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Constrained .

SIAM journal on imaging sciences

Mang A, Biros G.
PMID: 29075361
SIAM J Imaging Sci. 2016;9(3):1154-1194. doi: 10.1137/15M1010919. Epub 2016 Aug 30.

We propose regularization schemes for deformable registration and efficient algorithms for their numerical approximation. We treat image registration as a variational optimal control problem. The deformation map is parametrized by its velocity. Tikhonov regularization ensures well-posedness. Our scheme augments...

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Mang A, Biros G. Constrained . SIAM J Imaging Sci. 2016;9(3):1154-1194doi: 10.1137/15M1010919.
Mang, A., & Biros, G. (2016). Constrained . SIAM journal on imaging sciences, 9(3), 1154-1194. https://doi.org/10.1137/15M1010919
Mang, Andreas, and Biros, George. "Constrained ." SIAM journal on imaging sciences vol. 9,3 (2016): 1154-1194. doi: https://doi.org/10.1137/15M1010919
Mang A, Biros G. Constrained . SIAM J Imaging Sci. 2016;9(3):1154-1194. doi: 10.1137/15M1010919. Epub 2016 Aug 30. PMID: 29075361; PMCID: PMC5654641.
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Pathogenesis of Fibrosis Produced by Asbestos and Man-Made Mineral Fibers: What Makes a Fiber Fibrogenic?.

Inhalation toxicology

Churg A, Wright J, Gilks B, Dai J.
PMID: 26368597
Inhal Toxicol. 2000 Jan;12:15-26. doi: 10.1080/08958378.2000.11463227.

Recent studies have revealed a wide array of molecular and cellular changes in cells and whole lungs exposed to asbestos fibers, changes that are presumed to be related to asbestos-induced fibrogenesis. These include generation of reactive oxygen species (ROS),...

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Churg A, Wright J, Gilks B, et al. Pathogenesis of Fibrosis Produced by Asbestos and Man-Made Mineral Fibers: What Makes a Fiber Fibrogenic?. Inhal Toxicol. 2000;12:15-26doi: 10.1080/08958378.2000.11463227.
Churg, A., Wright, J., Gilks, B., & Dai, J. (2000). Pathogenesis of Fibrosis Produced by Asbestos and Man-Made Mineral Fibers: What Makes a Fiber Fibrogenic?. Inhalation toxicology, 1215-26. https://doi.org/10.1080/08958378.2000.11463227
Churg, A, et al. "Pathogenesis of Fibrosis Produced by Asbestos and Man-Made Mineral Fibers: What Makes a Fiber Fibrogenic?." Inhalation toxicology vol. 12 (2000): 15-26. doi: https://doi.org/10.1080/08958378.2000.11463227
Churg A, Wright J, Gilks B, Dai J. Pathogenesis of Fibrosis Produced by Asbestos and Man-Made Mineral Fibers: What Makes a Fiber Fibrogenic?. Inhal Toxicol. 2000 Jan;12:15-26. doi: 10.1080/08958378.2000.11463227. PMID: 26368597.
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The Space Physics Environment Data Analysis System (SPEDAS).

Space science reviews

Angelopoulos V, Cruce P, Drozdov A, Grimes EW, Hatzigeorgiu N, King DA, Larson D, Lewis JW, McTiernan JM, Roberts DA, Russell CL, Hori T, Kasahara Y, Kumamoto A, Matsuoka A, Miyashita Y, Miyoshi Y, Shinohara I, Teramoto M, Faden JB, Halford AJ, McCarthy M, Millan RM, Sample JG, Smith DM, Woodger LA, Masson A, Narock AA, Asamura K, Chang TF, Chiang CY, Kazama Y, Keika K, Matsuda S, Segawa T, Seki K, Shoji M, Tam SWY, Umemura N, Wang BJ, Wang SY, Redmon R, Rodriguez JV, Singer HJ, Vandegriff J, Abe S, Nose M, Shinbori A, Tanaka YM, UeNo S, Andersson L, Dunn P, Fowler C, Halekas JS, Hara T, Harada Y, Lee CO, Lillis R, Mitchell DL, Argall MR, Bromund K, Burch JL, Cohen IJ, Galloy M, Giles B, Jaynes AN, Le Contel O, Oka M, Phan TD, Walsh BM, Westlake J, Wilder FD, Bale SD, Livi R, Pulupa M, Whittlesey P, DeWolfe A, Harter B, Lucas E, Auster U, Bonnell JW, Cully CM, Donovan E, Ergun RE, Frey HU, Jackel B, Keiling A, Korth H, McFadden JP, Nishimura Y, Plaschke F, Robert P, Turner DL, Weygand JM, Candey RM, Johnson RC, Kovalick T, Liu MH, McGuire RE, Breneman A, Kersten K, Schroeder P.
PMID: 30880847
Space Sci Rev. 2019;215(1):9. doi: 10.1007/s11214-018-0576-4. Epub 2019 Jan 22.

With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data...

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Angelopoulos V, Cruce P, Drozdov A, et al. The Space Physics Environment Data Analysis System (SPEDAS). Space Sci Rev. 2019;215(1):9doi: 10.1007/s11214-018-0576-4.
Angelopoulos, V., Cruce, P., Drozdov, A., Grimes, E. W., Hatzigeorgiu, N., King, D. A., Larson, D., Lewis, J. W., McTiernan, J. M., Roberts, D. A., Russell, C. L., Hori, T., Kasahara, Y., Kumamoto, A., Matsuoka, A., Miyashita, Y., Miyoshi, Y., Shinohara, I., Teramoto, M., Faden, J. B., Halford, A. J., McCarthy, M., Millan, R. M., Sample, J. G., Smith, D. M., Woodger, L. A., Masson, A., Narock, A. A., Asamura, K., Chang, T. F., Chiang, C. Y., Kazama, Y., Keika, K., Matsuda, S., Segawa, T., Seki, K., Shoji, M., Tam, S. W. Y., Umemura, N., Wang, B. J., Wang, S. Y., Redmon, R., Rodriguez, J. V., Singer, H. J., Vandegriff, J., Abe, S., Nose, M., Shinbori, A., Tanaka, Y. M., UeNo, S., Andersson, L., Dunn, P., Fowler, C., Halekas, J. S., Hara, T., Harada, Y., Lee, C. O., Lillis, R., Mitchell, D. L., Argall, M. R., Bromund, K., Burch, J. L., Cohen, I. J., Galloy, M., Giles, B., Jaynes, A. N., Le Contel, O., Oka, M., Phan, T. D., Walsh, B. M., Westlake, J., Wilder, F. D., Bale, S. D., Livi, R., Pulupa, M., Whittlesey, P., DeWolfe, A., Harter, B., Lucas, E., Auster, U., Bonnell, J. W., Cully, C. M., Donovan, E., Ergun, R. E., Frey, H. U., Jackel, B., Keiling, A., Korth, H., McFadden, J. P., Nishimura, Y., Plaschke, F., Robert, P., Turner, D. L., Weygand, J. M., Candey, R. M., Johnson, R. C., Kovalick, T., Liu, M. H., McGuire, R. E., Breneman, A., Kersten, K., & Schroeder, P. (2019). The Space Physics Environment Data Analysis System (SPEDAS). Space science reviews, 215(1), 9. https://doi.org/10.1007/s11214-018-0576-4
Angelopoulos, V, et al. "The Space Physics Environment Data Analysis System (SPEDAS)." Space science reviews vol. 215,1 (2019): 9. doi: https://doi.org/10.1007/s11214-018-0576-4
Angelopoulos V, Cruce P, Drozdov A, Grimes EW, Hatzigeorgiu N, King DA, Larson D, Lewis JW, McTiernan JM, Roberts DA, Russell CL, Hori T, Kasahara Y, Kumamoto A, Matsuoka A, Miyashita Y, Miyoshi Y, Shinohara I, Teramoto M, Faden JB, Halford AJ, McCarthy M, Millan RM, Sample JG, Smith DM, Woodger LA, Masson A, Narock AA, Asamura K, Chang TF, Chiang CY, Kazama Y, Keika K, Matsuda S, Segawa T, Seki K, Shoji M, Tam SWY, Umemura N, Wang BJ, Wang SY, Redmon R, Rodriguez JV, Singer HJ, Vandegriff J, Abe S, Nose M, Shinbori A, Tanaka YM, UeNo S, Andersson L, Dunn P, Fowler C, Halekas JS, Hara T, Harada Y, Lee CO, Lillis R, Mitchell DL, Argall MR, Bromund K, Burch JL, Cohen IJ, Galloy M, Giles B, Jaynes AN, Le Contel O, Oka M, Phan TD, Walsh BM, Westlake J, Wilder FD, Bale SD, Livi R, Pulupa M, Whittlesey P, DeWolfe A, Harter B, Lucas E, Auster U, Bonnell JW, Cully CM, Donovan E, Ergun RE, Frey HU, Jackel B, Keiling A, Korth H, McFadden JP, Nishimura Y, Plaschke F, Robert P, Turner DL, Weygand JM, Candey RM, Johnson RC, Kovalick T, Liu MH, McGuire RE, Breneman A, Kersten K, Schroeder P. The Space Physics Environment Data Analysis System (SPEDAS). Space Sci Rev. 2019;215(1):9. doi: 10.1007/s11214-018-0576-4. Epub 2019 Jan 22. PMID: 30880847; PMCID: PMC6380193.
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The two-fluid dynamics and energetics of the asymmetric magnetic reconnection in laboratory and space plasmas.

Nature communications

Yamada M, Chen LJ, Yoo J, Wang S, Fox W, Jara-Almonte J, Ji H, Daughton W, Le A, Burch J, Giles B, Hesse M, Moore T, Torbert R.
PMID: 30523290
Nat Commun. 2018 Dec 06;9(1):5223. doi: 10.1038/s41467-018-07680-2.

Magnetic reconnection is a fundamental process in magnetized plasma where magnetic energy is converted to plasma energy. Despite huge differences in the physical size of the reconnection layer, remarkably similar characteristics are observed in both laboratory and magnetosphere plasmas....

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Yamada M, Chen LJ, Yoo J, et al. The two-fluid dynamics and energetics of the asymmetric magnetic reconnection in laboratory and space plasmas. Nat Commun. 2018;9(1):5223doi: 10.1038/s41467-018-07680-2.
Yamada, M., Chen, L. J., Yoo, J., Wang, S., Fox, W., Jara-Almonte, J., Ji, H., Daughton, W., Le, A., Burch, J., Giles, B., Hesse, M., Moore, T., & Torbert, R. (2018). The two-fluid dynamics and energetics of the asymmetric magnetic reconnection in laboratory and space plasmas. Nature communications, 9(1), 5223. https://doi.org/10.1038/s41467-018-07680-2
Yamada, M, et al. "The two-fluid dynamics and energetics of the asymmetric magnetic reconnection in laboratory and space plasmas." Nature communications vol. 9,1 (2018): 5223. doi: https://doi.org/10.1038/s41467-018-07680-2
Yamada M, Chen LJ, Yoo J, Wang S, Fox W, Jara-Almonte J, Ji H, Daughton W, Le A, Burch J, Giles B, Hesse M, Moore T, Torbert R. The two-fluid dynamics and energetics of the asymmetric magnetic reconnection in laboratory and space plasmas. Nat Commun. 2018 Dec 06;9(1):5223. doi: 10.1038/s41467-018-07680-2. PMID: 30523290; PMCID: PMC6283883.
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ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints.

Computers in biology and medicine

Pezoulas VC, Kourou KD, Mylona E, Papaloukas C, Liontos A, Biros D, Milionis OI, Kyriakopoulos C, Kostikas K, Milionis H, Fotiadis DI.
PMID: 35007991
Comput Biol Med. 2021 Dec 27;141:105176. doi: 10.1016/j.compbiomed.2021.105176. Epub 2021 Dec 27.

The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need...

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Pezoulas VC, Kourou KD, Mylona E, et al. ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Comput Biol Med. 2021;141:105176doi: 10.1016/j.compbiomed.2021.105176.
Pezoulas, V. C., Kourou, K. D., Mylona, E., Papaloukas, C., Liontos, A., Biros, D., Milionis, O. I., Kyriakopoulos, C., Kostikas, K., Milionis, H., & Fotiadis, D. I. (2021). ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Computers in biology and medicine, 141105176. https://doi.org/10.1016/j.compbiomed.2021.105176
Pezoulas, Vasileios C, et al. "ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints." Computers in biology and medicine vol. 141 (2021): 105176. doi: https://doi.org/10.1016/j.compbiomed.2021.105176
Pezoulas VC, Kourou KD, Mylona E, Papaloukas C, Liontos A, Biros D, Milionis OI, Kyriakopoulos C, Kostikas K, Milionis H, Fotiadis DI. ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Comput Biol Med. 2021 Dec 27;141:105176. doi: 10.1016/j.compbiomed.2021.105176. Epub 2021 Dec 27. PMID: 35007991; PMCID: PMC8711179.
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Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach.

European journal of sport science

Giles B, Peeling P, Kovalchik S, Reid M.
PMID: 34781856
Eur J Sport Sci. 2021 Nov 15;1-20. doi: 10.1080/17461391.2021.2006800. Epub 2021 Nov 15.

PURPOSE: Recent explorations of tennis-specific movements have developed contemporary methods for identifying and classifying changes of direction (COD) during match-play. The aim of this research was to employ these new analysis techniques to objectively explore individual nuance and style...

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Giles B, Peeling P, Kovalchik S, et al. Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach. Eur J Sport Sci. 2021;1-20doi: 10.1080/17461391.2021.2006800.
Giles, B., Peeling, P., Kovalchik, S., & Reid, M. (2021). Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach. European journal of sport science, 1-20. https://doi.org/10.1080/17461391.2021.2006800
Giles, Brandon, et al. "Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach." European journal of sport science vol. (2021): 1-20. doi: https://doi.org/10.1080/17461391.2021.2006800
Giles B, Peeling P, Kovalchik S, Reid M. Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach. Eur J Sport Sci. 2021 Nov 15;1-20. doi: 10.1080/17461391.2021.2006800. Epub 2021 Nov 15. PMID: 34781856.
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Electronic patient-reported outcomes monitoring during lung cancer chemotherapy: A nested cohort within the PRO-TECT pragmatic trial (AFT-39).

Lung cancer (Amsterdam, Netherlands)

Mody GN, Stover AM, Wang M, King-Kallimanis BL, Jansen J, Henson S, Chung AE, Jonsson M, Bennett A, Smith AB, Wood WA, Deal A, Ginos B, Dueck AC, Schrag D, Basch E.
PMID: 34634754
Lung Cancer. 2021 Dec;162:1-8. doi: 10.1016/j.lungcan.2021.09.020. Epub 2021 Sep 30.

OBJECTIVES: Patients with lung cancer have high symptom burden and diminished quality of life. Electronic patient-reported outcome (PRO) platforms deliver repeated longitudinal surveys via web or telephone to patients and alert clinicians about concerning symptoms. This study aims to...

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Mody GN, Stover AM, Wang M, et al. Electronic patient-reported outcomes monitoring during lung cancer chemotherapy: A nested cohort within the PRO-TECT pragmatic trial (AFT-39). Lung Cancer. 2021;162:1-8doi: 10.1016/j.lungcan.2021.09.020.
Mody, G. N., Stover, A. M., Wang, M., King-Kallimanis, B. L., Jansen, J., Henson, S., Chung, A. E., Jonsson, M., Bennett, A., Smith, A. B., Wood, W. A., Deal, A., Ginos, B., Dueck, A. C., Schrag, D., & Basch, E. (2021). Electronic patient-reported outcomes monitoring during lung cancer chemotherapy: A nested cohort within the PRO-TECT pragmatic trial (AFT-39). Lung cancer (Amsterdam, Netherlands), 1621-8. https://doi.org/10.1016/j.lungcan.2021.09.020
Mody, Gita N, et al. "Electronic patient-reported outcomes monitoring during lung cancer chemotherapy: A nested cohort within the PRO-TECT pragmatic trial (AFT-39)." Lung cancer (Amsterdam, Netherlands) vol. 162 (2021): 1-8. doi: https://doi.org/10.1016/j.lungcan.2021.09.020
Mody GN, Stover AM, Wang M, King-Kallimanis BL, Jansen J, Henson S, Chung AE, Jonsson M, Bennett A, Smith AB, Wood WA, Deal A, Ginos B, Dueck AC, Schrag D, Basch E. Electronic patient-reported outcomes monitoring during lung cancer chemotherapy: A nested cohort within the PRO-TECT pragmatic trial (AFT-39). Lung Cancer. 2021 Dec;162:1-8. doi: 10.1016/j.lungcan.2021.09.020. Epub 2021 Sep 30. PMID: 34634754.
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Regulation of GluA1 phosphorylation by d-amphetamine and methylphenidate in the cerebellum.

Addiction biology

Cutando L, Puighermanal E, Castell L, Tarot P, Bertaso F, Bonnavion P, de Kerchove d'Exaerde A, Isingrini E, Galante M, Dallerac G, Pascoli V, Lüscher C, Giros B, Valjent E.
PMID: 33368923
Addict Biol. 2021 Jul;26(4):e12995. doi: 10.1111/adb.12995. Epub 2020 Dec 26.

Prescription stimulants, such as d-amphetamine or methylphenidate are used to treat suffering from attention-deficit hyperactivity disorder (ADHD). They potently release dopamine (DA) and norepinephrine (NE) and cause phosphorylation of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunit GluA1 in the striatum....

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Cutando L, Puighermanal E, Castell L, et al. Regulation of GluA1 phosphorylation by d-amphetamine and methylphenidate in the cerebellum. Addict Biol. 2020;26(4):e12995doi: 10.1111/adb.12995.
Cutando, L., Puighermanal, E., Castell, L., Tarot, P., Bertaso, F., Bonnavion, P., de Kerchove d'Exaerde, A., Isingrini, E., Galante, M., Dallerac, G., Pascoli, V., Lüscher, C., Giros, B., & Valjent, E. (2021). Regulation of GluA1 phosphorylation by d-amphetamine and methylphenidate in the cerebellum. Addiction biology, 26(4), e12995. https://doi.org/10.1111/adb.12995
Cutando, Laura, et al. "Regulation of GluA1 phosphorylation by d-amphetamine and methylphenidate in the cerebellum." Addiction biology vol. 26,4 (2021): e12995. doi: https://doi.org/10.1111/adb.12995
Cutando L, Puighermanal E, Castell L, Tarot P, Bertaso F, Bonnavion P, de Kerchove d'Exaerde A, Isingrini E, Galante M, Dallerac G, Pascoli V, Lüscher C, Giros B, Valjent E. Regulation of GluA1 phosphorylation by d-amphetamine and methylphenidate in the cerebellum. Addict Biol. 2021 Jul;26(4):e12995. doi: 10.1111/adb.12995. Epub 2020 Dec 26. PMID: 33368923.
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Blue light photodynamic therapy with 5-aminolevulinic acid in refractory mycosis fungoides: A prospective, open-label study.

Journal of the American Academy of Dermatology

Severson KJ, Cumsky HJL, Brumfiel CM, Janeczek MC, Ginos BF, Kosiorek HE, Besch-Stokes J, Patel MH, Rule WG, DiCaudo DJ, Rosenthal AC, Pittelkow MR, Mangold AR.
PMID: 33493572
J Am Acad Dermatol. 2021 Oct;85(4):969-971. doi: 10.1016/j.jaad.2021.01.053. Epub 2021 Jan 23.

No abstract available.

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Severson KJ, Cumsky HJL, Brumfiel CM, et al. Blue light photodynamic therapy with 5-aminolevulinic acid in refractory mycosis fungoides: A prospective, open-label study. J Am Acad Dermatol. 2021;85(4):969-971doi: 10.1016/j.jaad.2021.01.053.
Severson, K. J., Cumsky, H. J. L., Brumfiel, C. M., Janeczek, M. C., Ginos, B. F., Kosiorek, H. E., Besch-Stokes, J., Patel, M. H., Rule, W. G., DiCaudo, D. J., Rosenthal, A. C., Pittelkow, M. R., & Mangold, A. R. (2021). Blue light photodynamic therapy with 5-aminolevulinic acid in refractory mycosis fungoides: A prospective, open-label study. Journal of the American Academy of Dermatology, 85(4), 969-971. https://doi.org/10.1016/j.jaad.2021.01.053
Severson, Kevin J, et al. "Blue light photodynamic therapy with 5-aminolevulinic acid in refractory mycosis fungoides: A prospective, open-label study." Journal of the American Academy of Dermatology vol. 85,4 (2021): 969-971. doi: https://doi.org/10.1016/j.jaad.2021.01.053
Severson KJ, Cumsky HJL, Brumfiel CM, Janeczek MC, Ginos BF, Kosiorek HE, Besch-Stokes J, Patel MH, Rule WG, DiCaudo DJ, Rosenthal AC, Pittelkow MR, Mangold AR. Blue light photodynamic therapy with 5-aminolevulinic acid in refractory mycosis fungoides: A prospective, open-label study. J Am Acad Dermatol. 2021 Oct;85(4):969-971. doi: 10.1016/j.jaad.2021.01.053. Epub 2021 Jan 23. PMID: 33493572.
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Transform Quantization for CNN Compression.

IEEE transactions on pattern analysis and machine intelligence

Young S, Wang Z, Taubman D, Girod B.
PMID: 34048338
IEEE Trans Pattern Anal Mach Intell. 2021 May 28; doi: 10.1109/TPAMI.2021.3084839. Epub 2021 May 28.

In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization. Previous CNN quantization techniques tend to ignore the joint statistics of weights and activations, producing sub-optimal CNN performance at a given quantization bit-rate, or consider...

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Young S, Wang Z, Taubman D, et al. Transform Quantization for CNN Compression. IEEE Trans Pattern Anal Mach Intell. 2021;doi: 10.1109/TPAMI.2021.3084839.
Young, S., Wang, Z., Taubman, D., & Girod, B. (2021). Transform Quantization for CNN Compression. IEEE transactions on pattern analysis and machine intelligence, . https://doi.org/10.1109/TPAMI.2021.3084839
Young, Sean, et al. "Transform Quantization for CNN Compression." IEEE transactions on pattern analysis and machine intelligence vol. (2021). doi: https://doi.org/10.1109/TPAMI.2021.3084839
Young S, Wang Z, Taubman D, Girod B. Transform Quantization for CNN Compression. IEEE Trans Pattern Anal Mach Intell. 2021 May 28; doi: 10.1109/TPAMI.2021.3084839. Epub 2021 May 28. PMID: 34048338.
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Coupling brain-tumor biophysical models and diffeomorphic image registration.

Computer methods in applied mechanics and engineering

Scheufele K, Mang A, Gholami A, Davatzikos C, Biros G, Mehl M.
PMID: 31857736
Comput Methods Appl Mech Eng. 2019 Apr 15;347:533-567. doi: 10.1016/j.cma.2018.12.008. Epub 2019 Jan 07.

We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a framework for joint image registration and biophysical inversion and we apply it to analyze MR images of glioblastomas (primary brain tumors). We have two applications in mind. The first one...

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Scheufele K, Mang A, Gholami A, et al. Coupling brain-tumor biophysical models and diffeomorphic image registration. Comput Methods Appl Mech Eng. 2019;347:533-567doi: 10.1016/j.cma.2018.12.008.
Scheufele, K., Mang, A., Gholami, A., Davatzikos, C., Biros, G., & Mehl, M. (2019). Coupling brain-tumor biophysical models and diffeomorphic image registration. Computer methods in applied mechanics and engineering, 347533-567. https://doi.org/10.1016/j.cma.2018.12.008
Scheufele, Klaudius, et al. "Coupling brain-tumor biophysical models and diffeomorphic image registration." Computer methods in applied mechanics and engineering vol. 347 (2019): 533-567. doi: https://doi.org/10.1016/j.cma.2018.12.008
Scheufele K, Mang A, Gholami A, Davatzikos C, Biros G, Mehl M. Coupling brain-tumor biophysical models and diffeomorphic image registration. Comput Methods Appl Mech Eng. 2019 Apr 15;347:533-567. doi: 10.1016/j.cma.2018.12.008. Epub 2019 Jan 07. PMID: 31857736; PMCID: PMC6922029.
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Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities.

Journal of clinical medicine

Jarrett AM, Faghihi D, Ii DAH, Lima EABF, Virostko J, Biros G, Patt D, Yankeelov TE.
PMID: 32370195
J Clin Med. 2020 May 02;9(5). doi: 10.3390/jcm9051314.

Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications...

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Jarrett AM, Faghihi D, Ii DAH, et al. Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J Clin Med. 2020;9(5)doi: 10.3390/jcm9051314.
Jarrett, A. M., Faghihi, D., Ii, D. A. H., Lima, E. A. B. F., Virostko, J., Biros, G., Patt, D., & Yankeelov, T. E. (2020). Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. Journal of clinical medicine, 9(5), . https://doi.org/10.3390/jcm9051314
Jarrett, Angela M, et al. "Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities." Journal of clinical medicine vol. 9,5 (2020). doi: https://doi.org/10.3390/jcm9051314
Jarrett AM, Faghihi D, Ii DAH, Lima EABF, Virostko J, Biros G, Patt D, Yankeelov TE. Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J Clin Med. 2020 May 02;9(5). doi: 10.3390/jcm9051314. PMID: 32370195; PMCID: PMC7290915.
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Showing 13 to 24 of 158 entries