Automatic estimation of left atrial function from short axis CINE-MRI using machine learning

Introduction The importance of atrial mechanical dysfunction in atrial and ventricular pathologies is becoming increasingly recognised. Although machine learning (ML) tools have the ability to automatically estimate atrial function, to date ML techniques have not been used to automatically estimate atrial volumes and functional parameters directly from short axis CINE MRI. Purpose We introduce a convolutional neural network (CNN) to automatically segment the left atria (LA) in CINE-MRI. As a demonstration of the clinical utility of this technique, we calculated LA and left ventricular (LV) ejection fractions automatically from CINE images. Methods Short axis CINE MRI stacks, covering both ventricles and atria, were obtained in a 1.5T Philips Ingenia scanner. A 2D bSSFP ECG-gated protocol was used (FA=60°, TE/TR=1.5/2.9 ms), typical FOV =385 x 310 x 150 mm3, acquisition matrix = 172 x 140, slice thickness = 10 mm, reconstructed with resolution 1.25 x 1.25 x 10 mm3, 30–50 cardiac phases. Images were collected from 37 AF patients in sinus rythm at the time of scan (31–72 years old, 75% male, 18 with paroxysmal AF (PAF), 19 with persistent AF (persAF)). To automatically segment the LA, we used a dedicated CNN that follows a U-Net architecture and was trained in 715 images of the LA, manually segmented by an expert. Data augmentation techniques that included noise addition and linear and non-linear image transforms were also used to increase the training dataset. Ventricular structures, including the LV blood pool, were automatically segmented in these images using a CNN previously trained for this task. Volumetric time plots of LA and LV volume were produced and used to automatically compute maximal and minimal volumes, from which LA and LV ejection fractions (EFs) were assessed. A Bland-Altman analysis compared these automatically computed LA volumes and LA EFs with clinical manual estimates from the same scanning session. Results The CNN achieved very good quality LA segmentations when compared to manual ones (Fig a,b): Dice coefficients (0.90±0.07), median contour distances (0.50±1.12mm) and Hausdorff distances (6.70±6.16mm). Bland-Altman analyses show very good agreement between automatic and manual LA volumes and EFs (Fig e). A moderate linear correlation between LA and LV EFs in AF patients was found (Fig d). The measured LA EF was higher for PAF (29±8%) than PersAF patients (21±11%), although non-significantly (t-test p-value: 0.10). Conclusions We present a reliable automatic method to perform LA segmentations from CINE MRI across the entire cardiac cycle. This approachs opens up the possibility of automatically calculating more sophisticated biomarkers of LA function which take into account information about LA volumes across the entire cardiac cycle, including biomarkers of LA booster pump function. Figure 1 Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): British Heart Foundation; EPSRC/Wellcome Centre for Medical Engineering

Hemodynamic changes due to stent placement in bifurcating intracranial aneurysms

Object The aim of this study was to measure changes in intraaneurysm flow dynamics and mechanical stresses resulting from the placement of Neuroform stents in bifurcating intracranial aneurysm models. Methods A digital particle image velocimetry (DPIV) system was used to measure the pulsatile velocity and shear stress fields within the aneurysm and at the aneurysm neck—parent artery interface. The DPIV system provides an instantaneous two-dimensional measurement of the temporal and spatial variations of the velocity vector field of the flow inside the aneurysm pouch and the parent vessel, providing information on both the temporal and spatial variations of the velocity field during the entire cardiac cycle. The corresponding shear stress field was then computed from the velocity field data. A flexible silicone model of bifurcating intracranial aneurysms was used. Two Neuroform stents with a 60- to 65-µm strut thickness and an 11% metal/artery ratio were placed in a Y-configuration, and measurements were obtained after placing the stents. Conclusions Two three-dimensional vortices of different strengths persisted within the aneurysm during the entire cardiac cycle. The peak velocity and strength of these vortices were reduced after placing the two bifurcating stents. The effect of placing the Neuroform stent across the neck of a bifurcating intracranial aneurysm was shown to reduce the magnitude of the velocity of the jet entering the sac by as much as 11%. Nevertheless, the effect of the stents was particularly noticeable at the end of the cardiac cycle, when the residual vorticity and shear stresses inside the sac were decreased by more than 40%.