OBJECTIVES Calculation of accurate T1 relaxivity (r1) values for gadolinium-based magnetic resonance contrast agents (GBCAs) is a complex process. As such, often referenced r1 values for the GBCAs at 1.5 T, 3 T, and 7 T are based on measurements obtained in media that are not clinically relevant, derived from only a small number of concentrations, or available for only a limited number of GBCAs. This study derives the r1 values of the 8 commercially available GBCAs in human whole blood at 1.5 T, 3 T, and 7 T. MATERIALS AND METHODS Eight GBCAs were serially diluted in human whole blood, at 7 concentrations from 0.0625 to 4 mM. A custom-built phantom held the dilutions in air-tight cylindrical tubes maintained at 37 ± 0.5°C by a heat-circulating system. Images were acquired using inversion recovery sequences with inversion times from 30 milliseconds to 10 seconds at 1.5 T and 3 T as well as 60 milliseconds to 5 seconds at 7 T. A custom MATLAB program was used to automate signal intensity measurements from the images acquired of the phantom. SigmaPlot was used to calculate T1 relaxation times and, finally, r1. RESULTS Measured r1 values in units of s[BULLET OPERATOR]mM at 1.5 T (3 T/7 T) were 3.9 ± 0.2 (3.4 ± 0.4/2.8 ± 0.4) for Gd-DOTA, 4.6 ± 0.2 (4.5 ± 0.3/4.2 ± 0.3) for Gd-DO3A-butrol, 4.3 ± 0.4 (3.8 ± 0.2/3.1 ± 0.4) for Gd-DTPA, 6.2 ± 0.5 (5.4 ± 0.3/4.7 ± 0.1) for Gd-BOPTA, 4.5 ± 0.1 (3.9 ± 0.2/3.7 ± 0.2) for Gd-DTPA-BMA, 4.4 ± 0.2 (4.2 ± 0.2/4.3 ± 0.2) for Gd-DTPA-BMEA, 7.2 ± 0.2 (5.5 ± 0.3/4.9 ± 0.1) for Gd-EOB-DTPA, and 4.4 ± 0.6 (3.5 ± 0.6/3.4 ± 0.1) for Gd-HP-DO3A. The agents can be stratified by relaxivity, with a significant additional dependency on field strength. CONCLUSIONS This report quantifies, for the first time, T1 relaxivity for all 8 gadolinium chelates in common clinical use worldwide, at current relevant field strengths, in human whole blood at physiological temperature (37°C). The measured r1 values differ to a small degree from previously published values, where such comparisons exist, with the current r1 measurements being that most relevant to clinical practice. The macrocyclic agents, with the exception of Gd-DO3A-butrol, have slightly lower r1 values when compared with the 2 much less stable linear agents, Gd-DTPA-BMA and Gd-DTPA-BMEA. The 2 agents with hepatobiliary excretion, Gd-EOB-DTPA and Gd-BOPTA, have, at 1.5 and 3 T, substantially higher r1 values than all other agents.
Magnetic resonance imaging (MRI) contrast agents are pharmaceuticals used widely in MRI examinations. Gadolinium-based MRI contrast agents (GBCAs) are by far the most commonly used. To date, nine GBCAs have been commercialized for clinical use, primarily indicated in the central nervous system, vasculature, and whole body. GBCAs primarily lower the T(1) in vivo to create higher signal in T(1)-weighted MRI scans where GBCAs are concentrated. GBCAs are unique among pharmaceuticals, being water proton relaxation catalysts whose effectiveness is characterized by a rate constant known as relaxivity. The relaxivity of each GBCAs depends on a variety of factors that are discussed in terms of both the existing agents and future molecular imaging agents under study by current researchers. Current GBCAs can be divided into four different structural types (macrocyclic, linear, ionic, and nonionic) based on the chemistry of the chelating ligands whose primary purpose is to protect the body from dissociation of the relatively toxic Gd(3+) ion from the ligand. This article discusses how the chemical structure influences inherent and in vivo stability toward dissociation, and how it affects important formulation properties. Although GBCAs have a lower rate of serious adverse events than iodinated contrast agents, they still present some risk.
BackgroundPreoperative differentiation between malignant and benign soft‐tissue masses is important for treatment decisions.Purpose/HypothesisTo construct/validate a radiomics‐based machine method for differentiation between malignant and benign soft‐tissue masses.Study TypeRetrospective.PopulationIn all, 206 cases.Field Strength/SequenceThe T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352–550/2.75–19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700–6370/40–120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.AssessmentTwelve machine‐learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).Statistical Tests1) Demographic characteristics: a one‐way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver‐operating characteristic curve (AUC) and accuracy (ACC) values.ResultsThe LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.Data ConclusionA machine‐learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft‐tissue masses.Evidence Level3Technical EfficacyStage 2 J. Magn. Reson. Imaging 2020;52:873–882.
Magnetic resonance imaging (MRI) has now been used clinically for more than 30 years. Today, MRI serves as the primary diagnostic modality for many clinical problems. In this article, historical developments in the field of MRI will be discussed with a focus on technological innovations. Topics include the initial discoveries in nuclear magnetic resonance that allowed for the advent of MRI as well as the development of whole-body, high field strength, and open MRI systems. Dedicated imaging coils, basic pulse sequences, contrast-enhanced, and functional imaging techniques will also be discussed in a historical context. This article describes important technological innovations in the field of MRI, together with their clinical applicability today, providing critical insights into future developments.
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