Search

Your search keyword '"Lebel, R."' showing total 358 results
Start Over Author "Lebel, R."
358 results on '"Lebel, R."'

1. Performance characterization of a novel deep learning-based MR image reconstruction pipeline

Author

Lebel, R. Marc

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

A novel deep learning-based magnetic resonance imaging reconstruction pipeline was designed to address fundamental image quality limitations of conventional reconstruction to provide high-resolution, low-noise MR images. This pipeline's unique aims were to convert truncation artifact into improved image sharpness while jointly denoising images to improve image quality. This new approach, now commercially available at AIR Recon DL (GE Healthcare, Waukesha, WI), includes a deep convolutional neural network (CNN) to aid in the reconstruction of raw data, ultimately producing clean, sharp images. Here we describe key features of this pipeline and its CNN, characterize its performance in digital reference objects, phantoms, and in-vivo, and present sample images and protocol optimization strategies that leverage image quality improvement for reduced scan time. This new deep learning-based reconstruction pipeline represents a powerful new tool to increase the diagnostic and operational performance of an MRI scanner., Comment: 18 pages, 10 figures

Published
2020

2. Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction

Author

Souza, Roberto, Bento, Mariana, Nogovitsyn, Nikita, Chung, Kevin J., Lebel, R. Marc, and Frayne, Richard

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Physics - Medical Physics, Statistics - Machine Learning
Abstract

The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two element network combinations were evaluated for the four possible image-k-space domain configurations: a) W-net II, b) W-net KK, c) W-net IK, and d) W-net KI were evaluated. Selected promising four element networks (WW-nets) were also examined. Two configurations of each network were compared: 1) Each coil channel processed independently, and 2) all channels processed simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil k-space datasets were used in the experiments. Normalized root mean squared error, peak signal to noise ratio, visual information fidelity and visual inspection were used to assess the reconstructed images against the fully sampled reference images. Our results indicated that networks that operate solely in the image domain are better suited when processing individual channels of multi-channel data independently. Dual domain methods are more advantageous when simultaneously reconstructing all channels of multi-channel data. Also, the appropriate cascade of U-nets compared favorably (p < 0.01) to the previously published, state-of-the-art Deep Cascade model in in three out of four experiments.

Published
2019

4. Tracer Kinetic Models as Temporal Constraints during DCE-MRI reconstruction

Author

Lingala, Sajan Goud, Guo, Yi, Lebel, R. Marc, Zhu, Yinghua, Bliesener, Yannick, Law, Meng, and Nayak, Krishna S.

Subjects
Physics - Medical Physics
Abstract

Purpose: To apply tracer kinetic models as temporal constraints during reconstruction of under-sampled dynamic contrast enhanced (DCE) MRI. Methods: A library of concentration v.s time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under-sampling experiments are performed on a brain tumor DCE digital reference object (DRO) at different signal to noise levels (SNR=20-40) at (k-t) space under-sampling factor (R=20), and 12 brain tumor in- vivo 3T datasets at (R=20-40). The approach is compared against an existing compressed sensing based temporal finite-difference (tFD) reconstruction approach. Results: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts-Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO based experiments showed good fidelity in recovery of kinetic maps from 20-fold under- sampled data at SNRs between 10-30. In-vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at R>=20. Conclusions: Tracer kinetic models can be applied as temporal constraints during DCE-MRI reconstruction, enabling more accurate reconstruction from under- sampled data. The approach is flexible, can use several kinetic models, and does not require tuning of regularization parameters., Comment: Submitted to Medical Physics. 9 figures; 1 table; 32 pages

Published
2017
Full Text
View/download PDF

5. Improving Xenon‐129 lung ventilation image SNR with deep‐learning based image reconstruction.

Author

Stewart, Neil J., de Arcos, Jose, Biancardi, Alberto M., Collier, Guilhem J., Smith, Laurie J., Norquay, Graham, Marshall, Helen, Brau, Anja C. S., Lebel, R. Marc, and Wild, Jim M.

Subjects
SINGULAR value decomposition, CHRONIC obstructive pulmonary disease, IMAGE reconstruction, IMAGE denoising, DEEP learning
Abstract

Purpose: To evaluate the feasibility and utility of a deep learning (DL)‐based reconstruction for improving the SNR of hyperpolarized 129Xe lung ventilation MRI. Methods: 129Xe lung ventilation MRI data acquired from patients with asthma and/or chronic obstructive pulmonary disease (COPD) were retrospectively reconstructed with a commercial DL reconstruction pipeline at five different denoising levels. Quantitative imaging metrics of lung ventilation including ventilation defect percentage (VDP) and ventilation heterogeneity index (VHI) were compared between each set of DL‐reconstructed images and alternative denoising strategies including: filtering, total variation denoising and higher‐order singular value decomposition. Structural similarity between the denoised and original images was assessed. In a prospective study, the feasibility of using SNR gains from DL reconstruction to allow natural‐abundance xenon MRI was evaluated in healthy volunteers. Results: 129Xe ventilation image SNR was improved with DL reconstruction when compared with conventionally reconstructed images. In patients with asthma and/or COPD, DL‐reconstructed images exhibited a slight positive bias in ventilation defect percentage (1.3% at 75% denoising) and ventilation heterogeneity index (˜1.4) when compared with conventionally reconstructed images. Additionally, DL‐reconstructed images preserved structural similarity more effectively than data denoised using alternative approaches. DL reconstruction greatly improved image SNR (greater than threefold), to a level that 129Xe ventilation imaging using natural‐abundance xenon appears feasible. Conclusion: DL‐based image reconstruction significantly improves 129Xe ventilation image SNR, preserves structural similarity, and leads to a minor bias in ventilation metrics that can be attributed to differences in the image sharpness. This tool should help facilitate cost‐effective 129Xe ventilation imaging with natural‐abundance xenon in the future. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

14. Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Author

Mikkelsen, Mark, Rimbault, Daniel L., Barker, Peter B., Bhattacharyya, Pallab K., Brix, Maiken K., Buur, Pieter F., Cecil, Kim M., Chan, Kimberly L., Chen, David Y.-T., Craven, Alexander R., Cuypers, Koen, Dacko, Michael, Duncan, Niall W., Dydak, Ulrike, Edmondson, David A., Ende, Gabriele, Ersland, Lars, Forbes, Megan A., Gao, Fei, Greenhouse, Ian, Harris, Ashley D., He, Naying, Heba, Stefanie, Hoggard, Nigel, Hsu, Tun-Wei, Jansen, Jacobus F.A., Kangarlu, Alayar, Lange, Thomas, Lebel, R. Marc, Li, Yan, Lin, Chien-Yuan E., Liou, Jy-Kang, Lirng, Jiing-Feng, Liu, Feng, Long, Joanna R., Ma, Ruoyun, Maes, Celine, Moreno-Ortega, Marta, Murray, Scott O., Noah, Sean, Noeske, Ralph, Noseworthy, Michael D., Oeltzschner, Georg, Porges, Eric C., Prisciandaro, James J., Puts, Nicolaas A.J., Roberts, Timothy P.L., Sack, Markus, Sailasuta, Napapon, Saleh, Muhammad G., Schallmo, Michael-Paul, Simard, Nicholas, Stoffers, Diederick, Swinnen, Stephan P., Tegenthoff, Martin, Truong, Peter, Wang, Guangbin, Wilkinson, Iain D., Wittsack, Hans-Jörg, Woods, Adam J., Xu, Hongmin, Yan, Fuhua, Zhang, Chencheng, Zipunnikov, Vadim, Zöllner, Helge J., and Edden, Richard A.E.

Published
2019
Full Text
View/download PDF

15. Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging

Author

Allen, Timothy J., primary, Henze Bancroft, Leah C., additional, Unal, Orhan, additional, Estkowski, Lloyd D., additional, Cashen, Ty A., additional, Korosec, Frank, additional, Strigel, Roberta M., additional, Kelcz, Frederick, additional, Fowler, Amy M., additional, Gegios, Alison, additional, Thai, Janice, additional, Lebel, R. Marc, additional, and Holmes, James H., additional

Published
2023
Full Text
View/download PDF

19. Big GABA: Edited MR spectroscopy at 24 research sites

Author

Mikkelsen, Mark, Barker, Peter B., Bhattacharyya, Pallab K., Brix, Maiken K., Buur, Pieter F., Cecil, Kim M., Chan, Kimberly L., Chen, David Y.-T., Craven, Alexander R., Cuypers, Koen, Dacko, Michael, Duncan, Niall W., Dydak, Ulrike, Edmondson, David A., Ende, Gabriele, Ersland, Lars, Gao, Fei, Greenhouse, Ian, Harris, Ashley D., He, Naying, Heba, Stefanie, Hoggard, Nigel, Hsu, Tun-Wei, Jansen, Jacobus F.A., Kangarlu, Alayar, Lange, Thomas, Lebel, R. Marc, Li, Yan, Lin, Chien-Yuan E., Liou, Jy-Kang, Lirng, Jiing-Feng, Liu, Feng, Ma, Ruoyun, Maes, Celine, Moreno-Ortega, Marta, Murray, Scott O., Noah, Sean, Noeske, Ralph, Noseworthy, Michael D., Oeltzschner, Georg, Prisciandaro, James J., Puts, Nicolaas A.J., Roberts, Timothy P.L., Sack, Markus, Sailasuta, Napapon, Saleh, Muhammad G., Schallmo, Michael-Paul, Simard, Nicholas, Swinnen, Stephan P., Tegenthoff, Martin, Truong, Peter, Wang, Guangbin, Wilkinson, Iain D., Wittsack, Hans-Jörg, Xu, Hongmin, Yan, Fuhua, Zhang, Chencheng, Zipunnikov, Vadim, Zöllner, Helge J., and Edden, Richard A.E.

Published
2017
Full Text
View/download PDF

30. Effects of emotional context on impulse control

Author

Brown, Matthew R.G., Lebel, R. Marc, Dolcos, Florin, Wilman, Alan H., Silverstone, Peter H., Pazderka, Hannah, Fujiwara, Esther, Wild, T. Cameron, Carroll, Alan M., Hodlevskyy, Oleksandr, Zedkova, Lenka, Zwaigenbaum, Lonnie, Thompson, Angus H., Greenshaw, Andrew J., and Dursun, Serdar M.

Published
2012
Full Text
View/download PDF

31. Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI

Author

Koch, Kevin M., primary, Sherafati, Mohammad, additional, Arpinar, V. Emre, additional, Bhave, Sampada, additional, Ausman, Robin, additional, Nencka, Andrew S., additional, Lebel, R. Marc, additional, McKinnon, Graeme, additional, Kaushik, S. Sivaram, additional, Vierck, Douglas, additional, Stetz, Michael R., additional, Fernando, Sujan, additional, and Mannem, Rajeev, additional

Published
2021
Full Text
View/download PDF

38. Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites

Author

Považan, Michal, Mikkelsen, Mark, Berrington, Adam, Bhattacharyya, Pallab K, Brix, Maiken K, Buur, Pieter F, Cecil, Kim M, Chan, Kimberly L, Chen, David Y T, Craven, Alexander R, Cuypers, Koen, Dacko, Michael, Duncan, Niall W, Dydak, Ulrike, Edmondson, David A, Ende, Gabriele, Ersland, Lars, Forbes, Megan A, Gao, Fei, Greenhouse, Ian, Harris, Ashley D, He, Naying, Heba, Stefanie, Hoggard, Nigel, Hsu, Tun-Wei, Jansen, Jacobus F A, Kangarlu, Alayar, Lange, Thomas, Lebel, R Marc, Li, Yan, Lin, Chien-Yuan E, Liou, Jy-Kang, Lirng, Jiing-Feng, Liu, Feng, Long, Joanna R, Ma, Ruoyun, Maes, Celine, Moreno-Ortega, Marta, Murray, Scott O, Noah, Sean, Noeske, Ralph, Noseworthy, Michael D, Oeltzschner, Georg, Porges, Eric C, Prisciandaro, James J, Puts, Nicolaas A J, Roberts, Timothy P L, Sack, Markus, Sailasuta, Napapon, Saleh, Muhammad G, Schallmo, Michael-Paul, Simard, Nicholas, Stoffers, Diederick, Swinnen, Stephan P, Tegenthoff, Martin, Truong, Peter, Wang, Guangbin, Wilkinson, Iain D, Wittsack, Hans-Jörg, Woods, Adam J, Xu, Hongmin, Yan, Fuhua, Zhang, Chencheng, Zipunnikov, Vadim, Zöllner, Helge J, Edden, Richard A E, Barker, Peter B, Považan, Michal, Mikkelsen, Mark, Berrington, Adam, Bhattacharyya, Pallab K, Brix, Maiken K, Buur, Pieter F, Cecil, Kim M, Chan, Kimberly L, Chen, David Y T, Craven, Alexander R, Cuypers, Koen, Dacko, Michael, Duncan, Niall W, Dydak, Ulrike, Edmondson, David A, Ende, Gabriele, Ersland, Lars, Forbes, Megan A, Gao, Fei, Greenhouse, Ian, Harris, Ashley D, He, Naying, Heba, Stefanie, Hoggard, Nigel, Hsu, Tun-Wei, Jansen, Jacobus F A, Kangarlu, Alayar, Lange, Thomas, Lebel, R Marc, Li, Yan, Lin, Chien-Yuan E, Liou, Jy-Kang, Lirng, Jiing-Feng, Liu, Feng, Long, Joanna R, Ma, Ruoyun, Maes, Celine, Moreno-Ortega, Marta, Murray, Scott O, Noah, Sean, Noeske, Ralph, Noseworthy, Michael D, Oeltzschner, Georg, Porges, Eric C, Prisciandaro, James J, Puts, Nicolaas A J, Roberts, Timothy P L, Sack, Markus, Sailasuta, Napapon, Saleh, Muhammad G, Schallmo, Michael-Paul, Simard, Nicholas, Stoffers, Diederick, Swinnen, Stephan P, Tegenthoff, Martin, Truong, Peter, Wang, Guangbin, Wilkinson, Iain D, Wittsack, Hans-Jörg, Woods, Adam J, Xu, Hongmin, Yan, Fuhua, Zhang, Chencheng, Zipunnikov, Vadim, Zöllner, Helge J, Edden, Richard A E, and Barker, Peter B

Abstract

Background The hardware and software differences between MR vendors and individual sites influence the quantification of MR spectroscopy data. An analysis of a large data set may help to better understand sources of the total variance in quantified metabolite levels. Purpose To compare multisite quantitative brain MR spectroscopy data acquired in healthy participants at 26 sites by using the vendor-supplied single-voxel point-resolved spectroscopy (PRESS) sequence. Materials and Methods An MR spectroscopy protocol to acquire short-echo-time PRESS data from the midparietal region of the brain was disseminated to 26 research sites operating 3.0-T MR scanners from three different vendors. In this prospective study, healthy participants were scanned between July 2016 and December 2017. Data were analyzed by using software with simulated basis sets customized for each vendor implementation. The proportion of total variance attributed to vendor-, site-, and participant-related effects was estimated by using a linear mixed-effects model. P values were derived through parametric bootstrapping of the linear mixed-effects models (denoted Pboot). Results In total, 296 participants (mean age, 26 years ± 4.6; 155 women and 141 men) were scanned. Good-quality data were recorded from all sites, as evidenced by a consistent linewidth of N-acetylaspartate (range, 4.4-5.0 Hz), signal-to-noise ratio (range, 174-289), and low Cramér-Rao lower bounds (≤5%) for all of the major metabolites. Among the major metabolites, no vendor effects were found for levels of myo-inositol (Pboot > .90), N-acetylaspartate and N-acetylaspartylglutamate (Pboot = .13), or glutamate and glutamine (Pboot = .11). Among the smaller resonances, no vendor effects were found for ascorbate (Pboot = .08), aspartate (Pboot > .90), glutathione (Pboot > .90), or lactate (Pboot = .28). Conclusion Multisite multivendor single-voxel MR spectroscopy studies performed at 3.0 T can yield results that are coheren

Published
2020

39. Defining the Effect of the 16p11.2 Duplication on Cognition, Behavior, and Medical Comorbidities

Author

D'Angelo, D., Lebon, S., Chen, Q., Martin-Brevet, S., Snyder, L. G., Hippolyte, L., Hanson, E., Maillard, A. M., Faucett, W. A., Mace, A., Pain, A., Bernier, R., Chawner, S. J. R. A., David, A., Andrieux, J., Aylward, E., Baujat, G., Caldeira, I., Conus, P., Ferrari, C., Forzano, F., Gerard, M., Goin-Kochel, R. P., Grant, E., Hunter, J. V., Isidor, B., Jacquette, A., Jonch, A. E., Keren, B., Lacombe, D., Le Caignec, C., Martin, C. L., Mannik, K., Metspalu, A., Mignot, C., Mukherjee, P., Owen, M. J., Passeggeri, M., Rooryck-Thambo, C., Rosenfeld, J. A., Spence, S. J., Steinman, K. J., Tjernagel, J., Van Haelst, M., Shen, Y., Draganski, B., Sherr, E. H., Ledbetter, D. H., van den Bree, M. B. M., Beckmann, J. S., Spiro, J. E., Reymond, A., Jacquemont, S., Chung, W. K., Knoers, N. V. A. M., Martinet, D., Belfiore, M., Cuvellier, J. -C., Devries, B., Delrue, M. -A., Doco-Fenzy, M., Lebel, R., Leheup, B., Lewis, S., Mencarelli, M. A., Minet, J. -C., Vincent-Delorme, C., Moerman, A., Mucciolo, M., Ounap, K., Rajcan-Separovic, E., Renieri, A., Sanlaville, D., Faas, B. H., Koolen, D. A., Vulto-Van Silfhout, A., de Leeuw, N., Rosanfeld, J. A., Filges, I., Achatz, E., Roetzer, K. M., Bonneau, D., Guichet, A., Lazaro, L., Plessis, G., Kroisel, P. M., Reis, A., Jonveaux, P., Chantot-Bastaraud, S., Rauch, A., Demeer, B., Nordgren, A., Labalme, A., Ferrarini, A., Ramelli, G. P., Guilmatre, A., Joly-Helas, G., Haize, S., Layet, V., Le Gallic, S., de Freminville, B., Touraine, R., Van Binsbergen, E., Mathieu-Dramard, M., Barth, M., Blaumeiser, B., Masurel, A., Cailler, P., Olivier-Faivre, L., Malacarne, M., Coutton, C., Dieterich, K., Satre, V., Wallgren-Pettersson, C., Tensgrom, C., Kaksonen, S., Duban-Bedu, B., Holder, M., Rossi, M., Gaillard, D., Bock, D., Bednarek, N., Guillin, O., Bizzarri, V., Flori, E., Silengo, M., Kooy, R. F., Aboura, A., Beri, M., Delobel, B., Drunat, S., Jaros, Z., Kolk, A., Reigo, A., Zufferey, F., Beckmann, N., Faravelli, F., Alupay, H., Aaronson, B., Ackerman, S., Ankenman, K., Anwar, A., Atwell, C., Bowe, A., Beaudet, A. L., Benedetti, M., Berg, J., Berman, J., Berry, L. N., Bibb, A. L., Blaskey, L., Brennan, J., Brewton, C. M., Buckner, R., Bukshpun, P., Burko, J., Cali, P., Cerban, B., Chang, Y., Cheong, M., Chow, V., Chu, Z., Chudnovskaya, D., Cornew, L., Dale, C., Dell, J., Dempsey, A. G., Deschamps, T., Earl, R., Edgar, J., Elgin, J., Endre, J., Evans, Y. L., Findlay, A., Fischbach, G. D., Fisk, C., Fregeau, B., Gaetz, B., Gaetz, L., Garza, S., Gerdts, J., Glenn, O., Gobuty, S. E., Golembski, R., Greenup, M., Heiken, K., Hines, K., Hinkley, L., Jackson, F. I., Jenkins, J., Jeremy, R. J., Johnson, K., Kanne, S. M., Kessler, S., Khan, S. Y., Ku, M., Kuschner, E., Laakman, A. L., Lam, P., Lasala, M. W., Lee, H., La, K., Levy, S., Lian, A., Llorens, A. V., Loftus, K., Luks, T. L., Marco, E. J., Martin, S., Martin, A. J., Marzano, G., Masson, C., Mcgovern, K. E., Keehn, R. M., Miller, D. T., Miller, F. K., Moss, T. J., Murray, R., Nagarajan, S. S., Nowell, K. P., Owen, J., Paal, A. M., Packer, A., Page, P. Z., Paul, B. M., Peters, A., Peterson, D., Poduri, A., Pojman, N. J., Porche, K., Proud, M. B., Qasmieh, S., Ramocki, M. B., Reilly, B., Roberts, T. P. L., Shaw, D., Sinha, T., Smith, B., Snow, A., Swarnakar, V., Thieu, T., Triantafallou, C., Vaughan, R., Wakahiro, M., Wallace, A., Ward, T., Wenegrat, J., Wolken, A., Blaumeiser, Bettina, Kooy, Frank, Other departments, Cardiff University Experiences of Children With Copy Number Variants (ECHO) Study, 16p11.2 European Consortium, Simons Variation in Individuals Project (VIP) Consortium, Knoers, VA., Martinet, D., Belfiore, M., Cuvellier, JC., de Vries, B., Delrue, MA., Doco-Fenzy, M., Lebel, R., Leheup, B., Lewis, S., Mencarelli, MA., Minet, JC., Vincent-Delorme, C., Moerman, A., Mucciolo, M., Ounap, K., Rajcan-Separovic, E., Renieri, A., Sanlaville, D., Faas, BH., Koolen, DA., Vulto-van Silfhout, A., de Leeuw, N., Rosenfeld, JA., Filges, I., Achatz, E., Roetzer, KM., Bonneau, D., Guichet, A., Lazaro, L., Plessis, G., Kroisel, PM., Reis, A., Jonveaux, P., Chantot-Bastaraud, S., Rauch, A., Demeer, B., Nordgren, A., Labalme, A., Ferrarini, A., Ramelli, GP., Guilmatre, A., Joly-Helas, G., Haize, S., Layet, V., Le Gallic, S., de Fréminville, B., Touraine, R., Van Binsbergen, E., Mathieu-Dramard, M., Barth, M., Blaumeiser, B., Masurel, A., Cailler, P., Olivier-Faivre, L., Malacarne, M., Coutton, C., Dieterich, K., Satre, V., Wallgren-Pettersson, C., Tensgrom, C., Kaksonen, S., Duban-Bedu, B., Holder, M., Rossi, M., Gaillard, D., Bock, D., Bednarek, N., Guillin, O., Bizzarri, V., Flori, E., Silengo, M., Kooy, RF., Aboura, A., Beri, M., Delobel, B., Drunat, S., Jaros, Z., Kolk, A., Reigo, A., Zufferey, F., Beckmann, N., Faravelli, F., Alupay, H., Aaronson, B., Ackerman, S., Ankenman, K., Anwar, A., Atwell, C., Bowe, A., Beaudet, AL., Benedetti, M., Berg, J., Berman, J., Berry, LN., Bibb, AL., Blaskey, L., Brennan, J., Brewton, CM., Buckner, R., Bukshpun, P., Burko, J., Cali, P., Cerban, B., Chang, Y., Cheong, M., Chow, V., Chu, Z., Chudnovskaya, D., Cornew, L., Dale, C., Dell, J., Dempsey, AG., Deschamps, T., Earl, R., Edgar, J., Elgin, J., Olson, JE., Evans, YL., Findlay, A., Fischbach, GD., Fisk, C., Fregeau, B., Gaetz, B., Gaetz, L., Garza, S., Gerdts, J., Glenn, O., Gobuty, SE., Golembski, R., Greenup, M., Heiken, K., Hines, K., Hinkley, L., Jackson, FI., Jenkins J.<Suffix>3rd</Suffix>, Jeremy, RJ., Johnson, K., Kanne, SM., Kessler, S., Khan, SY., Ku, M., Kuschner, E., Laakman, AL., Lam, P., Lasala, MW., Lee, H., LaGuerre, K., Levy, S., Lian Cavanagh, A., Llorens, AV., Loftus Campe, K., Luks, TL., Marco, EJ., Martin, S., Martin, AJ., Marzano, G., Masson, C., McGovern, KE., McNally Keehn, R., Miller, DT., Miller, FK., Moss, TJ., Murray, R., Nagarajan, SS., Nowell, KP., Owen, J., Paal, AM., Packer, A., Page, PZ., Paul, BM., Peters, A., Peterson, D., Poduri, A., Pojman, NJ., Porche, K., Proud, MB., Qasmieh, S., Ramocki, MB., Reilly, B., Roberts, TP., Shaw, D., Sinha, T., Smith-Packard, B., Snow Gallagher, A., Swarnakar, V., Thieu, T., Triantafallou, C., Vaughan, R., Wakahiro, M., Wallace, A., Ward, T., Wenegrat, J., Wolken, A., Human genetics, Amsterdam Neuroscience - Complex Trait Genetics, and Amsterdam Reproduction & Development (AR&D)

Subjects
Male, 0301 basic medicine, Proband, Pediatrics, Autism Spectrum Disorder, Developmental Disabilities, Chromosome Disorders, Comorbidity, Nonverbal learning disorder, Cohort Studies, Cognition, 0302 clinical medicine, Cerebellum, Chromosome Duplication, Gene duplication, Copy-number variation, Non-U.S. Gov't, Child, 2. Zero hunger, Intelligence quotient, Research Support, Non-U.S. Gov't, Middle Aged, Psychiatry and Mental health, Microcephaly, Female, Schizophrenic Psychology, Chromosome Deletion, Psychology, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9], Human, Adult, medicine.medical_specialty, Adolescent, DNA Copy Number Variations, Research Support, Nervous System Malformations, Article, Chromosomes, Young Adult, 03 medical and health sciences, Intellectual Disability, Journal Article, medicine, Humans, Autistic Disorder, Preschool, Psychiatry, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Epilepsy, Pair 16, Other Research Radboud Institute for Health Sciences [Radboudumc 0], Case-control study, Autism Spectrum Disorder/epidemiology, Autism Spectrum Disorder/genetics, Autistic Disorder/epidemiology, Autistic Disorder/genetics, Case-Control Studies, Cerebellum/abnormalities, Child, Preschool, Chromosome Disorders/epidemiology, Chromosome Disorders/genetics, Chromosomes, Human, Pair 16/genetics, Developmental Disabilities/epidemiology, Developmental Disabilities/genetics, Epilepsy/epidemiology, Epilepsy/genetics, Intellectual Disability/epidemiology, Intellectual Disability/genetics, Microcephaly/epidemiology, Microcephaly/genetics, Nervous System Malformations/epidemiology, Nervous System Malformations/genetics, Schizophrenia/epidemiology, Schizophrenia/genetics, medicine.disease, 030104 developmental biology, Chromosomes, Human, Pair 16, Schizophrenia, Autism, Human medicine, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 167711.pdf (Publisher’s version ) (Closed access) IMPORTANCE: The 16p11.2 BP4-BP5 duplication is the copy number variant most frequently associated with autism spectrum disorder (ASD), schizophrenia, and comorbidities such as decreased body mass index (BMI). OBJECTIVES: To characterize the effects of the 16p11.2 duplication on cognitive, behavioral, medical, and anthropometric traits and to understand the specificity of these effects by systematically comparing results in duplication carriers and reciprocal deletion carriers, who are also at risk for ASD. DESIGN, SETTING, AND PARTICIPANTS: This international cohort study of 1006 study participants compared 270 duplication carriers with their 102 intrafamilial control individuals, 390 reciprocal deletion carriers, and 244 deletion controls from European and North American cohorts. Data were collected from August 1, 2010, to May 31, 2015 and analyzed from January 1 to August 14, 2015. Linear mixed models were used to estimate the effect of the duplication and deletion on clinical traits by comparison with noncarrier relatives. MAIN OUTCOMES AND MEASURES: Findings on the Full-Scale IQ (FSIQ), Nonverbal IQ, and Verbal IQ; the presence of ASD or other DSM-IV diagnoses; BMI; head circumference; and medical data. RESULTS: Among the 1006 study participants, the duplication was associated with a mean FSIQ score that was lower by 26.3 points between proband carriers and noncarrier relatives and a lower mean FSIQ score (16.2-11.4 points) in nonproband carriers. The mean overall effect of the deletion was similar (-22.1 points; P < .001). However, broad variation in FSIQ was found, with a 19.4- and 2.0-fold increase in the proportion of FSIQ scores that were very low (100) compared with the deletion group (P < .001). Parental FSIQ predicted part of this variation (approximately 36.0% in hereditary probands). Although the frequency of ASD was similar in deletion and duplication proband carriers (16.0% and 20.0%, respectively), the FSIQ was significantly lower (by 26.3 points) in the duplication probands with ASD. There also were lower head circumference and BMI measurements among duplication carriers, which is consistent with the findings of previous studies. CONCLUSIONS AND RELEVANCE: The mean effect of the duplication on cognition is similar to that of the reciprocal deletion, but the variance in the duplication is significantly higher, with severe and mild subgroups not observed with the deletion. These results suggest that additional genetic and familial factors contribute to this variability. Additional studies will be necessary to characterize the predictors of cognitive deficits.

Published
2016
Full Text
View/download PDF

41. Improvement of late gadolinium enhancement image quality using a deep learning–based reconstruction algorithm and its influence on myocardial scar quantification

Author

van der Velde, Nikki, primary, Hassing, H. Carlijne, additional, Bakker, Brendan J., additional, Wielopolski, Piotr A., additional, Lebel, R. Marc, additional, Janich, Martin A., additional, Kardys, Isabella, additional, Budde, Ricardo P. J., additional, and Hirsch, Alexander, additional

Published
2020
Full Text
View/download PDF

44. Functional magnetic resonance imaging study of working memory several years after pediatric concussion

Author

Brooks, Brian L., primary, Virani, Shane, additional, Khetani, Aneesh, additional, Carlson, Helen, additional, Jadavji, Zeanna, additional, Mauthner, Micaela, additional, Low, Trevor A., additional, Plourde, Vickie, additional, MacMaster, Frank P., additional, Bray, Signe, additional, Harris, Ashley D., additional, Lebel, Catherine, additional, Lebel, R. Marc, additional, Esser, Michael J., additional, Yeates, Keith Owen, additional, and Barlow, Karen M., additional

Published
2020
Full Text
View/download PDF

45. Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites

Author

Považan, Michal, primary, Mikkelsen, Mark, additional, Berrington, Adam, additional, Bhattacharyya, Pallab K., additional, Brix, Maiken K., additional, Buur, Pieter F., additional, Cecil, Kim M., additional, Chan, Kimberly L., additional, Chen, David Y.T., additional, Craven, Alexander R., additional, Cuypers, Koen, additional, Dacko, Michael, additional, Duncan, Niall W., additional, Dydak, Ulrike, additional, Edmondson, David A., additional, Ende, Gabriele, additional, Ersland, Lars, additional, Forbes, Megan A., additional, Gao, Fei, additional, Greenhouse, Ian, additional, Harris, Ashley D., additional, He, Naying, additional, Heba, Stefanie, additional, Hoggard, Nigel, additional, Hsu, Tun-Wei, additional, Jansen, Jacobus F. A., additional, Kangarlu, Alayar, additional, Lange, Thomas, additional, Lebel, R. Marc, additional, Li, Yan, additional, Lin, Chien-Yuan E., additional, Liou, Jy-Kang, additional, Lirng, Jiing-Feng, additional, Liu, Feng, additional, Long, Joanna R., additional, Ma, Ruoyun, additional, Maes, Celine, additional, Moreno-Ortega, Marta, additional, Murray, Scott O., additional, Noah, Sean, additional, Noeske, Ralph, additional, Noseworthy, Michael D., additional, Oeltzschner, Georg, additional, Porges, Eric C., additional, Prisciandaro, James J., additional, Puts, Nicolaas A. J., additional, Roberts, Timothy P. L., additional, Sack, Markus, additional, Sailasuta, Napapon, additional, Saleh, Muhammad G., additional, Schallmo, Michael-Paul, additional, Simard, Nicholas, additional, Stoffers, Diederick, additional, Swinnen, Stephan P., additional, Tegenthoff, Martin, additional, Truong, Peter, additional, Wang, Guangbin, additional, Wilkinson, Iain D., additional, Wittsack, Hans-Jörg, additional, Woods, Adam J., additional, Xu, Hongmin, additional, Yan, Fuhua, additional, Zhang, Chencheng, additional, Zipunnikov, Vadim, additional, Zöllner, Helge J., additional, Edden, Richard A.E., additional, and Barker, Peter B., additional

Published
2020
Full Text
View/download PDF

49. Extraction of a vascular function for a fully automated dynamic contrast‐enhanced magnetic resonance brain image processing pipeline.

Author

Loos, Wallace S., Souza, Roberto, Andersen, Linda B., Lebel, R. Marc, and Frayne, Richard

Subjects
CONTRAST-enhanced magnetic resonance imaging, IMAGE processing, MAGNETIC resonance imaging, GLIOBLASTOMA multiforme, BRAIN imaging
Abstract

Purpose: To develop a deep‐learning model that leverages the spatial and temporal information from dynamic contrast‐enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling. Methods: Patients with glioblastoma multiforme were scanned post‐resection approximately every 2 months using a high spatial and temporal resolution DCE MR imaging sequence (≈5 s and ≈2 cm3). A region over the transverse sinus was manually drawn in the dynamic T1‐weighted images to provide a ground truth VF. The manual regions and their resulting VF curves were used to train a deep‐learning model based on a 3D U‐net architecture. The model concurrently utilized the spatial and temporal information in DCE MR images to predict the VF. In order to analyze the contribution of the spatial and temporal terms, different weighted combinations were examined. The manual and deep‐learning predicted regions and VF curves were compared. Results: Forty‐three patients were enrolled in this study and 155 DCE MR scans were processed. The 3D U‐net was trained using a loss function that combined the spatial and temporal information with different weightings. The best VF curves were obtained when both spatial and temporal information were considered. The predicted VF curve was similar to the manual ground truth VF curves. Conclusion: The use of spatial and temporal information improved VF curve prediction relative to when only the spatial information is used. The method generalized well for unseen data and can be used to automatically estimate a VF curve suitable for quantitative PK modeling. This method allows for a more efficient clinical pipeline and may improve automation of permeability mapping. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

50. The magnet system of the Relativistic Heavy Ion Collider (RHIC)

Author

Greene, A., Anerella, M., Cozzolino, J., Escallier, J., Fisher, D., Ganetis, G., Ghosh, A., Gupta, R., Jain, A., Kahn, S., Kelly, E., Lebel, R., Morgan, G., Morgillo, A., Mulhall, S., Muratore, J., Plate, S., Prodell, A., Rehak, M., Sampson, W., Thomas, R., Thompson, P., Wanderer, P., and Willen, E.

Subjects
Ion accelerators -- Design and construction, Storage rings -- Design and construction, Superconducting magnets -- Design and construction, Heavy ion collisions -- Research, Business, Electronics, Electronics and electrical industries
Abstract

The Relativistic Heavy Ion Collider now under construction at Brookhaven National Laboratory (BNL) is a colliding ring accelerator to be completed in 1999. Through collisions of heavy ions it is hoped to observe the creation of matter at extremely high temperatures and densities, similar to what may have occurred in the original 'Big Bang'. The collider rings will consist of 1740 superconducting magnet elements. Some of these elements are being manufactured by industrial partners (Northrop Grumman and Everson Electric). Others are being constructed or assembled at BNL. A description is given of the magnet designs, the plan for manufacturing and test results. In the manufacturing of the magnets, emphasis has been placed on uniformity of their performance and on quality. Results so far indicate that this emphasis has been very successful.

Published
1996

Catalog

Books, media, physical & digital resources
See catalog results