A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.

TitleA Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.
Publication TypeJournal Article
Year of Publication2014
AuthorsWan, T, Bloch NB, Danish S, Madabhushi A
Date Published11/2014

In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new treatment modality - laser induced interstitial thermal therapy (LITT) for treating neurological disorders. Magnetic resonance (MR) guided LITT has recently emerged as a minimally invasive alternative to craniotomy for local treatment of brain diseases (such as glioblastoma multiforme (GBM), epilepsy). However, LITT is currently only practised as an investigational procedure world-wide due to lack of data on longer term patient outcome following LITT. There is thus a need to quantitatively evaluate treatment related changes between post- and pre-LITT in terms of MR imaging markers. In order to validate LeFiR, we tested the scheme on a synthetic brain dataset (SBD) and in two real clinical scenarios for treating GBM and epilepsy with LITT. Four experiments under different deformation profiles simulating localized ablation effects of LITT on MRI were conducted on 286 pairs of SBD images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid (UniG) placement scheme, a speeded-up robust features (SURF) based method, and a scale-invariant feature transform (SIFT) based method as well as a generic free-form deformation (FFD) approach. The LeFiR method achieved average 90% improvement in recovering the local deformation compared to 82% for the uniform grid placement, 62% for the SURF based approach, and 16% for the generic FFD approach. On the real GBM and epilepsy data, the quantitative results showed that LeFiR outperformed UniG by 28% improvement in average.

PDF Link


Alternate JournalNeurocomputing

 *IEEE COPYRIGHT NOTICE: 1997 IEEE. * Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

*COPYRIGHT NOTICE:* These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.