Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods

Stress native T1 and native T2 mapping compared to myocardial perfusion reserve in long-term follow-up of severe Covid-19

Severe Covid-19 may cause a cascade of cardiovascular complications beyond viral pneumonia. The severe inflammation may affect the microcirculation which can be assessed by cardiovascular magnetic resonance (CMR) imaging using quantitative perfusion mapping and calculation of myocardial perfusion reserve (MPR). Furthermore, native T1 and T2 mapping have previously been shown to identify changes in myocardial perfusion by the change in native T1 and T2 during adenosine stress. However, the relationship between native T1, native T2, ΔT1 and ΔT2 with myocardial perfusion and MPR during long-term follow-up in severe Covid-19 is currently unknown. Therefore, patients with severe Covid-19 (n = 37, median age 57 years, 24% females) underwent 1.5 T CMR median 292 days following discharge. Quantitative myocardial perfusion (ml/min/g), and native T1 and T2 maps were acquired during adenosine stress, and rest, respectively. Both native T1 (R² = 0.35, p < 0.001) and native T2 (R² = 0.28, p < 0.001) correlated with myocardial perfusion. However, there was no correlation with ΔT1 or ΔT2 with MPR, respectively (p > 0.05 for both). Native T1 and native T2 correlate with myocardial perfusion during adenosine stress, reflecting the coronary circulation in patients during long-term follow-up of severe Covid-19. Neither ΔT1 nor ΔT2 can be used to assess MPR in patients with severe Covid-19.

Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19

Background

Patients with a history of COVID-19 infection are reported to have cardiac abnormalities on cardiovascular magnetic resonance (CMR) during convalescence. However, it is unclear whether these abnormalities were present during the acute COVID-19 illness and how they may evolve over time.

Methods

We prospectively recruited unvaccinated patients hospitalized with acute COVID-19 (n = 23), and compared them with matched outpatient controls without COVID-19 (n = 19) between May 2020 and May 2021. Only those without a past history of cardiac disease were recruited. We performed in-hospital CMR at a median of 3 days (IQR 1-7 days) after admission, and assessed cardiac function, edema and necrosis/fibrosis, using left and right ventricular ejection fraction (LVEF, RVEF), T1-mapping, T2 signal intensity ratio (T2SI), late gadolinium enhancement (LGE) and extracellular volume (ECV). Acute COVID-19 patients were invited for follow-up CMR and blood tests at 6 months.

Results

The two cohorts were well matched in baseline clinical characteristics. Both had normal LVEF (62 ± 7 vs. 65 ± 6%), RVEF (60 ± 6 vs. 58 ± 6%), ECV (31 ± 3 vs. 31 ± 4%), and similar frequency of LGE abnormalities (16 vs. 14%; all p > 0.05). However, measures of acute myocardial edema (T1 and T2SI) were significantly higher in patients with acute COVID-19 when compared to controls (T1 = 1,217 ± 41 ms vs. 1,183 ± 22 ms; p = 0.002; T2SI = 1.48 ± 0.36 vs. 1.13 ± 0.09; p < 0.001). All COVID-19 patients who returned for follow up (n = 12) at 6 months had normal biventricular function, T1 and T2SI.

Conclusion

Unvaccinated patients hospitalized for acute COVID-19 demonstrated CMR imaging evidence of acute myocardial edema, which normalized at 6 months, while biventricular function and scar burden were similar when compared to controls. Acute COVID-19 appears to induce acute myocardial edema in some patients, which resolves in convalescence, without significant impact on biventricular structure and function in the acute and short-term. Further studies with larger numbers are needed to confirm these findings.

Dynamic Cardiac Magnetic Resonance Fingerprinting During Vasoactive Breathing Maneuvers: First Results

BACKGROUND

Cardiac magnetic resonance fingerprinting (cMRF) enables simultaneous mapping of myocardial T1 and T2 with very short acquisition times. Breathing maneuvers have been utilized as a vasoactive stress test to dynamically characterize myocardial tissue in vivo. We tested the feasibility of sequential, rapid cMRF acquisitions during breathing maneuvers to quantify myocardial T1 and T2 changes.

METHODS

We measured T1 and T2 values using conventional T1 and T2-mapping techniques (modified look locker inversion [MOLLI] and T2-prepared balanced-steady state free precession), and a 15 heartbeat (15-hb) and rapid 5-hb cMRF sequence in a phantom and in 9 healthy volunteers. The cMRF_5-hb sequence was also used to dynamically assess T1 and T2 changes over the course of a vasoactive combined breathing maneuver.

RESULTS

In healthy volunteers, the mean myocardial T1 of the different mapping methodologies were: MOLLI 1,224 ± 81 ms, cMRF_15-hb 1,359 ± 97 ms, and cMRF_5-hb 1,357 ± 76 ms. The mean myocardial T2 measured with the conventional mapping technique was 41.7 ± 6.7 ms, while for cMRF_15-hb 29.6 ± 5.8 ms and cMRF_5-hb 30.5 ± 5.8 ms. T2 was reduced with vasoconstriction (post-hyperventilation compared to a baseline resting state) (30.15 ± 1.53 ms vs. 27.99 ± 2.07 ms, p = 0.02), while T1 did not change with hyperventilation. During the vasodilatory breath-hold, no significant change of myocardial T1 and T2 was observed.

CONCLUSIONS

cMRF_5-hb enables simultaneous mapping of myocardial T1 and T2, and may be used to track dynamic changes of myocardial T1 and T2 during vasoactive combined breathing maneuvers.

Compact MR-compatible ergometer and its application in cardiac MR under exercise stress: A preliminary study

Purpose

To develop a compact MR‐compatible ergometer for exercise stress and to initially evaluate the reproducibility of myocardial native T1 and myocardial blood flow (MBF) measurements during exercise stress performed on this ergometer.

Methods

The compact ergometer consists of exercise, workload, and data processing components. The exercise stress can be achieved by pedaling on a pair of cylinders at a predefined frequency with adjustable resistances. Ten healthy subjects were recruited to perform cardiac MRI scans twice in a 3.0T MR scanner, at different days to assess reproducibility. Myocardial native T1 and MBF were acquired at rest and during a moderate exercise. The reproducibility of the two tests was determined by the intra‐group correlation coefficient (ICC) and coefficient of variation (CoV).

Results

The mean exercise intensity in this pilot study was 45 Watts (W), with an exercise duration of 5 min. Stress induced a significant increase in systolic blood pressure (from 113 ± 11 mmHg to 141 ± 12, P < 0.05) and maximal increase in heart rate by 74 ± 19%. The rate pressure product increased two‐fold (P < 0.001). Excellent reproducibility was demonstrated in native T1 during the exercise (CoV = 3.0%), whereas the reproducibility of MBF and myocardial perfusion reserve during the exercise was also good (CoV = 10.7% and 8.8%, respectively).

Conclusion

This pilot study demonstrated that it is possible to acquire reproducible measurements of myocardial native T1 and MBF during the exercise stress in healthy volunteers using our new compact ergometer.

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MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps.

Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion).

Results: MOCOnet achieved fast image registration (<1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p < 0.001), whereas the baseline method reduced it to 15.8±15.6 (p < 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007).

Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.