Multiple Scans Research Articles

Osseointegration and Bone Resorption of Scapula Tip Free Flaps in Mandibular Reconstruction.

Bone resorption of more conventional vascularized bone grafts have been well described showing minimal resorption over time. Few studies have evaluated osseous union and bone resorption in scapula tip free flaps (STFF) in the reconstruction of mandibulectomy defects. We aimed to describe our series on STFF with respect to osseous union and bone resorption over time. Retrospective chart review of patients receiving STFF from January 2014-January 2017 (n=25). A neuroradiologist analyzed follow-up CT scans to assess (1) STFF complete, partial, or no osseous union with native mandible and (2) STFF volume change over time in a subset with multiple follow-up scans (n=18). Twenty-three of 25 patients (92%) showed complete or partial STFF osseous union with native mandible either distally or proximally. STFF volume change ranged from +4.8 to -54% (median -0.5%) over median follow-up interval of 23 months. History of chemoradiation therapy, bisphophonate use, sex, age, or smoking history did not correlate with bone resorption. STFFs shows high rates of osseous union and limited bone resorption that is equivalent to, or less than, vascularized fibular and iliac crest flaps. Clinically, this translates into both optimal healing andfunctional and cosmetic outcomes, especiallyin the setting of prior therapies. 4 Laryngoscope, 133:2597-2602, 2023.

Recent Developments in Carbon-11 Chemistry and Applications for First-In-Human PET Studies.

Positron emission tomography (PET) is a molecular imaging technique that makes use of radiolabelled molecules for in vivo evaluation. Carbon-11 is a frequently used radionuclide for the labelling of small molecule PET tracers and can be incorporated into organic molecules without changing their physicochemical properties. While the short half-life of carbon-11 (11C; t½ = 20.4 min) offers other advantages for imaging including multiple PET scans in the same subject on the same day, its use is limited to facilities that have an on-site cyclotron, and the radiochemical transformations are consequently more restrictive. Many researchers have embraced this challenge by discovering novel carbon-11 radiolabelling methodologies to broaden the synthetic versatility of this radionuclide. This review presents new carbon-11 building blocks and radiochemical transformations as well as PET tracers that have advanced to first-in-human studies over the past five years.

Development of flank lesions in growing pigs after weaning: A case study.

Flank lesions in pigs are a common yet poorly understood consequence of damaging social behavior. One group of pigs on a commercial farm with group lactation and late weaning, and with the history of flank lesions was studied. Skin lesions on the flanks, including linear and circular lesions, and tail lesions on 69 pigs were recorded six times during 5 weeks after weaning at the age of 9 weeks. Nosing behavior was scanned during six sessions with multiple scans. The associations of age, trunk whiteness, weight gain, sow parity, litter size, sex, and tail lesions with the number of circular and linear lesions were analyzed using linear mixed models. The number of linear lesions increased as pigs aged, and pigs with a higher weight gain had more linear lesions. Moreover, pigs with a whiter trunk color were scored with more lesions of both types. According to descriptive behavior data, nosing and biting behaviors were most frequent during weeks 2-4 after weaning at the age of 11-13 weeks. On average, seven circular flank lesions were found per pig during the experiment, at the age of 10-14 weeks. After the peak on day 17, their occurrence decreased. Skin lesion occurrence was related to a lighter skin color on the trunks of pigs. We recommend reporting skin color in connection with lesion scoring results. Nosing behavior and flank lesions both peaked from 2 to 4 weeks after weaning, suggesting that nosing behavior contributed to lesion development during this time.

Light-induced ordering of nanodomains in lithium tantalate as a result of multiple scanning by IR laser irradiation

The ordering of a light-induced structure of isolated circular ferroelectric nanodomains was discovered in lithium tantalate crystals under multiple scanning by infrared laser irradiation. The effect was considered as domain arising and growth under the action of alternating in sign pyroelectric field arising during sample heating and subsequent cooling. The circular domains appeared due to 1D to 2D shape transformation after the second scan and grew during subsequent scanning by merging with arising domains accompanied by shape restoration. The ordering of the domain pattern during multiple scanning characterized by an increase in the peak of the autocorrelation function was attributed to domain interaction. This mechanism was confirmed by computer simulation using the kinetic approach based on the analogy between the growth of domains and crystals. It was demonstrated that the quasi-regular pattern of one-size circular domains could be created by scanning with shift. The discovered domain ordering effect is similar to that in magnetic materials.

Scene point cloud understanding and reconstruction technologies in 3D space

Scene point cloud understanding and reconstruction technologies in 3D space

Luminescence principle and characteristics of imaging plate

The imaging plate (IP) is a reusable detector for detecting radiation particles in a complex electromagnetic field environment, and it is widely used as a detection medium in laser-accelerated particle beam diagnostic equipment. Therefore, it is necessary to study the performance characteristics and physical mechanism of IP. An electron source with known activity is used to explore the performance characteristics of IP. A <sup>90</sup>Sr/<sup>90</sup>Y electron source is used to measure the time attenuation curve, calibrate the absolute sensitivity, and study the law of multiple scanning of BAS-SR and BAS-TR. In the case of a longer irradiation, the fading cannot be neglected, and the attenuation curves are modified. The time attenuation characteristics indicate that the IP should be cooled after irradiation, and the scanning should be carried out in the slow decay process to reduce the influence of the reading error in the decay process. The absolute sensitivity of BAS-SR and BAS-TR to <sup>90</sup>Sr/<sup>90</sup>Y source are (0.033±0.002) PSL/<i>e</i> and (0.018±0.0038) PSL/<i>e</i> (photostimulated light, PSL), respectively, which are consistent with the results of most absolute sensitivity. The absolute sensitivity is closely related to the type of IP, scanning equipment, and experimental environment. In addition, the energy spectrum integral effect of the broad spectrum <i>β</i> source has a significant influence on the absolute sensitivity. This method is only suitable for the rough evaluation of the sensitivity characteristic parameters of the IP. Multiple scanning approximately satisfies the double exponential function distribution, which is consistent with the physical model. The characteristics of IP are determined by its storage principle. The fluorescence layer of IP is composed of typical electron trapping materials <i>M</i>F<i>X</i> (<i>M</i> = Ca, Sr, Ba; <i>X</i> = C1, Br, I) alkaline earth metal fluorhalide BaFBr. When the IP is irradiated, a large number of free electron-hole pairs are excited by the deposited energy in the material, and the free electrons will be captured by the electron trap, so the fluorescence layer of the IP records the radiation particles’ information through the energy deposited. In this paper, we study three kinds of models. Based on the models, a photo-stimulated luminescence model is proposed to describe the electron transfer process. The photo-stimulated luminescence model describes the physical mechanism of energy deposition, information storage, and information scanning of radiation particles. The relationship between the physical mechanism and characteristics is explained effectively by combining the microscopic mathematical model with the macroscopic physical phenomenon. It provides a specific data basis for the subsequent application of IPs in laser plasma diagnostic experiments.

A Multienergy Computed Tomography Method Based on a Blind Decomposition Model for Multivoltage X-Ray Transmission Images

Multienergy computed tomography (CT) can be implemented based on traditional CT systems to reduce the hardware cost. However, prior knowledge of the X-ray spectra or material composition and multiple scans are required in most such implementation methods, which imposes an extra burden. To avoid this, a method based on a blind decomposition model of multivoltage X-ray transmission images is proposed in this paper. The model considers X-ray scattering as an important factor resulting in errors of theoretical and measured polychromatic X-ray transmission. Since X-ray scatter has low-frequency characteristics, it is estimated via local averaging of the errors and subtracted from the errors. The solution algorithm for the model is derived with the Karush–Kuhn–Tucker conditions. In addition, image segmentation of the projection or reconstructed image is not needed. Samples representing different imaging situations are used in the experiments. Beam hardening artifacts are obviously reduced in the reconstructed images obtained with the proposed method. The relative errors of the X-ray energies of different component materials are calculated for the reconstructed images. For the first sample, the errors are less than 1% in the reconstructed images obtained with the proposed method and are seen to be the best among all compared methods. For the second sample, the errors are less than 10% in most reconstructed images obtained with the proposed method, whereas they are larger than 10% in most reconstructed images obtained with the compared methods. These results demonstrate the effectiveness of the proposed method.

Growth kinetics of the adsorbed layer of poly(bisphenol A carbonate) and its effect on the glass transition behavior in thin films.

The glass transition behavior of thin films of poly(bisphenol A carbonate) (PBAC) was studied employing ellipsometry. The glass transition temperature increases with the reduction of the film thickness. This result is attributed to the formation of an adsorbed layer with a reduced mobility compared to bulk PBAC. Therefore, for the first time, the growth kinetics of the adsorbed layer of PBAC was investigated, prepared by leaching samples from a 200 nm thin film which were annealed for several times at three different temperatures. The thickness of each prepared adsorbed layer was measured by multiple scans using atomic force microscopy (AFM). Additionally, an unannealed sample was measured. Comparison of the measurements of the unannealed and the annealed samples provides proof of a pre-growth regime for all annealing temperatures which was not observed for other polymers. For the lowest annealing temperature after the pre-growth stage only a growth regime with a linear time dependence is observed. For higher annealing temperatures the growth kinetics changes from a linear to a logarithmic growth regime at a critical time. At the longest annealing times the films showed signs of dewetting where segments of the adsorbed film were removed from the substrate (dewetting by desorption). The dependence of the surface roughness of the PBAC surface on annealing time also confirmed that the films annealed at highest temperatures for the longest times desorbed from the substrate.

3D-QCNet - A pipeline for automated artifact detection in diffusion MRI images.

Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying and removing them is essential to ensure the accuracy and viability of any post-processing carried out on these scans. This makes quality control (QC) a crucial first step prior to any analysis of dMRI data. Several QC methods for artifact detection exist, however they suffer from problems like requiring manual intervention and the inability to generalize across different artifacts and datasets. In this paper, we propose an automated deep learning (DL) pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion volumes for automatic artifact detection. Our method is validated on 9000 volumes sourced from 7 large clinical datasets spanning different acquisition protocols (with different gradient directions, high and low b-values, single-shell and multi-shell acquisitions) from multiple scanners. Additionally, they represent diverse subject demographics including age, sex and the presence or absence of pathologies. Our QC method is found to accurately generalize across this heterogenous data by correctly detecting 92% artifacts on average across our test set. This consistent performance over diverse datasets underlines the generalizability of our method, which currently is a significant barrier hindering the widespread adoption of automated QC techniques. Thus, 3D-QCNet can be integrated into diffusion pipelines to effectively automate the arduous and time-intensive process of artifact detection.

Automatic Extrinsic Calibration of Dual LiDARs With Adaptive Surface Normal Estimation

Solutions equipped with multiple light detection and ranging (LiDAR) systems have been widely used in several fields including mobile mapping, navigation, robot, and others. Accurate and robust extrinsic calibration between multiple scanners is necessary for the integration of point cloud data. An automatic method for the calibration of dual LiDARs with adaptive surface normal estimation is presented in this article. Specifically, this approach begins with environment detection from different positions and attitudes. A novel surface normal estimation method is conducted to take account of the uneven distribution of point cloud density and the edge information of planes. Finally, the calibration parameters are calculated by iteratively minimizing the cost function that consists of implicit point-to-plane distances. The experimental results on simulation and real-world data demonstrate that for different types of LiDAR, the proposed algorithm can achieve high-accuracy calibration in different scenes, without manual intervention. The rotation and translation calibration errors between Velodyne LiDARs are less than 1° and 0.02 m, respectively.

High-Resolution 3D MRI With Deep Generative Networks via Novel Slice-Profile Transformation Super-Resolution.

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo.

MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T1, T2*, PD, and susceptibility) and B1 simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments. Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching. A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods. The proposed QPM method can map T1, T2*, PD, susceptibility, and B1 simultaneously within a scan time that can be applied to human subjects.

CT Evaluation of Long-Term Changes in Common Bile Duct Diameter after Cholecystectomy.

The present study aimed to investigate the frequency and extent of compensatory common bile duct (CBD) dilatation after cholecystectomy, assess the time between cholecystectomy and CBD dilatation, and identify potentially useful CT findings suggestive of obstructive CBD dilatation. This retrospective study included 121 patients without biliary obstruction who underwent multiple CT scans before and after cholecystectomy at a single center between 2009 and 2011. The maximum short-axis diameters of the CBD and intrahepatic duct (IHD) were measured on each CT scan. In addition, the clinical and CT findings of 11 patients who were initially excluded from the study because of CBD stones or periampullary tumors were examined to identify distinguishing features between obstructive and non-obstructive CBD dilatation after cholecystectomy. The mean (standard deviation) short-axis maximum CBD diameter of 121 patients was 5.6 (± 1.9) mm in the axial plane before cholecystectomy but increased to 7.9 (± 2.6) mm after cholecystectomy (p < 0.001). Of the 106 patients with a pre-cholecystectomy axial CBD diameter of < 8 mm, 39 (36.8%) showed CBD dilatation of ≥ 8 mm after cholecystectomy. Six of the 17 patients with longterm (> 2 years) serial follow-up CT scans (35.3%) eventually showed a significant (> 1.5-fold) increase in the axial CBD diameter, all within two years after cholecystectomy. Of the 121 patients without obstruction or related symptoms, only one patient (0.1%) showed IHD dilatation > 3 mm after cholecystectomy. In contrast, all 11 patients with CBD obstruction had abdominal pain and abnormal laboratory indices, and 81.8% (9/11) had significant dilatation of the IHD and CBD. Compensatory non-obstructive CBD dilatation commonly occurs after cholecystectomy to a similar extent as obstructive dilatation. However, the presence of relevant symptoms, significant IHD dilatation, or further CBD dilatation 2-3 years after cholecystectomy should raise suspicion of CBD obstruction.

3D-Printed All-Metal Terahertz Multibeam Lens Antenna Based on Photonic Crystal

This paper presents a realization of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> -band multibeam lens antenna based on 3D printing. It extends all-metal Luneburg lens to terahertz band for the first time, and demonstrates the application potential of 3D-printed antenna in the associated communication and radar systems. Compared with the traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> -band antenna, the structure of our antenna is simple, which is only composed of a Luneburg lens and a waveguide feeder array. On this basis, we manufacture an antenna prototype through the process of projection micro-stereolithography 3D printing combined with magnetron metal sputtering for verification. It has the advantages of low cost, high precision and integrated processing. The measured results of the prototype indicate that the reflection coefficients are below –10dB within 110GHz to 170GHz, and its bandwidth is 42.8%. Also, a gain higher than 20dBi is obtained at the center frequency, and multiple beams scan loss is lower than 1.6dB from –45°to +45°, which agree well with the simulation results.

Superimposition of intraoral scans of an edentulous arch with implants and implant-supported provisional restoration, implementing a novel implant prosthetic scan body.

Purpose To describe a technique utilizing a novel prosthetic scan body, that assists the accurate merging of multiple scans (intra- and extraoral) of the interim prosthesis and edentulous arch with dental implants, during rehabilitation with a fixed implant-supported prosthesis.Methods Intraoral scanning (Trios 3, 3Shape) of an interim implant-supported prosthesis was performed, subsequently followed by another scan, using five scan bodies, placed onto the implant abutments (SRA, Bone level, Straumann AG). Successively, the newly designed prosthetic scan bodies were attached to the abutment copings of the interim prosthesis, for extraoral scanning. Utilizing an implant library designed for the prosthetic scan body, the three scans were merged, providing all the necessary information for the digital design and fabrication of the fixed implant-supported prosthesis.Conclusions The described clinical technique enabled effective and accurate superimposition of intra- and extraoral scans of the implant prosthesis. Superimposed data, including that of the position of dental implants and anatomy of soft tissue, provided essential information for the fabrication of a definitive implant-supported prosthesis. The novel prosthetic scan bodies attached to the implant prosthesis, assisted in merging intra- and extraoral scans, thus facilitating the rehabilitation of maxillary and/or mandibular edentulous dental arches. Further research is required to assess the accuracy of the proposed technique.

Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data

Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full potential of multi-modal sensor suites, radar sensors are essential for safety critical tasks and are already installed in most new vehicles today. In this paper, we address the problem of semantic segmentation of moving objects in radar point clouds to enhance the perception of the environment with another sensor modality. Instead of aggregating multiple scans to densify the point clouds, we propose a novel approach based on the self-attention mechanism to accurately perform sparse, single-scan segmentation. Our approach, called Gaussian Radar Transformer, includes the newly introduced Gaussian transformer layer, which replaces the softmax normalization by a Gaussian function to decouple the contribution of individual points. To tackle the challenge of the transformer to capture long-range dependencies, we propose our attentive up- and downsampling modules to enlarge the receptive field and capture strong spatial relations. We compare our approach to other state-of-the-art methods on the RadarScenes data set and show superior segmentation quality in diverse environments, even without exploiting temporal information.

Securing Medical Datasets using Block Chain Technology

Medical data sets are crucial in modern healthcare as they provide valuable information that helps in advancing medical research, improving patient care, and making informed decisions. They allow healthcare providers to track patient health records, evaluate treatments and outcomes, and identify trends and patterns in disease diagnosis and management. Additionally, medical data sets are used for training AI models that can assist healthcare professionals in diagnosing and treating diseases, providing accurate and timely patient care, and reducing medical errors.&#x0D; In recent years millions of patient records have already been compromised due to data thefts in the healthcare industry ranging from patient’s private health data to hospitals accounting, reporting, DNA structures etc. are all being taken. Blockchain technology, which is regarded as a trustworthy way to maintain documents, could be utilized to protect full security and confidentiality. This approach would not only safeguard patient data but also due to decentralized structure of a technology it also lead to more efficient treatment of patients as information would be readily and instantly available. Therefore, proper implementation of the blockchain system will not only increase individual service but also remove the need for external parties to provide security.&#x0D; The study proposed a model that would unites almost all of the leading healthcare providers, by combining all Electronic Health Record (EHR) system including hospitals, clinics, and specialized Laboratories as well. MedBloc, a blockchain-based safe EHR system that offers accessibility, safety, and protection is described. It allows both patients and healthcare professionals to view and exchange medical records, by extracting important ideas that can be used to create multiple blockchain systems and vulnerability scanning that address security flaws. This article also presents blockchain vulnerability management options by analyzing some assaults upon blockchain, including hashing, connectivity, smart contract and privacy breach cyberattacks. It has been proven difficult as a result of trustworthy attack analysis techniques. Therefore, we explore the blockchain's user privacy in this paper and consider potential fixes. We systematically divide the three kinds of blockchain attack strategies, then mentioned the associated attack and defense techniques that rely on such divisions.

Wave shadowing effects on phase resolved wave spectra estimated from RADAR backscatter: Part 2: The conflation technique and the deconvolution techniques for the mitigation of wave shadowing effects

This is the second part of a short two series concerned with wave shadowing which is acknowledged as the primary source of error involved in the estimation of phase resolved sea wave spectra from RADAR backscatter measurement. A new technique termed conflation is introduced that uses multiple RADAR scan data sets to build a multi-segment data set together with a corresponding theoretic model. Using simulation the conflation methodology is compared against the so called spectral averaging technique that is the standard approach for estimating phase resolved wave spectra. Conflation is found to provide superior estimates, in particular making it possible to avoid the need to impose the wave dispersion relationship though an additional computational step as is necessary in spectral averaging. A second technique based upon deconvolution is also introduced for mitigating wave shadowing errors. However because deconvolution is a very mature process well covered in the signal process and linear system theory literature the discussion of this second method is very brief.

Effect of the respiratory motion of pulmonary nodules on CT-guided percutaneous transthoracic needle biopsy.

Computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) is highly affected by respiratory motion; however, respiratory motion of target nodule during the PTNB and its effect on CT-guided lung biopsy have not been studied. To investigate the effect of the respiratory motion of pulmonary nodules on CT-guided PTNB. We retrospectively reviewed the procedural CT scans of 426 pulmonary nodules that underwent PTNB during quiet breathing. Maximal and average respiratory motions were measured using the difference of table position of the targeted nodule between multiple procedural scans. Diagnostic performance, complications, and technical factors of PTNB in nodules with large motion (maximal motion >1 cm) were compared with those in nodules with small motion (≤1 cm). The mean maximal and average respiratory motions between tidal volume breathing were 5.4 ± 4.4 and 2.7 ± 2.6 mm, respectively. Sensitivity and accuracy were 93.1% and 96.1% in nodules with large motion, compared with 94.7% and 95.9% in nodules with small motion, respectively. Respiratory targeting (P < 0.001), needle modulation (P < 0.001), motion artifact of target (P < 0.001), target disappearance from scans (P < 0.001), and number of performed CT scans (P < 0.001) were significantly higher in the large motion group, with no significant difference in radiation dose and complications between the groups. The respiratory motion of pulmonary nodules during CT-guided PTNB may cause technical difficulties but does not affect diagnostic performance nor complications associated with PTNB.

General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network

The general psychopathology factor (p-factor) represents shared variance across mental disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive Development (ABCD) Study offers an unprecedented opportunity to investigate functional networks (FNs) from functional magnetic resonance imaging (fMRI) associated with the psychopathology of an adolescent cohort (n > 10,000). However, the heterogeneities associated with the use of multiple sites and multiple scanners in the ABCD Study need to be overcome to improve the prediction of the p-factor using fMRI. We proposed a scanner-generalization neural network (SGNN) to predict the individual p-factor by systematically reducing the scanner effect for resting-state functional connectivity (RSFC). We included 6905 adolescents from 18 sites whose fMRI data were collected using either Siemens or GE scanners. The p-factor was estimated based on the Child Behavior Checklist (CBCL) scores available in the ABCD study using exploratory factor analysis. We evaluated the Pearson's correlation coefficients (CCs) for p-factor prediction via leave-one/two-site-out cross-validation (LOSOCV/LTSOCV) and identified important FNs from the weight features (WFs) of the SGNN. The CCs were higher for the SGNN than for alternative models when using both LOSOCV (0.1631 ± 0.0673 for the SGNN vs. 0.1497 ± 0.0710 for kernel ridge regression [KRR]; p < 0.05 from a two-tailed paired t-test) and LTSOCV (0.1469 ± 0.0381 for the SGNN vs. 0.1394 ± 0.0359 for KRR; p = 0.01). It was found that (a) the default-mode and dorsal attention FNs were important for p-factor prediction, and (b) the intra-visual FN was important for scanner generalization. We demonstrated the efficacy of our novel SGNN model for p-factor prediction while simultaneously eliminating scanner-related confounding effects for RSFC.