  Real-time 3D navigation during minimally invasive procedures is an essential
yet challenging task, especially when considerable tissue motion is involved.
To balance image acquisition speed and resolution, only 2D images or
low-resolution 3D volumes can be used clinically. In this paper, a real-time
and registration-free framework for dynamic shape instantiation, generalizable
to multiple anatomical applications, is proposed to instantiate high-resolution
3D shapes of an organ from a single 2D image intra-operatively. Firstly, an
approximate optimal scan plane was determined by analyzing the pre-operative 3D
statistical shape model (SSM) of the anatomy with sparse principal component
analysis (SPCA) and considering practical constraints . Secondly, kernel
partial least squares regression (KPLSR) was used to learn the relationship
between the pre-operative 3D SSM and a synchronized 2D SSM constructed from 2D
images obtained at the approximate optimal scan plane. Finally, the derived
relationship was applied to the new intra-operative 2D image obtained at the
same scan plane to predict the high-resolution 3D shape intra-operatively. A
major feature of the proposed framework is that no extra registration between
the pre-operative 3D SSM and the synchronized 2D SSM is required. Detailed
validation was performed on studies including the liver and right ventricle
(RV) of the heart. The derived results (mean accuracy of 2.19mm on patients and
computation speed of 1ms) demonstrate its potential clinical value for
real-time, high-resolution, dynamic and 3D interventional guidance.
