Genetic algorithm-based optimization for the geometric design of a novel orthopedic implant

OBJECTIVE

A tendon-transfer is a reconstructive orthopedic surgery where tendons are re-routed from a non-functioning muscle and attached to a functioning muscle. Prior work has shown that using a passive implanted device in the ECRL-to-FDP tendon-transfer surgery significantly improves hand grasping function. However, it is still unclear how hand-function improvement, measured by finger joint range of motion and torque, is dependent on the implant's geometry and location within the tendon network. This paper presents a genetic algorithm that determines the device's optimal geometry and location.

METHODS

hand biomechanical simulation platform was developed to model hand function and also model the tendon-transfer surgery with and without the implant. Finger kinematics and joint torque were used to develop three unique objective functions to optimize the implant's parameters.

RESULTS

The optimized device resulted in an 11X increase in finger kinematics with only a 0.9\% decrease in joint torque when compared with the biomechanical function enabled by the current suture-based surgery.

CONCLUSION

Designing implantable devices that modify musculoskeletal function is challenging. Factors like tendon routing and joint kinematics create a complex nonlinear system when considering biomechanical function. A genetic algorithm is an effective tool to tackle these nonlinear landscapes to produce optimized designs.

SIGNIFICANCE

The state-of-the-art surgical procedure to repair high median-ulnar nerve palsy leads to poor hand function and severely limits the patient's ability to perform activities of daily life. This work provides a method for defining relevant objective functions for hand biomechanical function and then uses those objective functions with genetic algorithms to optimize the geometry of an orthopedic implant across multiple variables. The achieved biomechanical function is significantly better than the hand function enabled by the current surgical procedure.

Beam geometry selection using sequential beam addition

PURPOSE

The selection of optimal beam geometry has been of interest since the inception of conformal radiotherapy. The authors report on sequential beam addition, a simple beam geometry selection method, for intensity modulated radiation therapy.

METHODS

The sequential beam addition algorithm (SBA) requires definition of an objective function (score) and a set of candidate beam geometries (pool). In the first iteration, the optimal score is determined for each beam in the pool and the beam with the best score selected. In the next iteration, the optimal score is calculated for each beam remaining in the pool combined with the beam selected in the first iteration, and the best scoring beam is selected. The process is repeated until the desired number of beams is reached. The authors selected three treatment sites, breast, lung, and brain, and determined beam arrangements for up to 11 beams from a pool comprised of 25 equiangular transverse beams. For the brain, arrangements were additionally selected from a pool of 22 noncoplanar beams. Scores were determined for geometries comprised equiangular transverse beams (EQA), as well as two tangential beams for the breast case.

RESULTS

In all cases, SBA resulted in scores superior to EQA. The breast case had the strongest dependence on beam geometry, for which only the 7-beam EQA geometry had a score better than the two tangential beams, whereas all SBA geometries with more than two beams were superior. In the lung case, EQA and SBA scores monotonically improved with increasing number of beams; however, SBA required fewer beams to achieve scores equivalent to EQA. For the brain case, SBA with a coplanar pool was equivalent to EQA, while the noncoplanar pool resulted in slightly better scores; however, the dose-volume histograms demonstrated that the differences were not clinically significant.

CONCLUSIONS

For situations in which beam geometry has a significant effect on the objective function, SBA can identify arrangements equivalent to equiangular geometries but using fewer beams. Furthermore, SBA provides the value of the objective function as the number of beams is increased, allowing the planner to select the minimal beam number that achieves the clinical goals. The method is simple to implement and could readily be incorporated into an existing optimization system.

Similar-case-based optimization of beam arrangements in stereotactic body radiotherapy for assisting treatment planners

Objective. To develop a similar-case-based optimization method for beam arrangements in lung stereotactic body radiotherapy (SBRT) to assist treatment planners. Methods. First, cases that are similar to an objective case were automatically selected based on geometrical features related to a planning target volume (PTV) location, PTV shape, lung size, and spinal cord position. Second, initial beam arrangements were determined by registration of similar cases with the objective case using a linear registration technique. Finally, beam directions of the objective case were locally optimized based on the cost function, which takes into account the radiation absorption in normal tissues and organs at risk. The proposed method was evaluated with 10 test cases and a treatment planning database including 81 cases, by using 11 planning evaluation indices such as tumor control probability and normal tissue complication probability (NTCP). Results. The procedure for the local optimization of beam arrangements improved the quality of treatment plans with significant differences (P < 0.05) in the homogeneity index and conformity index for the PTV, V10, V20, mean dose, and NTCP for the lung. Conclusion. The proposed method could be usable as a computer-aided treatment planning tool for the determination of beam arrangements in SBRT.

Customized prediction of respiratory motion with clustering from multiple patient interaction

Information processing of radiotherapy systems has become an important research area for sophisticated radiation treatment methodology. Geometrically precise delivery of radiotherapy in the thorax and upper abdomen is compromised by respiratory motion during treatment. Accurate prediction of the respiratory motion would be beneficial for improving tumor targeting. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, that is, customized prediction with multiple patient interactions using neural network (CNN). For the preprocedure of prediction for individual patient, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the intraprocedure, the proposed CNN used neural networks (NN) for a part of the prediction and the extended Kalman filter (EKF) for a part of the correction. The prediction accuracy of the proposed method was investigated with a variety of prediction time horizons using normalized root mean squared error (NRMSE) values in comparison with the alternate recurrent neural network (RNN). We have also evaluated the prediction accuracy using the marginal value that can be used as the reference value to judge how many signals lie outside the confidence level. The experimental results showed that the proposed CNN can outperform RNN with respect to the prediction accuracy with an improvement of 50%.

A self-adaptive case-based reasoning system for dose planning in prostate cancer radiotherapy

PURPOSE

Prostate cancer is the most common cancer in the male population. Radiotherapy is often used in the treatment for prostate cancer. In radiotherapy treatment, the oncologist makes a trade-off between the risk and benefit of the radiation, i.e., the task is to deliver a high dose to the prostate cancer cells and minimize side effects of the treatment. The aim of our research is to develop a software system that will assist the oncologist in planning new treatments.

METHODS

A nonlinear case-based reasoning system is developed to capture the expertise and experience of oncologists in treating previous patients. Importance (weights) of different clinical parameters in the dose planning is determined by the oncologist based on their past experience, and is highly subjective. The weights are usually fixed in the system. In this research, the weights are updated automatically each time after generating a treatment plan for a new patient using a group based simulated annealing approach.

RESULTS

The developed approach is analyzed on the real data set collected from the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. Extensive experiments show that the dose plan suggested by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better.

CONCLUSIONS

The developed case-based reasoning system enables the use of knowledge and experience gained by the oncologist in treating new patients. This system may play a vital role to assist the oncologist in making a better decision in less computational time; it utilizes the success rate of the previously treated patients and it can also be used in teaching and training processes.