Purpose: The inhomogeneity of flip angle distribution is a major challenge impeding the application of high-field MRI. We report a method combining spatially selective excitation using generalized spatial encoding magnetic fields (SAGS) with radiofrequency (RF) shimming to achieve homogeneous excitation. This method can be an alternative approach to address the challenge of B1+ inhomogeneity using nonlinear gradients.
Methods: We proposed a two-step algorithm that jointly optimizes the combination of nonlinear spatial encoding magnetic fields and the combination of multiple RF transmitter coils and then optimizes the locations, RF amplitudes, and phases of the spokes.
Results: Our results show that jointly designed SAGS and RF shimming can provide a more homogeneous flip angle distribution than using SAGS or RF shimming alone. Compared with RF shimming alone, our approach can reduce the relative standard deviation of flip angle by 56% and 52% using phantom and human head data, respectively.
Conclusion: The jointly designed SAGS and RF shimming method can be used to achieve homogeneous flip angle distributions when fully parallel RF transmission is not available. Magn Reson Med 78:577-587, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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http://dx.doi.org/10.1002/mrm.26397 | DOI Listing |
Magn Reson Med
January 2025
Department of Radiology, Stanford University, Stanford, California, USA.
Purpose: To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.
Methods: We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.
AJNR Am J Neuroradiol
January 2025
From the Department of Radiology, Medical University of South Carolina, Charleston, SC, USA (MVS, HRC, WD, JHC, JAC, MGM, STS, DRR), College of Medicine, Medical University of South Carolina, Charleston, SC, USA (HW, EY).
Background And Purpose: Magnetic Resonance Imaging is widely used to assess disease burden in multiple sclerosis (MS). This study aimed to evaluate the effectiveness of a commercially available k-nearest neighbors (k-NN) software in quantifying white matter lesion (WML) burden in MS. We compared the software's WML quantification to expert radiologists' assessments.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Department of Radiology, University Hospital of Strasbourg, 1 Avenue Moliere, 67098 Strasbourg, France.
Purpose: Compressed Sensing (CS) is an emerging technique to accelerate MRI acquisitions. The aim of this study was to assess the reliability and accuracy of cartilage thickness measurements in the knee using a CS-enabled isotropic 3D Fast Spin-Echo (FSE) sequence on a 3-T MRI scanner.
Methods: Twenty-eight tibial condyle sections were collected from 14 adult patients who underwent total knee arthroplasty.
Front Neurol
December 2024
Department of Neurology, Headache Outpatient Clinic, Medical University of Innsbruck, Innsbruck, Austria.
Background: There is evidence that iron metabolism may play a role in the underlying pathophysiological mechanism of migraine. Studies using (=1/ ) relaxometry, a common MRI-based iron mapping technique, have reported increased values in various brain structures of migraineurs, indicating iron accumulation compared to healthy controls.
Purpose: To investigate whether there are short-term changes in during a migraine attack.
Radiat Oncol
December 2024
Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands.
Background And Purpose: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learning (DL) features to clinical features increased Local Control (LC) prediction accuracy, their combined potential to predict LC remains unexplored. We examined whether a model using a combination of radiomics, DL and clinical features achieves better accuracy than models using only a subset of these features.
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