Show simple item record

Quantumâ inspired algorithm for radiotherapy planning optimization

dc.contributor.authorPakela, Julia M.
dc.contributor.authorTseng, Huan‐hsin
dc.contributor.authorMatuszak, Martha M.
dc.contributor.authorTen Haken, Randall K.
dc.contributor.authorMcShan, Daniel L.
dc.contributor.authorEl Naqa, Issam
dc.date.accessioned2020-02-05T15:05:16Z
dc.date.availableWITHHELD_12_MONTHS
dc.date.available2020-02-05T15:05:16Z
dc.date.issued2020-01
dc.identifier.citationPakela, Julia M.; Tseng, Huan‐hsin ; Matuszak, Martha M.; Ten Haken, Randall K.; McShan, Daniel L.; El Naqa, Issam (2020). "Quantumâ inspired algorithm for radiotherapy planning optimization." Medical Physics 47(1): 5-18.
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/153600
dc.publisherSpringer
dc.publisherWiley Periodicals, Inc.
dc.subject.otheradaptive radiotherapy
dc.subject.otherquantum tunneling optimization
dc.subject.othersimulated annealing
dc.subject.otherIMRT
dc.titleQuantumâ inspired algorithm for radiotherapy planning optimization
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153600/1/mp13840.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153600/2/mp13840_am.pdf
dc.identifier.doi10.1002/mp.13840
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceWebb S. Optimization of conformal radiotherapy dose distributions by simulated annealing. Phys Med Biol. 1989; 34: 1349 â 1370.
dc.identifier.citedreferenceIonQ Inc. A true quantum leap; 2018. https://ionq.co/
dc.identifier.citedreferenceIntel. Reinventing data processing with quantum computing. https://www.intel.com/content/www/us/en/research/quantum-computing.html
dc.identifier.citedreferencerigetti. QPU Specifications; 2019. https://rigetti.com/qpu
dc.identifier.citedreferenceGoogle AI. A Preview of Bristlecone. Google’s New Quantum Processor; 2018 March 5. https://ai.googleblog.com/2018/03/a-preview-of-bristlecone-googles-new.html
dc.identifier.citedreferenceApolloni B, Carvalho C, de Falco D. Quantum stochastic optimization. Stoch Proc Appl. 1989; 33: 233 â 244.
dc.identifier.citedreferenceFarhi E, Goldstone J, Gutmann S, Lapan J, Lundgren A, Preda D. A quantum adiabatic evolution algorithm applied to random instances of an NPâ complete problem. Science. 2001; 292: 472 â 475.
dc.identifier.citedreferenceMukherjee S, Chakrabarti BK. Multivariable optimization: quantum annealing and computation. Eur Phys Jâ Spec Top. 2015; 224: 17 â 24.
dc.identifier.citedreferenceNazareth DP, Spaans JD. First application of quantum annealing to IMRT beamlet intensity optimization. Phys Med Biol. 2015; 60: 4137 â 4148.
dc.identifier.citedreferenceSantoro GE, MartoŠák R, Tosatti E, Car R. Theory of quantum annealing of an Ising spin glass. Science. 2002; 295: 2427 â 2430.
dc.identifier.citedreferenceMorita S, Nishimori H. Mathematical foundation of quantum annealing. J Math Phys. 2008; 49: 125210.
dc.identifier.citedreferenceKadowaki T, Nishimori H. Quantum annealing in the transverse Ising model. Phys Rev E. 1998; 58: 5355.
dc.identifier.citedreferenceZettili N. Quantum Mechanics: Concepts and Applications. New York, NY: John Wiley & Sons, Incorporated; 2009.
dc.identifier.citedreferenceKuech TF. Metalâ organic vapor phase epitaxy of compound semiconductors. Mater Sci Rep. 1987; 2: 1 â 49.
dc.identifier.citedreferenceLeys MR, Veenvliet H. A study of the growth mechanism of epitaxial GaAs as grown by the technique of metal organic vapour phase epitaxy. J Cryst Growth. 55, 145 â 153.
dc.identifier.citedreferenceBertsimas D, Tsitsiklis J. Simulated annealing. Stat Sci. 1993; 8: 10 â 15.
dc.identifier.citedreferenceHajek B. Cooling schedules for optimal annealing. Mathe Oper Res. 1988; 13: 311 â 329.
dc.identifier.citedreferenceKessler ML, McShan DL, Epelman MA, et al. Costlets: a generalized approach to cost functions for automated optimization of IMRT treatment plans. Optimization Eng. 2005; 6: 421 â 448.
dc.identifier.citedreferenceMatuszak MM, Larsen EW, Fraass BA. Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function. Med Phys. 2007; 34: 507 â 520.
dc.identifier.citedreferenceMacFarlane M, Hoover DA, Wong E, Goldman P, Battista JJ, Chen JZ. A fast inverse direct aperture optimization algorithm for intensityâ modulated radiation therapy. Med Phys. 2019; 46: 1127 â 1139.
dc.identifier.citedreferenceHärdenmark B, Liander A, Rehbinder H, Löf J, Robinson D. P3IMRT: direct machine parameter optimization. Pinnacle White Paper; 2004: 983.
dc.identifier.citedreferenceLuo Y, McShan DL, Matuszak MM, et al. A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmallâ cell lung cancer (NSCLC) for responseâ adapted radiotherapy. Med Phys. 2018; 45: 3980 â 3995.
dc.identifier.citedreferenceTseng Hâ H, Luo Y, Cui S, Chien Jâ T, Ten Haken RK, Naqa IE. Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys. 2017; 44: 6690 â 6705.
dc.identifier.citedreferenceDâ Wave Systems Inc. Dâ Wave Previews Nextâ Generation Quantum Computing Platform; 2019 February. https://www.dwavesys.com/press-releases/d-wave-previews-next-generation-quantum-computing-platform
dc.identifier.citedreferenceBortfeld T. IMRT: a review and preview. Phys Med Biol. 2006; 51: R363 â R379.
dc.identifier.citedreferenceCitrin DE. Recent developments in radiotherapy. N Engl J Med. 2017; 377: 2200 â 2201.
dc.identifier.citedreferenceCho B. Intensityâ modulated radiation therapy: a review with a physics perspective. Radiat Oncol J. 2018; 36: 1 â 10.
dc.identifier.citedreferenceBortfeld T, Schmidtâ Ullrich R, Neve W, Wazer DE. Imageâ Guided IMRT. Berlin, Heidelberg: Springer; 2006.
dc.identifier.citedreferenceZhang Y, Merritt M. Doseâ volumeâ based IMRT fluence optimization: a fast leastâ squares approach with differentiability. Linear Algebra Appl. 2008; 428: 1365 â 1387.
dc.identifier.citedreferenceLimâ Reinders S, Keller BM, Alâ Ward S, Sahgal A, Kim A. Online adaptive radiation therapy. Int J Radiat Oncol Biol Phys. 2017; 99: 994 â 1003.
dc.identifier.citedreferencePreskill J. Quantum computing and the entanglement frontier. arXiv preprint arXiv:12035813; 2012.
dc.identifier.citedreferenceNielsen MA, Chuang IL. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge: Cambridge University Press; 2010: http://csis.pace.edu/ctappert/cs837-18spring/QC-textbook.pdf. Accessed 1/29/2019.
dc.identifier.citedreferenceGrover LK. From Schrödingerâ s equation to the quantum search algorithm. Am J Phys. 2001; 69: 769 â 777.
dc.identifier.citedreferenceDâ Wave Systems Inc. Dâ Wave Announces Dâ Wave 2000Q Quantum Computer and First System Order; 2017 January:24; https://www.dwavesys.com/press-releases/d-wave%C2%A0announces%C2%A0d-wave-2000q-quantum-computer-and-first-system-order
dc.identifier.citedreferenceIBM. Quantum devices and simulators. https://www.research.ibm.com/ibm-q/technology/devices/
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.