An optimized framework for cone-beam computed tomography-based online evaluation for proton therapy

BACKGROUND

Clinical evidence has demonstrated that proton therapy can achieve comparable tumor control probabilities compared to conventional photon therapy but with the added benefit of sparing healthy tissues. However, proton therapy is sensitive to inter-fractional anatomy changes. Online pre-fraction evaluation can effectively verify proton dose before delivery to patients, but there is a lack of guidelines for implementing this workflow.

PURPOSE

The purpose of this study is to develop a cone-beam CT-based (CBCT) online evaluation framework for proton therapy that enables knowledge transparency and evaluates the efficiency and accuracy of each essential component.

METHODS

Twenty-three patients with various lesion sites were included to conduct a retrospective study of implementing the proposed CBCT evaluation framework for the clinic. The framework was implemented on the RayStation 11B Research platform. Two synthetic CT (sCT) methods, corrected CBCT (cCBCT), and virtual CT (vCT), were used, and the ground truth images were acquired from the same-day deformed quality assurance CT (dQACT) for the comparisons. The evaluation metrics for the framework include time efficiency, dose-difference distributions (gamma passing rates), and water equivalent thickness (WET) distributions.

RESULTS

The mean online CBCT evaluation times were 1.6 ± 0.3 min and 1.9 ± 0.4 min using cCBCT and vCT, respectively. The dose calculation and deformable image registration dominated the evaluation efficiency, and accounted for 33% and 30% of the total evaluation time, respectively. The sCT generation took another 19% of the total time. Gamma passing rates were greater than 91% and 97% using 1%/1 mm and 2%/2 mm criteria, respectively. When the appropriate sCT was chosen, the target mean WET difference from the reference were less than 0.5 mm. The appropriate sCT method choice determined the uncertainty for the framework, with the cCBCT being superior for head-and-neck patient evaluation and vCT being better for lung patient evaluation.

CONCLUSIONS

An online CBCT evaluation framework was proposed to identify the use of the optimal sCT algorithm regarding efficiency and dosimetry accuracy. The framework is extendable to adopt advanced imaging methods and has the potential to support online adaptive radiotherapy to enhance patient benefits. It could be implemented into clinical use in the future.

Dose mimicking based strategies for online adaptive proton therapy of head and neck cancer

OBJECTIVE

To compare a not adapted (NA) robust planning strategy with three fully automated online adaptive proton therapy (OAPT) workflows based on the same optimization method: dose mimicking (DM). The added clinical value and limitations of the OAPT methods are investigated for head and neck cancer (HNC) patients.&#xD;&#xD;&#xD;Approach: The three OAPT strategies aimed at compensating for inter-fractional anatomical changes by mimiking different dose distributions on corrected cone beam CT images (corrCBCTs). Order by complexity, the OAPTs were: (1) online adaptive dose restoration (OADR) where the approved clinical dose on the planning-CT (pCT) was mimicked, (2) online adaptation using DM of the deformed clinical dose from the pCT to corrCBCTs (OADEF), and (3) online adaptation applying DM to a predicted dose on corrCBCTs (OAML). Adaptation was only applied in fractions where the target coverage criteria were not met (D98% < 95% of the prescribed dose). For 10 HNC patients, the accumulated dose distributions over the 35 fractions were calculated for NA, OADR, OADEF, and OAML.&#xD;&#xD;Main results: Higher target coverage was observed for all OAPT strategies compared to no adaptation. OADEF and OAML outperformed both NA and OADR and were comparable in terms of target coverage to initial clinical plans. However, only OAML provided comparable NTCP values to those from the clinical dose without statistically significant differences. When the NA initial plan was evaluated on corrCBCTs, 51% of fractions needed adaptation. The adaptation rate decreased significantly to 25% when the last adapted plan with OADR was selected for delivery, to 16% with OADEF, and to 21% with OAML. The reduction was even greater when the best plan among previously generated adapted plans (instead of the last one) was selected.&#xD;&#xD;Significance: The implemented OAPT strategies provided superior target coverage compared to no adaptation, higher OAR sparing, and fewer required adaptations.

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans

Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Online Adaptive Prediction of Human Motion Intention Based on sEMG

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

AFFIRM Online: Utilising an Affirmative Cognitive-Behavioural Digital Intervention to Improve Mental Health, Access, and Engagement among LGBTQA+ Youth and Young Adults

Digital mental health interventions may enable access to care for LGBTQA+ youth and young adults that face significant threats to their wellbeing. This study describes the preliminary efficacy of AFFIRM Online, an eight-session manualised affirmative cognitive behavioural group intervention delivered synchronously. Participants (Mage = 21.17; SD = 4.52) had a range of sexual (e.g., queer, lesbian, pansexual) and gender (e.g., non-binary, transgender, cisgender woman) identities. Compared to a waitlist control (n = 50), AFFIRM Online participants (n = 46) experienced significantly reduced depression (b = -5.30, p = 0.005, d = 0.60) and improved appraisal of stress as a challenge (b = 0.51, p = 0.005, d = 0.60) and having the resources to meet those challenges (b = 0.27, p = 0.059, d = 0.39) as well active coping (b = 0.36, p = 0.012, d = 0.54), emotional support (b = 0.38, p = 0.017, d = 0.51), instrumental support (b = 0.58, p < 0.001, d = 0.77), positive framing (b = 0.34, p = 0.046, d = 0.42), and planning (b = 0.41, p = 0.024, d = 0.49). Participants reported high acceptability. This study highlights the potential of digital interventions to impact LGBTQA+ youth mental health and explores the feasibility of digital mental health to support access and engagement of youth with a range of identities and needs (e.g., pandemic, lack of transportation, rural locations). Findings have implications for the design and delivery of digital interventions for marginalised youth and young adults.

A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision

Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thoroughly investigated. Using a humanoid robot controlled by a neural network using a precision-weighted prediction error minimization mechanism, it is suggested that both increased and decreased sensory precision could induce the behavioral rigidity characterized by resistance to change that is characteristic of autistic behavior. Specifically, decreased sensory precision caused any error signals to be disregarded, leading to invariability of the robot's intention, while increased sensory precision caused an excessive response to error signals, leading to fluctuations and subsequent fixation of intention. The results may provide a system-level explanation of mechanisms underlying different types of behavioral rigidity in autism spectrum and other psychiatric disorders. In addition, our findings suggest that symptoms caused by decreased and increased sensory precision could be distinguishable by examining the internal experience of patients and neural activity coding prediction error signals in the biological brain.

Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection

The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.

Online adaptation and over-trial learning in macaque visuomotor control

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning.