Regional contrast agent quantification in a mouse model of myocardial infarction using 3D cardiac T1 mapping

<p>Abstract</p> <p>Background</p> <p>Quantitative relaxation time measurements by cardiovascular magnetic resonance (CMR) are of paramount importance in contrast-enhanced studies of experimental myocardial infarction. First, compared to qualitative measurements based on signal intensity changes, they are less sensitive to specific parameter choices, thereby allowing for better comparison between different studies or during longitudinal studies. Secondly, T₁measurements may allow for quantification of local contrast agent concentrations. In this study, a recently developed 3D T₁mapping technique was applied in a mouse model of myocardial infarction to measure differences in myocardial T₁before and after injection of a liposomal contrast agent. This was then used to assess the concentration of accumulated contrast agent.</p> <p>Materials and methods</p> <p>Myocardial ischemia/reperfusion injury was induced in 8 mice by transient ligation of the LAD coronary artery. Baseline quantitative T₁maps were made at day 1 after surgery, followed by injection of a Gd-based liposomal contrast agent. Five mice served as control group, which followed the same protocol without initial surgery. Twenty-four hours post-injection, a second T₁measurement was performed. Local ΔR₁values were compared with regional wall thickening determined by functional cine CMR and correlated to <it>ex vivo </it>Gd concentrations determined by ICP-MS.</p> <p>Results</p> <p>Compared to control values, pre-contrast T₁of infarcted myocardium was slightly elevated, whereas T₁of remote myocardium did not significantly differ. Twenty-four hours post-contrast injection, high ΔR₁values were found in regions with low wall thickening values. However, compared to remote tissue (wall thickening > 45%), ΔR₁was only significantly higher in severe infarcted tissue (wall thickening < 15%). A substantial correlation (<it>r </it>= 0.81) was found between CMR-based ΔR₁values and Gd concentrations from <it>ex vivo </it>ICP-MS measurements. Furthermore, regression analysis revealed that the effective relaxivity of the liposomal contrast agent was only about half the value determined <it>in vitro</it>.</p> <p>Conclusions</p> <p>3D cardiac T₁mapping by CMR can be used to monitor the accumulation of contrast agents in contrast-enhanced studies of murine myocardial infarction. The contrast agent relaxivity was decreased under <it>in vivo </it>conditions compared to <it>in vitro </it>measurements, which needs consideration when quantifying local contrast agent concentrations.</p

Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI

Background: Left ventricular (LV) and right ventricular (RV) function have an important impact on symptom occurrence, disease progression and exercise tolerance in pressure overload-induced heart failure, but particularly RV functional changes are not well described in the relevant aortic banding mouse model. Therefore, we quantified time-dependent alterations in the ventricular morphology and function in two models of hypertrophy and heart failure and we studied the relationship between RV and LV function during the transition from hypertrophy to heart failure. Methods: MRI was used to quantify RV and LV function and morphology in healthy (n = 4) and sham operated (n = 3) C57BL/6 mice, and animals with a mild (n = 5) and a severe aortic constriction (n = 10). Results: Mice subjected to a mild constriction showed increased LV mass (P0.05). Animals with a severe constriction progressively developed LV hypertrophy (P<0.001), depressed LVEF (P<0.001), followed by a declining RVEF (P<0.001) and the development of pulmonary remodeling, as compared to controls during a 10-week follow-up. Myocardial strain, as a measure for local cardiac function, decreased in mice with a severe constriction compared to controls (P<0.05). Conclusions: Relevant changes in mouse RV and LV function following an aortic constriction could be quantified using MRI. The well-controlled models described here open opportunities to assess the added value of new MRI techniques for the diagnosis of heart failure and to study the impact of new therapeutic strategies on disease progression and symptom occurrence

3D Fiber Orientation in Atherosclerotic Carotid Plaques

Atherosclerotic plaque rupture is the primary trigger of fatal cardiovascular events. Fibrillar collagen in atherosclerotic plaques and their directionality are anticipated to play a crucial role in plaque rupture. This study aimed assessing 3D fiber orientations and architecture in atherosclerotic plaques for the first time.Seven carotid plaques were imaged ex-vivo with a state-of-the-art Diffusion Tensor Imaging (DTI) technique, using a high magnetic field (9.4. Tesla) MRI scanner. A 3D spin-echo sequence with uni-polar diffusion sensitizing pulsed field gradients was utilized for DTI and fiber directions were assessed from diffusion tensor measurements. The distribution of the 3D fiber orientations in atherosclerotic plaques were quantified and the principal fiber orientations (circumferential, longitudinal or radial) were determined.Overall, 52% of the fiber orientations in the carotid plaque specimens were closest to the circumferential direction, 34% to the longitudinal direction, and 14% to the radial direction. Statistically no significant difference was measured in the amount of the fiber orientations between the concentric and eccentric plaque sites. However, concentric plaque sites showed a distinct structural organization, where the principally longitudinally oriented fibers were closer to the luminal side and the principally circumferentially oriented fibers were located more abluminally. The acquired unique information on 3D plaque fiber direction will help understanding pathobiological mechanisms of atherosclerotic plaque progression and pave the road to more realistic biomechanical plaque modeling for rupture assessment

Labeling galectin-3 for the assessment of myocardial infarction in rats

Background: Galectin-3 is a beta-galactoside-binding lectin expressed in most of tissues in normal conditions and overexpressed in myocardium from early stages of heart failure (HF). It is an established biomarker associated with extracellular matrix (ECM) turnover during myocardial remodeling. The aim of this study is to test the ability of I-123-galectin-3 (IG3) to assess cardiac remodeling in a model of myocardial infarction (MI) using imaging techniques. Methods: Recombinant galectin-3 was labeled with iodine-123 and in vitro binding assays were conducted to test I-123-galectin-3 ability to bind to ECM targets. For in vivo studies, a rat model of induced-MI was used. Animals were subjected to magnetic resonance and micro-SPETC/micro-CT imaging two (2 W-MI) or four (4 W-MI) weeks after MI. Sham rats were used as controls. Pharmacokinetic, biodistribution, and histological studies were also performed after intravenous administration of IG3. Results: In vitro studies revealed that IG3 shows higher binding affinity (measured as counts per minute, cpm) (p < 0.05) to laminin (2.45 +/- 1.67 cpm), fibronectin (4.72 +/- 1.95 cpm), and collagen type I (1.88 +/- 0.53 cpm) compared to bovine serum albumin (BSA) (0.88 +/- 0.31 cpm). Myocardial quantitative IG3 uptake (\%ID/g) was higher (p < 0.01) in the infarct of 2 W-MI rats (0.15 +/- 0.04\%) compared to control (0.05 +/- 0.03\%). IG3 infarct uptake correlates with the extent of scar (r(s) = 1, p = 0.017). Total collagen deposition in the infarct (percentage area) was higher (p < 0.0001) at 2 W-MI (24.2 +/- 5.1\%) and 4 W-MI (30.4 +/- 7.5\%) compared to control (1.9 +/- 1.1\%). However, thick collagen content in the infarct (square micrometer stained) was higher at 4 W-MI (20.5 +/- 11.2 mu m(2)) compared to control (4.7 +/- 2.0 mu m(2), p < 0.001) and 2 W-MI (10.6 +/- 5.1 mu m(2), p < 0.05). Conclusions: This study shows, although preliminary, enough data to consider IG3 as a potential contrast agent for imaging of myocardial interstitial changes in rats after MI. Labeling strategies need to be sought to improve in vivo IG3 imaging, and if proven, galectin-3 might be used as an imaging tool for the assessment and treatment of MI patients.This work was supported by Centro Nacional de Investigaciones Cardiovasculares (CNIC) through the Cardio-Image program (TA and CPM).S

Myocardial perfusion MRI shows impaired perfusion of the mouse hypertrophic left ventricle

There is growing consensus that myocardial perfusion deficits play a pivotal role in the transition from compensated to overt decompensated hypertrophy. The purpose of this study was to systematically study myocardial perfusion deficits in the highly relevant model of pressure overload induced hypertrophy and heart failur

Acute cellular and vascular responses to photodynamic therapy using EGFR-targeted nanobody-photosensitizer conjugates studied with intravital optical imaging and magnetic resonance imaging

Targeted photodynamic therapy (PDT) has the potential to selectively damage tumor tissue and to increase tumor vessel permeability. Here we characterize the tissue biodistribution of two EGFR-targeted nanobody-photosensitizer conjugates (NB-PS), the monovalent 7D12-PS and the biparatopic 7D12-9G8-PS. In addition, we report on the local and acute phototoxic effects triggered by illumination of these NB-PS which have previously shown to lead to extensive tumor damage. Methods: Intravital microscopy and the skin-fold chamber model, containing OSC-19-luc2-cGFP tumors, were used to investigate: a) the fluorescence kinetics and distribution, b) the vascular response and c) the induction of necrosis after illumination at 1 or 24 h post administration of 7D12-PS and 7D12-9G8-PS. In addition, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of a solid tumor model was used to investigate the microvascular status 2 h after 7D12-PS mediated PDT. Results: Image analysis showed significant tumor colocalization for both NB-PS which was higher for 7D12-9G8-PS. Intravital imaging showed clear tumor cell membrane localization 1 and 2 h after administration of 7D12-9G8-PS, and fluorescence in or close to endothelial cells in normal tissue for both NB-PS. PDT lead to vasoconstriction and leakage of tumor and normal tissue vessels in the skin-fold chamber model. DCE-MRI confirmed the reduction of tumor perfusion after 7D12-PS mediated PDT. PDT induced extensive tumor necrosis and moderate normal tissue damage, which was similar for both NB-PS conjugates. This was significantly reduced when illumination was performed at 24 h com

Multiparametric Immunoimaging Maps Inflammatory Signatures in Murine Myocardial Infarction Models.

In the past 2 decades, research on atherosclerotic cardiovascular disease has uncovered inflammation to be a key driver of the pathophysiological process. A pressing need therefore exists to quantitatively and longitudinally probe inflammation, in preclinical models and in cardiovascular disease patients, ideally using non-invasive methods and at multiple levels. Here, we developed and employed in vivo multiparametric imaging approaches to investigate the immune response following myocardial infarction. The myocardial infarction models encompassed either transient or permanent left anterior descending coronary artery occlusion in C57BL/6 and Apoe-/-mice. We performed nanotracer-based fluorine magnetic resonance imaging and positron emission tomography (PET) imaging using a CD11b-specific nanobody and a C-C motif chemokine receptor 2-binding probe. We found that immune cell influx in the infarct was more pronounced in the permanent occlusion model. Further, using 18F-fluorothymidine and 18F-fluorodeoxyglucose PET, we detected increased hematopoietic activity after myocardial infarction, with no difference between the models. Finally, we observed persistent systemic inflammation and exacerbated atherosclerosis in Apoe-/- mice, regardless of which infarction model was used. Taken together, we showed the strengths and capabilities of multiparametric imaging in detecting inflammatory activity in cardiovascular disease, which augments the development of clinical readouts.This work was supported by National Institute of Health grants R01HL143814 (to Dr Fayad), P01HL131478 (Drs Fayad and Mulder), P41EB025815 (Drs Liu and Gropler ), R35HL145212 (Dr Liu), and R35HL139598 (Dr Nahrendorf) and award K22CA226040 (Dr Rashidian). This work was also supported by an Innovation Research Fund Basic Research Award from the Dana-Farber Cancer Institute (Dr Rashidian). Dr Maier was supported by Deutsche Forschungsgemeinschaft grants (MA 7059/1 and MA 7059/3-1) and is part of SFB1425 funded by the Deutsche Forschungsgemeinschaft (project no. 422681845). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.S

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score ≥5, despite EVT treatment. Methods: We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives. Results: From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0–4 patients, 27–61 (3–6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99–163 (21–34%) were correctly identified by the models. Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice