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The proposed image-guidance system is the first practical approach to dynamic dose calculation, outperforming earlier solutions in terms of robustness, ease of use, and functional completeness.

The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland, and the capability of intraoperatively localizing implanted seeds provides potential for dose evaluation and optimization during therapy. REDMAPS is a recently reported algorithm that carries out seed localization by detecting, matching, and reconstructing seeds in only a few seconds from three acquired x-ray images. In this paper, we present an automatic pose correction (APC) process that is combined with REDMAPS to allow for both more accurate seed reconstruction and the use of images with relatively large pose errors. APC uses a set of reconstructed seeds as a fiducial and corrects the image pose by minimizing the overall projection error. The seed matching and APC are iteratively computed until a stopping condition is met. Simulations and clinical studies show that APC significantly improves the reconstructions with an overall average matching rate of ≥ 99.4%, reconstruction error of ≤ 0.5 mm, and the matching solution optimality of ≥ 99.8%.

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Prostate brachytherapy is a treatment for prostate cancer using radioactive seeds that are permanently implanted in the prostate. The treatment success depends on adequate coverage of the target gland with a therapeutic dose, while sparing the surrounding tissue. Since seed implantation is performed under transrectal ultrasound (TRUS) imaging, intraoperative localization of the seeds in ultrasound can provide physicians with dynamic dose assessment and plan modification. However, since all seeds cannot be seen in the ultrasound images, registration between ultrasound and fluoroscopy is a practical solution for intraoperative dosimetry. In this manuscript, we introduce a new image-based nonrigid registration method that obviates the need for manual seed segmentation in TRUS images and compensates for the prostate displacement and deformation due to TRUS probe pressure. First, we filter the ultrasound images for subsequent registration using thresholding and Gaussian blurring. Second, a computationally efficient point-to-volume similarity metric, an affine transformation and an evolutionary optimizer are used in the registration loop. A phantom study showed final registration errors of 0.84 ± 0.45 mm compared to ground truth. In a study on data from 10 patients, the registration algorithm showed overall seed-to-seed errors of 1.7 ± 1.0 mm and 1.5 ± 0.9 mm for rigid and nonrigid registration methods, respectively, performed in approximately 30 seconds per patient.

Needle insertion simulators find use in a number of medical interventions, such as prostate brachytherapy. A needle insertion simulator has three main components: the needle model, the tissue model, and the model of interaction between the needle and the tissue. In this paper, a new methodology is introduced for the joint modeling of tissue and needle-tissue interactions. The approach consists of the measurement of tissue motion using ultrasound, and of the needle position and base force. Tissue motion is determined using a correlation-based algorithm that processes the ultrasound radiofrequency data. The tissue elastic parameters and the parameters of the tissue-needle interaction model are determined by using numerical optimization to match the response of the needle insertion model to the measured data. Phantom experiments were carried out in which a brachytherapy needle was inserted into a two-layer non-homogeneous phantom mimicking a prostate and its surrounding tissue. Experimental results show good agreement with the model obtained. In particular, the parameters of a three-parameter force model were identified for each layer of the phantom to fit the measured force to the simulated one. Also, the Young's modulus of each layer was identified to match the measured and simulated nodal axial displacements. This is the first report of the use of ultrasound radiofrequency data to characterize tissue motion during needle insertion. As the method is non-invasive and does not involve ionizing radiation, its application in patient studies is feasible.

Abstract.A needle-tissue interaction model is an essential part of every needle insertion simulator. In this paper, a new experimental method for the modeling of needle-tissue interaction is presented. The method consists of measuring needle and tissue displacements with ultrasound, measuring needle base forces, and using a deformation simulation model to identify the parameters of a needle-tissue interaction model. The feasibility of this non-invasive approach was demonstrated in an experiment in which a brachytherapy needle was inserted into a prostate phantom. Ultrasound radio-frequency data and the time-domain cross-correlation method, often used in ultrasound elastography, were used to generate the tissue displacement field during needle insertion. A three-parameter force density model was assumed for the needle-tissue interaction. With the needle displacement, tissue displacement and needle base forces as input data, finite element simulations were carried out to adjust the model parameters to achieve a good fit between simulated and measured data.