Proton beam radiotherapy: report of the first patient treated at the Centro Nazionale di Adroterapia Oncologica (CNAO) [National Center of Oncologic Hadron Therapy]

Proton beam radiotherapy, an innovative treatment modality, allows delivery of high radiation doses to the target while sparing surrounding healthy structures. The Centro Nazionale di Adroterapia Oncologica (CNAO), equipped with a synchrotron and capable of using both protons and ions, initiated its clinical activity in September 2011. The first treatment of a skull base tumor with protons is reported here. The case of a 26-year-old man with an intracranial low-grade chondrosarcoma of the right petroclival junction is discussed with emphasis on technical and clinical details. Two previous surgical interventions had achieved partial removal of the tumor and the patient was treated with protons for residual disease. The prescribed dose was 70 GyE in 35 fractions of 2 GyE. Treatment was completed with minimal acute toxicity consisting of grade 1 alopecia and nausea. Nine months after treatment the disease is locally controlled. Use of high-energy protons at CNAO is a safe and effective means of treating a tumor located near critical normal structures.

Cancer-associated fibroblasts and M2-polarized macrophages synergize during prostate carcinoma progression

Inflammation is now acknowledged as an hallmark of cancer. Cancer-associated fibroblasts (CAFs) force a malignant cross talk with cancer cells, culminating in their epithelial-mesenchymal transition and achievement of stemness traits. Herein, we demonstrate that stromal tumor-associated cells cooperate to favor malignancy of prostate carcinoma (PCa). Indeed, prostate CAFs are active factors of monocyte recruitment toward tumor cells, mainly acting through stromal-derived growth factor-1 delivery and promote their trans-differentiation toward the M2 macrophage phenotype. The relationship between M2 macrophages and CAFs is reciprocal, as M2 macrophages are able to affect mesenchymal-mesenchymal transition of fibroblasts, leading to their enhanced reactivity. On the other side, PCa cells themselves participate in this cross talk through secretion of monocyte chemotactic protein-1, facilitating monocyte recruitment and again macrophage differentiation and M2 polarization. Finally, this complex interplay among cancer cells, CAFs and M2 macrophages, cooperates in increasing tumor cell motility, ultimately fostering cancer cells escaping from primary tumor and metastatic spread, as well as in activation of endothelial cells and their bone marrow-derived precursors to drive de novo angiogenesis. In keeping with our data obtained in vitro, the analysis of patients affected by prostate cancers at different clinical stages revealed a clear increase in the M2/M1 ratio in correlation with clinical values. These data, coupled with the role of CAFs in carcinoma malignancy to elicit expression of stem-like traits, should focus great interest for innovative strategies aimed at the co-targeting of inflammatory cells and fibroblasts to improve therapeutic efficacy.

CyberKnife robotic image-guided stereotactic radiotherapy for oligometastic cancer : A prospective evaluation of 95 patients/118 lesions

PURPOSE

To evaluate the outcome of robotic CyberKnife (Accuray Inc. Sunnyvale, USA)-based stereotactic radiotherapy (CBK-SRT) for oligometastic cancer patients.

PATIENTS AND METHODS

Between May 2007 and December 2009, 95 patients with a total of 118 lesions underwent CBK-SRT (median dose 24 Gy in 3 fractions).

INCLUSION CRITERIA

adult patients with limited volume cancer; suitability for SRT but not for other local therapies. Primary diagnoses included breast, lung, head and neck, gastrointestinal and other malignancies. Prostate cancer patients were excluded. Concomitant systemic therapy was given in 40 % of cases and median follow-up was 12 months. Toxicity and tumor response were evaluated using the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer (RTOG/EORTC) Scale and Response Evaluation Criteria in Solid Tumors RECIST.

RESULTS

Toxicity was rare and observed mainly in patients with comorbidities or uncontrolled cancer. Out of 87 evaluable lesions, complete radiological response, partial response, stabilization and progressive disease were observed in 15 (17 %), 25 (29 %), 34 (39 %) and 13 (15 %) lesions, respectively. Upon restricting the analysis to lesions treated with CBK-SRT alone (no concomitant therapy), response- and local control (LC) rates remained similar. Actuarial 3-year in-field progression-free survival- (i.e. LC), progression-free survival- (PFS) and overall-survival (OS) rates were 67.6, 18.4, and 31.2 %, respectively. LC was reduced in cases of early recurrence. OS- and cause-specific survival (CSS) rates were significantly lower in patients treated for visceral lesions. Failures were predominantly out-field.

CONCLUSION

CBK-SRT is a feasible therapeutic approach for oligometastastic cancer patients that provides long-term in-field tumor control with a low toxicity profile. Further investigations should focus on dose escalation and optimization of the combination with systemic therapies.

The role of regularization in deformable image registration for head and neck adaptive radiotherapy

Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis.

Mechatronic design of a fully integrated camera for mini-invasive surgery

This paper describes the design features of an innovative fully integrated camera candidate for mini-invasive abdominal surgery with single port or transluminal access. The apparatus includes a CMOS imaging sensor, a light-emitting diode (LED)-based unit for scene illumination, a photodiode for luminance detection, an optical system designed according to the mechanical compensation paradigm, an actuation unit for enabling autofocus and optical zoom, and a control logics based on microcontroller. The bulk of the apparatus is characterized by a tubular shape with a diameter of 10 mm and a length of 35 mm. The optical system, composed of four lens groups, of which two are mobile, has a total length of 13.46 mm and an effective focal length ranging from 1.61 to 4.44 mm with a zoom factor of 2.75×, with a corresponding angular field of view ranging from 16° to 40°. The mechatronics unit, devoted to move the zoom and the focus lens groups, is implemented adopting miniature piezoelectric motors. The control logics implements a closed-loop mechanism, between the LEDs and photodiode, to attain automatic control light. Bottlenecks of the design and some potential issues of the realization are discussed. A potential clinical scenario is introduced.

An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates

In the radiation treatment of moving targets with external surrogates, information on tumor position in real time can be extracted by using accurate correlation models. A fuzzy environment is proposed here to correlate input surrogate data with tumor motion estimates in real time. In this study, two different data clustering approaches were analyzed due to their substantial effects on the fuzzy modeler performance. Moreover, a comparative investigation was performed on two fuzzy-based and one neuro-fuzzy-based inference systems with respect to state-of-the-art models. Finally, due to the intrinsic interpatient variability in fuzzy models' performance, a model selectivity algorithm was proposed to select an adaptive fuzzy modeler on a case-by-case basis. The performance of multiple and adaptive fuzzy logic models were retrospectively tested in 20 patients treated with CyberKnife real-time tumor tracking. Final results show that activating adequate model selection of our fuzzy-based modeler can significantly reduce tumor tracking errors.

Dosimetric assessment of a novel metal artifact reduction method in CT images

The aim of this study was to assess the ability of metal artifact reduction (MAR) algorithm in restoring the CT image quality while correcting the tissue density information for the accurate estimation of the absorbed dose. A phantom filled with titanium (low-Z metal) and Cerrobend (high-Z metal) inserts was used for this purpose. The MAR algorithm was applied to phantom's CT dataset. Static intensity-modulated radiation therapy (IMRT) plans, including five beam angles, were designed and optimized on the uncorrected images to deliver 10 Gy on the simulated target. Monte Carlo dose calculation was computed on uncorrected, corrected, and ground truth image datasets. It was firstly verified that MAR methodology was able to correct HU errors due to the metal presence. In the worst situation (high-Z phantom), the image difference, uncorrected ground truth and corrected ground truth, went from -4.4 ± 118.8 HU to 0.4 ± 10.8 HU, respectively. Secondly, it was observed that the impact of dose errors estimation depends on the atomic number of the metal: low-Z inserts do not produce significant dose inaccuracies, while high-Z implants substantially influence the computation of the absorbed dose. In this latter case, dose errors in the PTV region were up to 23.56% (9.72% mean value) when comparing the uncorrected vs. the ground truth dataset. After MAR correction, errors dropped to 0.11% (0.10% mean value). In conclusion, it was assessed that the new MAR algorithm is able to restore image quality without distorting mass density information, thus producing a more accurate dose estimation.

Computer assisted planning of autologous fat grafting in breast

Autologous fat grafting is an emerging and promising surgical technique in regenerative medicine, and its application is quickly spreading in plastic and reconstructive surgery of the breast. However, despite the advantages of the technique, surgical complications may occur, such as implanted tissue necrosis and resorption and onset of microcalcifications. In view of the hypothesis that the uniformity of the lipoaspirate transplantation is related to graft survival and a lower probability of complications, we developed an interactive lipomodeling planning software application based on a genetic algorithm that allows automatic optimization of the uniformity of fat tissue distribution. The input dataset consists of a 3D model of the patient's thorax, created from MRI scans, on which relevant structures are segmented. The developed software was tested starting from either an automatically generated plan or an initial guess of the optimal surgical plan, and in both cases the application yielded a consistent improvement in the planned fat tissue distribution by optimizing the position of the insertion points and the direction of the insertion pathways. On the basis of the simulations performed, the use of genetic algorithms for optimization of the geometry of autologous fat transfer in the breast proved to be effective. These results will foster further activities focused on the comparison of predicted optimized geometries and those obtained in real surgical cases as a means of obtaining a deeper knowledge of the potential influence of a uniform fat tissue distribution on the quality of the surgical outcome. The presented application is also put forward as representing a noteworthy step towards the clinical application of computer assisted planning tools in breast surgery.

Real-time tumour tracking in particle therapy: technological developments and future perspectives

A key challenge in radiation oncology is accurate delivery of the prescribed dose to tumours that move because of respiration. Tumour tracking involves real-time target localisation and correction of radiation beam geometry to compensate for motion. Uncertainties in tumour localisation are important in particle therapy (proton therapy, carbon-ion therapy) because charged particle beams are highly sensitive to geometrical and associated density and radiological variations in path length, which will affect the treatment plan. Target localisation and motion compensation methods applied in x-ray photon radiotherapy require careful performance assessment for clinical applications in particle therapy. In this Review, we summarise the efforts required for an application of real-time tumour tracking in particle therapy, by comparing and assessing competing strategies for time-resolved target localisation and related clinical outcomes in x-ray radiation oncology.

Comparative assessment of 3D surface scanning systems in breast plastic and reconstructive surgery

In this work, we compared accuracy, repeatability, and usability in breast surface imaging of 2 commercial surface scanning systems and a hand-held laser surface scanner prototype coupled with a patient's motion acquisition and compensation methodology. The accuracy of the scanners was assessed on an anthropomorphic phantom, and to evaluate the usability of the scanners on humans, thorax surface images of 3 volunteers were acquired. Both the intrascanner repeatability and the interscanner comparative accuracy were assessed. The results showed surface-to-surface distance errors inferior to 1 mm and to 2 mm, respectively, for the 2 commercial scanners and for the prototypical one. Moreover, comparable performances of the 3 scanners were found when used for acquiring the breast surface. On the whole, this study demonstrated that handheld laser surface scanners coupled with subject motion compensation methods lend themselves as competitive technologies for human body surface modeling.

Ranking of stopping criteria for log domain diffeomorphic demons application in clinical radiation therapy

Deformable Image Registration is a complex optimization algorithm with the goal of modeling a non-rigid transformation between two images. A crucial issue in this field is guaranteeing the user a robust but computationally reasonable algorithm. We rank the performances of four stopping criteria and six stopping value computation strategies for a log domain deformable registration. The stopping criteria we test are: (a) velocity field update magnitude, (b) vector field Jacobian, (c) mean squared error, and (d) harmonic energy. Experiments demonstrate that comparing the metric value over the last three iterations with the metric minimum of between four and six previous iterations is a robust and appropriate strategy. The harmonic energy and vector field update magnitude metrics give the best results in terms of robustness and speed of convergence.

Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy

PURPOSE

The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment.

METHOD

We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT.

RESULTS

Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found.

CONCLUSION

The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

WE-E-213CD-02: Gaussian Weighted Multi-Atlas Based Segmentation for Head and Neck Radiotherapy Planning

PURPOSE

To develop a multi-atlas segmentation strategy for IMRT head and neck therapy planning.

METHODS

The method was tested on thirty-one head and neck simulation CTs, without demographic or pathology pre-clustering. We compare Fixed Number (FN) and Thresholding (TH) selection (based on normalized mutual information ranking) of the atlases to be included for current patient segmentation. Next step is a pairwise demons Deformable Registration (DR) onto current patient CT. DR was extended to automatically compensate for patient different field of view. Propagated labels are combined according to a Gaussian Weighted (GW) fusion rule, adapted to poor soft tissues contrast. Agreement with manual segmentation was quantified in terms of Dice Similarity Coefficient (DSC). Selection methods, number of atlases used, as well as GW, average and majority voting fusion were discriminated by means of Friedman Test (a=5%). Experimental tuning of the algorithm parameters was performed on five patients, deriving an optimal configuration for each structure.

RESULTS

DSC reduction was not significant when ten or more atlases are selected, whereas DSC for single most similar atlas selection is 10% lower in median. DSC of FN selection rule were significantly higher for most structures. Tubular structures may benefit from computing average contour rather than looking at the singular voxel contribution, whereas the best performing strategy for all other structures was GW. When half database is selected, final median DSC were 0.86, 0.80, 0.51, 0.81, 0.69 and 0.79 for mandible, spine, optical nerves, eyes, parotids and brainstem respectively.

CONCLUSION

We developed an efficient algorithm for multiatlas based segmentation of planning CT volumes, based on DR and GW. FN selection of database atlases is foreseen to increase computational efficiency. The absence of clinical pre-clustering and specific imaging protocol on database subjects makes the results closer to real clinical application. "Progetto Roberto Rocca" funded by the Fondazione Fratelli Agostino and Enrico Rocca, Italy.

Optical eye tracking system for noninvasive and automatic monitoring of eye position and movements in radiotherapy treatments of ocular tumors

A noninvasive eye tracking system based on infrared 3-D video-oculographic techniques is proposed for the automatic monitoring of eye position and orientation in external beam radiotherapy of ocular tumors. The presented method can be applied for the real-time estimation of lesion position and tumor-beam misalignments, allowing automatic patient setup and eye movement gated treatments. A prototypal eye tracker was developed and tested on five subjects, achieving gaze estimation errors of 0.5° and eye monitoring frequencies of 125 Hz. The proposed application can potentially improve quality and efficacy of ocular radiotherapy treatments, currently based on invasive, qualitative, and manual control procedures.

Mean-shifted surface curvature algorithm for automatic bone shape segmentation in orthopedic surgery planning: a sensitivity analysis

The results of recent studies concerning statistical bone atlases and automated shape analysis are promising with a view to widening the use of surface models in orthopedic clinical practice, both in pre-operative planning and in the intra-operative stages. In this domain, automatic shape analysis is strongly advocated because it offers the opportunity to detect morphological and clinical landmarks with superior repeatability in comparison to human operators. Surface curvatures have been proposed extensively for segmentation and labeling of image and surface regions based on their appearance and shape. The surface curvature is an invariant that can be exploited for reliable detection of geometric features. In this paper, we investigate the potentiality of the algorithm termed mean-shift (MS), as applied to a non-linear combination of the minimum and maximum curvatures of a surface. We exploited a sensitivity analysis of the algorithm parameters across increasing surface resolutions. Results obtained with femur and pelvic bone surface data, reconstructed from cadaveric CT scans, demonstrated that the information content derived by the MS non-linear curvature overcomes both the mean and the Gaussian curvatures and the original non-linear curvature. By applying a threshold-based clustering algorithm to the curvature distribution, we found that the number of clusters yielded by the MS non-linear curvature is significantly lower (by a factor of up to 6) than that obtained by using the original non-linear curvature. In conclusion, this study provides valuable insights into the use of surface curvature for automatic shape analysis.

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.

Scale Invariant Feature Transform as feature tracking method in 4D imaging: a feasibility study

We propose the use of Scale Invariant Feature Transform (SIFT) as a method able to extract stable landmarks from 4D images and to quantify internal motion. We present a preliminary validation of the SIFT method relying on expert user identification of landmarks and then apply it to 4D lung CT and liver MRI data. Results demonstrate SIFT capabilities as an operator-independent feature tracking method.

Simulation of functional tricuspid regurgitation using an isolated porcine heart model

BACKGROUND AND AIM OF THE STUDY

The results of tricuspid annuloplasty to treat functional tricuspid regurgitation (FTR) are sometimes suboptimal, and alternative techniques are needed. In the absence of reliable FTR models, and in an effort to minimize the need for animal experiments, a reproducible bench-model was developed of FTR, that allowed the simulation of the anatomic features of the condition.

METHODS

A fresh porcine heart was mounted on a rigid support that was placed into a basin filled with saline; a closed circuit was then created with a centrifugal pump, equipped with connection tubes. The inflow tube of the pump conveyed saline from the basin to the pump; the outflow cannula was inserted through the pulmonary artery, across the pulmonary valve, into the right ventricle. The pump was activated to pressurize the right ventricle, thus inducing tricuspid valve regurgitation (TVR). The regurgitant flow through the valve was quantified using a flow-meter. Radiopaque markers were sutured to the head of each papillary muscle and to the tricuspid annulus, in order to trace the geometric changes of the tricuspid valve at increasing pump rates, using fluoroscopy. The efficacy of the bench-model was validated with 10 hearts.

RESULTS

The TVR was increased proportionally with the right ventricular pressure (RVP) (TVR = 0.089xRVP - 1.515; R2 = 0.89). The increase in TVR was associated with increases in the annular-to-papillary muscles distance (APML) (TVR = 0.059xAPML - 2.94; R2 = 0.96), of the inter-papillary muscles distance (PMD) (TVR = 0.058xPMD - 8.58; R2 = 0.94), and of the triscuspid annular dilatation (TAD) (TVR = 0.05xTAD - 1.85; R2 = 0.89). Of these parameters, APML was the strongest predictor of TVR.

CONCLUSION

The porcine heart bench model represents a reproducible system to simulate the physiopathology of FTR, and has the potential to serve as a complementary method for the evaluation of new 'in vitro' technologies and therapies for FTR.

Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy

PURPOSE

The goal of this study is to investigate the advantages of large scale optimization methods vs conventional classification techniques in predicting acute toxicity for urinary bladder and rectum due to prostate irradiation.

METHODS

Clinical and dosimetric data of 321 patients undergoing prostate conformal radiotherapy were recorded. Gastro-intestinal and genito-urinary acute toxicities were scored according to the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer (RTOG/EORTC) scale. Patients were classified in two categories to separate mild (Grade < 2) from severe toxicity levels (Grade > 2). Machine learning methods at different complexity were implemented to predict toxicity as a function of multiple variables. The first approach consisted of a large scale optimization method, based on genetic algorithms (GAs) and artificial neural networks (ANN). The second approach was a binary classifier based on support vector machines (SVM).

RESULTS

The ANN and SVM-based solutions showed comparable prediction accuracy, exhibiting an area under the receiver operating characteristic (ROC) curve of 0.7. Different sensitivity and specificity features were measured for the two approaches. The ANN algorithm showed enhanced sensitivity if combined with appropriate classification criteria.

CONCLUSIONS

The results demonstrate that high sensitivity in toxicity prediction can be achieved with optimized ANNs, that are put forward to represent a valuable support in medical decisions. Future studies will be focused on enlarging the available patient database to increase the reliability of toxicity prediction algorithms and to define optimal classification criteria.

Functional evaluation and rehabilitation engineering

Life is complex and all about movement, which allows us to interact with the environment and communicate with each other. The human nervous system is capable of performing a simultaneous and integrated control of 100-150 mechanical degrees of freedom of movement in the body via tensions generated by about 700 muscles. In its widest context, movement is carried out by a sensory motor system comprising multiple sensors (visual,auditory, and proprioceptive),multiple actuators (muscles acting on the skeletal system),and an intermediary processor that can be summarized as a multiple-input–multiple-output nonlinear dynamic time-varying control system. This grand control system is capable of responding with remarkable accuracy,speed, appropriateness,versatility, and adaptability to a wide spectrum of continuous and discrete stimuli and conditions and is certainly orders of magnitude more complex and sophisticated than the most advanced robotic systems currently available. In the last decades,a great deal of research has been carried out in the fields of functional evaluation of human performance and rehabilitation engineering. These fields combine knowledge, concepts, and methods from across many disciplines (e.g., biomechanics,neuroscience, and physiology), with the aim of developing apparatuses and methods fort he measurement and analysis of complex sensory motor performance and the ultimate goal of enhancing the execution of different tasks in both healthy people and persons with reduced capabilities from different causes (injury, disease, amputation,and neural degeneration).

Intra-fraction setup variability: IR optical localization vs. X-ray imaging in a hypofractionated patient population

BACKGROUND

The purpose of this study is to investigate intra-fraction setup variability in hypo-fractionated cranial and body radiotherapy; this is achieved by means of integrated infrared optical localization and stereoscopic kV X-ray imaging.

METHOD AND MATERIALS

We analyzed data coming from 87 patients treated with hypo-fractionated radiotherapy at cranial and extra-cranial sites. Patient setup was realized through the ExacTrac X-ray 6D system (BrainLAB, Germany), consisting of 2 infrared TV cameras for external fiducial localization and X-ray imaging in double projection for image registration. Before irradiation, patients were pre-aligned relying on optical marker localization. Patient position was refined through the automatic matching of X-ray images to digitally reconstructed radiographs, providing 6 corrective parameters that were automatically applied using a robotic couch. Infrared patient localization and X-ray imaging were performed at the end of treatment, thus providing independent measures of intra-fraction motion.

RESULTS

According to optical measurements, the size of intra-fraction motion was (median ± quartile) 0.3 ± 0.3 mm, 0.6 ± 0.6 mm, 0.7 ± 0.6 mm for cranial, abdominal and lung patients, respectively. X-ray image registration estimated larger intra-fraction motion, equal to 0.9 ± 0.8 mm, 1.3 ± 1.2 mm, 1.8 ± 2.2 mm, correspondingly.

CONCLUSION

Optical tracking highlighted negligible intra-fraction motion at both cranial and extra-cranial sites. The larger motion detected by X-ray image registration showed significant inter-patient variability, in contrast to infrared optical tracking measurement. Infrared localization is put forward as the optimal strategy to monitor intra-fraction motion, featuring robustness, flexibility and less invasivity with respect to X-ray based techniques.

Evaluation and commissioning of a surface based system for respiratory sensing in 4D CT

The purpose of this study is to assess the temporal and reconstruction accuracy of a surface imaging system, the GateCT under ideal conditions, and compare the device with a commonly used respiratory surrogate: the Varian RPM. A clinical CT scanner, run in cine mode, was used with two optical devices, GateCT and RPM, to detect respiratory motion. A radiation detector, GM-10, triggers the X-ray on/off to GateCT system, while the RPM is directly synchronized with the CT scanner through an electronic connection. Two phantoms were imaged: the first phantom translated on a rigid plate along the anterior-posterior (AP) direction, and was used to assess the temporal synchronization of each optical system with the CT scanner. The second phantom, consisting of five spheres translating 3 cm peak-to-peak in the superior-inferior direction, was used to assess the quality of rebinned images created by GateCT and RPM. Calibration assessment showed a nearly perfect synchronization with the scanner for both the RPM and GateCT systems, thus demonstrating the good performance of the radiation detector. Results for the volume rebinning test showed discrepancies in volumes for the 3D reconstruction (compared to ground truth) of up to 36% for GateCT and up to 40% for RPM. No statistical difference was proven between the two systems in volume sorting. Errors are mainly due to phase detection inaccuracies and to the large motion of the phantom. This feasibility study assessed the consistency of two optical systems in synchronizing the respiratory signal with the image acquisition. A new patient protocol based on both RPM and GateCT will be soon started.

Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study

The use of external surrogates to predict tumor motion in real-time for extra-cranial sites requires the use of accurate correlation models. This is extremely challenging when motion prediction is to be performed over several breathing cycles, as occurs for realtime tumor tracking with Cyberknife® Synchrony®. In this work we compare three different approaches to infer tumor motion based on external surrogates, since no comparative study is available to assess the accuracy of correlation models in tumor tracking over a long time period. We selected 20 cases in a database of 130 patients treated with realtime tumor tracking by means of the Synchrony® module. The implemented correlation models comprise linear/quadratic correlation, artificial neural networks and fuzzy logic. The accuracy of each correlation model is evaluated on the basis of ground truth tumor position information acquired during treatment, as detected by means of stereoscopic X-ray imaging. Results show that the implemented models achieve an error reduction with respect to Synchrony®, measured at the 95% confidence level, up to 10.8% for the fuzzy logic approach. This latter is able to partly reduce the incidence of tumor tracking errors above 6 mm, resulting in improved accuracy for larger discrepancies. In conclusion, complex models are suggested to predict tumor motion over long time periods. This leads to an effective improvement with respect to Cyberknife® Synchrony®. Future studies will investigate the sensitivity of the implemented models to the input database, in order to define optimal strategies.

Uncertainties in Lung Motion Prediction Relying on External Surrogate: A 4DCT Study in Regular vs. Irregular Breathers

This paper examines the uncertainty in estimating lung motion from external surrogates for lung cancer patients with regular and irregular breathing. 4DCT data sets were analyzed using a template matching algorithm to track the spatial movement of vessel bifurcations in 12 patients. The detected internal movement of features in 3D was retrospectively synchronized with the RPM surrogate signal, and the correlation index R2 and the prediction error were computed. Patients were classified into two groups depending on the presence or not of irregularities in their breathing pattern. Peak-to-peak values of feature motion in the SI direction ranged from 0.8 mm (upper lung) to 25.3 mm (lower lung). Some patients exhibited large motion also in the latero-lateral (10.6 mm) and anterior-posterior (12.2 mm) directions. The median ± quartile of R2 in SI direction was 0.89 ± 0.09. Prediction error values were up to 4.2 mm (95th percentile) with a maximum value of 4.9 mm. Statistical differences between regular and irregular breathers were found for R2, while prediction error depended only on the range of motion. This study is relevant for image guided radiotherapy methods that rely on external surrogates to monitor motion.