Purpose: To analyze the flip angle dependence and to optimize the statistical precision of a fast three-dimensional (3D) T1 mapping technique based on the variable flip angle (VFA) method. The proposed single flip angle (1FA) approach acquires only a single 3D spoiled gradient echo data set for each time point of the dynamical series in combination with a longer baseline measurement.
Theory And Methods: The optimal flip angle for the dynamic series can be calculated as αdyn,opt = arccos[(2E1 - 1)/(2 - E1 )] (with E1 = exp(-TR /T1 )) by minimizing the statistical error of T1 . T1 maps of a liquid phantom with step-wise increasing concentrations of contrast agent were measured using a saturation recovery (SR) and a VFA/1FA technique with 11 flip angles. The standard deviation of the parameter maps was defined as statistical error of the 1FA measurement.
Results: The measured statistical error of the 1FA technique as a function of αdyn agrees with the derived theoretical curve. The optimal flip angle increases from about 5° for T1 = 2629 ms to 30° for T1 = 137 ms. The relative deviation between 1FA and SR measurements varies between -2.9 % and +10.3 %. Measurements in vivo confirm the expression for the optimal flip angle.
Conclusion: The proposed flip angle-optimized 1FA technique optimizes the precision of T1 values in dynamic phantom measurements.
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http://dx.doi.org/10.1002/mrm.25199 | 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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