True Location Research Articles

Stereo slit-scanning tomography of the anterior segment of the human eye

We demonstrate a new tomographic imaging device for 2D cross-sectional and 3D volumetric images of the anterior eye segment. The optical system combines optical sectioning with a stereoscopic camera setup to determine the true spatial location of the ocular structures. Moreover, we propose a calibration procedure to estimate the triangulation function that links the stereo camera pixel indices with the eye-referenced 3D spatial coordinates. The proposed instrument will image the entire anterior chamber and contiguous structures, including the corneal limbus, sclera and eyelids. Furthermore, the system can obtain corneal topography. Finally, we studied the repeatability of the instrument and its agreement with a commercial Scheimpflug imaging tomographer in a group of healthy subjects.

A dynamic solvent chamber propagation estimation framework using RNN for warm solvent injection in heterogeneous reservoirs

Warm solvent injection (WSI), injecting low-temperature solvent into formations to reduce the viscosity of heavy oil, is a clean technology for heavy oil production through reducing greenhouse gas emissions and water usage. The success of WSI operation depends on the uniform development and propagation of solvent chambers in reservoirs. However, reservoir heterogeneity stemming from shale barriers plays a detrimental role in the conformance of solvent chamber development and oil production rate. In this work, we developed a novel recurrent neural network (RNN)-based framework with the capability of efficiently tracking and estimating the solvent chamber positions in heterogeneous reservoirs based on only production time-series data. The developed estimation model utilizes the “sequence-to-sequence" mapping methodology to correlate observed production time-series sequence and solvent chamber edge sequence via a long short-term memory (LSTM) algorithm. The trained RNN models exhibit high accuracy, evidenced by the predicted dynamic solvent chamber locations match the corresponding true locations from numerical simulation, with a high coefficient of determination (R2) and a low mean squared error. Specifically, the achieved R2 values exceed 0.98 on both the training and testing data. The developed RNN-based workflow was tested via several cases from both regularly- and irregularly-shaped shale barriers, and the results were promising. The predicted solvent chambers showed strong agreement with those obtained from numerical simulations. The major benefits of this workflow include reducing computational time and saving overall monitoring and tracking costs for conventional techniques. The present work would provide a good demonstration of the capability of practical integration of machine learning methods in solving engineering problems.

Rejection of Surface Clutter in Far Sidelobes for Airborne Radars

In airborne radar, false alarms caused by surface clutter returns through antenna sidelobes need to be suppressed to detect the target of interest. Sidelobe blanking is widely used to reject false alarms. Ideally, an auxiliary antenna radiation pattern should cover entire sidelobes of the main antenna radiation pattern. However, this may not always be satisfied, especially in the case of far sidelobes (FSL) of the main antenna, for various reasons. In this work, rejection techniques for false alarms caused by surface clutter returns through FSL are proposed. The distortion of the measured angle and velocity of the surface clutter are explained, and a measurement process for estimating the true location and velocity of the clutter is derived. Subsequently, two techniques for false alarm rejection are presented. The first technique is based on the look-up table (LUT) of FSL, which is robust but not practical due to the time and cost required to build a LUT with sufficient resolution. In the second technique, false alarms are rejected when they satisfy predefined criteria derived from their common characteristics, and flight test results confirm that false alarms can be effectively rejected by applying the second technique.

Towards a Generalized Bayesian Model of Reconstructive Memory

AbstractPrior knowledge has long been known to shape reconstruction from memory. An individual stimulus from a category is often remembered to be closer to the center of that category than its true location. This effect, together with more complex memory effects that involve prior knowledge at multiple levels of abstraction, has been successfully explained by the Category Adjustment Model (CAM; Huttenlocher et al. Journal of Experimental Psychology: General, 129(2), 220, 2000) and its extensions. However, recent experimental results diverge from CAM’s predictions showing that reconstructive memory for atypical category examples is influenced by the category center less than that of typical category examples. To unify these findings, we propose a generalized Bayesian model of reconstructive memory, called the generalized CAM model (g-CAM). We demonstrate through simulations that g-CAM can account for previously known effects of reconstructive memory, while additionally capturing recent empirical findings involving atypical category examples.

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids.

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Behavioural interference at event boundaries reduces long-term memory performance in the virtual water maze task without affecting working memory performance

Narrative episodic memory of movie clips can be retroactively impaired by presenting unrelated stimuli coinciding with event boundaries. This effect has been linked with rapid hippocampal processes triggered by the offset of the event, that are alternatively related either to memory consolidation or with working memory processes. Here we tested whether this effect extended to spatial memory, the temporal specificity and extent of the interference, and its effect on working- vs long-term memory. In three computerized adaptations of the Morris Water Maze, participants learned the location of an invisible target over three trials each. A second spatial navigation task was presented either immediately after finding the target, after a 10-s delay, or no second task was presented (control condition). A recall session, in which participants indicated the learned target location with 10 ‘pin-drop’ trials for each condition, was performed after a 1-h or a 24-h break. Spatial memory was measured by the mean distance between pins and the true location. Results indicated that the immediate presentation of the second task led to worse memory performance, for both break durations, compared to the delayed condition. There was no difference in performance between the delayed presentation and the control condition. Despite this long-term memory effect, we found no difference in the rate of performance improvement during the learning session, indicating no effect of the second task on working memory. Our findings are in line with a rapid process, linked to the offset of an event, that is involved in the early stages of memory consolidation.

From Do-It-Yourself Design to Discovery: A Comprehensive Approach to Hyperspectral Imaging from Drones

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification

Groundwater Contaminant Source Identification (GCSI) is important for addressing environmental concerns. Currently, it is widely achieved using the Simulation/Optimization (S/O) method. However, the utilization of optimization techniques may cause high computation costs and parameter equifinality issues. We introduce two innovative GCSI methods, Direct Forward Machine Learning (DFML) and One-Hot Machine Learning (OHML), utilizing the classical Artificial Neuro Network (ANN) model in the machine learning field. Both new methods eliminate the need for an optimization algorithm in GCSI, thus reducing the construction effort and improving efficiency. The first method, DFML can directly estimate eight parameters, providing valuable insights into the contaminant location, release history, and aquifer properties. The second method, OHML can estimate the spatial probability distribution of the contaminant location through one-hot encoding, addressing uncertainties in the contaminant source location estimations realized by DFML.Evaluations demonstrate that both methods exhibit satisfying performances. DFML can estimate contaminant location and aquifer properties with high accuracy; and estimate the release history information with moderate accuracy. The OHML correctly assigns the higher contaminant probabilities to regions containing true contaminant locations. The combination of DFML and OHML offers a comprehensive framework. This study contributes valid methodologies to the GCSI field.

Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm

The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. This paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the adaptive simulated annealing (ASA) algorithm is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the sum of absolute values of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

The significance of multimodality approach in the management of non-lesional drug-resistant focal parietal lobe epilepsies.

Due to extensive connectivity of the parietal lobe, non-lesional drug-resistant (DRE) parietal lobe epilepsies (PLEs) are difficult to localize and often imitate other epilepsies. Therefore, patients with PLEs have low rates of seizure freedom following epilepsy surgery. Previous studies have highlighted the need to combine EEG and semiology for more accurate localization of PLEs. As sophisticated tools for localization become more available, the use of multiple different neuroimaging and neurophysiologic diagnostic tests may more readily identify PLE. We hereby report a unique case of a complex localization in a non-lesional PLE, which was initially falsely localized to frontal lobe. This case underscores the utility of voxel-based morphometry (VBM) in identifying an epileptogenic lesion on a non-lesional MRI and the significance of multimodality approach including PET, magnetoencephalopathy (MEG), interictal and ictal EEG, semiology and cortical stimulation for accurate localization of PLEs. Understanding epilepsy through multimodality approach in this fashion can help with accurate localization especially in difficulty to localize and deceptive non-lesional PLEs. PLAIN LANGUAGE SUMMARY: Parietal lobe epilepsies are hard to pinpoint in the brain and can mimic other types of epilepsy, especially when brain MRIs appear normal. As sophisticated tools for locating epilepsies in the brain become more available, using multiple diagnostic tests may help identify parietal lobe epilepsies more easily. We describe a unique case of a parietal lobe epilepsy patient with normal brain MRI whose epilepsy was initially misidentified as being in the frontal lobe. Using various advanced diagnostic tests, we accurately found the epilepsy's true location in the parietal lobe and successfully treated the patient with surgery.

Machine Learning for the Visually Impaired: Benchmarking Object Detection Models

This research paper benchmarks object detection models, a form of machine learning, to determine which algorithm would be most beneficial for the visually impaired to locate objects that are used in everyday life. As most benchmarking experiments test object detection models with a variety of objects, it is essential to test the models using images of more relevant objects to find the most suitable algorithm. The models are tested using still images from the COCO database. Pretrained models employing five of the most popular object detection algorithms are used to process the images and find each model’s detection accuracy. To simulate real life scenarios, these objects may be partially hidden or at a distance. For each image, the models return a list of detections providing the names, confidence rating, and location of each object detected. These results will be filtered to remove detections with low confidence ratings as well as detections of irrelevant objects. The remaining results are compared to the dataset of object names and locations provided by the COCO database to calculate the distance between the predicted object locations and the true location. The algorithms will be ranked based on the number of failed detections, the time taken to analyze each image, and the accuracy of each object’s determined location.

Lock Protocol : a new robust workflow for a very high first pass pulmonary vein isolation during atrial fibrillation ablation

Abstract Introduction Pulmonary vein isolation (PVI) is the gold standard for atrial fibrillation (AF) ablation. First pass PVI is often demanding when a raw geometry reconstruction is used in combination with an inaccurate automarks annotation, accounting for discontinuous or non-transmural lesions and related AF recurrences. We hypothesized that a magnetic-post processing of left atrial geometry in combination with an LSI-guided high power short duration (HPSD) automarks annotation could improve procedural efficiency. Purpose The aim of this study was to determine the impact on procedural outcomes of the new "Lock PVI protocol" during AF ablation with the new voxel mode EnSite X mapping system. Methods We included in the study consecutive patients with AF scheduled for pulmonary vein isolation (PVI) at our Departement between January 2021 and December 2022. Patients were divided in two Groups, the Lock-PVI Group and the control No Lock-PVI Group. The "Lock PVI" Group was ablated with the new Ensite X System using the new protocol, which consisted in (1) performing a road map with contact-force annotation at potential PVI sites after High Density reconstruction, (2) shaving the magnetic geometry reconstruction to unearth true 3D location of contact points, (3) Use of 3D-Automarks color-coded with a Lesion Index (LSI) target of 5.5 (anterior) and 4.5 (posterior) (4) Inter-lesion distance <6 mm (5) High Power Short Duration Approach (50 W). The No-Lock PVI was represented by patients ablated with and HPSD ablation with the previous version of the EnSite Precision mapping suit as per local standard practice. Groups were compared in term of procedural duration, radiological exposure and "first pass" isolation. Results Forty-four patients (22 "Lock PVI" and 22 controls mean age 63,3±8 years, 65,9% male, 47,7% persistent AF), for a total of 88 PVI circles were enrolled. Procedural duration was similar between approaches (121,1±43,6 vs 125,3±30,1 min; P=0,72) but RF time was shorter in the "Lock PVI" group (19,6±4 vs 29,55±9,1 min; P<0,001), although with with a longer fluoroscopy (19,5±10,1 vs 13,4±4,9 min; P=0,01). Of note, first pass isolation was significantly higher in the "Lock PVI" group: 40/44 (90,9%) vs 28/44 (63,6%) circles (P=0,002). Conclusion The new "Lock PVI" approach resulted in consistently better first-pass isolation (>90%) with less RF time when compared to standard of care PVI.Graphical AbstractLock PVI

LOCK PROTOCOL: A NEW ROBUST WORKFLOW FOR A VERY HIGH FIRST PASS PULMONARY VEIN ISOLATION DURING ATRIAL FIBRILLATION ABLATION

Abstract Introduction Pulmonary vein isolation (PVI) is the gold standard for atrial fibrillation (AF) ablation. First pass PVI is often demanding when a raw geometry reconstruction is used in combination with an inaccurate automarks annotation, accounting for discontinuous or non–transmural lesions and related AF recurrences. We hypothesized that a magnetic–post processing of left atrial geometry in combination with an LSI–guided high power short duration (HPSD) automarks annotation could improve procedural efficiency. Purpose Aim of the study was to determine the impact on procedural outcomes of the new “Lock PVI protocol” during AF ablation with the new voxel mode EnSite X mapping system. Methods We included in the study consecutive patients with AF scheduled for pulmonary vein isolation (PVI) at our Departement between January 2021 and December 2022. Patients were divided in two Groups, the Lock–PVI Group and the control No Lock–PVI Group. The “Lock PVI” Group was ablated with the new Ensite X System using the new protocol, which consisted in (1) performing a road map with contact–force annotation at potential PVI sites after High Density reconstruction, (2) shaving the magnetic geometry reconstruction to unearth true 3D location of contact points, (3) Use of 3D–Automarks color–coded with a Lesion Index (LSI) target of 5.5 (anterior) and 4.5 (posterior) (4) Inter–lesion distance £6 mm (5) High Power Short Duration Approach (50 W). The No–Lock PVI was represented by patients ablated with and HPSD ablation with the previous version of the EnSite Precision mapping suit as per local standard practice. Groups were compared in term of procedural duration, radiological exposure and “first pass” isolation. Results Forty–four patients (22 “Lock PVI” and 22 controls mean age 63,3±8 years, 65,9% male, 47,7% persistent AF), for a total of 88 PVI circles were enrolled. Procedural duration was similar between approaches (121,1±43,6 vs 125,3±30,1 min; P=0,72) but RF time was shorter in the “Lock PVI” group (19,6±4 vs 29,55±9,1 min; P<0,001), even if with a longer fluoroscopy (19,5±10,1 vs 13,4±4,9 min; P=0,01). Of note, first pass isolation was significantly higher in the “Lock PVI” group: 40/44 (90,9%) vs 28/44 (63,6%) circles (P=0,002). Conclusion The new “Lock PVI” approach allowed a consistently better first pass isolation (>90%) with less RF time when compared to standard of care PVI.

A Hypothesis on the Pillars of Hercules and their True Location

A Hypothesis on the Pillars of Hercules and their True Location

Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration

The ability to detect the absolute location of sensory stimuli can be quantified with either error-based metrics derived from single-trial localization errors or regression-based metrics derived from a linear regression of localization responses on the true stimulus locations. Here we tested the agreement between these two approaches in estimating accuracy and precision in a large sample of 188 subjects who localized auditory stimuli from different azimuthal locations. A subsample of 57 subjects was subsequently exposed to audiovisual stimuli with a consistent spatial disparity before performing the sound localization test again, allowing us to additionally test which of the different metrics best assessed correlations between the amount of crossmodal spatial recalibration and baseline localization performance. First, our findings support a distinction between accuracy and precision. Localization accuracy was mainly reflected in the overall spatial bias and was moderately correlated with precision metrics. However, in our data, the variability of single-trial localization errors (variable error in error-based metrics) and the amount by which the eccentricity of target locations was overestimated (slope in regression-based metrics) were highly correlated, suggesting that intercorrelations between individual metrics need to be carefully considered in spatial perception studies. Secondly, exposure to spatially discrepant audiovisual stimuli resulted in a shift in bias toward the side of the visual stimuli (ventriloquism aftereffect) but did not affect localization precision. The size of the aftereffect shift in bias was at least partly explainable by unspecific test repetition effects, highlighting the need to account for inter-individual baseline differences in studies of spatial learning.

Reconstruction of internal heat source in biological tissue using parallel particle swarm optimization

With the rapid development of engineering thermophysics, researches on human biological heat transfer phenomena has gradually shifted from qualitative to quantitative. The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. Differing from traditional numerical methods for solving heat conduction, this paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the parallel particle swarm optimization (PPSO) is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the absolute value of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

The nature of chronic rejection after lung transplantation: a murine orthotopic lung transplant study.

Chronic rejection is a major complication post-transplantation. Within lung transplantation, chronic rejection was considered as airway centred. Chronic Lung Allograft Dysfunction (CLAD), defined to cover all late chronic complications, makes it more difficult to understand chronic rejection from an immunological perspective. This study investigated the true nature, timing and location of chronic rejection as a whole, within mouse lung transplantation. 40 mice underwent an orthotopic left lung transplantation, were sacrificed at day 70 and evaluated by histology and in vivo µCT. For timing and location of rejection, extra grafts were sacrificed at day 7, 35, 56 and investigated by ex vivo µCT or single cell RNA (scRNA) profiling. Chronic rejection originated as innate inflammation around small arteries evolving toward adaptive organization with subsequent end-arterial fibrosis and obliterans. Subsequently, venous and pleural infiltration appeared, followed by airway related bronchiolar folding and rarely bronchiolitis obliterans was observed. Ex vivo µCT and scRNA profiling validated the time, location and sequence of events with endothelial destruction and activation as primary onset. Against the current belief, chronic rejection in lung transplantation may start as an arterial response, followed by responses in venules, pleura, and, only in the late stage, bronchioles, as may be seen in some but not all patients with CLAD.

Mapping the global geography of cybercrime with the World Cybercrime Index.

Cybercrime is a major challenge facing the world, with estimated costs ranging from the hundreds of millions to the trillions. Despite the threat it poses, cybercrime is somewhat an invisible phenomenon. In carrying out their virtual attacks, offenders often mask their physical locations by hiding behind online nicknames and technical protections. This means technical data are not well suited to establishing the true location of offenders and scholarly knowledge of cybercrime geography is limited. This paper proposes a solution: an expert survey. From March to October 2021 we invited leading experts in cybercrime intelligence/investigations from across the world to participate in an anonymized online survey on the geographical location of cybercrime offenders. The survey asked participants to consider five major categories of cybercrime, nominate the countries that they consider to be the most significant sources of each of these types of cybercrimes, and then rank each nominated country according to the impact, professionalism, and technical skill of its offenders. The outcome of the survey is the World Cybercrime Index, a global metric of cybercriminality organised around five types of cybercrime. The results indicate that a relatively small number of countries house the greatest cybercriminal threats. These findings partially remove the veil of anonymity around cybercriminal offenders, may aid law enforcement and policymakers in fighting this threat, and contribute to the study of cybercrime as a local phenomenon.

Morphologic prognostic factor for thoracoabdominal aortic dilation after acute type A dissection repair.

Risk factors for late-term aortic dilation after acute type A aortic dissection repair have not been well examined. The goal of this study was to determine the relationship between the abdominal aortic true lumen location and thoraco-abdominal aortic dilation after surgical repair for acute type A aortic dissection. Patients who were preoperatively diagnosed with acute type A aortic dissection between April 2014 and July 2022 were included in this study. We evaluated the renal artery-level dissected aortic morphology and classified the study population into 2 groups: the ventral (those with the true lumen located on the ventral side) and the dorsal (other patients not assigned to the ventral group) groups, based on the location of the true lumen. Aortic dilation was defined as thoraco-abdominal aortic expansion ≥5 mm on 1-year postoperative computed tomography images. We examined 49 surgical patients who were assigned to the ventral (n = 22) and dorsal (n = 27) groups. The number of patients with ≥5 mm thoraco-abdominal aortic dilation after the operation was significantly higher in the ventral group than in the dorsal group (90.9% vs 51.9%, P = 0.009). The multivariable logistic regression analysis showed that the ventral type was an independent prognostic factor for thoraco-abdominal aortic dilation after the operation (odds ratio, 6.01; 95% confidence interval, 1.56-23.77; P = 0.009). The location of the true lumen of the abdominal aorta in acute type A aortic dissection may be a prognostic factor for thoraco-abdominal aortic dilation after surgical repair.