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pro vyhledávání: '"MacDonald, M A"'
Acquiring accurate external respiratory data during functional Magnetic Resonance Imaging (fMRI) is challenging, prompting the exploration of machine learning methods to estimate respiratory variation (RV) from fMRI data. Respiration induces head mot
Externí odkaz:
http://arxiv.org/abs/2410.19802
Motivation: Alzheimer's Disease hallmarks include amyloid-beta deposits and brain atrophy, detectable via PET and MRI scans, respectively. PET is expensive, invasive and exposes patients to ionizing radiation. MRI is cheaper, non-invasive, and free f
Externí odkaz:
http://arxiv.org/abs/2405.02109
Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for periphera
Externí odkaz:
http://arxiv.org/abs/2405.00219
Autor:
Gianchandani, Neha, Dibaji, Mahsa, Ospel, Johanna, Vega, Fernando, Bento, Mariana, MacDonald, M. Ethan, Souza, Roberto
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that can provide
Externí odkaz:
http://arxiv.org/abs/2310.11385
In this work, an image translation model is implemented to produce synthetic amyloid-beta PET images from structural MRI that are quantitatively accurate. Image pairs of amyloid-beta PET and structural MRI were used to train the model. We found that
Externí odkaz:
http://arxiv.org/abs/2309.00569
Background: Mental illness can lead to adverse outcomes such as homelessness and police interaction and understanding of the events leading up to these adverse outcomes is important. Predictive models may help identify individuals at risk of such adv
Externí odkaz:
http://arxiv.org/abs/2307.11211
In this work, a denoising Cycle-GAN (Cycle Consistent Generative Adversarial Network) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images from simulated low-field, low resoluti
Externí odkaz:
http://arxiv.org/abs/2307.06338
Autor:
Addeh, Abdoljalil, Vega, Fernando, Williams, Rebecca J, Golestani, Ali, Pike, G. Bruce, MacDonald, M. Ethan
In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model f
Externí odkaz:
http://arxiv.org/abs/2307.05426
Autor:
Ross, J. S., Ralph, J. E., Zylstra, A. B., Kritcher, A. L., Robey, H. F., Young, C. V., Hurricane, O. A., Callahan, D. A., Baker, K. L., Casey, D. T., Doeppner, T., Divol, L., Hohenberger, M., Pape, S. Le, Pak, A., Patel, P. K., Tommasini, R., Ali, S. J., Amendt, P. A., Atherton, L. J., Bachmann, B., Bailey, D., Benedetti, L. R., Hopkins, L. Berzak, Betti, R., Bhandarkar, S. D., Bionta, R. M., Birge, N. W., Bond, E. J., Bradley, D. K., Braun, T., Briggs, T. M., Bruhn, M. W., Celliers, P. M., Chang, B., Chapman, T., Chen, H., Choate, C., Christopherson, A. R., Clark, D. S., Crippen, J. W., Dewald, E. L., Dittrich, T. R., Edwards, M. J., Farmer, W. A., Field, J. E., Fittinghoff, D., Frenje, J., Gaffney, J., Johnson, M. Gatu, Glenzer, S. H., Grim, G. P., Haan, S., Hahn, K. D., Hall, G. N., Hammel, B. A., Harte, J., Hartouni, E., Heebner, J. E., Hernandez, V. J., Herrmann, H., Herrmann, M. C., Hinkel, D. E., Ho, D. D., Holder, J. P., Hsing, W. W., Huang, H., Humbird, K. D., Izumi, N., Jarrott, L. C., Jeet, J., Jones, O., Kerbel, G. D., Kerr, S. M., Khan, S. F., Kilkenny, J., Kim, Y., Kleinrath, H. Geppert, Kleinrath, V. Geppert, Kong, C., Koning, J. M., Kroll, J. J., Landen, O. L., Langer, S., Larson, D., Lemos, N. C., Lindl, J. D., Ma, T., MacDonald, M. J., MacGowan, B. J., Mackinnon, A. J., MacLaren, S. A., MacPhee, A. G., Marinak, M. M., Mariscal, D. A., Marley, E. V., Masse, L., Meaney, K., Meezan, N. B., Michel, P. A., Millot, M., Milovich, J. L., Moody, J. D., Moore, A. S., Morton, J. W., Murphy, T., Newman, K., Di Nicola, J. -M. G., Nikroo, A., Nora, R., Patel, M. V., Pelz, L. J., Peterson, J. L., Ping, Y., Pollock, B. B., Ratledge, M., Rice, N. G., Rinderknecht, H., Rosen, M., Rubery, M. S., Salmonson, J. D., Sater, J., Schiaffino, S., Schlossberg, D. J., Schneider, M. B., Schroeder, C. R., Scott, H. A., Sepke, S. M., Sequoia, K., Sherlock, M. W., Shin, S., Smalyuk, V. A., Spears, B. K., Springer, P. T., Stadermann, M., Stoupin, S., Strozzi, D. J., Suter, L. J., Thomas, C. A., Town, R. P. J., Tubman, E. R., Volegov, P. L., Weber, C. R., Widmann, K., Wild, C., Wilde, C. H., Van Wonterghem, B. M., Woods, D. T., Woodworth, B. N., Yamaguchi, M., Yang, S. T., Zimmerman, G. B.
An experimental program is currently underway at the National Ignition Facility (NIF) to compress deuterium and tritium (DT) fuel to densities and temperatures sufficient to achieve fusion and energy gain. The primary approach being investigated is i
Externí odkaz:
http://arxiv.org/abs/2111.04640
Publikováno v:
In International Journal of Heat and Fluid Flow February 2024 105