Left Ventricular Ejection Fraction Assessment: Unraveling the Bias between Area- and Volume-based Estimates

Application of multi-planar reconstruction technique in endovascular repair of aortic dissection

BACKGROUND

Endovascular repair of aortic dissection is an effective method commonly used in the treatment of Stanford type B aortic dissection. Stent placement during the operation was one-time and could not be repeatedly adjusted during the operation. Therefore, it is of great significance for cardiovascular physicians to fully understand the branch status, position, angle, and other information regarding aortic arch dissection before surgery.

AIM

To provide more references for clinical cardiovascular physicians to develop treatment plans.

METHODS

Data from 153 patients who underwent endovascular repair of aortic dissection at our hospital between January 2021 and December 2022 were retrospectively collected. All patients underwent multi-slice spiral computed tomography angiography. Based on distinct post-image processing techniques, the patients were categorized into three groups: Multiplanar reconstruction (MPR) (n = 55), volume reconstruction (VR) (n = 46), and maximum intensity projection (MIP) (n = 52). The detection rate of aortic rupture, accuracy of the DeBakey classification, rotation, and tilt angles of the C-arm during the procedure, dispersion after stent release, and the incidence of late complications were recorded and compared.

RESULTS

The detection rates of interlayer rupture in the MPR and VR groups were significantly higher than that in the MIP group (P < 0.05). The detection rates of DeBakey subtypes I, II, and III in the MPR group were higher than those in the MIP group, and the detection rate of type III in the MPR group was significantly higher than that in the VR group (P < 0.05). There was no statistically significant difference in the detection rates of types I and II compared to the VR group (P > 0.05). The scatter rate of markers and the incidence of complications in the MPR group were significantly lower than those in the VR and MIP groups (P < 0.05).

CONCLUSION

The application of MPR in the endovascular repair of aortic dissection has improved the detection rate of dissection rupture, the accuracy of anatomical classification, and safety.

Combining Statistical Shape Model and Principal Component Analysis to Estimate Left Ventricular Volume and Ejection Fraction

Left ventricular ejection fraction (LVEF) assessment is instrumental for cardiac health diagnosis, patient management, and patient eligibility for participation in clinical studies. Due to its non-invasiveness and low operational cost, ultrasound (US) imaging is the most commonly used imaging modality to image the heart and assess LVEF. Even though 3D US imaging technology is becoming more available, cardiologists dominantly use 2D US imaging to visualize the LV blood pool and interpret its area changes between end-systole and end-diastole. Our previous work showed that LVEF estimates based on area changes are significantly lower than the true volume-based estimates by as much as 13%,¹ which could lead to unnecessary and costly therapeutic decisions. Acquiring volumetric information about the LV blood pool necessitates either time-consuming 3D reconstruction or 3D US image acquisition. Here, we propose a method that leverages on a statistical shape model (SSM) constructed from 13 landmarks depicting the LV endocardial border to estimate a new patient's LV volume and LVEF. Two methods to estimate the 3D LV geometry with and without size normalization were employed. The SSM was built using the 13 landmarks from 50 training patient image datasets. Subsequently, the Mahalanobis distance (with size normalization) or the vector distance (without size normalization) between an incoming patient's LV landmarks and each shape in the SSM were used to determine the weights each training patient contributed to describing the new, incoming patient's LV geometry and associated blood pool volume. We tested the proposed method to estimate the LV volumes and LVEF for 16 new test patients. The estimated LVEFs based on Mahalanobis distance and vector distance were within 2.9% and 1.1%, respectively, of the ground truth LVEFs calculated from the 3D reconstructed LV volumes. Furthermore, the viability of using fewer principal components (PCs) to estimate the LV volume was explored by reducing the number of PCs retained when projecting landmarks onto PCA space. LVEF estimated based on 3 PCs, 5 PCs, and 10 PCs are within 6.6%, 5.4%, and 3.3%, respectively, of LVEF estimates using the full set of 39 PCs.

A Statistical Shape Model Approach for Computing Left Ventricle Volume and Ejection Fraction Using Multi-plane Ultrasound Images

Assessing the left ventricular ejection fraction (LVEF) accurately requires 3D volumetric data of the LV. Cardiologists either have no access to 3D ultrasound (US) systems or prefer to visually estimate LVEF based on 2D US images. To facilitate the consistent estimation of the end-diastolic and end-systolic blood pool volume and LVEF based on 3D data without extensive complicated manual input, we propose a statistical shape model (SSM) based on 13 key anchor points-the LV apex (1), mitral valve hinges (6), and the midpoints of the endocardial contours (6)-identified from the LV endocardial contour of the tri-plane 2D US images. We use principal component analysis (PCA) to identify the principle modes of variation needed to represent the LV shapes, which enables us to estimate an incoming LV as a linear combination of the principle components (PC). For a new, incoming patient image, its 13 anchor points are projected onto the PC space; its shape is compared to each LV shape in the SSM based on Mahalanobis distance, which is normalized with respect to the LV size, as well as direct vector distance (i.e., PCA distance), without any size normalization. These distances are used to determine the weight each training shape in the SSM contributes to the description of the new patient LV shape. Finally, the new patient's LV systolic and diastolic volumes are estimated as the weighted average of the training volumes in the SSM. To assess our proposed method, we compared the SSM-based estimates of diastolic, systolic, stroke volumes, and LVEF with those computed directly from 16 tri-plane 2D US imaging datasets using the GE Echo-Pac PC clinical platform. The estimated LVEF based on Mahalanobis distance and PCA distance were within 6.8% and 1.7% of the reference LVEF computed using the GE Echo-Pac PC clinical platform.