Emerging technologies for cancer therapy using accelerated particles

Evaluating an analytical prediction algorithm of positron emitter distributions in patient data for PET monitoring of carbon ion therapy: A simulation study

In vivo treatment monitoring in ion therapy is one of the key issues for improving the treatment quality assurance procedures. Range verification is one of the most relevant and yet complex task used for in vivo treatment monitoring. In carbon ion therapy, positron emission tomography is the most widely used method. This technique exploits the β+-activity of positron emitters created by nuclear interactions between the incoming beam and the irradiated tissue. Currently, high computational efforts and time-consuming Monte Carlo simulation platforms are typically used to predict positron emitter distributions. In order to avoid time-consuming simulations, an extended filtering approach was suggested to analytically predict positron emitter profiles from depth dose distributions in carbon ion therapy. The purpose of this work is to investigate such an analytical prediction model in patient anatomies of varying complexity, highlighting its potential and the need of further improvements, especially in highly heterogeneous anatomies where many air cavities are present in the beam path. The accuracy of range verification showed a mean relative error of ∼3% and a deviation between the simulation and the prediction below 2mm for the three patient cases analysed: a brain case and two head and neck cases. Additional investigations demonstrated the region of applicability of the method for cases of patient data. The analytical method enables range verification in carbon ion therapy by replacing computing-intensive Monte Carlo simulations and thus minimize the PET monitoring burden on the clinical workflow.

Real-time delivered dose assessment in carbon ion therapy of moving targets

Objective. Real-time adaptive particle therapy is being investigated as a means to maximize the treatment delivery accuracy. To react to dosimetric errors, a system for fast and reliable verification of the agreement between planned and delivered doses is essential. This study presents a clinically feasible, real-time 4D-dose reconstruction system, synchronized with the treatment delivery and motion of the patient, which can provide the necessary feedback on the quality of the delivery.Approach. A GPU-based analytical dose engine capable of millisecond dose calculation for carbon ion therapy has been developed and interfaced with the next generation of the dose delivery system (DDS) in use at Centro Nazionale di Adroterapia Oncologica (CNAO). The system receives the spot parameters and the motion information of the patient during the treatment and performs the reconstruction of the planned and delivered 4D-doses. After each iso-energy layer, the results are displayed on a graphical user interface by the end of the spill pause of the synchrotron, permitting verification against the reference dose. The framework has been verified experimentally at CNAO for a lung cancer case based on a virtual phantom 4DCT. The patient's motion was mimicked by a moving Ionization Chamber (IC) 2D-array.Mainresults. For the investigated static and 4D-optimized treatment delivery cases, real-time dose reconstruction was achieved with an average pencil beam dose calculation speed up to more than one order of magnitude smaller than the spot delivery. The reconstructed doses have been benchmarked against offline log-file based dose reconstruction with the TRiP98 treatment planning system, as well as QA measurements with the IC 2D-array, where an average gamma-index passing rate (3%/3 mm) of 99.8% and 98.3%, respectively, were achieved.Significance. This work provides the first real-time 4D-dose reconstruction engine for carbon ion therapy. The framework integration with the CNAO DDS paves the way for a swift transition to the clinics.

Opportunities and challenges of upright patient positioning in radiotherapy

Objective.Upright positioning has seen a surge in interest as a means to reduce radiotherapy (RT) cost, improve patient comfort, and, in selected cases, benefit treatment quality. In particle therapy (PT) in particular, eliminating the need for a gantry can present massive cost and facility footprint reduction. This review discusses the opportunities of upright RT in perspective of the open challenges.Approach.The clinical, technical, and workflow challenges that come with the upright posture have been extracted from an extensive literature review, and the current state of the art was collected in a synergistic perspective from photon and particle therapy. Considerations on future developments and opportunities are provided.Main results.Modern image guidance is paramount to upright RT, but it is not clear which modalities are essential to acquire in upright posture. Using upright MRI or upright CT, anatomical differences between upright/recumbent postures have been observed for nearly all body sites. Patient alignment similar to recumbent positioning was achieved in small patient/volunteer cohorts with prototype upright positioning systems. Possible clinical advantages, such as reduced breathing motion in upright position, have been reported, but limited cohort sizes prevent resilient conclusions on the treatment impact. Redesign of RT equipment for upright positioning, such as immobilization accessories for various body regions, is necessary, where several innovations were recently presented. Few clinical studies in upright PT have already reported promising outcomes for head&neck patients.Significance.With more evidence for benefits of upright RT emerging, several centers worldwide, particularly in PT, are installing upright positioning devices or have commenced upright treatment. Still, many challenges and open questions remain to be addressed to embed upright positioning firmly in the modern RT landscape. Guidelines, professionals trained in upright patient positioning, and large-scale clinical studies are required to bring upright RT to fruition.

Deep learning-based voxel sampling for particle therapy treatment planning

Objective.Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency.Approach.Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based onP-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels.Main results.On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection.Significance.The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

The rationale for a carbon ion radiation therapy facility in Australia

Australia has taken a collaborative nationally networked approach to achieve particle therapy capability. This supports the under-construction proton therapy facility in Adelaide, other potential proton centres and an under-evaluation proposal for a hybrid carbon ion and proton centre in western Sydney. A wide-ranging overview is presented of the rationale for carbon ion radiation therapy, applying observations to the case for an Australian facility and to the clinical and research potential from such a national centre.

Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization

Objective.We demonstrate a novel focus stacking technique to improve spatial resolution of single-event particle radiography (pRad), and exploit its potential for 3D feature detection.Approach.Focus stacking, used typically in optical photography and microscopy, is a technique to combine multiple images with different focal depths into a single super-resolution image. Each pixel in the final image is chosen from the image with the largest gradient at that pixel's position. pRad data can be reconstructed at different depths in the patient based on an estimate of each particle's trajectory (called distance-driven binning; DDB). For a given feature, there is a depth of reconstruction for which the spatial resolution of DDB is maximal. Focus stacking can hence be applied to a series of DDB images reconstructed from a single pRad acquisition for different depths, yielding both a high-resolution projection and information on the features' radiological depth at the same time. We demonstrate this technique with Geant4 simulated pRads of a water phantom (20 cm thick) with five bone cube inserts at different depths (1 × 1 × 1 cm3) and a lung cancer patient.Main results.For proton radiography of the cube phantom, focus stacking achieved a median resolution improvement of 136% compared to a state-of-the-art maximum likelihood pRad reconstruction algorithm and a median of 28% compared to DDB where the reconstruction depth was the center of each cube. For the lung patient, resolution was visually improved, without loss in accuracy. The focus stacking method also enabled to estimate the depth of the cubes within few millimeters accuracy, except for one shallow cube, where the depth was underestimated by 2.5 cm.Significance.Focus stacking utilizes the inherent 3D information encoded in pRad by the particle's scattering, overcoming current spatial resolution limits. It further opens possibilities for 3D feature localization. Therefore, focus stacking holds great potential for future pRad applications.

Carbon Ions for Hypoxic Tumors: Are We Making the Most of Them?

Hypoxia, which is associated with abnormal vessel growth, is a characteristic feature of many solid tumors that increases their metastatic potential and resistance to radiotherapy. Carbon-ion radiation therapy, either alone or in combination with other treatments, is one of the most promising treatments for hypoxic tumors because the oxygen enhancement ratio decreases with increasing particle LET. Nevertheless, current clinical practice does not yet fully benefit from the use of carbon ions to tackle hypoxia. Here, we provide an overview of the existing experimental and clinical evidence supporting the efficacy of C-ion radiotherapy in overcoming hypoxia-induced radioresistance, followed by a discussion of the strategies proposed to enhance it, including different approaches to maximize LET in the tumors.