Quantitative Assessment of Perfusion – Where Are We Now?

Clinical Translation of Three-Dimensional Scar, Diffusion Tensor Imaging, Four-Dimensional Flow, and Quantitative Perfusion in Cardiac MRI: A Comprehensive Review

Cardiovascular magnetic resonance (CMR) imaging is a versatile tool that has established itself as the reference method for functional assessment and tissue characterisation. CMR helps to diagnose, monitor disease course and sub-phenotype disease states. Several emerging CMR methods have the potential to offer a personalised medicine approach to treatment. CMR tissue characterisation is used to assess myocardial oedema, inflammation or thrombus in various disease conditions. CMR derived scar maps have the potential to inform ablation therapy—both in atrial and ventricular arrhythmias. Quantitative CMR is pushing boundaries with motion corrections in tissue characterisation and first-pass perfusion. Advanced tissue characterisation by imaging the myocardial fibre orientation using diffusion tensor imaging (DTI), has also demonstrated novel insights in patients with cardiomyopathies. Enhanced flow assessment using four-dimensional flow (4D flow) CMR, where time is the fourth dimension, allows quantification of transvalvular flow to a high degree of accuracy for all four-valves within the same cardiac cycle. This review discusses these emerging methods and others in detail and gives the reader a foresight of how CMR will evolve into a powerful clinical tool in offering a precision medicine approach to treatment, diagnosis, and detection of disease.

Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI

BACKGROUND

Quantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation.

PURPOSE

To evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis.

STUDY TYPE

Retrospective.

POPULATION

In all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test).

FIELD STRENGTH/SEQUENCE

3.0T/2D multislice saturation recovery T1 -weighted gradient echo sequence.

ASSESSMENT

Accuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL-based processing were compared to the results obtained with the manually processed images.

STATISTICAL TESTS

Bland-Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator.

RESULTS

The mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland-Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per-myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90].

DATA CONCLUSION

We showed high accuracy, compared to manual processing, for the DL-based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1689-1696.

Optimal repetition time for free breathing myocardial arterial spin labeling

The aim of this study was to improve the scan efficiency of ASL in the myocardium. Free breathing FAIR-ASL scans with different TRs were compared, while keeping the acquisition time constant. Scans were named by the trigger pulse that started each acquisition: every two (TP1), four (TP2) and six (TP3) cardiac cycles. TP2 offered the best alternative with a coefficient of variation of 17.15% intrasession and 36.85% intersession. Mean MBF increased by 0.22 ± 0.41 ml/g/min with mild stress.

Optimization of dual-saturation single bolus acquisition for quantitative cardiac perfusion and myocardial blood flow maps

BACKGROUND

In-vivo quantification of cardiac perfusion is of great research and clinical value. The dual-bolus strategy is universally used in clinical protocols but has known limitations. The dual-saturation acquisition strategy has been proposed as a more accurate alternative, but has not been validated across the wide range of perfusion rates encountered clinically. Dual-saturation acquisition also lacks a clinically-applicable procedure for optimizing parameter selection. Here we present a comprehensive validation study of dual-saturation strategy in vitro and in vivo.

METHODS

The impact of saturation time and profile ordering in acquisitions was systematically analyzed in a phantom consisting of 15 tubes containing different concentrations of contrast agent. In-vivo experiments in healthy pigs were conducted to evaluate the effect of R2* on the definition of the arterial input function (AIF) and to evaluate the relationship between R2* and R1 variations during first-pass of the contrast agent. Quantification by dual-saturation perfusion was compared with the reference-standard dual-bolus strategy in 11 pigs with different grades of myocardial perfusion.

RESULTS

Adequate flow estimation by the dual-saturation strategy is achieved with myocardial tissue saturation times around 100 ms (always <30 ms of AIF), with the lowest echo time, and following a signal model for contrast conversion that takes into account the residual R2* effect and profile ordering. There was a good correlation and agreement between myocardial perfusion quantitation by dual-saturation and dual-bolus techniques (R(2) = 0.92, mean difference of 0.1 ml/min/g; myocardial perfusion ranges between 0.18 and 3.93 ml/min/g).

CONCLUSIONS

The dual-saturation acquisition strategy produces accurate estimates of absolute myocardial perfusion in vivo. The procedure presented here can be applied with minimal interference in standard clinical procedures.