US-20260126506-A1 - Implicit Field Estimator for Spatio-Temporally Varying Field Imperfections

Abstract

A method for magnetic resonance imaging acquires clean source data that is free of field imperfections, and target data that has field imperfections. The source data and target data are k-space data acquired under conditions of matching static B0 inhomogeneity and relaxation-based signal evolution in time. A neural network is trained using the source data and the target data to learn a mapping from temporal and gradient-based features to field GRAPPA-like kernels representing phase inhomogeneity effects. The kernels are used to reconstruct an image-domain phase inhomogeneity map which varies over time across a region of interest. An MRI image is then reconstructed using this extracted spatial-temporal phase imperfection to correct for field imperfection effects.

Inventors

• Zachary Andrew Shah
• Daniel Raz Abraham
• Kawin Setsompop

Assignees

• THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY

Dates

Publication Date

20260507

Application Date

20251105

Claims (6)

1. 1 . A method for magnetic resonance imaging comprising: a) acquiring data by an MRI scanner for calibrating gradient imperfections, wherein the data comprises a source dataset and a target dataset, wherein the source dataset and the target dataset are each acquired under conditions with different gradient imperfections but are matched in relaxation-based signal evolution and static B 0 inhomogeneity; b) using the source dataset and the target dataset to learn a mapping from temporal-based and gradient-based features to Fourier-weighted multi-coil kernels representing phase inhomogeneity effects; wherein the Fourier-weighted multi-coil kernels are time-dependent and transform points in the source dataset to nearby points in the target dataset; c) extracting a spatial-temporally-varying image-domain phase inhomogeneity across a region of interest using the Fourier-weighted multi-coil kernels and spatial sensitivity maps of receive coils; d) reconstructing an MRI image from the target dataset using the extracted image-domain phase inhomogeneity to correct for field imperfection effects due to high slew rate acquisition sequences.
2. 2 . The method of claim 1 wherein the target dataset has field imperfections and the source dataset is free of the field imperfections.
3. 3 . The method of claim 1 wherein acquiring the source dataset and the target dataset by an MRI scanner comprises acquiring the target dataset by sampling k-t space along a target trajectory, and acquiring the source dataset comprises sampling k-t space surrounding the target trajectory.
4. 4 . The method of claim 1 wherein acquiring the source dataset comprises using a fully-phase encoded acquisition, an echo planar time resolved imaging acquisition, or an accelerated echo planar spectroscopic imaging acquisition for mapping imperfection-free k-space Nyquist-resolved in time.
5. 5 . The method in claim 1 wherein acquiring the target dataset is supplemented with target acquisitions at additional Fourier offsets to increase the fidelity of the calibration fit and allow lower-resolution k-t mapping of the source dataset.
6. 6 . The method of claim 1 wherein acquiring source dataset and target dataset by an MRI scanner comprises acquiring the source dataset in a calibration scan and subsequently acquiring the target dataset in a target scan, where the target scan matches a B 0 inhomogeneity and relaxation-induced signal evolution of the calibration scan in time.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. patent application Ser. No. 19/200,007 filed May 6, 2025, which claims priority from U.S. Provisional Patent Application 63/642,958 filed May 6, 2024, both of which are incorporated herein by reference. STATEMENT OF FEDERALLY SPONSORED RESEARCH This invention was made with Government support under contracts MH116173, EB033206, and EB019437 awarded by the National Institutes of Health. The Government has certain rights in the invention. FIELD OF THE INVENTION The present invention relates generally to medical imaging. More specifically, it relates to methods for rapid magnetic resonance imaging techniques. BACKGROUND OF THE INVENTION In magnetic resonance imaging (MRI), it is desirable to acquire images as fast as possible for a number of clinical reasons. However, such rapid imaging requires pushing the system to its limit, leading to system imperfections such as from eddy currents and gradient nonlinearities that can cause artifacts like blurring, distortion, and signal loss. Nuclear magnetic resonance (NMR) field probes can accurately measure these imperfections to achieve high-quality imaging, but these probes require additional hardware and cost. SUMMARY OF THE INVENTION The present invention provides a data-driven approach to gradient field characterization. This enables enhanced image reconstruction for high-slew MRI without the need for external hardware, potentially revolutionizing fast acquisition MRI techniques and broadening their application. The present invention provides methods, devices and systems for imaging-based approaches to estimate gradient imperfections by leveraging encoding capability of modern multi-channel receivers and neural networks for implicit Fourier phase representation. MRI companies could use this invention to acquire and reconstruct scans with rapid acquisition sequences without requiring external hardware purchases or installation, permitting higher imaging quality for high slew-rate sequences. To algorithmically estimate field imperfections, we acquire sets of measurements with different field imperfections. For example, a first set of scanner data is free of field imperfections (which we designate the source data), and a second set of scanner data has field imperfections present (which we call the target data). Then, for given sets of target and clean data, we find a time-dependent kernel that transforms of a set of points in the source data to the data in the target scan. Such a time-parameterized mapping is a Fourier-weighted multi-coil kernel which mimics the spatio-temporal field imperfection experienced during the target scan. Using this kernel and the spatial sensitivity maps of our receive coils, we can then extract the image-domain phase inhomogeneity across the region of interest using projections in a basis spanned by the multiple receive channels. This phase map can then be used in image reconstruction to correct for any field imperfection effects. This method to estimate field imperfections algorithmically involves and efficient calibration scan, which uses an imaging sequence design that collects the source scanner data to which the target sequence desiring characterization can be referenced against. This method also involves a technique for field estimation, which uses a data-driven approach to retrieve the field inhomogeneity map from such scans at each timepoint along the scan. The efficient calibration scan, which samples k-t space with a time-resolved imaging sequence. Examples include fully-phase encoded imaging, accelerated variants of echo-planar spectroscopic imaging (EPSI), and echo-planar time-resolved imaging (EPTI). For added efficiency, the target acquisition can be acquired at Fourier offsets to match nominally high-resolution trajectory data to a lower-resolution clean k-t dataset. For 3D characterization, a 2D acquisition can be performed at multiple slice positions to characterize cross-term interactions in orthogonal encoding axes, while only requiring an efficient collection of 2D k-space in the clean k-t dataset, instead viewed at multiple characterizing slice positions. For characterizing system imperfections that are not subject-dependent, this calibration can be done a priori on a phantom before the subject enters the scanner. For scan prescriptions requiring subject-dependent gradient utilization, an efficient EPTI k-t sampling method presents a more efficient encoding from which a fully-sampled k-t space can be interpolated with minimal eddy currents at play in under one minute. Field estimation algorithm. After the calibration scan has been completed, we collect the target scan along a desired trajectory, where this target scan matches to the B0 inhomogeneity and relaxation-induced signal evolution of the calibration scan in time. Thus, any differences in the signals at the sampled k-t coordinates collected between the calibration and targ