Joint design of k T -points trajectories and RF pulses under explicit SAR and power constraints in the large flip angle regime

Magnetic Resonance in Med

Vignaud

Amadon

et al. 2016

Self Cite

Section: Methodsmentioning

confidence: 99%

“…Step 4 . Starting from this feasible but likely suboptimal solution, proceed with the joint optimization of the M‐slices problem, where blipped k‐space trajectories and RF coefficients are further simultaneously optimized in a slice‐specific manner.…”

Section: Methodsmentioning

confidence: 99%

In vivo demonstration of whole‐brain multislice multispoke parallel transmit radiofrequency pulse design in the small and large flip angle regimes at 7 Tesla

Magnetic Resonance in Med

Vignaud

Amadon

et al. 2016

Self Cite

“…The k T ‐points pulse design consists in homogenizing the flip angle (FA) distribution in a region of interest by optimizing simultaneously RF complex coefficients, k‐space locations and durations of each k T ‐point sub‐pulse. With N kT sub‐pulses on a pTx system equipped with N Ch transmission channels, and using the vectors x , k , and t to represent, respectively, all sub‐pulse RF complex coefficients, 3D k‐space locations, and sub‐pulse durations, the optimization problem can be expressed as:

\underset{x, k, t}{arg min} \frac{1}{α_{T} \sqrt{N_{v}}} {‖ A (x, k, t) - α_{T} I ‖}_{2}, (x, k, t) \in {double-struckC}^{N_{italicCh} N_{italickT}} \times {double-struckR}^{3 N_{italickT}} \times {double-struckR}^{N_{kT}}

where

α_{T}

is the targeted FA (a scalar), I is the identity vector spanning the N v voxels in the region of interest, and A is the Bloch operator returning a FA for each voxel depending on its

B_{1}^{+}

and Δf 0 values.…”

Section: Theorymentioning

confidence: 99%

SmartPulse, a machine learning approach for calibration‐free dynamic RF shimming: Preliminary study in a clinical environment

Tomi‐Tricot

Magnetic Resonance in Med

Thirion

et al. 2019

Self Cite

Purpose A calibration‐free pulse design method is introduced to alleviate B1+ artifacts in clinical routine with parallel transmission at high field, dealing with significant inter‐subject variability, found for instance in the abdomen. Theory and Methods From a dual‐transmit 3T scanner, a database of B1+ and off‐resonance abdominal maps from 50 subjects was first divided into 3 clusters based on mutual affinity between their respective tailored kT‐points pulses. For each cluster, a kT‐points pulse was computed, minimizing normalized root‐mean‐square flip angle deviations simultaneously for all subjects comprised in it. Using 30 additional subjects' field distributions, a machine learning classifier was trained on this 80‐labeled‐subject database to recognize the best pulse from the 3 ones available, relying only on patient features accessible from the preliminary localizer sequence present in all protocols. This so‐called SmartPulse process was experimentally tested on an additional 53‐subject set and compared with other pulse types: vendor's hard calibration‐free dual excitation, tailored static radiofrequency shimming, universal and tailored kT‐points pulses. Results SmartPulse outperformed both calibration‐free approaches. Tailored static radiofrequency shimming yielded similar flip angle homogeneity for most patients but broke down for some while SmartPulse remained robust. Although flip angle homogeneity was systematically better with tailored kT‐points, the difference was barely noticeable on in vivo images. Conclusion The proposed method paves the way toward an efficient trade‐off between tailored and universal pulse design approaches for large inter‐subject variability. With no need for on‐line field mapping or pulse design, it can fit seamlessly into a clinical protocol.

“…[28][29][30] This was achieved using MatLab's (MathWorks, Natick, MA) built-in active-set algorithm on a laptop computer (Intel Core i7-4712HQ CPU, NVI-DIA Quadro K1100m GPU). [28][29][30] This was achieved using MatLab's (MathWorks, Natick, MA) built-in active-set algorithm on a laptop computer (Intel Core i7-4712HQ CPU, NVI-DIA Quadro K1100m GPU).…”

Section: K T -Points Pulse Designmentioning

confidence: 99%

“…A prototypical k T -points pulse design algorithm minimized the FA NRMSE, i.e., the root-mean-square error normalized to the target FA over the volume of interest, by optimizing simultaneously RF complex coefficients, k T -point locations, pulse and subpulse durations under SAR and hardware constraints. [28][29][30] This was achieved using MatLab's (MathWorks, Natick, MA) built-in active-set algorithm on a laptop computer (Intel Core i7-4712HQ CPU, NVI-DIA Quadro K1100m GPU). FAs were evaluated by numerical integration of Bloch's equation.…”

Section: K T -Points Pulse Designmentioning

confidence: 99%

B₁ artifact reduction in abdominal DCE‐MRI using k_T‐points: First clinical assessment of dynamic RF shimming at 3T

Tomi‐Tricot

Magnetic Resonance Imaging

Mauconduit

et al. 2017

Self Cite