Contrast-optimized composite image derived from multigradient echo cardiac magnetic resonance imaging improves reproducibility of myocardial contours and T2* measurement

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1 - and T2 -weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2 *, and T1ρ ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

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.

Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI

BACKGROUND

Extraction of liver parenchyma is an important step in the evaluation of R2*-based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T2*-thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability.

PURPOSE

To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R2*-based HIC.

STUDY TYPE

Retrospective analysis of clinical data.

SUBJECTS

Data from 511 MRI exams performed on 257 patients were analyzed.

FIELD STRENGTH/SEQUENCE

All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC.

ASSESSMENT

An automated method based on a multiscale vessel enhancement filter was investigated for three input data types-contrast-optimized composite image, T2* map, and R2* map-to segment blood vessels and extract liver tissue for R2*-based HIC assessment. Segmentation and R2* results obtained using this automated technique were compared with those from a reference T2*-thresholding technique performed by a radiologist.

STATISTICAL TESTS

The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R2* results, obtained with the automated and reference techniques.

RESULTS

Mean liver R2* values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04-1.05, R² > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87-88%. The T2*-thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R2* values compared to the automated method.

DATA CONCLUSION

The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1542-1551.

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.