A learning based fiducial-driven registration scheme for evaluating laser ablation changes in neurological disorders

Abstract

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.

Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings

“…In our recent work, 18 a fiducial driven based registration scheme was presented to evaluate LITT changes for GBM and epilepsy patients. Similarly, Viswanath et al 17,19 recently presented a quantitative approach via texture descriptors in conjunction with MP-MRI to evaluate post-LITT changes for prostate cancer.…”

Guest Editorial: Special issue on advanced computing for image-guided intervention

“…A different approach has been employed by [3] to model the deformation induced by LITT for neurological disorders.…”

“…In [1][2][3], from this issue, image processing algorithms are applied to support minimally invasive therapeutic procedures based on MRI imaging. In [2], Laser induced Interstitial Thermal Therapy (LITT) has been applied to treat prostate cancer and a quantitative analysis of MRI image features is presented which is shown to correlate well with the therapeutic response.…”

“…Toth et al [1] have addressed the image registration problem between preand post-radiated MRI to facilitate the evaluation of the therapeutic response after External Beam Radiation Treatment (EBRT) for the prostate cancer. A different approach has been employed by [3] to model the deformation induced by LITT for neurological disorders.…”

Scalable joint segmentation and registration framework for infant brain images

“…In particular, image segmentation [7–11] and registration [12, 13], which are the two most essential steps, are largely needed in many early brain development studies, such as longitudinal cortical analysis [14, 15] and infant atlas construction [16]. Recent patch based label fusion methods, such as local weighted label fusion method [17], non-local mean label fusion method [18], and sparse learning based label fusion methods [9–11, 19–21], have been successfully applied to medical image segmentations.…”