed for 17.5 quarters (range: 3-50). The final RAPIDS 2.0 risk engine accurately predicted the long-term trajectories of all nine biomarkers and nine outcomes in the hold-out validation sample. Similar accuracies in predictions were observed in each of the 14 subgroups studied. The RAPIDS 2.0 model demonstrated valid long-term predictions of outcomes in individuals with T2DM in the United States as a function of dynamic sequences of treatment use patterns. This highlights its potential to project long-term comparative effectiveness between alternative sequences of glucose-lowering treatment uses in the United States.

410. THE RELATIONSHIP BETWEEN GASTRIC CONDUIT EMPTYING, FUNCTIONAL ANATOMY AND PERISTALSIS: A DETAILED ANALYSIS USING DYNAMIC MAGNETIC RESONANCE IMAGING

Abstract Background Gastric conduit peristalsis post oesophagectomy is an important mediator of conduit emptying and consequently gastrointestinal symptoms and quality of life. The relationship between anatomical variations of the conduit, peristaltic function and transpyloric flow are incompletely defined. We aimed to analyse gastric conduit function using dynamic MRI to evaluate functional anatomy, quantify peristaltic function and measure emptying. Methods Eighteen patients with gastric conduit reconstruction post oesophagectomy and 12 controls with normal gastric anatomy were recruited. Dynamic and static sequences of MRI following ingestion of a semi solid meal were obtained. Peristalsis variables including Gastric Motor Index (GMI - Velocity x Amplitude) were measured at 4 time points throughout the MRI, and volume scans were taken at the beginning and end of the 20-minute scanning period. Nuclear Scintigraphy conduit emptying studies were obtained from 17/18 (94%) of patients. Quality of Life questionnaire data was collected. Results Nine (50%) conduits demonstrated coordinated peristalsis, 3(16.7%) had no measurable peristalsis and 6 (33.3%) had incoordinate peristalsis. Regarding conduits vs controls, there was no difference in peristaltic wave frequency (5 vs 5/90 seconds) or GMI (35.45 vs 36.5, p=0.61), but emptying was decreased (0.10% vs 15% p=0.01). Acute angulation of the conduit was associated with a slower emptying half time (444 vs 127 min, p=0.025). Conduit width and location of the pylorus relative to the hiatus made no significant difference to peristalsis or emptying. Quality of life scores were significantly better in controls compared with conduits. Conclusion Although coordinate peristalsis was present in many conduits, it did not appear to be a key mediator of conduit emptying. Conduit shape and location within the mediastinum significantly affected emptying. These data demonstrate that construction of the conduit at the index operation may be a key factor in long term function and reducing gastrointestinal symptoms. Dynamic MRI is a promising research and diagnostic tool to assess conduit function due to its capacity to accurately delineate the key elements of conduit function.

Clinical value of MR defecography: What additional knowledge is provided by the radiologist to the surgeon?

PurposeTo evaluate the clinical value of MR defecography in supporting surgical decision-making and management strategies in a consecutive series of patients. Material and methodsA consecutive series of patients with clinical suspicion of pelvic disorders who underwent MR defecography at a single university hospital from January 2021 to June 2024 were included. MR defecography was performed at rest (axial/sagittal and coronal T2-weighted sequences), during squeezing, straining, and evacuation (dynamic sagittal T2-weighted sequences). An expert surgeon assessed his satisfaction about the diagnostic information given by the prospective radiological report as: 1: insufficient, 2: sufficient, 3: good, 4: excellent. For patients who underwent surgery, the same surgeon assessed the usefulness of the MR defecography, using a score from 1 to 4 (1: discordant findings, useless exam; 2: concordant findings without new information; 3: concordant findings, useful new information; 4: concordant findings, extremely useful new information). ResultsA total of 218 patients (age 27 − 79, mean 60 ± 11; 193 females and 25 males) entered the analysis. For 211 of them (97 %), the radiologist was able to evaluate the images and determine an MR defecography-based diagnosis; in 7 patients (3 %), a diagnosis was not provided due to exam interruption for low patient’s compliance. In 195/211 patients (92 %), the radiological diagnosis matched the clinical suspect that prompted the examination. The satisfaction about the diagnostic information given by the radiological report of the 211 exams was assessed by the surgeon as follows: excellent (n = 99, 47 %), good (n = 48, 23 %), sufficient (n = 35, 16 %), and insufficient (n = 29, 14 %). Out of 211 patients, 73 (34.6 %) underwent surgery. MR defecography provided concordant findings with relevant additional information in 63 (86.3 %), concordant findings with additional information in 3 (4.1 %), concordant findings without additional information in 4 (5.5 %), and discordant findings in 3 (4.1 %). ConclusionsThe diagnostic information prospectively provided by MR defecography resulted to be good or excellent in 70 % of the patients, adding clinical value in the preoperative surgical setting in 86 % of those patients who underwent surgery.

Dynamic human–object interaction detection for feature exclusion in visual simultaneous localization and mapping (SLAM)

Visual simultaneous localization and mapping (SLAM) remains a focal point in robotics research, particularly in the realm of mobile robots. Despite the existence of robust methods such as ORBSLAM3, their effectiveness is limited in dynamic scenarios. The influence of moving entities in these scenarios poses challenges to data association, leading to compromised pose estimation accuracy. This paper proposes a novel approach that utilizes spatial reasoning to reduce the influence of dynamic entities present in an environment. Our approach, known as human–object interaction detection, identifies the dynamic nature of an object by evaluating the intersecting area between the bounding boxes of a person and the object. We tested our approach by extending the ORBSLAM3 RGB-D SLAM algorithm. Consequently, all ORB features associated with dynamic objects are filtered out from the ORBSLAM3 tracking thread. To validate our approach, we conducted evaluations on highly dynamic sequences extracted from the TUM RGB-D dataset. Our results exhibited a significant performance enhancement over ORBSLAM3. Furthermore, in comparison to other state-of-the-art research, our results remained competitive, given the simplicity of our approach.

Limited Utility of Dynamic Contrast Enhancement Imaging Sequences Within the PI-RADS v2.1 Classification Scheme: A Retrospective Cross-Sectional Study of MRI Reports.

Background/Objective: We sought to characterize the proportion of peripheral zone lesions "upgraded" within the PI-RADS v2.1 protocol using DCE imaging sequences in a large patient population undergoing multiparametric prostate MRI. Methods: A retrospective review of radiologist reports for 2742 prostate MRI exams at 2 large Alberta teaching hospitals between January 2017 and January 2022 was conducted. Prostate specific antigen (PSA), prostate volume, sequence specific and overall PI-RADS scores, and lesion positivity for DCE were collected if present in the accompanying radiology report. Further, pathology reports of biopsies of the upgraded lesions within upgraded patients were reviewed to see if upgraded lesions were deemed clinically significant by gleason score/grade group. Results: The median age was 63 years, with a median PSA and PSA density of 7.5 ng/mL and 0.13 ng/mL2 respectively. A total of 1809 lesions were reported, with 69.4% of all lesions being DCE positive. Of the lesions within the peripheral zone, 548 were overall PI-RADS 4. A total of 87/2742 (3.2%) of patients were upgraded to a PI-RADS 4 by DCE imaging. Within these patients, 65 had pathology reports available, of which 18 had a clinically significant lesion at the upgrade site. Conclusion: Contrast enhancement is only beneficial for a very small portion of patients undergoing prostate MRI. Given the invasive nature of contrast enhanced studies, potential contrast induced side effects, added imaging time, and the cost of contrast agent, routine use of contrast for prostate MRI is questioned. Further studies are necessary to determine if it should be part of routine prostate MRI imaging protocols.

DFS-DETR: Detailed-Feature-Sensitive Detector for Small Object Detection in Aerial Images Using Transformer

Object detection in aerial images plays a crucial role across diverse domains such as agriculture, environmental monitoring, and security. Aerial images present several challenges, including dense small objects, intricate backgrounds, and occlusions, necessitating robust detection algorithms. This paper addresses the critical need for accurate and efficient object detection in aerial images using a Transformer-based approach enhanced with specialized methodologies, termed DFS-DETR. The core framework leverages RT-DETR-R18, integrating the Cross Stage Partial Reparam Dilation-wise Residual Module (CSP-RDRM) to optimize feature extraction. Additionally, the introduction of the Detail-Sensitive Pyramid Network (DSPN) enhances sensitivity to local features, complemented by the Dynamic Scale Sequence Feature-Fusion Module (DSSFFM) for comprehensive multi-scale information integration. Moreover, Multi-Attention Add (MAA) is utilized to refine feature processing, which enhances the model’s capacity for understanding and representation by integrating various attention mechanisms. To improve bounding box regression, the model employs MPDIoU with normalized Wasserstein distance, which accelerates convergence. Evaluation across the VisDrone2019, AI-TOD, and NWPU VHR-10 datasets demonstrates significant improvements in the mean average precision (mAP) values: 24.1%, 24.0%, and 65.0%, respectively, surpassing RT-DETR-R18 by 2.3%, 4.8%, and 7.0%, respectively. Furthermore, the proposed method achieves real-time inference speeds. This approach can be deployed on drones to perform real-time ground detection.