ArticleIEEE transactions on medical imaging2026
The Ritz Adjoint Method for MRI Pulse Design.
Article in IEEE transactions on medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
High-field magnetic resonance imaging (MRI) suffers from pronounced magnetic field inhomogeneities and subject-specific field variations, motivating the in-scanner design of individually tailored excitation pulses to exploit the full capabilities of high-field MRI. Contemporary methods may employ piecewise-constant (PWC) waveform parameterizations to design excitation pulses, which require many optimization variables and hinder rapid, in-scanner customization. We represent the radiofrequency (RF) and gradient waveforms in a global waveform basis, demonstrated using a Chebyshev polynomial basis, to reduce problem dimensionality and accelerate convergence, while ensuring waveform smoothness. The adjoint method efficiently computes derivatives of the excitation objective function with respect to the basis coefficients, which each influence an entire waveform. Graphics processing unit (GPU) acceleration of derivative computation further reduces computation time, while system and safety constraints are enforced throughout the optimization. Using this global waveform basis yields an approximate five- to ten-fold speedup in subject-specific (tailored) pulse optimization for non-selective excitations and, in the best case, comparable gains for slice-selective designs, making real-time, subject-specific pulse optimization feasible even for advanced pulse types.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.