Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy.

BACKGROUND: Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. PURPOSE: The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). METHODS: An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. RESULTS: Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. CONCLUSIONS: Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.

Multi-stage image registration based on list-mode proton radiographies for small animal proton irradiation: A simulation study.

We present a multi-stage and multi-resolution deformable image registration framework for image-guidance at a small animal proton irradiation platform. The framework is based on list-mode proton radiographies acquired at different angles, which are used to deform a 3D treatment planning CT relying on normalized mutual information (NMI) or root mean square error (RMSE) in the projection domain. We utilized a mouse X-ray micro-CT expressed in relative stopping power (RSP), and obtained Monte Carlo simulations of proton images in list-mode for three different treatment sites (brain, head and neck, lung). Rigid transformations and controlled artificial deformation were applied to mimic position misalignments, weight loss and breathing changes. Results were evaluated based on the residual RMSE of RSP in the image domain including the comparison of extracted local features, i.e. between the reference micro-CT and the one transformed taking into account the calculated deformation. The residual RMSE of the RSP showed that the accuracy of the registration framework is promising for compensating rigid (>97% accuracy) and non-rigid (∼95% accuracy) transformations with respect to a conventional 3D-3D registration. Results showed that the registration accuracy is degraded when considering the realistic detector performance and NMI as a metric, whereas the RMSE in projection domain is rather insensitive. This work demonstrates the pre-clinical feasibility of the registration framework on different treatment sites and its use for small animal imaging with a realistic detector. Further computational optimization of the framework is required to enable the use of this tool for online estimation of the deformation.

Impact of secondary particles on the magnetic field generated by a proton pencil beam: a finite-element analysis based on Geant4-DNA simulations.

PURPOSE: To investigate the static magnetic field generated by a proton pencil beam as a candidate for range verification by means of Monte Carlo simulations, thereby improving upon existing analytical calculations. We focus on the impact of statistical current fluctuations and secondary protons and electrons. METHODS: We considered a pulsed beam (10 µ ${\umu}$ s pulse duration) during the duty cycle with a peak beam current of 0.2 µ $\umu$ A and an initial energy of 100 MeV. We ran Geant4-DNA Monte Carlo simulations of a proton pencil beam in water and extracted independent particle phase spaces. We calculated longitudinal and radial current density of protons and electrons, serving as an input for a magnetic field estimation based on a finite element analysis in a cylindrical geometry. We made sure to allow for non-solenoidal current densities as is the case of a stopping proton beam. RESULTS: The rising proton charge density toward the range is not perturbed by energy straggling and only lowered through nuclear reactions by up to 15%, leading to an approximately constant longitudinal current. Their relative low density however (at most 0.37 protons/mm3 for the 0.2  µ ${\umu}$ A current and a beam cross-section of 2.5 mm), gives rise to considerable current density fluctuations. The radial proton current resulting from lateral scattering and being two orders of magnitude weaker than the longitudinal current is subject to even stronger fluctuations. Secondary electrons with energies above 10 eV, that far outnumber the primary protons, reduce the primary proton current by only 10% due to their largely isotropic flow. A small fraction of electrons (<1%), undergoing head-on collisions, constitutes the relevant electron current. In the far-field, both contributions to the magnetic field strength (longitudinal and lateral) are independent of the beam spot size. We also find that the nuclear reaction-related losses cause a shift of 1.3 mm to the magnetic field profile relative to the actual range, which is further enlarged to 2.4 mm by the electron current (at a distance of ρ = 50 $\rho =50$  mm away from the central beam axis). For ρ > 45 $\rho >45$  mm, the shift increases linearly. While the current density variations cause significant magnetic field uncertainty close to the central beam axis with a relative standard deviation (RSD) close to 100%, they average out at a distance of 10 cm, where the RSD of the total magnetic field drops below 2%. CONCLUSIONS: With the small influence of the secondary electrons together with the low RSD, our analysis encourages an experimental detection of the magnetic field through sensitive instrumentation, such as optical magnetometry or SQUIDs.

X-ray CT adaptation based on a 2D-3D deformable image registration framework using simulated in-room proton radiographies.

The aim of this work is to investigate in-room proton radiographies to compensate realistic rigid and non-rigid transformations in clinical-like scenarios based on 2D-3D deformable image registration (DIR) framework towards future clinical implementation of adaptive radiation therapy (ART). Monte Carlo simulations of proton radiographies (pRads) based on clinical x-ray CT of a head and neck, and a brain tumor patients are simulated for two different detector configurations (i.e. integration-mode and list-mode detectors) including high and low proton statistics. A realistic deformation, derived from cone beam CT of the patient, is applied to the treatment planning CT. Rigid inaccuracies in patient positioning are also applied and the effect of small, medium and large fields of view (FOVs) is investigated. A stopping criterion, as desirable in realistic scenarios devoid of ground truth proton CT (pCT), is proposed and investigated. Results show that rigid and non-rigid transformations can be compensated based on a limited number of low dose pRads. The root mean square error with respect to the pCT shows that the 2D-3D DIR of the treatment planning CT based on 10 pRads from integration-mode data and 2 pRads from list-mode data is capable of achieving comparable accuracy (∼90% and >90%, respectively) to conventional 3D-3D DIR. The dice similarity coefficient over the segmented regions of interest also verifies the improvement in accuracy prior to and after 2D-3D DIR. No relevant changes in accuracy are found between high and low proton statistics except for 2 pRads from integration-mode data. The impact of FOV size is negligible. The convergence of the metric adopted for the stopping criterion indicates the optimal convergence of the 2D-3D DIR. This work represents a further step towards the potential implementation of ART in proton therapy. Further computational optimization is however required to enable extensive clinical validation.

Deformable image registration of the treatment planning CT with proton radiographies in perspective of adaptive proton therapy.

The purpose of this work is to investigate the potentiality of using a limited number of in-room proton radiographies to compensate anatomical changes in adaptive proton therapy. The treatment planning CT is adapted to the treatment delivery scenario relying on 2D-3D deformable image registration (DIR). The proton radiographies, expressed in water equivalent thickness (WET) are simulated for both list-mode and integration-mode detector configurations in pencil beam scanning. Geometrical and analytical simulations of an anthropomorphic phantom in the presence of anatomical changes due to breathing are adopted. A Monte Carlo simulation of proton radiographies based on a clinical CT image in the presence of artificial anatomical changes is also considered. The accuracy of the 2D-3D DIR, calculated as root mean square error, strongly depends on the considered anatomical changes and is considered adequate for promising adaptive proton therapy when comparable to the accuracy of conventional 3D-3D DIR. In geometrical simulation, this is achieved with a minimum of eight/nine radiographies (more than 90% accuracy). Negligible improvement (sim1%) is obtained with the use of 180 radiographies. Comparing different detector configurations, superior accuracy is obtained with list-mode than integration-mode max (WET with maximum occurrence) and mean (average WET weighted by occurrences). Moreover, integration-mode max performs better than integration-mode mean. Results are minimally affected by proton statistics. In analytical simulation, the anatomical changes are approximately compensated (about 60%-70% accuracy) with two proton radiographies and minor improvement is observed with nine proton radiographies. In clinical data, two proton radiographies from list-mode have demonstrated better performance than nine from integration-mode (more than 100% and about 50%-70% accuracy, respectively), even avoiding the finer grid spacing of the last numerical optimization stage. In conclusion, the choice of detector configuration as well as the amount and complexity of the considered anatomical changes determine the minimum number of radiographies to be used.

Patient-specific CT calibration based on ion radiography for different detector configurations in 1H, 4He and 12C ion pencil beam scanning.

The empirical conversion of the treatment planning x-ray computed tomography (CT) image to ion stopping power relative to water causes dose calculation inaccuracies in ion beam therapy. A patient-specific calibration of the CT image is enabled by the combination of an ion radiography (iRad) with the forward-projection of the empirically converted CT image along the estimated ion trajectories. This work investigated the patient-specific CT calibration for list-mode and integration-mode detector configurations, with reference to a ground truth ion CT (iCT) image. Analytical simulations of idealized carbon ion and proton trajectories in a numerical anthropomorphic phantom and realistic Monte Carlo simulations of proton, helium and carbon ion pencil beam scanning in a clinical CT image of a head-and-neck patient were considered. Controlled inaccuracy and noise levels were applied to the calibration curve and to the iRad, respectively. The impact of the selection of slices and angles of the iRads, as well as the choice of the optimization algorithm, were investigated. Accurate and robust CT calibration was obtained in analytical simulations of straight carbon ion trajectories. Analytical simulations of non-straight proton trajectories due to scattering suggested limitations for integration-mode but not for list-mode. To make the most of integration-mode, a dedicated objective function was proposed, demonstrating the desired accuracy for sufficiently high proton statistics in analytical simulations. In clinical data the inconsistencies between the iRad and the forward-projection of the ground truth iCT image were in the same order of magnitude as the applied inaccuracies (up to 5%). The accuracy of the CT calibration were within 2%-5% for integration-mode and 1%-3% for list-mode. The feasibility of successful patient-specific CT calibration depends on detector technologies and is primarily limited by these above mentioned inconsistencies that slightly penalize protons over helium and carbon ions due to larger scattering and beam spot size.