Free breathing three-dimensional cardiac quantitative susceptibility mapping for differential cardiac chamber blood oxygenation – initial validation in patients with cardiovascular disease inclusive of direct comparison to invasive catheterization

Abstract

BackgroundDifferential blood oxygenation between left (LV) and right ventricles (RV; ΔSaO2) is a key index of cardiac performance; LV dysfunction yields increased RV blood pool deoxygenation. Deoxyhemoglobin increases blood magnetic susceptibility, which can be measured using an emerging cardiovascular magnetic resonance (CMR) technique, Quantitative Susceptibility Mapping (QSM) – a concept previously demonstrated in healthy subjects using a breath-hold 2D imaging approach (2DBHQSM). This study tested utility of a novel 3D free-breathing QSM approach (3DNAVQSM) in normative controls, and validated 3DNAVQSM for non-invasive ΔSaO2 quantification in patients undergoing invasive cardiac catheterization (cath).MethodsInitial control (n = 10) testing compared 2DBHQSM (ECG-triggered 2D gradient echo acquired at end-expiration) and 3DNAVQSM (ECG-triggered navigator gated gradient echo acquired in free breathing using a phase-ordered automatic window selection algorithm to partition data based on diaphragm position). Clinical testing was subsequently performed in patients being considered for cath, including 3DNAVQSM comparison to cine-CMR quantified LV function (n = 39), and invasive-cath quantified ΔSaO2 (n = 15). QSM was acquired using 3 T scanners; analysis was blinded to comparator tests (cine-CMR, cath).Results3DNAVQSM generated interpretable QSM in all controls; 2DBHQSM was successful in 6/10. Among controls in whom both pulse sequences were successful, RV/LV susceptibility difference (and ΔSaO2) were not significantly different between 3DNAVQSM and 2DBHQSM (252 ± 39 ppb [17.5 ± 3.1%] vs. 211 ± 29 ppb [14.7 ± 2.0%]; p = 0.39). Acquisition times were 30% lower with 3DNAVQSM (4.7 ± 0.9 vs. 6.7 ± 0.5 min, p = 0.002), paralleling a trend towards lower LV mis-registration on 3DNAVQSM (p = 0.14). Among cardiac patients (63 ± 10y, 56% CAD) 3DNAVQSM was successful in 87% (34/39) and yielded higher ΔSaO2 (24.9 ± 6.1%) than in controls (p < 0.001). QSM-calculated ΔSaO2 was higher among patients with LV dysfunction as measured on cine-CMR based on left ventricular ejection fraction (29.4 ± 5.9% vs. 20.9 ± 5.7%, p < 0.001) or stroke volume (27.9 ± 7.5% vs. 22.4 ± 5.5%, p = 0.013). Cath measurements (n = 15) obtained within a mean interval of 4 ± 3 days from CMR demonstrated 3DNAVQSM to yield high correlation (r = 0.87, p < 0.001), small bias (− 0.1%), and good limits of agreement (±8.6%) with invasively measured ΔSaO2.Conclusion3DNAVQSM provides a novel means of assessing cardiac performance. Differential susceptibility between the LV and RV is increased in patients with cine-CMR evidence of LV systolic dysfunction; QSM-quantified ΔSaO2 yields high correlation and good agreement with the reference of invasively-quantified ΔSaO2.

Iron imaging in myocardial infarction reperfusion injury

“…To mitigate this issue, we minimized the breath-hold time for each slice (<20 s) to limit cardiac and respiratory motion across the cardiac slice direction. Free-breathing cardiac-gated GRE acquisitions using respiration gating may have the potential to further improve image quality 49 .…”

Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping

“…In brain QSM, M c is typically chosen as the regions containing cerebrospinal fluid. 64 Note that Equation (6) does not model errors from other sources of error, such as when using parallel acquisitions. Using a proper noise weighting in nTFI can help to mitigate this model error (as well as fitting errors from Equation [5]) by assigning small values to w in the problematic voxels, and therefore reducing the influence of these problematic voxels during the optimization.…”

“…The magnetic fields generated by the magnetized susceptibility sources, henceforth termed the magnetization field, can then be measured by the scanner for susceptibility map reconstruction. Accordingly, QSM can be used to study tissue susceptibility sources such as deoxyheme iron in blood, [6][7][8][9][10][11][12] tissue nonheme iron, [13][14][15][16] myelin, 17 cartilage, 18,19 and calcification. [20][21][22] QSM robustness and reproducibility have been shown for brain applications, 23,24 and there are a wide range of QSM clinical applications, including deep brain stimulation target mapping, 25,26 cerebral cavernous malformation monitoring, [27][28][29] multiple sclerosis chronic inflammation imaging [30][31][32] and MRI follow-up without gadolinium, 31,33 neurodegeneration in Parkinson's disease [34][35][36][37] and Alzheimer's disease, 35,38,39 and more.…”

Investigation of the magnetic susceptibility properties of fresh and fixed mouse heart, liver, skeletal muscle and brain tissue

“…phase) distribution [2][3][4][5][6][7][8]. A multitude of clinical and preclinical studies illustrated how QSM can be applied in vivo; for example, to detect vascular abnormalities, visualize blood products and calcifications; microstructural changes and lesions, or map abnormalities in iron metabolism [9][10][11][12][13][14]. These studies have collectively demonstrated that pathological alterations in the brain, heart, muscle and abdomen in various diseases are associated with changes of tissue magnetic susceptibility.…”