Bicuspid Aortic Valve Morphology And Hemodynamics By Same-Day Echocardiography And Cardiac MRI

PURPOSE: This study investigated the impact of bicuspid aortic valve (BAV) on valve morphology and motion as well as proximal and aortic hemodynamics using a same-day echocardiography and cardiac MRI. METHODS: Transthoracic echocardiography, two-dimensional cine MRI of the aortic valve, and aortic 4D flow MRI were performed on the same day in 9 normofunctional BAV patients (age=41±12, 3 female), 4 BAV with moderate to severe aortic stenosis (AS) (age=63±5, 1 female), and 36 healthy tricuspid aortic valve controls (age=52±10, 21 female). Valve opening and closing timings and transvalvular peak velocity were measured using B-mode and Doppler echocardiogram, respectively. Valve orifice morphology at fully-opened state was characterized using cine MRI. Ascending aortic (AAo) wall shear stress (WSS) was measured using 4D flow MRI data. RESULTS: Valve motion timings were similar between BAV and controls. BAV was associated with increased orifice aspect ratio (1.44±0.11 vs. 1.10±0.13, P<0.001), transvalvular peak velocity (1.5±0.3 vs. 1.2±0.2 m/s, P<0.001) and maximum AAo WSS (1.62±0.31 vs. 0.91±0.24 Pa, P<0.001). Increased orifice aspect ratio was associated with the increase in transvalvular peak velocity (r=0.80, P < 0.0001) and maximum AAo WSS (r=0.83, P<0.0001). Transvalvular peak velocity was also positively correlated with maximum AAo WSS (r=0.83, P<0.0001). CONCLUSION: A same-day echo and MRI imaging allows for comprehensive assessment of the impact of aortic valve disease on valve function and hemodynamics. In this pilot application to BAV, we found increased orifice aspect ratio may be responsible for increased transvalvular peak velocity and maximum AAo WSS.

Image-Based Computational Hemodynamics Analysis of Systolic Obstruction in Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is a pathological condition characterized by an abnormal thickening of the myocardium. When affecting the medio-basal portion of the septum, it is named Hypertrophic Obstructive Cardiomyopathy (HOCM) because it induces a flow obstruction in the left ventricular outflow tract. In any type of HCM, the myocardial function can become compromised, possibly resulting in cardiac death. In this study, we investigated with computational analysis the hemodynamics of patients with different types of HCM. The aim was quantifying the effects of this pathology on the intraventricular blood flow and pressure gradients, and providing information potentially useful to guide the indication and the modality of the surgical treatment (septal myectomy). We employed an image-based computational approach, integrating fluid dynamics simulations with geometric and functional data, reconstructed from standard cardiac cine-MRI acquisitions. We showed that with our approach we can better understand the patho-physiological behavior of intraventricular blood flow dynamics due to the abnormal morphological and functional aspect of the left ventricle. The main results of our investigation are: (a) a detailed patient-specific analysis of the blood velocity, pressure and stress distribution associated to HCM; (b) a computation-based classification of patients affected by HCM that can complement the current clinical guidelines for the diagnosis and treatment of HOCM.

Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging

In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects—comprising healthy, obese, and diabetic patients—who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss. Networks were trained using 4-fold cross-validation (n = 80) and evaluated on an independent dataset (n = 20). Segmentation performances were compared to inter-intra observer bias with dice (DSC) and relative surface error (RSE). Both systole and diastole four-chamber area were correlated with total EAT volume (r = 0.77 and 0.74 respectively). Networks’ performances were equivalent to inter-observers’ bias (EAT: DSCInter = 0.76, DSCU-Net = 0.77, DSCFCNB = 0.76). U-net outperformed (p < 0.0001) FCNB on all metrics. Eventually, proposed multi-frame U-Net provided automated EAT area quantification with a 14.2% precision for the clinically relevant upper three quarters of EAT area range, scaling patients’ risk of EAT overload with 70% accuracy. Exploiting multi-frame U-Net in standard cine provided automated EAT quantification over a wide range of EAT quantities. The method is made available to the community through a FSLeyes plugin.

Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.

New cine magnetic resonance imaging parameters for the differential diagnosis of chronic intestinal pseudo-obstruction

Chronic intestinal pseudo-obstruction (CIPO) is a severe and refractory intestinal motility disorder whose diagnosis currently relies on subjective imaging assessments. Cine magnetic resonance imaging (MRI) may potentially improve the quantitative analysis of gastrointestinal motility; however, suitable CIPO detection parameters should be determined. Cine MRI was performed in seven patients with CIPO and 11 healthy controls. The logarithm of the Mahalanobis distance (x1) and distance variation per time (x2) were used as the original parameters to determine CIPO diagnostic thresholds. Furthermore, the correlation between cine MRI findings and CIPO severity was investigated. Threshold values of α = 1.10 and β = 0.15 for x1 and x2, respectively, produced a CIPO diagnosis sensitivity of 1.00 (7/7) and specificity of 0.82 (9/11). The resulting error was 0.11 (2/18). The two parameters were correlated (Pearson’s correlation coefficient: − 0.52). Any of the intestinal tracts of patients with severe CIPO requiring home parenteral nutrition belonged to the region defined by x1 ≥ 1.10 and x2 ≤ 0.15. Cine MRI is effective for the quantitative evaluation of small intestinal motility and CIPO diagnosis when using the abovementioned parameters and can be useful for treatment decision-making. However, these parameters have a wide distribution in healthy volunteers; this may complicate the detection of other disorders.

Developing a Technique for the Imaging-Based Measurement of ACL Elongation: A Proof of Principle

Towards the goal of obtaining non-invasive biomarkers reflecting the anterior cruciate ligament’s (ACL) loading capacity, this project aimed to develop a magnetic resonance imaging (MRI)-based method facilitating the measurement of ACL elongations during the execution of knee stress tests. An MRI-compatible, computer-controlled, and pneumatically driven knee loading device was designed to perform Lachman-like tests and induce ACL strain. A human cadaveric leg was used for test purposes. During the execution of the stress tests, a triggered real-time cine MRI sequence with a temporal resolution of 10 Hz was acquired in a parasagittal plane to capture the resultant ACL elongations. To test the accuracy of these measurements, the results were compared to in situ data of ACL elongation that were acquired by measuring the length changes of a surgical wire directly sutured to the ACL’s anteromedial bundle. The MRI-based ACL elongations ranged between 0.7 and 1.7 mm and agreed very well with in situ data (root mean square errors, RMSEs ≤ 0.25 mm), although peak elongation rates were underestimated by the MRI (RMSEs 0.19–0.36 mm/s). The high accuracy of elongation measurements underlines the potential of the technique to yield an imaging-based biomarker of the ACL’s loading capacity.