Standard Trajectory Research Articles

Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study.

Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 (COVID-19) pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee's experiences using existing tools. This study aims to demonstrate a feasible and efficient method to capture electronic health record (EHR) data that measure the volume and variety of pediatric resident clinical experiences from a continuity clinic; generate individual-, class-, and graduate-level benchmark data; and create a visualization for learners to quickly identify gaps in clinical experiences. This pilot was conducted in a large, urban pediatric residency program from 2016 to 2022. Through consensus, 5 pediatric faculty identified diagnostic groups that pediatric residents should see to be competent in outpatient pediatrics. Information technology consultants used International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding with each diagnostic group to extract EHR patient encounter data as an indicator of exposure to the specific diagnosis. The frequency (volume) and diagnosis types (variety) seen by active residents (classes of 2020-2022) were compared with class and graduated resident (classes of 2016-2019) averages. These data were converted to percentages and translated to a radar chart visualization for residents to quickly compare their current clinical experiences with peers and graduates. Residents were surveyed on the use of these data and the visualization to identify training gaps. Patient encounter data about clinical experiences for 102 residents (N=52 graduates) were extracted. Active residents (n=50) received data reports with radar graphs biannually: 3 for the classes of 2020 and 2021 and 2 for the class of 2022. Radar charts distinctly demonstrated gaps in diagnoses exposure compared with classmates and graduates. Residents found the visualization useful in setting clinical and learning goals. This pilot describes an innovative method of capturing and presenting data about resident clinical experiences, compared with peer and graduate benchmarks, to identify learning gaps that may result from disruptions or modifications in medical training. This methodology can be aggregated across specialties and institutions and potentially inform competence-based medical education.

Source-detector trajectory optimization for FOV extension in dental CBCT imaging

In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

Describing Kepler Orbits with the Ulianov Orbital Model

This paper presents the Ulianov Orbital Model (UOM), a simplified approach to two-body orbital mechanics. The UOM provides equations to calculate the standard ellipse parameters (a and b) and orbital trajectories and velocities from three UOM basic parameters (Ue, R0, and V0). It introduces a new kind of elliptical trigonometric functions, which simplify plotting orbital trajectories and their velocities over time and in elliptical angular steps. The Ulianov Elliptic Transform (UET), generates an impressive effect of rotating and scaling an ellipse, transferring its center from one of the foci to the geometric center of the ellipse. The UET offers a new and easy way to create and manipulate ellipses using both numerical and analytical methods.

Constraints on the early luminosity history of the Sun: applications to the Faint Young Sun problem

ABSTRACT Stellar evolution theory predicts that the Sun was fainter in the past, which can pose difficulties for understanding Earth’s climate history. One proposed solution to this Faint Young Sun (FYS) problem is a more luminous Sun in the past. In this paper, we address the robustness of the solar luminosity history using the yrec code to compute solar models including rotation, magnetized winds, and the associated mass-loss. We present detailed solar models, including their evolutionary history, which are in excellent agreement with solar observables. Consistent with prior standard models, we infer a high solar metal content. We provide predicted X-ray luminosities and rotation histories for usage in climate reconstructions and activity studies. We find that the Sun’s luminosity deviates from the standard solar model trajectory by at most 0.5 per cent during the Archean (corresponding to a radiative forcing of 0.849 W m$^{-2}$). The total mass-loss experienced by solar models is modest because of strong feedback between mass and angular momentum loss. We find a maximum mass-loss of $1.35 \times 10^{-3} \,{\rm M}_\odot$ since birth, at or below the level predicted by empirical estimates. The associated maximum luminosity increase falls well short of the level necessary to solve the FYS problem. We present compilations of paleotemperature and CO$_2$ reconstructions. One-dimensional ‘inverse’ climate models demonstrate a mismatch between the solar constant needed to reach high temperatures (e.g. 60–80 $^{\circ }$C) and the narrow range of plausible solar luminosities determined in this study. Maintaining a temperate Earth, however, is plausible given these conditions.

The Effectiveness of Artificial Intelligence-based Pedicle Screw Trajectory Planning in Patients With Different Levels of Bone Mineral Density.

Retrospective cohort study. To evaluate the effectiveness of pedicle screw trajectory planning based on artificial intelligence (AI) software in patients with different levels of bone mineral density (BMD). AI-based pedicle screw trajectory planning has potential to improve pullout force (POF) of screws. However, there is currently no literature investigating the efficacy of AI-based pedicle screw trajectory planning in patients with different levels of BMD. The patients were divided into 5 groups (group A-E) according to their BMD. The AI software utilizes lumbar spine CT data to perform screw trajectory planning and simulate AO screw trajectories for bilateral L3-5 vertebral bodies. Both screw trajectories were subdivided into unicortical and bicortical modes. The AI software automatically calculating the POF and pullout risk of every screw trajectory. The POF and risk of screw pullout for AI-planned screw trajectories and AO standard trajectories were compared and analyzed. Forty-three patients were included. For the screw sizes, AI-planned screws were greater in diameter and length than those of AO screws (P<0.05). In groups B-E, the AI unicortical trajectories had a POF of over 200N higher than that of AO unicortical trajectories. POF was higher in all groups for the AI bicortical screw trajectories compared with the AO bicortical screw trajectories (P<0.05). AI unicortical trajectories in groups B-E had a lower risk of screw pullout compared with that of AO unicortical trajectories (P<0.05). AI unicortical screw trajectory planning for lumbar surgery in patients with BMD of 40-120mg/cm3 can significantly improve screw POF and reduce the risk of screw pullout.

Effect of the cone-beam CT acquisition trajectory on image quality in spine surgery: experimental cadaver study

BACKGROUNDIntraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. PURPOSEThe objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. STUDY DESIGNExperimental cadaver study. METHODSA human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (−30° to +30°) and swivel (−25° to +25°). Subsequently, radiological evaluation was performed by 3 blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1–5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. RESULTSThe angulated acquisition trajectory significantly increased the score for subjective image quality (p<.001) as well as the clinical assessability of pedicle screw position (p<.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<.001) as well as clinical assessability of pedicle screw position (p<.001). CONCLUSIONSIn this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. CLINICAL SIGNIFICANCEThe knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery.

Machine learning for exoplanet detection in high-contrast spectroscopy: Combining cross-correlation maps and deep learning on medium-resolution integral-field spectra

The advent of high-contrast imaging instruments combined with medium-resolution spectrographs allows spectral and temporal dimensions to be combined with spatial dimensions to detect and potentially characterize exoplanets with higher sensitivity. We developed a new method to effectively leverage the spectral and spatial dimensions in integral-field spectroscopy (IFS) datasets using a supervised deep-learning algorithm to improve the detection sensitivity to high-contrast exoplanets. We began by applying a data transform whereby the four-dimensional (two spatial dimensions, one spectral dimension, and one temporal dimension) IFS datasets are replaced by four-dimensional cross-correlation coefficient tensors obtained by cross-correlating our data with young gas giant spectral template spectra. Thus, the spectral dimension is replaced by a radial velocity dimension and the rest of the dimensions are retained `as is'. This transformed data is then used to train machine learning (ML) algorithms. We trained a 2D convolutional neural network with temporally averaged spectral cubes as input, and a convolutional long short-term memory memory network that uses the temporal data as well. We compared these two models with a purely statistical (non-ML) exoplanet detection algorithm, which we developed specifically for four-dimensional datasets, based on the concept of the standardized trajectory intensity mean (STIM) map. We tested our algorithms on simulated young gas giant s inserted into a SINFONI dataset that contains no known exoplanet, and explored the sensitivity of algorithms to detect these exoplanets at contrasts ranging from $10^ $ to $10^ $ for different radial separations. We quantify the relative sensitivity of the algorithms by using modified receiver operating characteristic curves (mROCs). We discovered that the ML algorithms produce fewer false positives and have a higher true positive rate than the STIM-based algorithm. We also show that the true positive rate of ML algorithms is less impacted by changing radial separation than the STIM-based algorithm. Finally, we show that preserving the velocity dimension of the cross-correlation coefficients in the training and inference plays an important role in ML algorithms being more sensitive to the simulated young gas giant s. black In this paper we demonstrate that ML techniques have the potential to improve the detection limits and reduce false positives for directly imaged planets in IFS datasets, after transforming the spectral dimension into a radial velocity dimension through a cross-correlation operation and that the presence of the temporal dimension does not lead to increased sensitivity.

VegaEdge: Edge AI confluence for real-time IoT-applications in highway safety

Traditional highway safety and monitoring solutions, reliant on surveillance cameras, face limitations due to their dependence on high-speed internet connectivity and the remote processing of Artificial Intelligence (AI) algorithms. This reliance introduces latency, undermining the real-time detection and analysis crucial for highway applications. The fusion of AI with the Internet of Things (IoT) opens new avenues for highway safety and surveillance innovation. Yet, most existing solutions are confined to vehicle detection and tracking, hindered by edge-IoT platforms’ limited power and processing capabilities. Addressing these limitations, this paper presents VegaEdge, an AI framework optimized for edge-IoT devices capable of real-time vehicle detection and tracking, trajectory forecasting, and identifying anomalous driving behaviors, such as road departures, sudden stops, and hazardous merges. A novel lightweight anomaly detection algorithm based on trajectory prediction is used for identifying hazardous driving on highways. VegaEdge demonstrates its versatility and efficiency across various traffic conditions and roadway configurations and has been evaluated on platforms like the Nvidia Jetson Orin and Xavier NX. The Nvidia Jetson Orin processes up to 738 trajectories per second and detects up to 140 vehicles in a single frame. Additionally, the Carolinas Anomaly Dataset (CAD) an extension of the Carolinas Highway Dataset (CHD) is introduced. While CHD consists of standard highway vehicle videos and trajectories, CAD includes video data of anomalous driving behaviors, providing a crucial resource for enhancing anomaly detection algorithms. CAD is available at https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set.

Scan Time Reduction by Fewer Projections - an Approach for Circular and Spherical Trajectories

For most computed tomography scans a circular or helical trajectory is used to acquire the necessary radiographic projections. However, these standard trajectories are not necessarily the best choice since the shape of the inspected object is not considered which leads to a suboptimal sampling of the part. In contrast, workpiece-specific and more efficient scan strategies hold the potential for time and cost savings. Such adaptions might be of particular interest for future robotic CT systems that allow greater flexibility in the positioning of X-ray tube, object, and detector than conventional setups or multi-source applications with predefined positions by the setup. We propose a scan planning algorithm to adapt the acquisition trajectory considering the shape of the investigated part. For this task, we present an algorithm that is able to identify the projections from a set of projections that contribute most to the reconstruction of the object. As input information, only the localization of the area of interest and projections from the feasible range of motion are necessary. Also, a reasonable amount of projections to pick from needs to be provided, which can originate from an earlier scan or computer simulations that consider the range of motion of the available CT setup. The optimization method has been implemented for circular and spherical trajectories and can be extended towards arbitrary acquisition paths. Since for modern CT systems the duration for the scan itself takes significantly longer than the reconstruction, this approach appears to be well-suited to significantly reduce the throughput time if several identical parts need to be inspected. Additionally, new applications such as inline CT are conceivable and certain artifacts can be suppressed if spherical trajectories are used instead of circular ones. For the subsequent reconstruction itself no previous knowledge of the object is included, i.e. the reconstruction workflow does not need to be modified. We demonstrate the capabilities of the optimization method for two industrial relevant examples and show that a scan time reduction of more than 50 % is feasible for some cases by reducing the number of acquired projections. Furthermore, for all investigated projection numbers and parts the optimized trajectory yields an identical to clearly improved image quality in the reconstructed volume than the reference trajectories.

Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks

In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each axis during each operation may vary. There may even be out-of-control situations where the robot does not run according to the set welding trajectory, which may cause the robot and equipment to collide and be damaged. Therefore, a real-time judgment method for the welding robot trajectory is proposed. Firstly, multiple sets of axis data are obtained by running the welding robot, and the phase of the data is aligned by using a proposed algorithm, and then the Kendall correlation coefficient is used to identify and remove weak axis data. Secondly, the mean of multiple sets of axis data with strong correlation is calculated as the standard trajectory, and the trajectory threshold of the robot is set using the μ ± nσ method based on the trajectory deviation judgment sensitivity. Finally, the absolute difference between the real-time axis trajectory and the standard trajectory is used to determine the deviation of the running trajectory. When the deviation reaches the threshold, a forewarning starts. When the deviation exceeds the threshold + σ, the robot is stopped. Take the six-axis welding robot as an example, by collecting the axis data of the robot running multiple times under the same conditions, it is proved that the proposed method can accurately warn the deviation of the running trajectory. The research results have important practical value for the prevention of welding robot accidents in industrial production.

Standard Procedure-Guided Flight Trajectory Pattern Mining for Airport Terminal Airspace

Standard Procedure-Guided Flight Trajectory Pattern Mining for Airport Terminal Airspace

Does Neighborhood Socioeconomic Status Alter Memory Change Associated with a Cancer Diagnosis? Preliminary Evidence from the US Health and Retirement Study.

Neighborhood disadvantage has been linked to cognitive impairment, but little is known about the effect of neighborhood disadvantage on long-term cancer-related memory decline. Incident cancer diagnosis and memory (immediate and delayed recall, combined with proxy-reported memory) were assessed at biennial interviews in the US Health and Retirement Study (N = 13,293, 1998-2016). Neighborhood disadvantage was measured using the National Neighborhood Data Archive disadvantage index, categorized into tertiles (T1: least disadvantaged-T3: most disadvantaged). Linear mixed-effects models estimated the standardized memory trajectories in participants with or without cancer, by neighborhood disadvantage. Living in more disadvantaged neighborhoods was associated with worse mean memory function and steeper memory declines, regardless of cancer status. An incident cancer diagnosis was associated with an acute memory drop for those living in least disadvantaged neighborhoods but not more disadvantaged neighborhoods [T1: -0.05, 95% confidence interval (CI): -0.08, -0.01; T3: -0.13, 95% CI: -0.06, 0.03]. Cancer survivors in the least disadvantaged neighborhoods had a slight memory advantage in the years prior to diagnosis (T1: 0.09, 95% CI: 0.04, 0.13) and after diagnosis (T1: 0.07, 95% CI: 0.01, 0.13). An incident cancer diagnosis among those living in the least disadvantaged neighborhoods was associated with an acute memory drop at the time of diagnosis and a long-term memory advantage before and after diagnosis compared with cancer-free individuals in similar neighborhoods. These findings could inform interventions to promote cancer survivor's long-term aging. Future studies should investigate the social and biological pathways through which neighborhood socioeconomic status could influence cancer-related memory changes.

Dwell Times, Wavepacket Dynamics, and Quantum Trajectories for Particles with Spin 1/2.

The theoretical connections between quantum trajectories and quantum dwell times, previously explored in the context of 1D time-independent stationary scattering applications, are here generalized for multidimensional time-dependent wavepacket applications for particles with spin 1/2. In addition to dwell times, trajectory-based dwell time distributions are also developed, and compared with previous distributions based on the dwell time operator and the flux-flux correlation function. Dwell time distributions are of interest, in part because they may be of experimental relevance. In addition to standard unipolar quantum trajectories, bipolar quantum trajectories are also considered, and found to relate more directly to the dwell time (and other quantum time) quantities of greatest relevance for scattering applications. Detailed calculations are performed for a benchmark 3D spin-1/2 particle application, considered previously in the context of computing quantum arrival times.

Trajectories of school refusal: sequence analysis using retrospective parent reports.

School refusal (SR) is a form of school attendance problem (SAP) that requires specific mental health care. Despite improvements in the definition of SAPs, the course of SR is not well characterized. To explore three-year patterns of SR course in children, as reported by their parents, we deployed an anonymous web-based survey. We defined SR onset as the absence of ≥ 2 school weeks during one academic year, combined with emotional distress. We defined standard SR trajectories using sequence analysis of parents' recollection of three consecutive years of school attendance. We obtained 1970 responses, 1328 (67%) completed by a parent and meeting the definition of SR. Of these, 729 (55%) responses included three years of school attendance recollection. We identified five prototypical trajectories of SR: two profiles for children: beaded absences (n = 272), and rapid recovery (n = 132); and three for adolescents: prolonged recovery (n = 93), gradual decline (n = 89), and rapid decline (n = 143). We found five distinct trajectories of retrospective recall of SR course. Through pattern recognition, this typology could help with timely identification of SR and implementation of evidence-based interventions to optimize outcomes. Prospective replication of these findings and their field application is warranted.

X-ray beam diagnostics at the MID instrument of the European X-ray Free-Electron Laser Facility.

The Materials Imaging and Dynamics (MID) instrument at the European X-ray Free-Electron Laser Facility (EuXFEL) is equipped with a multipurpose diagnostic end-station (DES) at the end of the instrument. The imager unit in DES is a key tool for aligning the beam to a standard trajectory and for adjusting optical elements such as focusing lenses or the split-and-delay line. Furthermore, the DES features a bent-diamond-crystal spectrometer to disperse the spectrum of the direct beam to a line detector. This enables pulse-resolved characterization of the EuXFEL spectrum to provide X-ray energy calibration, and the spectrometer is particularly useful in commissioning special modes of the accelerator. Together with diamond-based intensity monitors, the imager and spectrometer form the DES unit which also contains a heavy-duty beamstop at the end of the MID instrument. Here, we describe the setup in detail and provide exemplary beam diagnostic results.

Multiple Leap Maneuver Trajectory Design and Tracking Method Based on Prescribed Performance Control during the Gliding Phase of Vehicles

A novel standard trajectory design and tracking guidance used in the multiple active leap maneuver mode for hypersonic glide vehicles (HGVs) is proposed in this paper. First, the dynamic equation and multiconstraint model are first established in the flight path coordinate system. Second, the reference drag acceleration-normalized energy (D-e) profile of the multiple active leap maneuver mode is quickly determined by the Newton iterative algorithm with a single design parameter. The range to go error is corrected by the drag acceleration profile update algorithm, and the drag acceleration error of the gliding terminal is corrected by the aerodynamic parameter estimation algorithm. Then, the reference drag acceleration tracking guidance law is designed based on the prescribed performance control method. Finally, the CAV-L vehicle model is used for numerical simulation. The results show that the proposed method can satisfy the design requirements of drag acceleration under multiple active leap maneuver modes, and the reference drag acceleration can be tracked precisely. The adaptability and robustness of the proposed method are verified by the Monte Carlo simulations under various combined deviation conditions.

A Study on Determination of Motor Data of a Base-Bleed Projectile based on Standard Ballistic Model

In this study, the methodology of determination of base bleed motor data for base bleed projectile based on the NATO standard trajectory model, especially STANAG 4355 Method 2 were presented. Ground combustion experiments and aerodynamic performance firing tests were conducted to determine the drag reduction motor data of the base bleed projectile and this data was described based on the NATO standard ballistic model. The derived drag reduction motor data were input into the ballistic equations to complete the ballistic model and it was confirmed that the calculated predicted trajectory from the ballistic model matched well with the measured trajectory from the aerodynamic performance firing tests.

Road traffic noise exposure assessment based on spatiotemporal data fusion

The current static assessment methods for road traffic noise exposure have limitations in representing spatiotemporal variations and only account for the noise exposure of registered residents. To address these issues, this study proposes an assessment method by fusing spatiotemporal noise and population distributions. This approach estimates spatiotemporal noise distributions through real-time noise monitoring data and an update model of noise source intensity calibrated by historical noise monitoring data and road segment speed data. Meanwhile, spatiotemporal population distributions are generated by aggregating standardized spatiotemporal trajectories of users extracted from mobile phone data. The proposed method is applied to evaluate the population exposure to traffic noise in a Chinese city in different hourly periods. The results demonstrate that this method is a quantitative tool for assessing road traffic noise exposure dynamically, providing policymakers with valuable information to locate hotspots of traffic noise over short periods of time.

An innovative method for calculating partial molar volume from a standard molecular trajectory

In this research, we present a novel method for calculating the partial molar volume (PMV) of system components through post-processing the output trajectory of a molecular dynamic simulation. The method involves employing linear regression and sampling small volumes from the system during the simulation time, to obtaining the PMV of its components. To validate our approach, simulations were conducted on two systems previously studied in the Journal of Molecular Liquids (386, 2023, 122498). A comparison between our results and experimental values revealed average relative deviations as minimal as 2.49 %, and specific sampling methods achieved even lower relative deviations, down to 1.21 % and 1.18 %. The average relative deviation across all components for the two systems was calculated at 7.24 %, translating to average deviations of 6.19 cm^3/mol for mesitylene and 7.80 cm^3/mol for isopropanol. To demonstrate the method's implications, we conducted simulations on a series of systems comprising model asphaltene, an organic solvent, and a chemical inhibitor. By analyzing the PMV results for the precipitant phase (asphaltene and inhibitor), we offer insights into the inhibition mechanism, showcasing how it enhances the solubility of the precipitants in the employed solvents. Finally, we aim to hypothesize a molecular-level explanation of the inhibition process through quantitative measures by comparing the size and number of nanoaggregates and exploring the molecular positioning within these aggregates. Our method introduces a valuable approach to calculate PMVs to comprehend the behavior of self-associating solutes and their interactions with solvents.

Challenging Cortical Explorations in Difficult-to-Localize Seizures: The Rationale and Usefulness of Perisylvian Paralimbic Explorations With Orthogonal Stereoelectroencephalography Depth Electrodes.

Stereoelectroencephalography (SEEG) is an invasive monitoring method designed to define and localize the epileptogenic zone (EZ) and explore the putative network responsible for the electroclinical seizures using anatomo-functional-electroclinical correlations. When indicated by semiology in selected patients, exploration of both limbic and paralimbic (PL) regions is indispensable. However, the PL cortex is located in deep and highly vascularized areas in proximity to the anterior Sylvian fissure and middle cerebral artery branches. Thus, those explorations are considered surgically challenging because of the multilobar location and fear of hemorrhagic events. Here, we discuss and illustrate the rationale and SEEG methodology approach in usefulness of exploring the PL regions using standard orthogonal SEEG depth electrode trajectories with the Talariach reference system. We retrospectively studied PL exploration from a cohort of 71 consecutive SEEG procedures from Nov 2019 to Nov 2022 and identified 31 patients who underwent PL trajectories. In 31 patients, there were 32 SEEG trajectories, and no major complications were observed. PL electrodes were consistently implanted in the C10/D10 coordinates of the Talariach reference coordinates. The most common confirmed EZ in our cohort was mesio-temporal (45%), followed by temporo-perisylvian regions (16%), ventromedial frontal (13%), and mesio-lateral temporal regions (13%). The PL contacts were involved in the EZ in 10 patients (32%). Of 31 patients, 25 underwent resective surgery, and 19 obtained Engel 1 outcome with a mean follow-up of 25 months (range 12-41 months) after surgery. The orthogonal perisylvian PL trajectories are feasible and useful in sampling multiple PL regions with single-electrode trajectories. In patients with perisylvian seizures, sampling PL structures may contribute to an improved understanding of seizure propagation and the optimal anatomic demarcation of the EZs in this surgically challenging region.