Ray contribution masks for structure adaptive sinogram filtering.

The patient dose in computed tomography (CT) imaging is linked to measurement noise. Various noise-reduction techniques have been developed that adapt structure preserving filters like anisotropic diffusion or bilateral filters to CT noise properties. We introduce a structure adaptive sinogram (SAS) filter that incorporates the specific properties of the CT measurement process. It uses a point-based forward projector to generate a local structure representation called ray contribution mask (RCM). The similarities between neighboring RCMs are used in an enhanced variant of the bilateral filtering concept, where the photometric similarity is replaced with the structural similarity. We evaluate the performance in four different scenarios: The robustness against reconstruction artifacts is demonstrated by a scan of a high-resolution-phantom. Without changing the modulation transfer function (MTF) nor introducing artifacts, the SAS filter reduces the noise level by 13.6%. The image sharpness and noise reduction capabilities are visually assessed on in vivo patient scans and quantitatively evaluated on a simulated phantom. Unlike a standard bilateral filter, the SAS filter preserves edge information and high-frequency components of organ textures well. It shows a homogeneous noise reduction behavior throughout the whole frequency range. The last scenario uses a simulated edge phantom to estimate the filter MTF for various contrasts: the noise reduction for the simple edge phantom exceeds 80%. For low contrasts at 55 Hounsfield units (HU), the mid-frequency range is slightly attenuated, at higher contrasts of approximately 100 HU and above, the MTF is fully preserved.

Fast time-of-flight camera based surface registration for radiotherapy patient positioning.

PURPOSE: This work introduces a rigid registration framework for patient positioning in radiotherapy, based on real-time surface acquisition by a time-of-flight (ToF) camera. Dynamic properties of the system are also investigated for future gating/tracking strategies. METHODS: A novel preregistration algorithm, based on translation and rotation-invariant features representing surface structures, was developed. Using these features, corresponding three-dimensional points were computed in order to determine initial registration parameters. These parameters became a robust input to an accelerated version of the iterative closest point (ICP) algorithm for the fine-tuning of the registration result. Distance calibration and Kalman filtering were used to compensate for ToF-camera dependent noise. Additionally, the advantage of using the feature based preregistration over an "ICP only" strategy was evaluated, as well as the robustness of the rigid-transformation-based method to deformation. RESULTS: The proposed surface registration method was validated using phantom data. A mean target registration error (TRE) for translations and rotations of 1.62 ± 1.08 mm and 0.07° ± 0.05°, respectively, was achieved. There was a temporal delay of about 65 ms in the registration output, which can be seen as negligible considering the dynamics of biological systems. Feature based preregistration allowed for accurate and robust registrations even at very large initial displacements. Deformations affected the accuracy of the results, necessitating particular care in cases of deformed surfaces. CONCLUSIONS: The proposed solution is able to solve surface registration problems with an accuracy suitable for radiotherapy cases where external surfaces offer primary or complementary information to patient positioning. The system shows promising dynamic properties for its use in gating/tracking applications. The overall system is competitive with commonly-used surface registration technologies. Its main benefit is the usage of a cost-effective off-the-shelf technology for surface acquisition. Further strategies to improve the registration accuracy are under development.

Value-based noise reduction for low-dose dual-energy computed tomography.

We introduce a value-based noise reduction method for Dual-Energy CT applications. It is based on joint intensity statistics estimated from high- and low-energy CT scans of the identical anatomy in order to reduce the noise level in both scans. For a given pair of measurement values, a local gradient ascension algorithm in the probability space is used to provide a noise reduced estimate. As a consequence, two noise reduced images are obtained. It was evaluated with synthetic data in terms of quantitative accuracy and contrast to noise ratio (CNR)-gain. The introduced method allows for reducing patient dose by at least 30% while maintaining the original CNR level. Additionally, the dose reduction potential was shown with a radiological evaluation on real patient data. The method can be combined with state-of-the-art filter-based noise reduction techniques, and makes low-dose Dual-Energy CT possible for the full spectrum of quantitative CT applications.