Preconditioned intensity-based prostate registration using statistical deformation models

Despite the common invisibility of cancerous lesions in transrectal ultrasound (TRUS), TRUS-guided random biopsy is considered the gold standard to diagnose prostate cancer. Pre-interventional magnetic resonance imaging (MRI) has been shown to improve the detection of malignancies but fast and accurate MRI/TRUS registration for multi-modal biopsy guidance remains challenging. In this work, we derive a statistical deformation model (SDM) from 50 automatically segmented patient datasets and propose a novel registration scheme based on a lesion-specific, anisotropic preconditioned similarity metric. The approach is validated on a dataset of 10 patients, showing landmark registration errors of 1.41 mm in the vicinity of suspicious areas.