Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients.

Objective. Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D computed tomography (CT), 4D magnetic resonance imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT.Approach. Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans.Main results. The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for targetD95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on targetD95%up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs.Significance. Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario.

Dosimetric effect of variable rectum and sigmoid colon filling during carbon ion radiotherapy to sacral chordoma.

PURPOSE: Carbon ion radiotherapy (CIRT) is sensitive to anatomical density variations. We examined the dosimetric effect of variable intestinal filling condition during CIRT to ten sacral chordoma patients. METHODS: For each patient, eight virtual computed tomography scans (vCTs) were generated by varying the density distribution within the rectum and the sigmoid in the planning computed tomography (pCT) with a density override approach mimicking a heterogeneous combination of gas and feces. Totally full and empty intestinal preparations were modelled. In addition, five different intestinal filling conditions were modelled by a mixed density pattern derived from two combined and weighted Gaussian distributions simulating gas and feces respectively. Finally, a patient-specific mixing proportion was estimated by evaluating the daily amount of gas detected in the cone beam computed tomography (CBCT). Dose distribution was recalculated on each vCT and dose volume histograms (DVHs) were examined. RESULTS: No target coverage degradation was observed at different vCTs. Rectum and sigma dose degradation ranged respectively between: [-6.7; 21.6]GyE and [-0.7; 15.4]GyE for D50%; [-377.4; 1197.9] and [-95.2; 1027.5] for AUC; [-1.2; 10.7]GyE and [-2.6; 21.5]GyE for D1%. CONCLUSIONS: Variation of intestinal density can greatly influence the penetration depth of charged particle and might compromise dose distribution. In particular cases, with large clinical target volume in very close proximity to rectum and sigmoid colon, it is appropriate to evaluate the amount of gas present in the daily CBCT images even if it is totally included in the reference planning structures.

Development and validation of a new set-up simulator dedicated to ocular proton therapy at CNAO.

An Eye Tracking System (ETS) is used at CNAO for providing a stable and reproducible ocular proton therapy (OPT) set-up, featuring a fixation light (FL) and monitoring stereo-cameras embedded in a rigid case. The aim of this work is to propose an ETS set-up simulation algorithm, that automatically provides the FL positioning in space, according to patient-specific gaze direction and avoiding interferences with patient, beam and collimator. Two configurations are provided: one in the CT room for acquiring images required for treatment planning with the patient lying on a couch, and one related to the treatment room with the patient sitting in front of the beam. Algorithm validation was performed reproducing ETS simulation (CT) and treatment (room) set-up for 30 patients previously treated at CNAO. The positioning accuracy of the device was quantified through a set of 14 control points applied to the ETS case and localizable both in the CT volume and in room X-ray images. Differences between the position of ETS reference points estimated by the algorithm and those measured by imaging systems are reported. The corresponding gaze direction deviation is on average 0.2° polar and 0.3° azimuth for positioning in CT room and 0.1° polar and 0.4° azimuth in the treatment room. The simulation algorithm was embedded in a clinically usable software application, which we assessed as capable of ensuring ETS positioning with an average accuracy of 2 mm in CT room and 1.5 mm in treatment room, corresponding to gaze direction deviations consistently lower than 1°.

Image guided particle therapy in CNAO room 2: implementation and clinical validation.

In this contribution we describe the implementation of a novel solution for image guided particle therapy, designed to ensure the maximal accuracy in patient setup. The presented system is installed in the central treatment room at Centro Nazionale di Adroterapia Oncologica (CNAO, Italy), featuring two fixed beam lines (horizontal and vertical) for proton and carbon ion therapy. Treatment geometry verification is based on robotic in-room imaging acquisitions, allowing for 2D/3D registration from double planar kV-images or 3D/3D alignment from cone beam image reconstruction. The calculated six degrees-of-freedom correction vector is transferred to the robotic patient positioning system, thus yielding automated setup error compensation. Sub-millimetre scale residual errors were measured in absolute positioning of rigid phantoms, in agreement with optical- and laser-based assessment. Sub-millimetre and sub-degree positioning accuracy was achieved when simulating setup errors with anthropomorphic head, thorax and pelvis phantoms. The in-house design and development allowed a high level of system customization, capable of replicating the clinical performance of commercially available products, as reported with preliminary clinical results in 10 patients.

Commissioning and quality assurance of an integrated system for patient positioning and setup verification in particle therapy.

In an increasing number of clinical indications, radiotherapy with accelerated particles shows relevant advantages when compared with high energy X-ray irradiation. However, due to the finite range of ions, particle therapy can be severely compromised by setup errors and geometric uncertainties. The purpose of this work is to describe the commissioning and the design of the quality assurance procedures for patient positioning and setup verification systems at the Italian National Center for Oncological Hadrontherapy (CNAO). The accuracy of systems installed in CNAO and devoted to patient positioning and setup verification have been assessed using a laser tracking device. The accuracy in calibration and image based setup verification relying on in room X-ray imaging system was also quantified. Quality assurance tests to check the integration among all patient setup systems were designed, and records of daily QA tests since the start of clinical operation (2011) are presented. The overall accuracy of the patient positioning system and the patient verification system motion was proved to be below 0.5 mm under all the examined conditions, with median values below the 0.3 mm threshold. Image based registration in phantom studies exhibited sub-millimetric accuracy in setup verification at both cranial and extra-cranial sites. The calibration residuals of the OTS were found consistent with the expectations, with peak values below 0.3 mm. Quality assurance tests, daily performed before clinical operation, confirm adequate integration and sub-millimetric setup accuracy. Robotic patient positioning was successfully integrated with optical tracking and stereoscopic X-ray verification for patient setup in particle therapy. Sub-millimetric setup accuracy was achieved and consistently verified in daily clinical operation.

Commissioning of an integrated platform for time-resolved treatment delivery in scanned ion beam therapy by means of optical motion monitoring.

The integrated use of optical technologies for patient monitoring is addressed in the framework of time-resolved treatment delivery for scanned ion beam therapy. A software application has been designed to provide the therapy control system (TCS) with a continuous geometrical feedback by processing the external surrogates tridimensional data, detected in real-time via optical tracking. Conventional procedures for phase-based respiratory phase detection were implemented, as well as the interface to patient specific correlation models, in order to estimate internal tumor motion from surface markers. In this paper, particular attention is dedicated to the quantification of time delays resulting from system integration and its compensation by means of polynomial interpolation in the time domain. Dedicated tests to assess the separate delay contributions due to optical signal processing, digital data transfer to the TCS and passive beam energy modulation actuation have been performed. We report the system technological commissioning activities reporting dose distribution errors in a phantom study, where the treatment of a lung lesion was simulated, with both lateral and range beam position compensation. The zero-delay systems integration with a specific active scanning delivery machine was achieved by tuning the amount of time prediction applied to lateral (14.61 ± 0.98 ms) and depth (34.1 ± 6.29 ms) beam position correction signals, featuring sub-millimeter accuracy in forward estimation. Direct optical target observation and motion phase (MPh) based tumor motion discretization strategies were tested, resulting in 20.3(2.3)% and 21.2(9.3)% median (IQR) percentual relative dose difference with respect to static irradiation, respectively. Results confirm the technical feasibility of the implemented strategy towards 4D treatment delivery, with negligible percentual dose deviations with respect to static irradiation.

Tumor tracking based on correlation models in scanned ion beam therapy: an experimental study.

Accurate dose delivery to extra-cranial lesions requires tumor motion compensation. An effective compensation can be achieved by real-time tracking of the target position, either measured in fluoroscopy or estimated through correlation models as a function of external surrogate motion. In this work, we integrated two internal/external correlation models (a state space model and an artificial neural network-based model) into a custom infra-red optical tracking system (OTS). Dedicated experiments were designed and conducted at GSI (Helmholtzzentrum für Schwerionenforschung). A robotic breathing phantom was used to reproduce regular and irregular internal target motion as well as external thorax motion. The position of a set of markers placed on the phantom thorax was measured with the OTS and used by the correlation models to infer the internal target position in real-time. Finally, the estimated target position was provided as input for the dynamic steering of a carbon ion beam. Geometric results showed that the correlation models transversal (2D) targeting error was always lower than 1.3 mm (root mean square). A significant decrease of the dosimetric error with respect to the uncompensated irradiation was achieved in four out of six experiments, demonstrating that phase shifts are the most critical irregularity for external/internal correlation models.

Robustness of external/internal correlation models for real-time tumor tracking to breathing motion variations.

In radiotherapy, organ motion mitigation by means of dynamic tumor tracking requires continuous information about the internal tumor position, which can be estimated relying on external/internal correlation models as a function of external surface surrogates. In this work, we propose a validation of a time-independent artificial neural networks-based tumor tracking method in the presence of changes in the breathing pattern, evaluating the performance on two datasets. First, simulated breathing motion traces were specifically generated to include gradually increasing respiratory irregularities. Then, seven publically available human liver motion traces were analyzed for the assessment of tracking accuracy, whose sensitivity with respect to the structural parameters of the model was also investigated. Results on simulated data showed that the proposed method was not affected by hysteretic target trajectories and it was able to cope with different respiratory irregularities, such as baseline drift and internal/external phase shift. The analysis of the liver motion traces reported an average RMS error equal to 1.10 mm, with five out of seven cases below 1 mm. In conclusion, this validation study proved that the proposed method is able to deal with respiratory irregularities both in controlled and real conditions.

Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications.

We propose a novel method for radio-opaque external marker localization in CT scans for infrared (IR) patient set-up in radiotherapy. Efforts were focused on the quantification of uncertainties in marker localization in the CT dataset as a function of algorithm performance. We implemented a 3-D approach to fiducial localization based on surface extraction and marker recognition according to geometrical prior knowledge. The algorithm parameters were optimized on a clinical CT dataset coming from 35 cranial and extra-cranial patients; the localization accuracy was benchmarked at variable image resolution versus laser tracker measurements. The applicability of conventional IR optical tracking systems for localizing external surrogates in daily patient set-up procedures was also investigated in 121 proton therapy treatment sessions. Our study shows that the implemented algorithm features surrogates localization with uncertainties lower than 0.3 mm and with a true positive rate of 90.1%, being this latter mainly influenced by fiducial homogeneity in the CT images. The reported clinical validation in proton therapy confirmed the submillimetric accuracy and the expected algorithm sensitivity. Geometrical prior knowledge allows judging the reliability of the extracted fiducial coordinates, ensuring the highest accuracy in patient set-up.

Development and validation of a prototypal neural networks-based tumor tracking method.

In radiotherapy, intra-fractional organ motion introduces uncertainties in target localization, leading to unacceptable inaccuracy in dose delivery. Especially in highly selective treatments, such as those delivered with particles beams instead of photons, organ motion may results in severe side effects and/or limited tumor control. Tumor tracking is a motion mitigation strategy that allows an almost continuous dose delivery while the beam is dynamically steered to match the position of the moving target in real-time. Currently, tumor tracking is applied clinically only in the CyberKnife system for photon radiotherapy, whereas neither clinical solutions nor dedicated methodologies are available for particle therapy. Consequently, the aim of the proposed study is to develop a neural networks-based prototypal tracking algorithm intended for particle therapy. We developed a method that exploits three independent neural networks to estimate the internal target position as a function of external surrogate signals. This method was tested on data relative to 20 patients treated with CyberKnife, whose performance was used as benchmark. Results show that the developed algorithm allows targeting error reduction with respect to the CyberKnife system, thus proving the potential value of artificial neural networks for the implementation of tumor tracking methodologies.