Left atrial (LA) dilatation has been associated with a large variety of car-diac diseases and results from either the chronic volume and/or pressure overload of the LA. Studies have shown that LA dilation has been associated with the severity of left ventricular (LV) diastolic dysfunction and it is also a recognized adverse prognostic marker in several cardiac diseases. Currently, clinical quantification of LA volumes is performed using either the bi-plane area-length method or the method of discs (Simpson’s method) on 2-D echocardiography images. However, these methods tend to underestimate LA volumes as compared to cardiovascular magnetic resonance (CMR) imaging. However, measuring LA volume from CMR using the above methods is time-consuming as it requires the acquisition of additional images and additional analysis time. In this paper, we propose a geometry-based reconstruction algorithm for computing the LA volume automatically for the entire cardiac cycle by combining information from both the short- and long-axis from CMR imaging. The inputs to our reconstruction algorithm are as follows: (i) a set of segmented short-axis contours and (ii) a set of segmented long-axis con-tours from the standard 2-chamber and 4-chamber views. Our approach consists of a series of iterative steps where the most basal short-axis contour is projected in the LA direction and subsequently morph to the patient-specific LA shape using the long-axis contours as guide. These series of morphing generate a left heart comprising both the LV and LA geometries with a planar basal surface. To reconstruct the LA cap, this planar basal surface is morphed into a hemisphere representing the closed surface of the LA using the long-axis contours as guide, thereby allowing us to reconstruct the closed LA shape and to calculate its volume.