Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms.

BACKGROUND Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion. PURPOSE We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle. METHODS Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations. RESULTS Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms. CONCLUSIONS A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Motion management strategies are crucial for radiotherapy of mobile tumours in order to ensure proper target coverage, save organs at risk and prevent interplay effects. We present a feasibility study for an inter-fractional, patient-specific motion model targeted at active beam scanning proton therapy. The model is designed to predict dense lung motion information from 2D abdominal ultrasound images. In a pretreatment phase, simultaneous ultrasound and magnetic resonance imaging are used to build a regression model. During dose delivery, abdominal ultrasound imaging serves as a surrogate for lung motion prediction. We investigated the performance of the motion model on five volunteer datasets. In two cases, the ultrasound probe was replaced after the volunteer has stood up between two imaging sessions. The overall mean prediction error is 2.9 mm and 3.4 mm after repositioning and therefore within a clinically acceptable range. These results suggest that the ultrasound-based regression model is a promising approach for inter-fractional motion management in radiotherapy

Ultrasound-based Motion Modelling for the Lungs in Scanned Proton Therapy

Respiratory motion poses a major challenge in image acquisition and image-guided interventions of thoracic and abdominal organs, such as the liver or lungs. In the field of radiotherapy, accurate knowledge of the organ motion is essential for precise radiation of the target volume while sparing surrounding healthy tissue and organs at risk. In this thesis, we present different tools and methods towards ultrasound-guided lung tumour tracking in scanned proton therapy with the main focus on respiratory motion modelling. We start off with introducing an ultrasound-based 4D magnetic resonance imaging (4D MRI) method for which simultaneously acquired ultrasound and partial MRI data is used to retrospectively reconstruct a time-resolved volumetric MR image. In the following, different motion modelling approaches are presented where 2D abdominal ultrasound images serve as a surrogate signal to predict complete lung motion information. First, we propose a novel approach based on a conditional generative adversarial network (cGAN) in conjunction with a state-of-the-art navigator-based 4D MRI. Second, we investigate the performance of a polynomial regression model when subject to ultrasound probe repositioning as required for fractionated treatments. Third, we propose a motion model based on Gaussian process regression (GPR) and analyse the impact of prediction errors on proton dose distributions with and without tumour tracking. All of these studies are based on simultaneously acquired ultrasound and 4D MRI data sets of two to eight healthy volunteers. For the dosimetric analysis, the motion patterns extracted from 4D MRI of healthy volunteers were combined with computed tomography (CT) scans of two lung cancer patients. In general, the mean or median prediction error was found to be below 3mm for intra-fractional motion modelling. Moreover, motion predictions based on GPR were shown to translate into clinically acceptable dose distributions, emphasising the great potential of ultrasound-guidance for motion mitigation in scanned proton therapy. From a treatment point of view, however, the dosimetric benefits of tumour tracking were found to be limited. Tumour tracking alone may not always be sufficient to restore clinically acceptable dose distributions and should be combined with other motion mitigation techniques such as rescanning

Electoral participation in pursuit of policy representation : ideological congruence and voter turnout

Published online: 07 Nov 2013In this article, we examine whether lack of ideological congruence with the viable party options discourages turnout, and under which conditions. We conceive congruence from the perspective of the individual citizen, and, drawing on policy-based arguments for non-voting, we hypothesize that: having no party in the political menu sharing similar views should especially reduce turnout of citizens holding extremist views and that this effect would be greatest in proportional electoral systems. Relying on data collected by the Comparative Study of Electoral Systems (CSES), we show that lack of congruence with the electoral menu reduces extremists’ turnout and does so particularly in PR systems

Respiratory Motion Compensation for the Robot-guided Laser Osteotome

PURPOSE: The use of a robot-guided laser osteotome for median sternotomy is impeded by prohibiting cutting inaccuracies due to respiration-induced motions of the thorax. With this paper, we advance today's methodologies in sternotomy procedures by introducing the concept of novel 3D functional cuts and a respiratory motion compensation algorithm for the computer-assisted and robot-guided laser osteotome, CARLO®. METHODS: We present a trajectory planning algorithm for performing 3D functional cuts at a constant cutting velocity. In addition, we propose the use of Gaussian process (GP) prediction in order to anticipate the sternum's pose providing enough time for the CARLO® device to adjust the position of the laser source. RESULTS: We analysed the performance of the proposed algorithms on a computer-based simulation framework of the CARLO® device. The median position error of the laser focal point has shown to be reduced from 0.22 mm without GP prediction to 0.19 mm with GP prediction. CONCLUSION: The encouraging simulation results support the proposed respiratory motion compensation algorithm for robot-guided laser osteotomy on the thorax. Successful compensation of the respiration-induced motion of the thorax opens doors for robot-guided laser sternotomy and the related novel cutting patterns. These functional cuts hold great potential to significantly improve postoperative sternal stability and therefore reduce pain and recovery time for the patient. By enabling functional cuts, we approach an important threshold moment in the history of osteotomy, creating innovative opportunities which reach far beyond the classic linear cutting patterns

Ultrasound-driven 4D MRI

We present an ultrasound-driven 4D magnetic resonance imaging (US-4DMRI) method for respiratory motion imaging in the thorax and abdomen. The proposed US-4DMRI comes along with a high temporal resolution, and allows for organ motion imaging beyond a single respiratory cycle. With the availability of the US surrogate both inside and outside the MR bore, 4D MR images can be reconstructed for 4D treatment planning and online respiratory motion prediction during radiotherapy. US-4DMRI relies on simultaneously acquired 2D liver US images and abdominal 2D MR multi-slice scans under free respiration. MR volumes are retrospectively composed by grouping the MR slices corresponding to the most similar US images. We present two different US similarity metrics: an intensity-based approach, and a similarity measure relying on predefined fiducials which are being tracked over time. The proposed method is demonstrated on MR liver scans of eight volunteers acquired over a duration of 5.5 min each at a temporal resolution of 2.6 Hz with synchronous US imaging at 14 Hz-17 Hz. Visual inspection of the reconstructed MR volumes revealed satisfactory results in terms of continuity in organ boundaries and blood vessels. In quantitative leave-one-out experiments, both US similarity metrics reach the performance level of state-of-the-art navigator-based approaches

Impact of internal target volume definition for pencil beam scanned proton treatment planning in the presence of respiratory motion variability for lung cancer: A proof of concept.

PURPOSE Motion management is crucial in scanned proton therapy for mobile tumours. Current motion mitigation approaches rely on single 4DCTs before treatment, ignoring respiratory variability. We investigate the consequences of respiratory variations on internal target volumes (ITV) definition and motion mitigation efficacy, and propose a probabilistic ITV based on 4DMRI. MATERIALS AND METHODS Four 4DCT(MRI) datasets, each containing 40 variable cycles of synthetic 4DCTs, were generated by warping single-phase CTs of two lung patients with motion fields extracted from two 4DMRI datasets. Two-field proton treatment plans were optimised on ITVs based on different parts of the 4DCT(MRI)s. 4D dose distributions were calculated by considering variable respiratory patterns. Different probabilistic ITVs were created by incorporating the voxels covered by the CTV in at least 25%, 50%, or 75% (ITV25, ITV50, ITV75) of the cycles, and compared with the conservative ITV encompassing all possible CTV positions. RESULTS Depending on the selected planning 4DCT, ITV volumes vary up to 20%, resulting in significant variation in CTV coverage for 4D treatments. Target coverage and homogeneity improved with the conservative ITV, but was associated with significantly increased lung dose (~1%). ITV25 and ITV50 led to acceptable plan quality in most cases without lung dose increments. ITV75 best minimised lung dose, but was insufficient to ensure coverage under all motion scenarios. CONCLUSION Irregular respiration significantly affects CTV coverage when ITVs are only defined by single 4DCTs. A probabilistic ITV50 provides an adequate compromise between target coverage and lung dose for most motion and patient scenarios investigated

Liver-ultrasound-guided lung tumour tracking for scanned proton therapy: a feasibility study

Pencil beam scanned (PBS) proton therapy of lung tumours is hampered by respiratory motion and the motion-induced density changes along the beam path. In this simulation study, we aim to investigate the effectiveness of proton beam tracking for lung tumours both under ideal conditions and in conjunction with a respiratory motion model guided by real-time ultrasound imaging of the liver. Multiple-breathing-cycle 4DMRIs of the thorax and abdominal 2D ultrasound images were acquired simultaneously for five volunteers. Deformation vector fields extracted from the 4DMRI, referred to as ground truth motion, were used to generate 4DCT(MRI) data sets of two lung cancer patients, resulting in 10 data sets with variable motion patterns. Given the 4DCT(MRI) and the corresponding ultrasound images as surrogate data, a patient-specific motion model was built. The model consists of an autoregressive model and Gaussian process regression for the temporal and spatial prediction, respectively. Two-field PBS plans were optimised on the reference CTs, and 4D dose calculations (4DDC) were used to simulate dose delivery for (a) unmitigated motion, (b) ideal 2D and 3D tracking (both beam adaption and 4DDC based on ground truth motion), and (c) realistic 2D and 3D tracking (beam adaption based on motion predictions, 4DDC on ground truth motion). Model-guided tracking retrieved clinically acceptable target dose homogeneity, as seen in a substantial reduction of the D5%-D95% compared to the non-mitigated simulation. Tracking in 2D and 3D resulted in a similar improvement of the dose homogeneity, as did ideal and realistic tracking simulations. In some cases, however, the tracked deliveries resulted in a shift towards higher or lower dose levels, leading to unacceptable target over- or under-coverage. The presented motion modelling framework was shown to be an accurate motion prediction tool for the use in proton beam tracking. Tracking alone, however, may not always effectively mitigate motion effects, making it necessary to combine it with other techniques such as rescanning

Liver-ultrasound based motion modelling to estimate 4D dose distributions for lung tumours in scanned proton therapy

Motion mitigation strategies are crucial for scanned particle therapy of mobile tumours in order to prevent geometrical target miss and interplay effects. We developed a patient-specific respiratory motion model based on simultaneously acquired time-resolved volumetric MRI and 2D abdominal ultrasound images. We present its effects on 4D pencil beam scanned treatment planning and simulated dose distributions. Given an ultrasound image of the liver and the diaphragm, principal component analysis and Gaussian process regression were applied to infer dense motion information of the lungs. 4D dose calculations for scanned proton therapy were performed using the estimated and the corresponding ground truth respiratory motion; the differences were compared by dose difference volume metrics. We performed this simulation study on 10 combined CT and 4DMRI data sets where the motion characteristics were extracted from 5 healthy volunteers and fused with the anatomical CT data of two lung cancer patients. Median geometrical estimation errors below 2 mm for all data sets and maximum dose differences of = 43.2% and = 16.3% were found. Moreover, it was shown that abdominal ultrasound imaging allows to monitor organ drift. This study demonstrated the feasibility of the proposed ultrasound-based motion modelling approach for its application in scanned proton therapy of lung tumours.</p