Biomechanical Modeling of the Prostate for Procedure Guidance and Simulation

Patient-specific Deformation Modelling via Elastography: Application to Image-guided Prostate Interventions

Image-guided prostate interventions often require the registration of preoperative magnetic resonance (MR) images to real-time transrectal ultrasound (TRUS) images to provide high-quality guidance. One of the main challenges for registering MR images to TRUS images is how to estimate the TRUS-probe-induced prostate deformation that occurs during TRUS imaging. The combined statistical and biomechanical modeling approach shows promise for the adequate estimation of prostate deformation. However, the right setting of the biomechanical parameters is very crucial for realistic deformation modeling. We propose a patient-specific deformation model equipped with personalized biomechanical parameters obtained from shear wave elastography to reliably predict the prostate deformation during image-guided interventions. Using data acquired from a prostate phantom and twelve patients with suspected prostate cancer, we compared the prostate deformation model with and without patient-specific biomechanical parameters in terms of deformation estimation accuracy. The results show that the patient-specific deformation model possesses favorable model ability, and outperforms the model without patient-specific biomechanical parameters. The employment of the patient-specific biomechanical parameters obtained from elastography for deformation modeling shows promise for providing more precise deformation estimation in applications that use computer-assisted image-guided intervention systems.

Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration

Registration and fusion of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland can provide high-quality guidance for prostate interventions. However, accurate MR-TRUS registration remains a challenging task, due to the great intensity variation between two modalities, the lack of intrinsic fiducials within the prostate, the large gland deformation caused by the TRUS probe insertion, and distinctive biomechanical properties in patients and prostate zones. To address these challenges, a personalized model-to-surface registration approach is proposed in this study. The main contributions of this paper can be threefold. First, a new personalized statistical deformable model (PSDM) is proposed with the finite element analysis and the patient-specific tissue parameters measured from the ultrasound elastography. Second, a hybrid point matching method is developed by introducing the modality independent neighborhood descriptor (MIND) to weight the Euclidean distance between points to establish reliable surface point correspondence. Third, the hybrid point matching is further guided by the PSDM for more physically plausible deformation estimation. Eighteen sets of patient data are included to test the efficacy of the proposed method. The experimental results demonstrate that our approach provides more accurate and robust MR-TRUS registration than state-of-the-art methods do. The averaged target registration error is 1.44 mm, which meets the clinical requirement of 1.9 mm for the accurate tumor volume detection. It can be concluded that the presented method can effectively fuse the heterogeneous image information in the elastography, MR, and TRUS to attain satisfactory image alignment performance.

Multiparametric 3D in vivo ultrasound vibroelastography imaging of prostate cancer: Preliminary results

PURPOSE

Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images.

METHODS

The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM.

RESULTS

The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leave-one-patient-out validation.

CONCLUSIONS

The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method.

Multi-parametric 3D quantitative ultrasound vibro-elastography imaging for detecting palpable prostate tumors

In this article, we describe a system for detecting dominant prostate tumors, based on a combination of features extracted from a novel multi-parametric quantitative ultrasound elastography technique. The performance of the system was validated on a data-set acquired from n = 10 patients undergoing radical prostatectomy. Multi-frequency steady-state mechanical excitations were applied to each patient's prostate through the perineum and prostate tissue displacements were captured by a transrectal ultrasound system. 3D volumetric data including absolute value of tissue elasticity, strain and frequency-response were computed for each patient. Based on the combination of all extracted features, a random forest classification algorithm was used to separate cancerous regions from normal tissue, and to compute a measure of cancer probability. Registered whole mount histopathology images of the excised prostate gland were used as a ground truth of cancer distribution for classifier training. An area under receiver operating characteristic curve of 0.82 +/- 0.01 was achieved in a leave-one-patient-out cross validation. Our results show the potential of multi-parametric quantitative elastography for prostate cancer detection for the first time in a clinical setting, and justify further studies to establish whether the approach can have clinical use.

Model-based registration of ex vivo and in vivo MRI of the prostate using elastography*

Registration of histopathology to in vivo magnetic resonance imaging (MRI) of the prostate is an important task that can be used to optimize in vivo imaging for cancer detection. Such registration is challenging due to the change in volume and deformation of the prostate during excision and fixation. One approach towards this problem involves the use of an ex vivo MRI of the excised prostate specimen, followed by in vivo to ex vivo MRI registration of the prostate. We propose a novel registration method that uses a patient-specific biomechanical model acquired using magnetic resonance elastography to deform the in vivo volume and match it to the surface of the ex vivo specimen. The forces that drive the deformations are derived from a region-based energy, with the elastic potential used for regularization. The incorporation of elastography data into the registration framework allows inhomogeneous elasticity to be assigned to the in vivo volume. We show that such inhomogeneity improves the registration results by providing a physical regularization of the deformation map. The method is demonstrated and evaluated on six clinical cases.

Model-based registration of ex vivo and in vivo MRI of the prostate using elastography

Registration of histopathology to in vivo magnetic resonance imaging (MRI) of the prostate is an important task that can be used to optimize in vivo imaging for cancer detection. Such registration is challenging due to the change in volume and deformation of the prostate during excision and fixation. One approach towards this problem involves the use of an ex vivo MRI of the excised prostate specimen, followed by in vivo to ex vivo MRI registration of the prostate. We propose a novel registration method that uses a patient-specific biomechanical model acquired using magnetic resonance elastography to deform the in vivo volume and match it to the surface of the ex vivo specimen. The forces that drive the deformations are derived from a region-based energy, with the elastic potential used for regularization. The incorporation of elastography data into the registration framework allows inhomogeneous elasticity to be assigned to the in vivo volume. We show that such inhomogeneity improves the registration results by providing a physical regularization of the deformation map. The method is demonstrated and evaluated on six clinical cases.