Rebecca Fahrig

TU‐B‐204B‐02: A Study of the Effect of Inline and Perpendicular Magnetic Fields on Beam Characteristics of Medical Linear Accelerator Electron Guns

Purpose: Integrated MRI‐linacs have the potential for real‐time volumetric imaging and targeting. Both inline (main MRI field parallel to linac beam) and perpendicular (main field orthogonal to linac beam) configurations are considered. The MRI fringe fields affect beam generation and transport, particularly the electron gun, where the electron energy is the lowest and thus magnetic fields have the largest effect. This work characterizes the electron gun behavior in external magnetic fields for inline and perpendicular configurations. Methods: Two electron guns were studied, the Litton L2087 and Varian VTC6364. Based on fringe field measurements of a 0.5T open bore MRI scanner (GE Signa SP), field strengths of 0–0.16T were computed with the finite elements method. Space charge beam simulations were performed for both inline and perpendicular configurations. Emitted current and beam deflection were determined. Results: For the inline configuration, the electron beam remains aligned with the gun axis. As the field strength increases, the emitted current has an initial plateau of constant value after which its value decreases to a minimum near 0.06T. The minimum of the emitted current is 26% and 21% from the zero field value for Litton and Varian guns respectively. Above 0.06T, the emitted current increases monotonically. For the perpendicular configuration, the electron beam is deflected from the gun axis even at small field strengths. The beam deflection increases with the magnetic field value, leading to a sharp decrease of the emitted current which completely vanishes at about 0.007T for Litton and 0.006T for Varian. Conclusion: For the inline configuration, there is always emitted current along the gun axis thus leading to the possibility to adapt the gun geometry for optimal beam generation compatible with the inline configuration. For the perpendicular configuration, magnetic shielding of the electron gun is required to avoid beam bending. Support: NIH T32‐CA09695

A photon recycling approach to the denoising of ultra-low dose X-ray sequences

PurposeClinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential consequences of such exposures fall under two categories, namely stochastic (mostly cancer) and deterministic risks (skin injury). According to the “as low as reasonably achievable” principle, the radiation dose can be lowered only if the necessary image quality can be maintained.MethodsOur work improves upon the existing patch-based denoising algorithms by utilizing a more sophisticated noise model to exploit non-local self-similarity better and this in turn improves the performance of low-rank approximation. The novelty of the proposed approach lies in its properly designed and parameterized noise model and the elimination of initial estimates. This reduces the computational cost significantly.ResultsThe algorithm has been evaluated on 500 clinical images (7 patients, 20 sequences, 3 clinical sites), taken at ultra-low dose levels, i.e. 50% of the standard low dose level, during electrophysiology procedures. An average improvement in the contrast-to-noise ratio (CNR) by a factor of around 3.5 has been found. This is associated with an image quality achieved at around 12 (square of 3.5) times the ultra-low dose level. Qualitative evaluation by X-ray image quality experts suggests that the method produces denoised images that comply with the required image quality criteria.ConclusionThe results are consistent with the number of patches used, and they demonstrate that it is possible to use motion estimation techniques and “recycle” photons from previous frames to improve the image quality of the current frame. Our results are comparable in terms of CNR to Video Block Matching 3D—a state-of-the-art denoising method. But qualitative analysis by experts confirms that the denoised ultra-low dose X-ray images obtained using our method are more realistic with respect to appearance.

Comparison of Default Patient Surface Model Estimation Methods

A patient model is useful for many clinical applications such as patient positioning, device placement, or dose estimation in case of X-ray imaging. A default or a-priori patient model can be estimated using learning based methods trained over a large database. Different methods can be used to estimate such a default model given a restricted number of the input parameters. We investigated different learning based estimation strategies using patient gender, height, and weight as the input to estimate a default patient surface model. We implemented linear regression, an active shape model, kernel principal component analysis and a deep neural network method. These methods are trained on a database containing about 2000 surface meshes. Using linear regression, we obtained a mean vertex error of 20.8±14.7mm for men and 17.8±11.6mm for women, respectively. While the active shape model and kernel PCA method performed better than linear regression, the results also revealed that the deep neural network outperformed all other methods with a mean vertex error of 15.6±9.5mm for male and 14.5±9.3mm for female models.

Dual-energy C-arm CT in the angiographic suite

Dual-energy CT techniques have demonstrated tremendous clinical value due to their ability to distinguish materials based on atomic number. C-arm CT is currently used to guide interventional procedures, but there are no commercially available systems that employ dual-energy material decomposition. This paper explores the feasibility of implementing a fast kV-switching technique to perform dual-energy C-arm CT on a clinical angiography system. As an initial proof of concept, a fast kV-switching scan with energies of 90 kV and 125 kV was compared to respective constant kV scans. During rapid kV-switching acquisitions, the energy produced by the tube at each pulse is up to 5% lower than the energy produced during kV-constant acquisitions. The small instability in the produced kV, measured as the standard deviation of the kV produced in each pulse, is up to 4 times higher for kV-switching acquisitions. These minor deficits resulted in a small reduction in contrast resolution of the fast kV-switching 3D reconstructions.

SU‐C‐220‐04: Electrostatic Focal Spot Correction for X‐Ray Tubes Operating in Strong Magnetic Fields

Purpose: To evaluate the effectiveness of electrostatic mechanisms for controlling the focal spot position in X‐ray tubes operating in the fringe field of an MR bore, and to determine a cathode design to implement the correction mechanism while not compromising normal X‐ray tube operation.Methods: Using a combination of theoretical calculations and high voltage vacuum standoff constraints, a cathode design was derived that is practical for standard X‐ray tube geometry. The crucial part of the design consists of two voltage‐biased deflection electrodes placed adjacent to the cathode. Space charge beam simulations were performed for the design to determine current density changes and beam deflection in the presence of a magnetic field. Phase space information from the beam simulation was input into a Monte Carlo engine to determine the effect of the cathode design on the X‐ray photon energy spectrum.Results: For a 0.07T magnetic field, a 120 kV cathode‐anode potential, a 14.4 mm cathode‐anode separation distance, and a 35 kV electrode potential, the deflection of the X‐ray tube focal spot is within 2 mm of the original position with slight distortions to focal spot shape. However, the curvature of the electron trajectories is altered resulting in a significant velocity component tangential to the anode that is on average 10 times larger than in the control case. The electron velocity changes coupled with slightly lower current density on the anode reduces the total number of photons generated by 7.5% without significantly altering the energy spectrum of the X‐ray photons Conclusions: The beam simulations demonstrate that focal spot deflection can be controlled to within reasonable values. The generated spectrum is not significantly different from that of a standard X‐ray tube, with only a moderate decrease in overall photon fluence. Work is underway to evaluate the cathode design in an experimental setting. Funding Sources: NIHR01 EB007626, Stanford Bio‐X Fellowship, and the Lucas Foundation

Intraoperative Brain Shift Compensation Using a Hybrid Mixture Model.

Brain deformation (or brain shift) during neurosurgical procedures such as tumor resection has a significant impact on the accuracy of neuronavigation systems. Compensating for this deformation during surgery is essential for effective guidance. In this paper, we propose a method for brain shift compensation based on registration of vessel centerlines derived from preoperative C-Arm cone beam CT (CBCT) images, to intraoperative ones. A hybrid mixture model (HdMM)-based non-rigid registration approach was formulated wherein, Student’s t and Watson distributions were combined to model positions and centerline orientations of cerebral vasculature, respectively. Following registration of the preoperative vessel centerlines to its intraoperative counterparts, B-spline interpolation was used to generate a dense deformation field and warp the preoperative image to each intraoperative image acquired. Registration accuracy was evaluated using both synthetic and clinical data. The former comprised CBCT images, acquired using a deformable anthropomorphic brain phantom. The latter meanwhile, consisted of four 3D digital subtraction angiography (DSA) images of one patient, acquired before, during and after surgical tumor resection. HdMM consistently outperformed a state-of-the-art point matching method, coherent point drift (CPD), resulting in significantly lower registration errors. For clinical data, the registration error was reduced from 3.73 mm using CPD to 1.55 mm using the proposed method.

Resolve Intraoperative Brain Shift as Imitation Game

Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 ± 0.55 mm.

A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model

PurposeWith the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system’s iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose.MethodTo enable such functionality in a clinical context with sufficiently high accuracy, we present an extension of our previous patient surface model by adding internal anatomical landmarks associated with certain (main) bones of the human skeleton, in particular the spine. We also investigate different approaches to positioning of these landmarks employing CT datasets with annotated internal landmarks as training data. The general pipeline of our proposed method comprises the following steps: First, we train an active shape model using an existing avatar database and segmented CT surfaces. This stage also includes a gravity correction procedure, which accounts for shape changes due to the fact that the avatar models were obtained in standing position, while the CT data were acquired with patients in supine position. Second, we match the gravity-corrected avatar patient surface models to surfaces segmented from the CT datasets. In the last step, we derive the spatial relationships between the patient surface model and internal anatomical landmarks.ResultWe trained and evaluated our method using cross-validation using 20 datasets, each containing 50 internal landmarks. We further compared the performance of four different generalized linear models’ setups to describe the positioning of the internal landmarks relative to the patient surface. The best mean estimation error over all the landmarks was achieved using lasso regression with a mean error of

Simulation of Realistic Low Dose Fluoroscopic Images from their High Dose Counterparts

12.19 \pm 6.98\ \hbox {mm}