title: Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric.
abstract: In this work, we present a novel, automated, registration method to fuse magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images of the prostate. Our methodology consists of: (1) delineating the prostate on MRI, (2) building a probabilistic model of prostate location on TRUS, and (3) aligning the MRI prostate segmentation to the TRUS probabilistic model. TRUS-guided needle biopsy is the current gold standard for prostate cancer (CaP) diagnosis. Up to 40% of CaP lesions appear isoechoic on TRUS, hence TRUS-guided biopsy cannot reliably target CaP lesions and is associated with a high false negative rate. MRI is better able to distinguish CaP from benign prostatic tissue, but requires special equipment and training. MRI-TRUS fusion, whereby MRI is acquired pre-operatively and aligned to TRUS during the biopsy procedure, allows for information from both modalities to be used to help guide the biopsy. The use of MRI and TRUS in combination to guide biopsy at least doubles the yield of positive biopsies. Previous work on MRI-TRUS fusion has involved aligning manually determined fiducials or prostate surfaces to achieve image registration. The accuracy of these methods is dependent on the reader's ability to determine fiducials or prostate surfaces with minimal error, which is a difficult and time-consuming task. Our novel, fully automated MRI-TRUS fusion method represents a significant advance over the current state-of-the-art because it does not require manual intervention after TRUS acquisition. All necessary preprocessing steps (i.e. delineation of the prostate on MRI) can be performed offline prior to the biopsy procedure. We evaluated our method on seven patient studies, with B-mode TRUS and a 1.5 T surface coil MRI. Our method has a root mean square error (RMSE) for expertly selected fiducials (consisting of the urethra, calcifications, and the centroids of CaP nodules) of 3.39 ± 0.85 mm.
authors.affiliation: Department of Biomedical Engineering, Rutgers University ; Department of Biomedical Engineering, Case Western Reserve University.
authors.forename: Rachel
authors.initials: R
authors.lastname: Sparks
authors.affiliation: Department of Radiology, Boston Medical Center & Boston University.
authors.forename: B Nicolas
authors.initials: BN
authors.lastname: Bloch
authors.affiliation: Lizzi Center for Biomedical Engineering, Riverside Research.
authors.forename: Ernest
authors.initials: E
authors.lastname: Feleppa
authors.affiliation: Centre for Medical Image Computing, University College London.
authors.forename: Dean
authors.initials: D
authors.lastname: Barratt
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi