Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI.

In-room cine-MRI guidance can provide non-invasive target localization during radiotherapy treatment. However, in order to cope with finite imaging frequency and system latencies between target localization and dose delivery, tumour motion prediction is required. This work proposes a framework for motion prediction dedicated to cine-MRI guidance, aiming at quantifying the geometric uncertainties introduced by this process for both tumour tracking and beam gating. The tumour position, identified through scale invariant features detected in cine-MRI slices, is estimated at high-frequency (25 Hz) using three independent predictors, one for each anatomical coordinate. Linear extrapolation, auto-regressive and support vector machine algorithms are compared against systems that use no prediction or surrogate-based motion estimation. Geometric uncertainties are reported as a function of image acquisition period and system latency. Average results show that the tracking error RMS can be decreased down to a [0.2; 1.2] mm range, for acquisition periods between 250 and 750 ms and system latencies between 50 and 300 ms. Except for the linear extrapolator, tracking and gating prediction errors were, on average, lower than those measured for surrogate-based motion estimation. This finding suggests that cine-MRI guidance, combined with appropriate prediction algorithms, could relevantly decrease geometric uncertainties in motion compensated treatments.

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.

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.