Influence of reference tube location on the measured sodium concentrations in calf muscles using a birdcage coil at 3T

PURPOSE: To investigate the influence of the sodium (Na) reference tube location in a birdcage coil on the quantification of Na in the calf muscle. Two correction methods were also evaluated. METHOD: Eight (4 × 20 mM, 4 × 30 mM Na) reference tubes were placed along the inner surface of the coil and one (30 mM Na) tube more centrally near the tibia. In two volunteers, four repeated UTE scans were acquired. In six calf muscles, the Na concentration was calculated based on each reference tube. Flip angle mapping of a homogenous Na phantom was used for correcting intensity values. Alternatively, a normalized intensity map was used for correcting the in vivo signal intensities. Results were given as range or SD of Na concentration measurements over the reference tubes. RESULTS: For calf Na measurements, there was limited space for positioning reference tubes away from coil B1 inhomogeneity. In both volunteers, the Na quantification depended greatly on the reference tube used with a range of up to 10 mM. The central tube location gave a Na quantification close to the mean of the other tubes. The flip angle and normalized signal intensity phantom-based correction methods decreased the quantification variation from 14.9% to 5.0% and 10.4% to 2.7%, respectively. Both correction methods had little influence (&lt; 2.3%) on quantification based on the central tube. CONCLUSION: Despite use of a birdcage coil, location of the reference tube had a great impact on Na quantification in the calf muscles. Although both correction methods did reduce this variation, placing the reference tube more centrally was found to give the most reliable results.</p

Cardiac T-2* mapping:Techniques and clinical applications

Cardiac T-2* mapping is a noninvasive MRI method that is used to identify myocardial iron accumulation in several iron storage diseases such as hereditary hemochromatosis, sickle cell disease, and beta-thalassemia major. The method has improved over the years in terms of MR acquisition, focus on relative artifact-free myocardium regions, and T-2* quantification. Several improvement factors involved include blood pool signal suppression, the reproducibility of T-2* measurement as affected by scanner hardware, and acquisition software. Regarding the T-2* quantification, improvement factors include the applied curve-fitting method with or without truncation of the signals acquired at longer echo times and whether or not T-2* measurement focuses on multiple segmental regions or the midventricular septum only. Although already widely applied in clinical practice, data processing still differs between centers, contributing to measurement outcome variations. State of the art T-2* measurement involves pixelwise quantification providing better spatial iron loading information than region of interest-based quantification. Improvements have been proposed, such as on MR acquisition for free-breathing mapping, the generation of fast mapping, noise reduction, automatic myocardial contour delineation, and different T-2* quantification methods. This review deals with the pro and cons of different methods used to quantify T-2* and generate T-2* maps. The purpose is to recommend a combination of MR acquisition and T-2* mapping quantification techniques for reliable outcomes in measuring and follow-up of myocardial iron overload. The clinical application of cardiac T-2* mapping for iron overload's early detection, monitoring, and treatment is addressed. The prospects of T-2* mapping combined with different MR acquisition methods, such as cardiac T-1 mapping, are also described. Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019

Diagnostic value of MRS-quantified brain tissue lactate level in identifying children with mitochondrial disorders

Magnetic resonance spectroscopy (MRS) of children with or without neurometabolic disease is used for the first time for quantitative assessment of brain tissue lactate signals, to elaborate on previous suggestions of MRS-detected lactate as a marker of mitochondrial disease. Multivoxel MRS of a transverse plane of brain tissue cranial to the ventricles was performed in 88 children suspected of having neurometabolic disease, divided into 'definite' (n = 17, >= 1 major criteria), 'probable' (n = 10, >= 2 minor criteria), 'possible' (n = 17, 1 minor criterion) and 'unlikely' mitochondrial disease (n = 44, none of the criteria). Lactate levels, expressed in standardized arbitrary units or relative to creatine, were derived from summed signals from all voxels. Ten 'unlikely' children with a normal neurological exam served as the MRS reference subgroup. For 61 of 88 children, CSF lactate values were obtained. MRS lactate level (> 12 arbitrary units) and the lactate-to-creatine ratio (L/Cr > 0.22) differed significantly between the definite and the unlikely group (p = 0.015 and p = 0.001, respectively). MRS L/Cr also differentiated between the probable and the MRS reference subgroup (p = 0.03). No significant group differences were found for CSF lactate. MRS-quantified brain tissue lactate levels can serve as diagnostic marker for identifying mitochondrial disease in children. MRS-detected brain tissue lactate levels can be quantified. MRS lactate and lactate/Cr are increased in children with mitochondrial disease. CSF lactate is less suitable as marker of mitochondrial disease

Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas

Background Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade. Purpose To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential in differentiating low- and high-grade gliomas. Material and Methods Thirty patients (16 men; mean age = 40.6 years) with low-grade (n = 13) and high-grade (n = 17) gliomas and known pathology, scanned with dynamic-susceptibility contrast and diffusion-weighted imaging were included retrospectively between March 2006 and March 2014. Three observers used three different methods to define the regions of interest: (i) circles at maximum perfusion and minimum apparent diffusion coefficient; (ii) freeform 2D encompassing the tumor at largest cross-section only; (iii) freeform 3D on all cross-sections. The dynamic-susceptibility contrast curve was analyzed voxelwise: maximum contrast enhancement; time-to-peak; wash-in rate; wash-out rate; and relative cerebral blood volume. The mean was calculated for all regions of interest. For 2D and 3D methods, histogram analysis yielded additional statistics: the minimum and maximum 5% and 10% pixel values of the tumor (min5%, min10%, max5%, max10%). Intraclass correlations coefficients (ICC) were calculated between observers. Low- and high-grade tumors were compared with independent t-tests or Mann-Whitney tests. Results ICCs were highest for 3D freeform (ICC = 0.836-0.986) followed by 2D freeform (ICC = 0.854-0.974) and circular regions of interest (0.141-0.641). High ICC and significant discrimination between low- and high-grade gliomas was found for the following optimized parameters: apparent diffusion coefficient (P <0.001; ICC = 0.641; mean; circle); time-to-peak (P = 0.015; ICC = 0.986; mean; 3D); wash-in rate (P = 0.004; ICC = 0.826; min10%; 3D); wash-out rate (P <0.001; ICC = 0.860; min10%; 2D); and relative cerebral blood volume (

Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information

Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization

Clinical Implications of Non-Steatotic Hepatic Fat Fractions on Quantitative Diffusion-Weighted Imaging of the Liver

Diffusion-weighted imaging (DWI) is an important diagnostic tool in the assessment of focal liver lesions and diffuse liver diseases such as cirrhosis and fibrosis. Quantitative DWI parameters such as molecular diffusion, microperfusion and their fractions, are known to be affected when hepatic fat fractions (HFF) are higher than 5.5% (steatosis). However, less is known about the effect on DWI for HFF in the normal non-steatotic range below 5.5%, which can be found in a large part of the population. The aim of this study was therefore to evaluate the diagnostic implications of non-steatotic HFF on quantitative DWI parameters in eight liver segments. For this purpose, eleven healthy volunteers (2 men, mean-age 31.0) were prospectively examined with DWI and three series of in-/out-of-phase dual-echo spoiled gradient-recalled MRI sequences to obtain the HFF and T-2*. DWI data were analyzed using the intravoxel incoherent motion (IVIM) model. Four circular regions (circle divide 22.3 mm) were drawn in each of eight liver segments and averaged. Measurements were divided in group 1 (HFF 5.5%). DWI parameters and T-2* were compared between the three groups and between the segments. It was observed that the molecular diffusion (0.85, 0.72 and 0.49610 23 mm(2)/s) and T-2* (32.2, 27.2 and 21.0 ms) differed significantly between the three groups of increasing HFF (2.18, 3.50 and 19.91%). Microperfusion and its fraction remained similar for different HFF. Correlations with HFF were observed for the molecular diffusion (r = -0.514,

Early detection of heart function abnormality by native T1:a comparison of two T1 quantification methods

Objective To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods. Methods Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed. Results Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p <0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p <0.001-0.040) by median quantification compared with six (p <0.001-0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053). Conclusion Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment