3D Cube Research Articles

Diagnostic value of three-dimensional cube fluid attenuated inversion recovery imaging and its axial MIP reconstruction in multiple sclerosis

BackgroundMagnetic resonance imaging is regarded as one of the most important markers for multiple sclerosis. It can detect lesions in order to establish dissemination in time and space, which would aid in the diagnosis. Two-dimensional FLAIR is a standard sequence in MS routine imaging because it suppresses cerebrospinal fluid signal, increasing contrast between lesions and CSF and improving white matter lesion detection. Newer 3D FLAIR sequences are expected to offer even more benefits, such as improved MS lesions detection and higher resolution due to thinner slice thickness. We aimed to compare the role of 3D Cube FLAIR imaging (versus standard 2D FLAIR) in the assessment of white matter lesions in MS patients, as well as to test the convenience of using maximum intensity projection (MIP) on 3D FLAIR images for faster and easier evaluation.ResultsThis study included 160 MS patients. A 1.5 T routine brain MRI scan was performed, which included a 2D FLAIR sequence, followed by a 3D-FLAIR sequence. All images were analyzed after 3D-FLAIR images were reformatted into axial MIP images. Lesions were counted in each sequence and classified into supra-tentorial (periventricular, deep white matter, and juxta-cortical), and infra-tentorial lesions, with the relative comparison of lesions numbers on 3D-FLAIR and MIP versus 2D-FLAIR expressed as a percentage increase or decrease. 3D FLAIR can significantly improve MS lesion detection in all areas of the brain when compared with 2D FLAIR results. At 2 mm reformatting, there is no difference in MS lesion detection between sagittal 3D FLAIR and axial MIP reconstruction, implying that the MIP algorithm can be used to simplify lesion detection by reducing the number of images while maintaining the same level of reliability.Conclusion3D FLAIR sequences should be added to conventional 2D FLAIR sequences in the MRI protocol when MS is suspected.

Influence of Depositional and Diagenetic Processes on Caprock Properties of CO2 Storage Sites in the Northern North Sea, Offshore Norway

Characterization of caprock shale is critical in CO2 storage site evaluation because the caprock shale acts as a barrier for the injected buoyant CO2 plume. The properties of shales are complex and influenced by various processes; hence, it is challenging to evaluate the caprock quality. An integrated approach is therefore necessary for assessing seal integrity. In this study, we investigated the caprock properties of the Lower Jurassic Drake Formation shales from the proposed CO2 storage site Aurora (the Longship/Northern Lights CCS project), located in the Horda Platform area, offshore Norway. Wireline logs from 50 exploration wells, various 2D seismic lines, and two 3D seismic cubes were used to investigate the variations of the caprock properties. The Drake Formation was subdivided into upper and lower Drake units based on the lithological variations observed. Exhumation and thermal gradient influencing the caprock properties were also analyzed. Moreover, rock physics diagnostics were carried out, and caprock property maps were generated using the average log values to characterize the Drake Formation shales. In addiiton, pre-stack seismic-inverted properties and post-stack seismic attributes were assessed and compared to the wireline log-based analysis. The sediment source controlled at 61° N significantly influenced the depositional environment of the studied area, which later influenced the diagenetic processes and had various caprock properties. The upper and lower Drake units represent similar geomechanical properties in the Aurora area, irrespective of significant lithological variations. The Drake Formation caprock shale near the injection site shows less-ductile to less-brittle brittleness values. Based on the caprock thickness and shaliness in the Aurora injection site, Drake Formation shale might act as an effective top seal. However, the effect of injection-induced pressure changes on caprock integrity needs to be evaluated.

Exploratory Seismic Geophysical Study of the Khanuqah Structure Area

Khanuqah structure area carried as the geophysics exploration study of Jeribe Formation target, the area located in the north of Iraq within Salah Aldeen Governorate, has been used geophysical data represented by 2D seismic lines were executed as exploratory surveys of 27 seismic lines, the data interpretations used by Petrel software (version 2018) based on the required aims, which are determined to achieve an update the structural model in Jeribe Fn. as a gas reservoir.The results show the Khanaqah structure includes five domes, the general direction of the structure axis is northwest-southeast, a gradient of its slope from the northwest structurally higher to the southwestern part structurally lower, the southeastern part of the study area is an open basin that increases the level of depth lines relative to the mentioned part.Seismic sections showed at the locations of structural domes the phenomenon of inter-spacing of seismic reflectors as tuning phenomena in reservoirs within Jeribe Fn. reservoir which is a direct index of the gas presence. To ensure tracking of extensions phenomena as a director for a gas presence, the seismic interpretations used a new technique by convert 2D seismic lines to 3D seismic cube through the reflections specifications publication of the khanaqah area, that shows the concerning of gaseous presence in the Khanaouqa-1 well and its disappearance in the Khanaouqa-2 well, it indicates the relationship between the location of the two wells at the structure, since Khanaouqa-1 well is structurally shallower than the Khanaouqa-2 well, which means that the well of Khanaouqa-2 is outside the scope of the gas patch, this was confirmed by the 3D seismic cube derived that showed the phenomenon presence on the site of Khanoqa-1 and did not appear in Khanoqa-2.Due to the importance of developing the Khanuqah structure as a worthy gas field, the research recommended drilling an exploratory well at the location of dome- B, whose coordinated in the study area penetrating the Euphrates Fn. bearing in mind that Khanuqa-1 did not locate on the center site of the domes nor on the center site of the direct phenomenon as evidence of the gas existence.

Addressing geometrical attributes and seismic imaging capability of fault systems in a world-class metal endowed region: Abitibi Greenstone Belt, Canada

With an excess of 75 Moz of gold produced, the Timmins-Porcupine mining camp in the Abitibi green stone belt is the largest gold producing camp in Canada. Most of the endowment is associated with two major, east-west striking, crustal fault zones, the Porcupine Destor fault and the Pipestone fault. The seismic survey presented in this study crosses these prolific fault systems in the Matheson area, which represents the eastern extension of the Timmins-Porcupine mining camp. The survey includes two receiver lines, the east and west profiles, with unequal receiver spacing among them and seismic shots only acquired along the east profile. After careful considerations, we treated the survey as a parallel geometry and designated the east and the west processing profiles to potentially image subsurface architecture in crystalline rock environment. We considered three scenarios to process the data: the entire survey, the east, and the west profiles individually considered. A full pseudo pre-stack time migrated 3D cube of the Archean crust documents the usefulness in imaging the extent of the geological features. By integrating the 2D and 3D seismic images with the local magnetic susceptibility isosurface model, we interpreted the Porcupine Destor fault to dip shallowly to the south and the Pipestone fault as steeply-dipping/subvertical. The techniques used here can be further applied to other areas with similar dense infrastructure requiring imperfect acquisition survey geometry, including crooked lines, to produce fully 3D or pseudo seismic cubes in such areas of great exploration interest.

Chaotic Search-and-Rescue-Optimization-Based Multi-Hop Data Transmission Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have applications in several fields, such as disaster management, underwater navigation, and environment monitoring. Since the nodes in UWSNs are restricted to inbuilt batteries, the effective utilization of available energy becomes essential. Clustering and routing approaches can be employed as energy-efficient solutions for UWSNs. However, the cluster-based routing techniques developed for conventional wireless networks cannot be employed for a UWSN because of the low bandwidth, spread stay, underwater current, and error probability. To resolve these issues, this article introduces a novel chaotic search-and-rescue-optimization-based multi-hop data transmission (CSRO-MHDT) protocol for UWSNs. When using the CSRO-MHDT technique, cluster headers (CHs) are selected and clusters are prearranged, rendering a range of features, including remaining energy, intracluster distance, and intercluster detachment. Additionally, the chaotic search and rescue optimization (CSRO) algorithm is discussed, which is created by incorporating chaotic notions into the classic search and rescue optimization (SRO) algorithm. In addition, the CSRO-MHDT approach calculates a fitness function that takes residual energy, distance, and node degree into account, among other factors. A distinctive aspect of the paper is demonstrated by the development of the CSRO algorithm for route optimization, which was developed in-house. To validate the success of the CSRO-MHDT method, a sequence of tests were carried out, and the results showed the CSRO-MHDT method to have a packet delivery ratio (PDR) of 88%, whereas the energy-efficient clustering routing protocol (EECRP), the fuzzy C-means and moth–flame optimization (FCMMFO), the fuzzy scheme and particle swarm optimization (FBCPSO), the energy-efficient grid routing based on 3D cubes (EGRC), and the low-energy adaptive clustering hierarchy based on expected residual energy (LEACH-ERE) methods have reached lesser PDRs of 83%, 81%, 78%, 77%, and 75%, respectively, for 1000 rounds. The CSRO-MHDT technique resulted in higher values of number of packets received (NPR) under all rounds. For instance, with 50 rounds, the CSRO-MHDT technique attained a higher NPR of 3792%.

Hybrid Modeling of Position-Dependent Dynamics of Thin-Walled Parts Using Shell Elements for Milling Simulation

Abstract This article presents a hybrid model to update the position-dependent structural dynamic parameters of thin-walled workpieces as the metal is removed during machining. The initial workpiece is modeled by shell elements, and its full stiffness and mass matrices are used to solve the eigenvalues and mode shapes to predict the frequency response function (FRF) at a fixed location. The model is calibrated using the experimentally measured FRF, which reduces the errors contributed by the uncertainties in the material properties and damping values. The optimized finite element (FE) model is then perturbed at discrete cutting locations to obtain the updated natural frequencies and mode shapes of the part without solving the computationally prohibitive eigenvalue problem. The accuracy of the model is further improved by using either full FE solutions or experimental measurements of FRFs at a few intermediate steps which reduce the accumulated perturbation errors along the tool path. The proposed method is verified in five-axis milling of a thin-walled twisted fan blade. It is shown that using shell elements reduces the computation effort by ∼20 times compared to the conventional three-dimensional (3D) cube elements. The experimental calibration of the numerical model at a few discrete locations reduces the prediction error of natural frequencies by about 50%.

Construction of Cubic Trigonometric Curves with an Application of Curve Modelling

This paper introduces new trigonometric basis functions (TBF) in polynomial and rational form with two shape parameters (SPs). Some classical characteristics, such as the partition of unity, positivity, symmetry, CHP, local control and invariance under affine transformation properties are proven mathematically and graphically. In addition, different continuity conditions at uniform knots (UK) are proven. Some open and closed curves from TBS and trigonometric rational B-spline (TRBS) are generated to test the applicability of the suggested technique, and the influence of the shape parameter is also noted. Furthermore, various objects, such as designing an alphabet, star, butterfly, leaf and 3D cube.

Influence of coin symmetry on infinite hitting times in quantum walks

Classical random walks on finite graphs have an underrated property: a walk from any vertex can reach every other vertex in finite time, provided they are connected. Discrete-time quantum walks on finite connected graphs however, can have infinite hitting times. This phenomenon is related to graph symmetry, as previously characterized by the group of direction-preserving graph automorphisms that trivially affect the coin Hilbert space. If a graph is symmetric enough (in a particular sense) then the associated quantum walk unitary will contain eigenvectors that do not overlap a set of target vertices, for any coin flip operator. These eigenvectors span the Infinite Hitting Time (IHT) subspace. Quantum states in the IHT subspace never reach the target vertices, leading to infinite hitting times. However, this is not the whole story: the graph of the 3D cube does not satisfy this symmetry constraint, yet quantum walks on this graph with certain symmetric coins can exhibit infinite hitting times. We study the effect of coin symmetry by analyzing the group of coin-permutation symmetries (CPS): graph automorphisms that act nontrivially on the coin Hilbert space but leave the coin operator invariant. Unitaries using highly symmetric coins with large CPS groups, such as the permutation-invariant Grover coin, are associated with higher probabilities of never arriving, as a result of their larger IHT subspaces.

Halloysite nanotube-based self-healing fluorescence hydrogels in fabricating 3D cube containing UV-sensitive QR code information

With the evolution of information technology, the development of smart materials for the information storage and encryption is in urgent need. Herein, halloysite nanotubes (HNTs)-based self-healing hydrogels were facilely prepared by using aryl arylboronic acid-bearing tetraphenylethylene (TPE) as crosslinker agent, in which the HNTs were covalently bonded into the polymeric matrix. With the addition of HNTs, the obtained hydrogel (H2) shows improved compression resistance performance and self-healing property, which can be customed in certain shapes. The TPE-crosslinked hydrogels (H1 and H2) are able to emit bright blue fluorescence centering at 457 nm when exposed to 365 nm light. By holding together with 1,4-phenylenebisboronic acid-crosslinked hydrogels (H3) with non-fluorescence property, the obtained cube is able to store UV-sensitive QR code information which is potentially to be recognized by QR code scanner or other smart tools.

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.

An effective multiple-image encryption algorithm based on 3D cube and hyperchaotic map

In accordance with the principle that the cube graph can be obtained by superimposing planes, a multiple-image encryption scheme based on hyperchaotic map and 3D cube is designed in this paper. Multiple images are segmented by columns, and a regular cube is obtained by stacking multiple fixed-size planes. Each stacked plane is considered as a stacked plane of the cube along the z-axis direction. Firstly, each tangent plane is rotated by different angles in the counterclockwise direction and then DNA encoded, followed by face swap, row swap, and column swap in turn. Then, the DNA-encoded image cube is subjected to DNA addition with the chaotic cube. Finally, two DNA sequences are randomly selected for DNA mutation. The mutated cube is decoded into a digital cube by DNA and the multiple-image encryption algorithm is completed. Theoretically, the cube used in the encryption process can be infinitely large, which means that the number and the size of 2D images that can be encrypted can be unrestricted. Experimental simulations and performance tests prove that the proposed image encryption scheme is effective and secure.

Posterior probability-based hydraulic unit division and prediction: A case study

Hydraulic units (HUs) with analogous petrophysical and flow characteristics are commonly employed to describe reservoirs. However, their prediction performances would be greatly influenced if the relationship between well-logging parameters and HUs is not well revealed. In this study, the model broke through the dimensionality of the traditional model and expanded the dimensionality of the logging intersection space to three dimensions. Specifically, HUs of cored samples were divided into 5 classes based on flow zone indicators, and the Bayes theorem was used to write a VB program based on the posterior probability to determine the HU class in the 3D cube. Scheme B (RT-2 & AC & SH) exhibited good prediction performances when applied to predict the 30% cored data, with an overall accuracy rate of 90.06%, which exceeded that of the artificial neural network (80.45%). Therefore, scheme B was applied to predict HUs of un-cored wells. Then, sequential instruction simulation was conducted on HUs of inter wells in the Petrel software, based on which well section analysis was performed to identify the relationship between lithology and predicted HUs. HUs of wells with similar production time were dynamically verified based on the perforation thickness data of HUs, which were combined with the production performance data to confirm the distribution rationality of predicted HUs along the well trajectory. When planning infill wells, injection and production wells should be deployed in the same HU class to ensure good connectivity. HU#2 and HU#3 with relatively good quality occupy more than 26% (7.78% + 18.93%) of the total geological space, and they should be the main targets in the future remaining oil exploitation. More attention should be paid in the future to refine the interpretation results of well-logging parameters.

A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data

Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then applies it to reconstruct the missing parts of the same dataset. We propose two improvements to the task: a rearrangement of the data, and a new deep-learning approach. We rearrange the traces of a 2D acquisition line as 3D data cubes, sorting the traces by the source and receiver coordinates. This 3D representation offers more information about the structure of the seismic events and allows a coherent reconstruction of them. However, learning the structure of events in 3D cubes is more complicated than in 2D images while the size of the training dataset is limited. Thus, we propose a specific architecture and training strategy to take advantage of 3D data samples, while benefiting from the simplicity of 2D reconstructions. Our proposed multidirectional convolutional neural network has two parallel branches trained to perform 2D reconstructions along the vertical and horizontal directions and a small 3D part that combines their results. We use our method to reconstruct data gaps resulting from several missing shots in a benchmark synthetic and a real land dataset. Compared to a conventional 3D U-net, our network learns to reconstruct the events more accurately. Compared to 2D U-nets, our method avoids the discontinuities that arise from the 2D reconstruction of each trace of the missing shot gathers.

Implant Related Artifact Around Metallic and Bio-Integrative Screws: A CT Scan 3D Hounsfield Unit Assessment

Category:Basic Sciences/Biologics; HindfootIntroduction/Purpose:Implant-related artifact (IRA) represents an important limitation in Computed Tomography (CT) assessment of osteotomy and fracture healing, bone-implant integration, and success of fusion procedures. Metallic implants are the most used in foot and ankle surgery and are recognized as important IRA generators. Absorbable and bio-integrative (BI) implants are attractive options for bone fixation when postoperative CT imaging is likely, particularly in procedures with higher risk for nonunion. However, the literature comparing IRA with metallic and BI implants is scarce. The objective of this study was to assess the degree of IRA around metallic and BI cannulated screws. Our hypothesis was that BI implants would demonstrate significantly decreased IRA around the inserted screws.Methods:In this cadaveric/CT imaging study, 2 below-knee specimens were used. Medial displacement calcaneal osteotomy was performed through a 5-cm long lateral heel approach. Calcaneal tuberosity was displaced medially by 10mm and fixed provisionally with 2 parallel K-wires under fluoroscopic guidance. Specimens were fixed with two headless 4.0 millimeters cannulated screws, either metallic or BI. Cone-beam CT imaging of both specimens was acquired following osteotomy screw fixation (120kVp, 5.5mA, 0.25mm isotropic). Using a dedicated software, the overall dispersion of Hounsfield units (HU) in a 3D cube of 30mm edge length was assessed. Four HU lines were then traced parallel to the screws, crossing the osteotomy site. Lines 1, 2, 3 and 4 were positioned respectively: In close proximity, over, inside the cannulation and away from the implant. The HU dispersion in these lines was measured and compared between metallic and BI implanted specimens, using T-tests/Wilcoxon analysis. P-values<0.05 were considered significant.Results:When compared to the BI implant, the average HU was pronouncedly and significantly increased around metallic implants in the lines with more close proximity (Line 1), over the screw wall (Line 2) and inside the screw cannulation (Line 3), when considering both the whole line extension inside the 3D cube as well as in a selected 8mm line-segment across the calcaneal osteotomy line: Line 1 (entire, 7.26 vs -159; selected, -5 vs -249) Line 2 (entire, 4.846 vs 108; selected, 6286 vs 151.2); Line 3 (entire, 1664 vs 144; selected, -277.7 vs 198.7) selected). However, across Line 4 (away from the implant), the HU dispersion was interestingly and significantly decreased around the metallic implant (entire, -49 vs 178.5; selected, -110 vs 221), potentially as result of beam hardening artifact, concentrating HU close to the metal, and shielding the surrounding cancellous bone from being accurately reconstructed.Conclusion:In this cadaveric imaging study, we compared Implant-related artifact (IRA) around metallic and bio-integrative (BI) implants utilized to fix medial displacement calcaneal osteotomies. We found metallic implants to demonstrate significantly and pronouncedly increased HU dispersion in in close proximity with the implants and significantly decreased dispersion more distantly from the implant, shielding the surrounding cancellous bone, and potentially hindering the assessment of bone density quality and bone/osteotomy/fusion healing in its neighborhood. BI implants represent an alternative to decrease these IRA effects. Additional clinical studies are needed to confirm and expand our findings.

Imaging crustal structures through a passive seismic imaging approach in a mining area in Saxony, Germany

Abstract. We have used several flooding-induced microseismic events that occurred in an abandoned mining area to image geological structures close to the hypocentres in the vicinity of the mine. The events have been located using a migration-based localization approach. We used the recorded full waveforms of these localized microseismic events and have processed these passive source data as if they resulted from active sources at the known hypocentre location and origin time defined by the applied location approach. The imaging was then performed using a focusing 3D prestack depth migration approach for the secondary P-wave arrivals. The needed 3D migration velocity model was taken from a recent 3D active (controlled-source) seismic survey in that area. We observed several clear and pronounced reflectors in our obtained 3D seismic image cube, some of them related to a major fault zone in that area and some correlating well with information from the nearby mining activities. We compared our results to the 3D seismic image cube obtained directly from the 3D active seismic survey and have found new structures with our approach that were not known yet, probably because of their steep dips which the 3D active seismic survey had not illuminated. The location of the hypocentres at depth with respect to the illumination angles of those structures proved to be favourable in that case, and our 3D passive image complements the 3D active seismic image in an elegant way, thereby revealing new structures that cannot be imaged otherwise with surface seismic configurations alone.

Assessment of Paleocene to lower Oligocene formations and basement to estimate the potential hydrocarbon reservoirs using seismic inversion: a case study in the Upper Assam Shelf, India

The upper Assam shelf is a self-slope basin in north-east India, filled with nearly 7 km of sedimentary rocks of tertiary period with the granite basement and various oil fields along the border of the Naga thrust. The major producing fields are structural and strati-structural. The study area is placed in between the Mikir hills and Naga thrust. The objective of the study is to identify potential hydrocarbon reservoir zones in the geologically complex south upper of the Assam shelf using estimates of acoustic impedance and porosity derived by 3D post-stack seismic inversion. Well data, such as sonic velocity and density logs, from two wells (namely, KA and TE) are used in the inversion and validation of results. Inversion results are used to build a geological model in the form of acoustic impedance from which we derive 3D porosity cube which are used for hydrocarbon potential in the Paleocene to lower Oligocene sands, and the Precambrian basement. Although the amplitude maps provide an indication of potential reservoirs, the extent of these zones are much better identified in the inverted impedance maps and the corresponding estimated high-porosity zones. The analysis predicted the potential reservoir rocks in the Sylhet, Kopili and Barail formations, in which the Sylhet and Kopili appear to have good potential zones. Near the vicinity of the Naga thrust belt, the proximity of potential reservoir is predicted in the Kopili, Sylhet formation and in the fractured basement, respectively.

FEDReD

Context. The Vela complex is a region of the sky that gathers several stellar and interstellar structures in a few hundred square degrees. Aims.Gaia data now allow us to obtain a 3D view of the Vela interstellar structures through the dust extinction. Methods. We used the FEDReD (Field Extinction-Distance Relation Deconvolver) algorithm on near-infrared 2MASS data, cross-matched with the Gaia DR2 catalogue, to obtain a 3D cube of extinction density. We applied the FellWalker algorithm to this cube to locate clumps and dense structures. Results. We analysed 18 million stars over 450 deg2 to obtain the extinction density of the Vela complex from 0.5 to 8 kpc at ℓ ∈ [250° ,280° ] and b ∈ [ − 10° ,5° ]. This cube reveals the complete morphology of known structures and relations between them. In particular, we show that the Vela Molecular Ridge is more likely composed of three substructures instead of four, as suggested by the 2D densities. These substructures form the shell of a large cavity. This cavity is visually aligned with the Vela supernova remnant but located at a greater distance. We provide a catalogue of location, distance, size, and total dust content of Interstellar Medium (ISM).

EFFECT OF DIFFERING EXERCISE INTENSITIES ON THE RESPONSE TIME OF GYMNASTS AND NON-GYMNASTS IN 3D CUBE MENTAL ROTATION TASK

The purpose of the present study was to examine the effect of different levels of exercise intensity on mental rotation performance in gymnasts vs. non-gymnasts'. Forty-one participants (18 females; mean age 20.94±0.89 years, height 1.65±0.03 m, body mass58.94±5.67 kg, and 23 males; mean age 21.26±0.99 years, height 1.70±0.05 m, body mass 66.87±4.52 kg) divided into two groups (i.e., gymnasts and non-gymnast) voluntarily took part in the present study. The two groups performed a 3D cube mental rotation task at rest, and then performed the same task preceded by short bouts of intense exercise at 60%, 80%, 100% and 120% of their maximum aerobic speed (MAS). The analyses of response times showed that gymnasts performed the mental rotation task faster following bouts of intense exercise than in rest condition, especially in 60% and 80% of MAS, whereas non-gymnasts increased their response times after moderate exercises (i.e., 60 et 80 % of MAS) and stabilized their performance (i.e., equally at the rest) after intense exercises. This finding highlights the specific physical expertise as a variable that can affect the influence of exercise on cognitive processing.

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients

PurposeThe identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this process is time-consuming and prone to errors. In this paper, we investigate the use of artificial intelligence–based methods to increase the accuracy and consistency of this process.MethodsWhole-body 18F-labelled fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography/computed tomography ([18F]FDG-PET/CT) scans (Philips Gemini TF) from 134 patients were retrospectively analysed. The thorax was automatically located, and then slices were fed into a U-Net to identify candidate regions. These regions were split into overlapping 3D cubes, which were individually predicted as positive or negative using a 3D CNN. From these predictions, pathological mediastinal nodes could be identified. A second cohort of 71 patients was then acquired from a different, newer scanner (GE Discovery MI), and the performance of the model on this dataset was tested with and without transfer learning.ResultsOn the test set from the first scanner, our model achieved a sensitivity of 0.87 (95% confidence intervals [0.74, 0.94]) with 0.41 [0.22, 0.71] false positives/patient. This was comparable to the performance of an expert. Without transfer learning, on the test set from the second scanner, the corresponding results were 0.53 [0.35, 0.70] and 0.24 [0.10, 0.49], respectively. With transfer learning, these metrics were 0.88 [0.73, 0.97] and 0.69 [0.43, 1.04], respectively.ConclusionModel performance was comparable to that of an expert on data from the same scanner. With transfer learning, the model can be applied to data from a different scanner. To our knowledge it is the first study of its kind to go directly from whole-body [18F]FDG-PET/CT scans to pathological mediastinal lymph node localisation.

Combined high-resolution 3D CUBE T1-weighted imaging and non-contrast-enhanced magnetic resonance venography for evaluation of vein stenosis in May-Thurner syndrome.

To explore the feasibility of high-resolution MRI 3-dimensional (3D) CUBE T1-weighted magnetic resonance imaging (MRI) in combination with non-contrast-enhanced (NCE) magnetic resonance venography (MRV) for the assessment of lumen stenosis in May-Thurner syndrome. Twenty-nine patients underwent computed tomography venography (CTV) and high-resolution MRI-CUBE T1, and NCE MRV acquisitions. ANOVA and LSD tests were used to compare the stenosis rate and narrowest and distal diameters of the vessel lumen. There were no significant differences in the estimated stenosis rate between CTV, CUBE T1, and NCE MRV (p = 0.768). However, there were significant differences in the measured stenosis diameters of the left common iliac vein (LCIV), with CTV giving the largest mean diameter and CUBE had the smallest mean diameter (p < 0.05). The measured normal LCIV diameters did not significantly differ between MRV and CUBE (p = 0.075) but were significantly larger on CTV than on MRV and CUBE (p < 0.05). Compared with CTV, a combination of CUBE and MRV could provide an improved assessment of the degree of lumen stenosis in May-Thurner syndrome and demonstrate acute thrombosis. MRI underestimates the diameter of the vessel in comparison with CTV. MRI can be a substitute tool for Duplex ultrasound and CTV.