A deformable multimodal image registration using PET/CT and TRUS for intraoperative focal prostate brachytherapy

Abstract

In this paper, a deformable registration method is proposed that enables automatic alignment of preoperative PET/CT to intraoperative ultrasound in order to achieve PET-determined focal prostate brachytherapy. Novel PET imaging agents such as prostate specific membrane antigen (PSMA) enables highly accurate identification of intra/extra-prostatic tumors. Incorporation of PSMA PET into the standard transrectal ultrasound (TRUS)-guided prostate brachytherapy will enable focal therapy, thus minimizing radiation toxicities. Our registration method requires PET/CT and TRUS volume as well as prostate segmentations. These input volumes are first rigidly registered by maximizing spatial overlap between the segmented prostate volumes, followed by the deformable registration. To achieve anatomically accurate deformable registration, we extract anatomical landmarks from both prostate boundary and inside the gland. Landmarks are extracted along the base-apex axes using two approaches: equiangular and equidistance. Three-dimensional thin-plate spline (TPS)-based deformable registration is then performed using the extracted landmarks as control points. Finally, the PET/CT images are deformed to the TRUS space by using the computed TPS transformation. The proposed method was validated on 10 prostate cancer patient datasets in which we registered post-implant CT to end-of-implantation TRUS. We computed target registration errors (TREs) by comparing the implanted seed positions (transformed CT seeds vs. intraoperatively identified TRUS seeds). The average TREs of the proposed method are 1.98±1.22 mm (mean±standard deviation) and 1.97±1.24 mm for equiangular and equidistance landmark extraction methods, respectively, which is better than or comparable to existing state-of-the-art methods while being computationally more efficient with an average computation time less than 40 seconds.

Real-time multimodal image registration with partial intraoperative point-set data

“…Recently, intensity-based deep learning methods have achieved reported TREs below 5 mm for the MRI-TRUS registration application explored in this paper ( Hu et al., 2018 ; Sun et al., 2018 ; Yan et al., 2018 ; Sultana et al., 2019 ;). The TREs for this application estimated in this work fall within the expected range found in the literature, however, it is difficult to make direct comparisons between our results and other methods due to variations in the quality of data (for example, due to different clinical setups, image acquisition protocols, and user experience) and validation methods.…”

“…The landmarks in our dataset comprised 309 pairs of points, including points defining the apex and base of the prostate, and various patient-specific landmarks including zonal structure boundaries, water-filled cysts, and calcifications ( Hamid et al., 2019 ). It should be noted that the overall spatial distribution of these landmarks may be representative of the full TRE distribution in this application ( Hu et al., 2008 ; Hahn et al., 2010 ; Karnik et al., 2010 ; Hu et al., 2011 ; Heinrich et al., 2012 ; Hu et al., 2012 ; Mitra et al, 2012a ; Mitra et al., 2012b ; De Silva et al., 2013 ; Sun et al., 2014 ; Fedorov et al., 2015 ; Hu et al., 2015 ; Sun et al., 2015 ; Yang et al., 2015 ; Zettinig et al., 2015 ; Wang et al., 2016 Onofrey et al., 2017 ; Hu et al., 2018 ; Sun et al., 2018 ; Yan et al., 2018 ; Sultana et al., 2019 ; Xu et al., 2020 ; Fu et al., 2021 ; Hu et al., 2021 ), but landmark-based TREs nevertheless provide a useful estimate of the errors associated with localising tumours within the prostate. The computational time was also recorded for each registration experiment.…”

Clinical Efficacy and Openness to New Challenges of Low Dose Rate Brachytherapy for Prostate Cancer

“…These new imaging techniques may make it possible to perform focal therapy, as described. These two methods can be combined to improve the current focal therapy procedures for PCa [55,56]. The utility of deformable registrations of PET/computed tomography (CT) and ultrasound to target PCa was demonstrated in this report.…”

JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans

“…Multi-modal Image Registration Methods Multi-modal image registration has been widely studied and applied in the medical area. The existing registration methods can be based on additional information like landmarks [28,33] and surface [40], or based on the voxel property, which operate directly on the image grey values without prior data introduced by the user or segmentation [22]. For the voxel-based methods, there are two typical strategies.…”