Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy

BACKGROUND

Ideally, inverse planning for HDR brachytherapy (BT) should include the pose of the needles which define the trajectory of the source. This would be particularly interesting when considering the additional freedom and accuracy in needle pose which robotic needle placement enables. However, needle insertion typically leads to tissue deformation, resulting in uncertainty regarding the actual pose of the needles with respect to the tissue.

PURPOSE

To efficiently address uncertainty during inverse planning for HDR BT in order to robustly optimize the pose of the needles before insertion, that is, to facilitate path planning for robotic needle placement.

METHODS

We use a form of stochastic linear programming to model the inverse treatment planning problem. To account for uncertainty, we consider random tissue displacements at the needle tip to simulate tissue deformation. Conventionally for stochastic linear programming, each simulated deformation is reflected by an addition to the linear programming problem which increases problem size and computational complexity substantially and leads to impractical runtime. We propose two efficient approaches for stochastic linear programming. First, we consider averaging dose coefficients to reduce the problem size. Second, we study weighting of the slack variables of an adjusted linear problem to approximate the full stochastic linear program. We compare different approaches to optimize the needle configurations and evaluate their robustness with respect to different amounts of tissue deformation.

RESULTS

Our results illustrate that stochastic planning can improve the robustness of the treatment with respect to deformation. The proposed approaches approximating stochastic linear programming better conform to the tissue deformation compared to conventional linear programming. They show good correlation with the plans computed after deformation while reducing the runtime by two orders of magnitude compared to the complete stochastic linear program. Robust optimization of needle configurations takes on average 59.42 s. Skew needle configurations lead to mean coverage improvements compared to parallel needles from 0.39 to 2.94 percentage points, when 8 mm tissue deformation is considered. Considering tissue deformations from 4  to 10 mm during planning with weighted stochastic optimization and skew needles generally results in improved mean coverage from 1.77 to 4.21 percentage points.

CONCLUSIONS

We show that efficient stochastic optimization allows selecting needle configurations which are more robust with respect to potentially negative effects of target deformation and displacement on the achievable prescription dose coverage. The approach facilitates robust path planning for robotic needle placement.

Experimental study of mosquito-inspired needle to minimize insertion force and tissue deformation

The aim of this work is to propose a mosquito-inspired (bioinspired) design of a surgical needle that can decrease the insertion force and the tissue deformation, which are the main causes of target inaccuracy during percutaneous procedures. The bioinspired needle was developed by mimicking the geometrical shapes of mosquito proboscis. Needle prototypes were manufactured and tested to determine optimized needle shapes and geometries. Needle insertion tests on a tissue-mimicking polyvinylchloride (PVC) gel were then performed to emulate the mosquito-proboscis stinging dynamics by applying vibration and insertion velocity during the insertion. An insertion test setup equipped with a sensing system was constructed to measure the insertion force and to assess the deformation of the tissue. It was discovered that using the proposed bioinspired design, the needle insertion force was decreased by 60% and the tissue deformation was reduced by 48%. This finding is significant for improving needle-based medical procedures.

Finite element simulation of multilayer model to simulate fine needle insertion mechanism into iliac crest for bone marrow biopsy

The main aim of this work is to use a finite element technique (FEM) to gain understanding about the bone marrow biopsy (BMB) needle insertion process and needle-tissue interactions in the human iliac crest. A multi-layer iliac crest model consists of stratum corneum, dermis, epidermis, hypodermis, cortical, and cancellous bone has been established. This paper proposes a FE model that examines all phases of tissue deformation, including puncture, cutting, needle-tissue interaction, and various stress-strain values for BMB needle during interaction. The results explain the needle-tissue interface and show the potential of this technique to estimate bone damage and tissue deformation for multiple needle dimensions, coefficient of friction, and penetration speeds. The insertion and extraction force of conical-shaped needles in the multi-layered iliac crest model decreased by 18.92% and 37.5%, respectively, as the needle diameter reduced from 11 G to 20 G. It has also been found that the significant insertion motion raises the deformation of the tissue due to the augmented frictional forces but reduces the strain perpendicular to the penetration direction closer to the needle tip. The simulation outcomes are helpful for the optimal design of fine biopsy needles used to perform the bone marrow biopsies.

FEMOSSA: Patient-specific finite element simulation of the prostate-rectum spacer placement, a predictive model for prostate cancer radiotherapy

PURPOSE

Major advances in delivery systems in recent years have turned radiotherapy (RT) into a more effective way to manage prostate cancer. Still, adjacency of organs at risk (OARs) can severely limit RT benefits. Rectal spacer implant in recto-prostatic space provides sufficient separation between prostate and rectum, and therefore, the opportunity for potential dose escalation to the target and reduction of OAR dose. Pretreatment simulation of spacer placement can potentially provide decision support to reduce the risks and increase the efficacy of the spacer placement procedure.

METHODS

A novel finite element method-oriented spacer simulation algorithm, FEMOSSA, was developed in this study. We used the finite element (FE) method to model and predict the deformation of rectum and prostate wall, stemming from hydrogel injection. Ten cases of prostate cancer, which undergone hydrogel placement before the RT treatment, were included in this study. We used the pre-injection organ contours to create the FE model and post-injection spacer location to estimate the distribution of the virtual spacer. Material properties and boundary conditions specific to each patient's anatomy were assigned. The FE analysis was then performed to determine the displacement vectors of regions of interest (ROIs), and the results were validated by comparing the virtually simulated contours with the real post-injection contours. To evaluate the different aspects of our method's performance, we used three different figures of merit: dice similarity coefficient (DSC), nearest neighbor distance (NND), and overlapped volume histogram (OVH). Finally, to demonstrate a potential dosimetric application of FEMOSSA, the predicted rectal dose after virtual spacer placement was compared against the predicted post-injection rectal dose.

RESULTS

Our simulation showed a realistic deformation of ROIs. The post-simulation (virtual spacer) created the same separation between prostate and rectal wall, as post-injection spacer. The average DSCs for prostate and rectum were 0.87 and 0.74, respectively. Moreover, there was a statistically significant increase in rectal contour similarity coefficient (P < 0.01). Histogram of NNDs showed the same overall shape and a noticeable shift from lower to higher values for both post-simulation and post-injection, indicative of the increase in distance between prostate and rectum. There was less than 2.2- and 2.1-mm averaged difference between the mean and fifth percentile NNDs. The difference between the OVH distances and the corresponding predicted rectal dose was, on average, less than 1 mm and 1.5 Gy, respectively.

CONCLUSIONS

FEMOSSA provides a realistic simulation of the hydrogel injection process that can facilitate spacer placement planning and reduce the associated uncertainties. Consequently, it increases the robustness and success rate of spacer placement procedure that in turn improves prostate cancer RT quality.