Browsing by Author "Sheng, Yang"

Purpose: To develop a heuristic greedy algorithm to generate automatic multi-leaf collimator (MLC) sequencing for spatial fractioned radiation therapy (SFRT) using 3D dynamic conformal arc (DCA).Methods and materials: One late stage lung cancer patient with simulated sphere target grid was included in this study. N_t spheres were equally spaced within the gross target volume (GTV). The sphere targets are 1.5 cm in diameter, 4.3 cm spacing for 6,9,10, and 12 targets scenarios, and 2.8cm spacing for one special 10 targets scenario. Optimization was designed to complete within one coplanar arc from 180° to 0° in a clockwise direction with 2° as the angle interval. The problem is formalized as finding optimal MLC sequencing to cover N_t targets with K control points (CPs) for each arc. The state of each target’s MLC opening at each CP is binary. The original NP-hard problem can be approximated to a feasible subproblem by the greedy approximation on each control point and using the heuristic approach for the initial point. The algorithm focuses on the normalized relative dose relationship as the object function during the optimization. The dose matrix for each step was rasterized and grouped based on Monte Carlo simulation as the pre-calculation process. The physical speed limitation of the MLC motion was considered in the optimization to achieve a realistic and deliverable final MLC sequencing solution. Four grid arrays (6, 9, 10, and 12 targets respectively) were tested for plan quality. The arc collimator angle was planned with both 0 and 30 degrees for comparison. Prescription was set to 20 Gy to one fraction. The delivered dose will be normalized to equalize the minimum target dose to the prescription dose. Key dosimetric endpoints including target mean dose, D5, and D95, were reported. Results: The complexity of this algorithm has been reduced by a factor of \frac{2^K}{2\left(K-1\right)}. The D95 deviations of all targets as the main focus object were within 2.88% in four grid arrays with 0°/30° collimator rotation angles, 4.3 cm spacing for 6,9,10 and 12 targets scenarios, and 2.8 cm spacing for one special 10 targets scenario. For all scenarios with 4.3 cm spacing, the mean valley-to-peak ratios were under 0.45 and were within the constraint that the dose of the other part of the tumor is no more than 45% of the max normalized D95 delivered target dose during the algorithm optimization. Conclusion: This algorithm is a feasible and practical method with high efficiency while delivering the prescription dose to small target volume for late stage cancer palliative management. The proposed solution provides decent coverage to the tumor volume as well as the valley-to-peak ratio. It provides a competitive alternative solution to the standard alloy grid delivery technique.

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Purpose: To develop and evaluate an automatic IMRT planning technique for prostate cancer utilizing prior expert plan's dose distribution as guidance.

Methods and Materials: In this study, the anatomical information of prostate cancer cases was parameterized and quantified into two measures: the percent distance-to-prostate (PDP) and the concaveness angle. Based on these two quantities, a plan atlas composed of 5 expert prostate IMRT plans was built out of a 70-case pool at our institution using k-medoids clustering analysis.

Extra 20 cases were used as query cases to evaluate the dose-guided automatic planning (DAP) scheme. Each query case was matched to an atlas case based on PTV-OAR anatomical features followed by deformable registration to enhance fine local matching. Using the deformation field, the expert dose in the matched atlas case was warped onto the query case, creating the goal dose conformal to the query case's anatomy. Dose volume histograms (DVHs) objectives were sampled from the goal dose to guide automatic IMRT treatment planning. Dosimetric comparison between DAP plans and clinical plans were performed.

Results: Generating goal dose is highly efficient by using MIM^TM workflows. The deformable registration provides high-quality goal dose tailored to query case's anatomy in terms of the dose falloff at the PTV-OAR boundary and the overall conformity. Automatic planning in Eclipse^TM takes ~2.5 min (~70 iterations) without human intervention. Compared to clinical plans, DAP plans improved the conformity index from 0.85±0.04 to 0.88±0.02 (p=0.0045), the bladder-gEUD from 40.7±3.2 Gy to 40.0±3.1 Gy (p=0.0003), and rectum-gEUD from 40.4±2.0 Gy to 39.9±2.1 Gy (p=0.0167). Other dosimetric parameter is similar (p>0.05): homogeneity indices are 7.4±0.9% and 7.1±1.5%, for DAP plans and clinical plans, respectively.

Conclusions: Dose-guided automatic treatment planning is feasible and efficient. Atlas-based patient-specific dose objectives can effectively guide the optimizer to achieve similar or better plan quality compared to clinical plans.

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Radiation has been utilized in medical domain for multiple purposes. Treating cancer using radiation has increasing popularity during the last century. Radiation beam is directed to the tumor cells while the surrounding healthy tissue is attempted to be avoided. Radiation therapy treatment planning serves the goal of delivering high concentrated radiation to the treatment volume while minimizing the normal tissue as much as possible. With the advent of more sophisticated delivery technology, treatment planning time increases over time. In addition, the treatment plan quality relies on the experience of the planner. Several computer assistance techniques emerged to help the treatment planning process, among which knowledge-based planning (KBP) has been successful in inverse planning IMRT. KBP falls under the umbrella of Knowledge Discovery in Databases (KDD) which originated in industry. The philosophy is to extract useful knowledge from previous application/data/observations to make predictions in the future practice. KBP reduces the iterative trial-and-error process in manual planning, and more importantly guarantees consistent plan quality. Despite the great potential of treatment planning KDD (TPKDD), three major challenges remain before TPKDD can be widely implemented in the clinical environment: 1. a good knowledge model asks for sufficient amount of training data to extract useful knowledge and is therefore less efficient; 2. a knowledge model is usually only applicable for the specific treatment site and treatment technique and is therefore less generalizable; 3. a knowledge model needs meticulous inspection before implementing in the clinic to verify the robustness.

This study aims at filling in the niche in TPKDD and improves current TPKDD workflow by tackling the aforementioned challenges. This study is divided into three parts. The first part of the study aims to improve the modeling efficiency by introducing an atlas-based treatment planning guidance. In the second part of the study, an automated treatment planning technique for whole breast radiation therapy (WBRT) is proposed to provide a solution for the area where TPKDD has not yet set foot on. In the third part of the study, several topics related to the knowledge model quality are addressed, including improving the model training workflow, identifying geometric novelty and dosimetric outlier case, building a global model and facilitating incremental learning.

I. Improvement of the modeling efficiency. First, a prostate cancer patient anatomy atlas was established to generate 3D dose distribution guidance for the new patient. The anatomy pattern of the prostate cancer patient was parameterized with two descriptors. Each training case was represented in 2D feature space. All training cases were clustered using the k-medoids algorithm. The optimal number of clusters was determined by the largest average silhouette width. For the new case, the most similar case in the atlas was identified and used to generate dose guidance. The anatomy of the atlas case and the query case was registered and the deformation field was applied to the 3D radiation dose of the atlas case. The deformed dose served as the goal dose for the query case. Dose volume objectives were then extracted from the goal dose to guide the inverse IMRT planning. Results showed that the plans generated with atlas guidance had similar dosimetric quality as compared to the clinical manual plans. The monitor units (MU) of the auto plan were also comparable with the clinical plan. Atlas-guided radiation therapy has proven to be effective and efficient in inverse IMRT planning.

II. Improvement of model generalization. An automatic WBRT treatment planning workflow was developed. First of all, an energy selection tool was developed based on previous single energy and dual energy WBRT plans. The DRR intensity histograms of training cases were collected and the principal component analysis (PCA) was performed to reduce the dimension of the histogram. First two components were used to represent each case and the classification was performed in the 2D space. This tool helps new patient to select appropriate energy based on the anatomy information. Secondly, an anatomy feature based random forest (RF) model was proposed to predict the fluence map for the patient. The model took the input of multiple anatomical features and output the fluence intensity of each pixel within the fluence map. Finally, a physics rule based method was proposed to further fine tune the fluence map to achieve optimal dose distribution within the irradiated volume. Extra validation cases were tested on the proposed workflow. Results showed similar dosimetric quality between auto plan and clinical manual plan. The treatment planning time was reduced from between 1-4 hours for the manual planning to within 1 minute for the auto planning. The proposed automatic WBRT planning technique has proven to be efficient.

III. Rapid learning of radiation therapy KBP. Several topics were analyzed in this part of the study. First of all, a systematic workflow was established to improve the KBP model quality. The workflow started with identifying geometric novelty case using the statistical metric “leverage”, followed by removing the novelty case. Then the dosimetric outlier was identified using studentized residual and then cleaned. The cleaned model was compared with the uncleaned model using the extra validation cases. This study used pelvic cases as an example. Results showed that the existence of novelty and outlier cases did degrade the model quality. The proposed statistical tools can effectively identify novelty and outlier cases. The workflow is able to improve the quality of the knowledge-based model.

Secondly, a clustering-based method was proposed to identify multiple geometric novelty cases and dosimetric outlier cases at the same time. One class support vector machine (OCSVM) was applied to the feature vectors of all training cases to generate one class of inliers while cases falling out of the frontier belonged to the novelty case group. Once the novelty cases were identified and cleaned, the robust regression followed by outlier identification (ROUT) was applied to all remaining cases to identify dosimetric outliers. A cleaned model was trained with the novelty and outlier free case pool and was tested using 10 fold cross validation. Initial training pool included intentionally added outlier cases to evaluate the efficacy of the proposed method. The model prediction on the inlier cases was compared with that of novelty and outlier cases. Results showed that the method can successfully identify geometric novelty and dosimetric outliers. The model prediction accuracy between the inliers and novelty/outliers was significantly different, indicating different dosimetric behavior between two groups. The proposed method proved to be effective in identifying multiple geometric novelty and dosimetric outliers.

Thirdly, a global model using the model tree and the clustering-based model was proposed to include cases with different clinical conditions and indications. The model tree is a combination of decision tree and linear regression, where all cases are branched into leaves and regression is performed within each leaf. A clustering-based model used k-means algorithm to segment all cases into more aggregated groups, and then the regression was performed within each small group. The overall philosophy of both the model tree and the clustering-based method is that cases with similar features have similar geometry-dosimetry relation. Training cases within small feature range gives better model accuracy. The proposed method proved to be effective in improving the model accuracy over the model trained with all cases without segmenting the cases.

At last, the incremental learning was analyzed in radiation therapy treatment planning model. This study tries to answer the question when model re-training should be invoked. In the clinical environment, it is often unnecessary to re-train the model whenever there is a new case. The scenario of incrementally adapting the model was simulated using the pelvic cases with different number of training cases and new incoming cases. The result showed that re-training was often necessary for small training dataset and as the number of cases increased, re-training became less frequent.

In summary, this study addressed three major challenges in TPKDD. In the first part, an atlas-guided treatment planning technique was proposed to improve the modeling efficiency. In the second part, an automatic whole breast radiation therapy treatment planning technique was proposed to tackle the issue where TPKDD has not yet resolved. In the final part, outlier analysis, global model training and incremental learning was further analyzed to facilitate rapid learning, which lay the foundation of future clinical implementation of radiation therapy knowledge models.

Introduction: The current head-and-neck (HN) fluence map generator tends to producehighly modulated fluence maps and therefore high monitor units (MUs) for each beam, which leads to more delivery uncertainty and leakage dose. This project implements diffu- sion into the training process and modifies the loss functions to mitigate this effect.

Methods: The dataset consists of 200 head-and-neck (HN) patients receiving intensity mod-ulated radiation therapy (IMRT) for training, 16 for validation, and 15 for testing. Two models were trained, one with-diffusion and one without. The original model was a con- ditional generative adversarial network (GAN) written in TensorFlow, the model without diffusion was written to be the PyTorch equivalent of the original model. After confirming the model was properly converted to PyTorch by comparing outputs, both new models were modified to use binary cross entropy for the GAN loss and mean absolute error as a third loss function for the generator. Hyperparameters were carefully selected based on the training script for the original model, and further tuned with trial and error. The diffusion was implemented based on Diffusion-GAN and the associated GitHub repository. The two new models were compared by plotting training loss vs epoch over 500 epochs. The two models were compared to the original model by comparing the output fluence maps to the ground truth using similarity index and comparing DVH statistics among the three models.

Results: The with-diffusion model and no-diffusion model achieved similar training loss.The diffusion model and no-diffusion model consistently delivered better parotid sparing than the original model and delivered less dose to four of the six tested OAR. The with- diffusion model delivered less dose to five of the six tested OAR. The diffusion model had the least MUs: 23% less than the original model and 3% less than the no-diffusion model. The diffusion model had lower D2cc: 4% less than the original model and 1% less than the no-diffusion model on average. All three plans deliver 95% of the prescription dose to nearly the same percentage of PTV volume.

Conclusion: Implementing diffusion does not provide a significant impact on training timeand training loss. However, it does enable comparable dose performance to both the no- diffusion and original models, while significantly reducing the total MU’s and 3D max 2cc relative to the original model and slightly reducing these metrics relative to the no-diffusion model, indicating smoother fluence modulation. In addition, both new models reduced dose to the right and left parotids relative to the original model, and to four of six tested OAR total, while the with-diffusion model consistently delivers less dose to OAR than the no- diffusion model. This indicates that both the new loss functions and diffusion reduce the overall dose to the OARs while preserving dose conformity around the target.

Purpose: Radiation therapy aims to deliver high energy radiation beam to eradicate cancer cells. Due to radiation toxicity to normal tissue, treatment planning process is needed to customize the radiation beam towards patient specific treatment geometry while minimizing radiation dose to the normal tissue. Treatment planning is often, however, a trial-and-error process to generate ultimate optimal dose distribution. Breast cancer radiation therapy is one of the most commonly seen treatment in modern radiation oncology department. Whole breast radiation therapy (WBRT) using electronic compensation is an iterative manual process which is time consuming. Our institution has been using artificial intelligence (AI) based planning tool for whole breast radiation therapy (WBRT) for 3 years. It is unclear how human planner interacts with AI in real clinical setting and whether the human planner can inject additional insight into well-established AI model. Therefore, the first aim of this study to model planners’ interaction with AI using deep neural network (NN). In addition, we proposed a multi-agent reinforcement learning based framework (MultiRL-FE) to self-interact with the treatment planning system with location awareness to improve plan quality via fluence editing.Methods: A total of 1151 patients have been treated since in-house AI-based planning tool was released for clinical use in 2019. All 526 patients treated with single energy beams were included in this study. The AI tool automatically generates fluence maps and creates “AI plan”. Then planner evaluates the plan and attempts manual fluence modification before physician’s approval (“final plan”). The manual-modification-value (MMV) of each beamlet is the difference between fluence maps in AI and “final plan”. The MMV was recorded for each planner. In the first aim, a deep NN using UNet3+ architecture was developed to predict MMV with AI fluence map, corresponding dose map and organ map in the beam’s eye view (BEV). Then the predicted MMV maps were applied on the initial “AI plan”s to generate AI-modified plans (“AI-m plan”). In the second aim, we developed MultiRL-FE to self-interact with a given plan to improve the plan quality. A simplified treatment planning system was built in the Python environment to train the agent. For each pixel in the fluence map, an individual agent was assigned to interact with the environment by editing fluence value and receive rewards based on projected beam ray’s dose profile. Asynchronous advantage actor critic (A3C) algorithm was used as the backbone for reinforcement learning agents’ training. To effectively train the agent, we developed the MultiRL-FE framework by embedding A3C in a fully convolutional neural network. To test the feasibility of the proposed framework, twelve patients from the same cohort were collected(6 for training and testing respectively). ”Final plans” were perturbed with 10% dose variation to evaluate the potential of the framework to improve the plan. The agent was designed to iteratively modify the fluence maps for 10 iterations. The modified fluence intensity was imported into the Eclipse treatment planning system for dose calculation. For both aims, plan quality was evaluated by dosimetric endpoints including breast PTV V95%(%), V105%(%), V110%(%), lung V20Gy(%) and heart V5Gy(%). Results: In the first aim, the “AI-m plans” generated by HAI network showed statistically significant improvement (p<.05) in hotspot control compared with the initial AI-plan, with an average of -25.2cc volume reduction in breast V105% and -0.805% decrease in Dmax. The planning target volume (PTV) coverage were similar to AI-plan and “final plan”. In the second aim of MultiRL-FE testing, the RL modified plans showed a substantial hotspot reduction from the initial plans. The average PTV V105%(%) of testing set was reduced from 77.78(\pm2.78) to 16.97 (\pm9.42), while clinical plans’ was 3.34(\pm2.73). Meanwhile, the modified plans showed improved dose coverage over the clinical plans, with 70.45(\pm3.94) compared to 65.44(\pm5.39) for V95%(%). Conclusions: In the first part of this study, we proposed a HAI model to enhance the clinical AI tool by reducing hotspot volume from a human perspective. By understanding and modeling the human-automation interaction , this study could advance the widespread clinical application of AI tools in radiation oncology departments with improved robustness and acceptability. In the second part, we developed a self-interactive treatment planning agent with multi-agents reinforcement learning. It offers the advantage of fast location-aware dose editing and can serve as an alternative optimization tool for intensity-modulated radiation therapy and electronic tissue compensation-based treatment planning.