Due to the large combinatorial problem, current beam orientation optimization algorithms for radiotherapy, such as column generation (CG), are typically heuristic or greedy in nature, leading to suboptimal solutions. We propose a reinforcement learning strategy using Monte Carlo Tree Search capable of finding a superior beam orientation set and in less time than CG.We utilized a reinforcement learning structure involving a supervised learning network to guide Monte Carlo tree search (GTS) to explore the decision space of beam orientation selection problem. We have previously trained a deep neural network (DNN) that takes in the patient anatomy, organ weights, and current beams, and then approximates beam fitness values, indicating the next best beam to add. This DNN is used to probabilistically guide the traversal of the branches of the Monte Carlo decision tree to add a new beam to the plan. To test the feasibility of the algorithm, we solved for 5-beam plans, using 13 test prostate cancer patients, different from the 57 training and validation patients originally trained the DNN. To show the strength of GTS to other search methods, performances of three other search methods including a guided search, uniform tree search and random search algorithms are also provided. On average GTS outperforms all other methods, it find a solution better than CG in 237 seconds on average, compared to CG which takes 360 seconds, and outperforms all other methods in finding a solution with lower objective function value in less than 1000 seconds. Using our guided tree search (GTS) method we were able to maintain a similar planning target volume (PTV) coverage within 1% error, and reduce the organ at risk (OAR) mean dose for body, rectum, left and right femoral heads, but a slight increase of 1% in bladder mean dose.
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions…
Dual-functional nanoparticles, with the property of aggregation-induced emission and the capability of reactive oxygen species, were used to achieve passive/active targeting of tumor. Good contrast in in vivo imaging and obvious therapeutic efficiency were realized with a low dose of AIE nanoparticles as well as a low power density of light, resulting in negligible side effects.
In this contribution, an image-guided therapy system supporting gynecologic radiation therapy is introduced. The overall workflow of the presented system starts with the arrival of the patient and ends with follow-up examinations by imaging and a superimposed visualization of the modeled device from a PACS system. Thereby, the system covers all treatments stages (pre-, intra- and postoperative) and has been designed and constructed by a computer scientist with feedback from an interdisciplinary team of physicians and engineers. This integrated medical system enables dispatch of diagnostic imag…
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
LASIGE, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院LASIGE实验室）；BioISI, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院BioISI实验室）；iBET – Instituto de Biologia Experimental e Tecnológica（生物实验与技术研究所）
Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term…
Purpose: To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time. Methods: A library of treatment plans is constructed by optimizing a plan for each breathing phase of a 4DCT. Treatments are delivered by simulation on a continuous sequence of synthetic CTs generated from real MRI sequences. During treatment, the plans for which the tumor is at a close distance to the current tumor position are selected to deliver their spots. Th…
The purpose of this study was to quantitatively evaluate the visibility and artifacts of commercially available fiducial markers in order to optimize their selection for image-guided stereotactic body radiation therapy (SBRT). From six different vendors, we selected 11 fiducials commonly used in image-guided radiation therapy (IGRT); the fiducials varied in material composition (gold, platinum, carbon), shape (cylindrical, notched/linear, coiled, ball-like, step), and size measured in terms of diameter (0.28-1.0 mm) and length (3.0-20.0 mm). Each fiducial was centered in 4-mm bolus within a 13…
Cone beam CT (CBCT) has been widely used for patient setup in image guided radiation therapy (IGRT). Radiation dose from CBCT scans has become a clinical concern. The purposes of this study are 1) to commission a GPU-based Monte Carlo (MC) dose calculation package gCTD for Varian On-Board Imaging (OBI) system and test the calculation accuracy, and 2) to quantitatively evaluate CBCT dose from the OBI system in typical IGRT scan protocols. We first conducted dose measurements in a water phantom. X-ray source model parameters used in gCTD are obtained through a commissioning process. gCTD accurac…
Two full rotating gantry with different nozzles (Multipurpose nozzle with MLC, Scanning Dedicated nozzle) with conventional cyclotron system is installed and under commissioning for various proton treatment options at Samsung Medical Center in Korea. The purpose of this study is to investigate neutron dose equivalent per therapeutic dose, H/D, to x-ray imaging equipment under various treatment conditions with monte carlo simulation. At first, we investigated H/D with the various modifications of the beam line devices (Scattering, Scanning, Multi-leaf collimator, Aperture, Compensator) at isoce…
In radiation therapy, mathematical methods have been used for optimizing treatment planning for delivery of sufficient dose to the cancerous cells while keeping the dose to critical surrounding structures minimal. This optimization problem can be modeled using mixed integer programming (MIP) whose solution gives the optimal beam orientation as well as optimal beam intensity. The challenge, however, is the computation time for this large scale MIP. We propose and investigate two novel heuristic approaches to reduce the computation time considerably while attaining high-quality solutions. We int…
During Stereotactic Body Radiotherapy (SBRT) for the treatment of prostate cancer, an inflatable endorectal balloon (ERB) may be used to reduce motion of the target and reduce the dose to the posterior rectal wall. This work assessed the dosimetric impact of manual interventions on ERB position in patients receiving prostate SBRT and investigated the impact of ERB interventions on prostate shape. Daily kilovoltage (kV) cone-beam computed tomography (CBCT) imaging was performed to localize the PTV, and an automated fusion with the planning images yielded displacements required for PTV re-locali…
Cycle-consistent Generative Adversarial Network Synthetic CT for MR-only Adaptive Radiation Therapy on MR-Linac
Dartmouth College（达特茅斯学院）；Thayer School of Engineering, Dartmouth College（达特茅斯学院塞耶工程学院）；Geisel School of Medicine, Dartmouth College（达特茅斯学院盖泽尔医学院）；Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心达特茅斯癌症中心）；Westchester Medical Center, New York Medical College（纽约医学院韦斯特切斯特医疗中心）
Adaptive Radiation Therapy (ART) was developed based on Image-guided Radiation Therapy (IGRT) and it is the trend of photon radiation therapy. To get a better use of Cone Beam CT (CBCT) images for ART, the CBCT system model was established based on Monte Carlo program and validated against the measurement. The BEAMnrc program was adopted to the KV x-ray tube. Both IOURCE-13 and ISOURCE-24 were chosen to simulate the path of beam particles. The measured Percentage Depth Dose (PDD) and lateral dose profiles under 1cm water were compared with the dose calculated by DOSXYZnrc program. The calculat…
The physical and clinical benefits of charged particle therapy (CPT) are well recognised and recent developments have led to the rapid emergence of facilities, resulting in wider adoption worldwide. Nonetheless, the availability of CPT and complete exploitation of dosimetric advantages are still limited by high facility costs and technological challenges. There are extensive ongoing efforts to improve upon these, which will lead to greater accessibility, superior delivery, and therefore better treatment outcomes. Several of these aspects can be addressed by developments to the beam delivery sy…
University of Tokyo Hospital（东京大学医院）；Kyushu University（九州大学）
Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. CBCT has found a wide variety of applications in patient positioning for image-guided radiation therapy, extracting radiomic information for designing patient-specific treatment, and computing fractional dose distributions for adaptive radiation therapy. However, CBCT images suffer from low soft-tissue contrast, noise, and artifacts compared to conventional fan-beam CT images. Therefore, it is essential to improve the image quality of CBCT.…
Pancreas stereotactic body radiotherapy treatment planning requires planners to make sequential, time consuming interactions with the treatment planning system (TPS) to reach the optimal dose distribution. We seek to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. The focus of pancreas SBRT planning is finding a balance between organs-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage…
The quality of SPECT Bremsstrahlung images of patients treated with Y-90 is poor, mainly because of scattered radiation and collimator septa penetration. To minimize the latter effect, High Energy (HE) or Medium Energy (ME) collimators can be used. Scatter correction is not possible through the methods commonly used for the diagnostic radionuclides (Tc-99m, etc.) because the Bremsstrahlung radiation does not have distinct photopeaks, but a broad spectrum of energies ranging from zero to the maximum one detectable by the gamma-camera crystal is registered. Scatter radiation and collimator septa…
In adaptive radiotherapy, deformable image registration is often conducted between the planning CT and treatment CT (or cone beam CT) to generate a deformation vector field (DVF) for dose accumulation and contour propagation. The auto propagated contours on the treatment CT may contain relatively large errors, especially in low contrast regions. A clinician inspection and editing of the propagated contours are frequently needed. The edited contours are able to meet the clinical requirement for adaptive therapy; however, the DVF is still inaccurate and inconsistent with the edited contours. The…
The next great leap toward improving treatment of cancer with radiation will require the combined use of online adaptive and magnetic resonance guided radiation therapy techniques with automatic X-ray beam orientation selection. Unfortunately, by uniting these advancements, we are met with a substantial expansion in the required dose information and consequential increase to the overall computational time imposed during radiation treatment planning, which cannot be handled by existing techniques for accelerating Monte Carlo dose calculation. We propose a deep convolutional neural network appro…
We previously proposed an intelligent automatic treatment planning framework for radiotherapy, in which a virtual treatment planner network (VTPN) was built using deep reinforcement learning (DRL) to operate a treatment planning system (TPS). Despite the success, the training of VTPN via DRL was time consuming. Also the training time is expected to grow with the complexity of the treatment planning problem, preventing the development of VTPN for more complicated but clinically relevant scenarios. In this study we proposed a knowledge-guided DRL (KgDRL) that incorporated knowledge from human pl…
The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation …
We provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. The method starts with an estimation of coarse scatter profile for a set of CBCT images. A total-variation denoising algorithm designed specifically for Poisson signal is then applied to derive the final scatter distribution. Qualitatively and quantitatively evaluations using Monte Carlo (MC) simulations, experimental CBCT phantom data, and \emph{in vivo} human data acquired for a clinical image guided radiation therapy were performed. Results show that the proposed algorithm can significantl…
