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Optimal control gradient precision trade-offs: Application to fast generation of DeepControl libraries for MRI

Vinding, Mads Sloth; Goodwin, David L.; Kuprov, Ilya; Lund, Torben Ellegaard


We have recently demonstrated supervised deep learning methods for rapid generation of radiofrequency pulses in magnetic resonance imaging (, Unlike the previous iterative optimization approaches, deep learning methods generate a pulse using a fixed number of floating-point operations - this is important in MRI, where patient-specific pulses preferably must be produced in real time. However, deep learning requires vast training libraries, which must be generated using the traditional methods, e.g., iterative quantum optimal control methods. Those methods are usually variations of gradient descent, and the calculation of the gradient of the performance metric with respect to the pulse waveform can be the most numerically intensive step. In this communication, we explore various ways in which the calculation of gradients in quantum optimal control theory may be accelerated. Four optimization avenues are explored: truncated commutator series expansions at zeroth and first order, a novel midpoint truncation scheme at first order, and the exact complex-step method. For the spin systems relevant to MRI, the first-order midpoint truncation is found to be sufficiently accurate, but also significantly faster than the machine precision gradient. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000140064
Veröffentlicht am 18.12.2021
DOI: 10.1016/j.jmr.2021.107094
Zitationen: 4
Web of Science
Zitationen: 3
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biologische Grenzflächen (IBG)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2021
Sprache Englisch
Identifikator ISSN: 1090-7807
KITopen-ID: 1000140064
HGF-Programm 43.35.02 (POF IV, LK 01) Functionality of Soft Matter and Biomolecular Systems
Erschienen in Journal of magnetic resonance
Verlag Elsevier
Band 333
Seiten Art.Nr. 107094
Nachgewiesen in Web of Science
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