Automated T(2) * measurements using supplementary field mapping to assess cardiac iron content

T2* assessment of the three coronary artery territories of the left ventricular wall by different monoexponential truncation methods

Objectives

This study aimed at evaluating left ventricular myocardial pixel-wise T2* using two truncation methods for different iron deposition T2* ranges and comparison of segmental T2* in different coronary artery territories.

Material and methods

Bright blood multi-gradient echo data of 30 patients were quantified by pixel-wise monoexponential T2* fitting with its R2 and SNR truncation. T2* was analyzed at different iron classifications. At low iron classification, T2* values were also analyzed by coronary artery territories.

Results

The right coronary artery has a significantly higher T2* value than the other coronary artery territories. No significant difference was found in classifying severe iron by the two truncation methods in any myocardial region, whereas in moderate iron, it is only apparent at septal segments. The R2 truncation produces a significantly higher T2* value than the SNR method when low iron is indicated.

Conclusion

Clear T2* differentiation between the three coronary territories by the two truncation methods is demonstrated. The two truncation methods can be used interchangeably in classifying severe and moderate iron deposition at the recommended septal region. However, in patients with low iron indication, different results by the two truncation methods can mislead the investigation of early iron level progression.

Cardiac T2 * mapping: Techniques and clinical applications

Cardiac T₂* 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 β‐thalassemia major. The method has improved over the years in terms of MR acquisition, focus on relative artifact‐free myocardium regions, and T₂* quantification. Several improvement factors involved include blood pool signal suppression, the reproducibility of T₂* measurement as affected by scanner hardware, and acquisition software. Regarding the T₂* 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 T₂* 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 T₂* 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 T₂* quantification methods. This review deals with the pro and cons of different methods used to quantify T₂* and generate T₂* maps. The purpose is to recommend a combination of MR acquisition and T₂* mapping quantification techniques for reliable outcomes in measuring and follow‐up of myocardial iron overload. The clinical application of cardiac T₂* mapping for iron overload's early detection, monitoring, and treatment is addressed. The prospects of T₂* mapping combined with different MR acquisition methods, such as cardiac T₁ mapping, are also described.

Level of Evidence: 4

Technical Efficacy Stage: 5

J. Magn. Reson. Imaging 2019.

Fast quantitative parameter maps without fitting: Integration yields accurate mono-exponential signal decay rates

PURPOSE

To develop a computationally fast and accurate algorithm for mono-exponential signal modelling and validate the new technique in the context of R2* mapping for iron overload assessment.

METHODS

An algorithm is introduced that directly calculates R2* values from a series of images based on integration of the mono-exponential signal decay curve. The algorithm is fast, because fitting is avoided and only arithmetic computations without iterations are applied. Precision and accuracy of the method is determined in comparison to the conventional log-linear (LL), nonlinear least-squares-based Levenberg-Marquardt (NLM), and squared nonlinear Levenberg-Marquardt (SQNLM) methods, which rely on iterative curve fitting.

RESULTS

In simulations, the signal integration based method consistently had the same or better accuracy than the LL, NLM, and SQNLM algorithms for R2* values ranging from 50 s^-1 to 1200 s^-1 . In phantoms and in vivo (12 participants), this method was robust over a wide range of R2* values and signal-to-noise ratios. Computation times were approximately 100, 1460, and 930 times faster than those of the LL, NLM, and SQNLM methods, respectively.

CONCLUSIONS

The fast signal integration method accurately calculates R2* maps. It has the potential to replace conventional, mono-exponential fitting methods for quantitative MRI such as R2* parameter mapping. Magn Reson Med 79:2978-2985, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Semi-automated myocardial segmentation of bright blood multi-gradient echo images improves reproducibility of myocardial contours and T2* determination

Objectives

Early detection of iron loading is affected by the reproducibility of myocardial contour assessment. A novel semi-automatic myocardial segmentation method is presented on contrast-optimized composite images and compared to the results of manual drawing.

Materials and methods

Fifty-one short-axis slices at basal, mid-ventricular and apical locations from 17 patients were acquired by bright blood multi-gradient echo MRI. Four observers produced semi-automatic and manual myocardial contours on contrast-optimized composite images. The semi-automatic segmentation method relies on vector field convolution active contours to generate the endocardial contour. After creating radial pixel clusters on the myocardial wall, a combination of pixel-wise coefficient of variance (CoV) assessment and k-means clustering establishes the epicardial contour for each segment.

Results

Compared to manual drawing, semi-automatic myocardial segmentation lowers the variability of T2* quantification within and between observers (CoV of 12.05 vs. 13.86% and 14.43 vs. 16.01%) by improving contour reproducibility (P < 0.001). In the presence of iron loading, semi-automatic segmentation also lowers the T2* variability within and between observers (CoV of 13.14 vs. 15.19% and 15.91 vs. 17.28%).

Conclusion

Application of semi-automatic myocardial segmentation on contrast-optimized composite images improves the reproducibility of T2* quantification.