Nonrigid registration improves MRI T2quantification in heart transplant patient follow-up

High-Spatial-Resolution 3D Whole-Heart MRI T2 Mapping for Assessment of Myocarditis

Background Clinical guidelines recommend the use of established T2 mapping sequences to detect and quantify myocarditis and edema, but T2 mapping is performed in two dimensions with limited coverage and repetitive breath holds. Purpose To assess the reproducibility of an accelerated free-breathing three-dimensional (3D) whole-heart T2 MRI mapping sequence in phantoms and participants without a history of cardiac disease and to investigate its clinical performance in participants with suspected myocarditis. Materials and Methods Eight participants (three women, mean age, 31 years ± 4 [standard deviation]; cohort 1) without a history of cardiac disease and 25 participants (nine women, mean age, 45 years ± 17; cohort 2) with clinically suspected myocarditis underwent accelerated free-breathing 3D whole-heart T2 mapping with 100% respiratory scanning efficiency at 1.5 T. The participants were enrolled from November 2018 to August 2020. Three repeated scans were performed on 2 separate days in cohort 1. Segmental variations in T2 relaxation times of the left ventricular myocardium were assessed, and intrasession and intersession reproducibility were measured. In cohort 2, segmental myocardial T2 values, detection of focal inflammation, and map quality were compared with those obtained from clinical breath-hold two-dimensional (2D) T2 mapping. Statistical differences were assessed using the nonparametric Mann-Whitney and Kruskal-Wallis tests, whereas the paired Wilcoxon signed-rank test was used to assess subjective scores. Results Whole-heart T2 maps were acquired in a mean time of 6 minutes 53 seconds ± 1 minute 5 seconds at 1.5 mm3 resolution. Breath-hold 2D and free-breathing 3D T2 mapping had similar intrasession (mean T2 change of 3.2% and 2.3% for 2D and 3D, respectively) and intersession (4.8% and 4.9%, respectively) reproducibility. The two T2 mapping sequences showed similar map quality (P = .23, cohort 2). Abnormal myocardial segments were identified with confidence (score 3) in 14 of 25 participants (56%) with 3D T2 mapping and only in 10 of 25 participants (40%) with 2D T2 mapping. Conclusion High-spatial-resolution three-dimensional (3D) whole-heart T2 mapping shows high intrasession and intersession reproducibility and helps provide T2 myocardial characterization in agreement with clinical two-dimensional reference, while enabling 3D assessment of focal disease with higher confidence. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Friedrich in this issue.

Accelerated free-breathing whole-heart 3D T2 mapping with high isotropic resolution

PURPOSE

To enable free-breathing whole-heart 3D T2 mapping with high isotropic resolution in a clinically feasible and predictable scan time. This 3D motion-corrected undersampled signal matched (MUST) T2 map is achieved by combining an undersampled motion-compensated T2 -prepared Cartesian acquisition with a high-order patch-based reconstruction.

METHODS

The 3D MUST-T2 mapping acquisition consists of an electrocardiogram-triggered, T2 -prepared, balanced SSFP sequence with nonselective saturation pulses. Three undersampled T2 -weighted volumes are acquired using a 3D Cartesian variable-density sampling with increasing T2 preparation times. A 2D image-based navigator is used to correct for respiratory motion of the heart and allow 100% scan efficiency. Multicontrast high-dimensionality undersampled patch-based reconstruction is used in concert with dictionary matching to generate 3D T2 maps. The proposed framework was evaluated in simulations, phantom experiments, and in vivo (10 healthy subjects, 2 patients) with 1.5-mm3 isotropic resolution. Three-dimensional MUST-T2 was compared against standard multi-echo spin-echo sequence (phantom) and conventional breath-held single-shot 2D SSFP T2 mapping (in vivo).

RESULTS

Three-dimensional MUST-T2 showed high accuracy in phantom experiments (R2 > 0.99). The precision of T2 values was similar for 3D MUST-T2 and 2D balanced SSFP T2 mapping in vivo (5 ± 1 ms versus 4 ± 2 ms, P = .52). Slightly longer T2 values were observed with 3D MUST-T2 in comparison to 2D balanced SSFP T2 mapping (50.7 ± 2 ms versus 48.2 ± 1 ms, P < .05). Preliminary results in patients demonstrated T2 values in agreement with literature values.

CONCLUSION

The proposed approach enables free-breathing whole-heart 3D T2 mapping with high isotropic resolution in about 8 minutes, achieving accurate and precise T2 quantification of myocardial tissue in a clinically feasible scan time.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.

A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T1 mapping MRI

PURPOSE

T₁ mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm.

METHODS

After nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the T₁ value. The algorithm was validated with cardiac T₁ mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T₁ relaxation with 2 and 3 parameters were tested.

RESULTS

The vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of T₁ values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T₁.

CONCLUSIONS

We have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T₁ mapping with SR sequences.

Impact of denoising on precision and accuracy of saturation-recovery-based myocardial T1 mapping

PURPOSE

To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T₁ mapping.

MATERIALS AND METHODS

This study introduces a fast and robust denoising method developed for magnetic resonance T₁ mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T₁ -weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T₁ mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T₁ values and standard deviation were extracted and compared in all studies.

RESULTS

Simulations on synthetic phantom showed signal-to-noise ratio and sharpness improvement with the proposed method in comparison with conventional denoising. In vivo results demonstrated that our method preserves accuracy, as no difference in mean T₁ values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [-40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [-60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T₁ values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03).

CONCLUSION

The proposed denoising method preserves accuracy and improves precision in myocardial T₁ mapping, with the potential to offer better map visualization and analysis.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388.

Joint Reconstruction of Multiple Images and Motion in MRI: Application to Free-Breathing Myocardial T₂Quantification

Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).

Signal decay mapping of myocardial edema using dual-contrast fast spin-echo MRI

PURPOSE

To introduce a dual-contrast fast spin-echo (dcFSE) sequence for signal decay mapping of myocardial edema.

MATERIALS AND METHODS

After consultation with the Institutional Review Board, 22 acute myocardial infarction (MI) patients were examined with magnetic resonance imaging (MRI) at 1.5T 2 days after revascularization. Edema was evaluated in 16 myocardial segments with an exponential fit for signal decay time (SDT) in dcFSE mapping and T2 signal intensity ratio for single-contrast FSE. Myocardial viability was evaluated in late gadolinium enhancement (LGE). A control group of 10 volunteers was examined for edema imaging. SDT was compared in segment groups: 1) with LGE in MI, 2) penumbra, 3) remote from LGE, 4) controls. Groups 1/3 and 3/4 were tested on difference. Three phantoms providing similar T2 but different T1 relaxation times (low, intermediate, high) were examined with dcFSE and multicontrast spin echo sequence as a reference.

RESULTS

The SDT/T2 ratio for segment groups was 1) 82msec/1.7 in segments with LGE; 2) 65msec/1.6 for penumbra, 3) 62msec/1.7 for remote segments, and 4) 50msec/1.6 in controls. In dcFSE group 1/3 (P < 0.0001) and in group 3/4 (P = 0.0002) SDT was significantly different. In single-contrast FSE the T2 ratio was not significantly different for both tests: 1/3 P = 0.1889; 3/4 P = 0.8879. T2 -overestimation of dcFSE was 23% in low, 29% in intermediate, and 35% in highly T1 contaminated phantoms.

CONCLUSION

dcFSE signal decay edema mapping is feasible in volunteers and patients. DcFSE SDT is superior to T2 ratio for detection of high-grade and diffuse myocardial edema. J. Magn. Reson. Imaging 2016;44:186-193.