Steven J Haker, Robert V. Mulkern, Joseph R Roebuck, Agnieszka Szot Barnes, Simon DiMaio, Nobuhiko Hata, and Clare M Tempany. 2005. “Magnetic Resonance Guided Prostate Interventions.” Top Magn Reson Imaging, 16, 5, Pp. 355-68.Abstract

We review our experience using an open 0.5-T magnetic resonance (MR) interventional unit to guide procedures in the prostate. This system allows access to the patient and real-time MR imaging simultaneously and has made it possible to perform prostate biopsy and brachytherapy under MR guidance. We review MR imaging of the prostate and its use in targeted therapy, and describe our use of image processing methods such as image registration to further facilitate precise targeting. We describe current developments with a robot assist system being developed to aid radioactive seed placement.

Lei Zhu, Steven Haker, and Allen Tannenbaum. 2005. “Mass Preserving Registration for Heart MR Images.” Med Image Comput Comput Assist Interv, 8, Pt 2, Pp. 147-54.Abstract

This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm.

Delphine Nain, Steven Haker, Aaron Bobick, and Allen R Tannenbaum. 2005. “Multiscale 3D Shape Analysis using Spherical Wavelets.” Med Image Comput Comput Assist Interv, 8, Pt 2, Pp. 459-67.Abstract

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.