Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise, to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporate a priori knowledge into iterative image reconstruction for example by adding additional constraints to the cost function, which penalize strong variations between neighboring voxels. However this approach to regularization in general poses a resolution noise trade–off because the stronger the regularization, and thus the noise reduction, the stronger the loss of spatial resolution and thus loss of anatomical detail. We propose a method which tries to improve this trade-off. One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The proposed reconstruction algorithm reconstructs voxel–specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade–off.
With modern CT scanners, detection and classification of coronary artery disease has become a routine applica- tion in cardiac CT. It poses a desirable non–invasive alternative to the invasive coronary angiography, which is the current clinical gold standard. However, the accuracy of cardiac CT depends on the spatial resolution of the imaging system. The limited spatial resolution leads to blooming artifacts, arising from hyper–dense calcification deposits in the arterial walls. This blooming leads to an overestimation of the degree of luminal narrowing and to loss of the morphology of the calcified region. We propose an image–based algorithm, which aims at removing the blooming and estimating the correct CT–value and morphology of the calcification. The method is based on the assumption, that each calcification consists of a compact region which has an almost constant density and attenuation. This knowledge is incorporated into an iterative deconvolution algorithm in image space. We quantitatively assess the accuracy of the proposed algorithm on analytically simulated phantom data. Qualita- tive results of clinical patient data are presented as well. In both cases, the proposed method outperforms the compared algorithms. The initial patient data results are promising. However, an ex vivo study has to be done to confirm the quantitative results of the simulation study with real specimen.
Dental imaging often requires to gather information from a curved plane that covers the upper and lower jaw. To acquire these data one may either use a CT or a DVT scan followed by extracting the desired curved plane from the volumetric CT data, or one may decide to work at much lower dose levels and acquire a panoramic radiograph, which is also known as an orthopantomogram, or short as panorama.
The panorama is acquired by moving the x-ray source and detector arrangement such that the x-ray cone intersects the curved plane in a preferably perpendicular way. Due to the small size of specialized panorama x-ray detectors, that often run in the so-called time-delayed integration (TDI) mode, the cone is collimated such that it is only a few millimeters wide in the fan direction. In this situation the fan angle is much smaller than the cone angle.
Assuming an imaging system based on flat detectors the panoramic imaging corresponds to digital x-ray tomosynthesis taken in a curved plane. Of course, panoramic imaging suffers from significant blurring between adjacent planes, as it is inherent to all tomosynthesis techniques. To reduce this intra plane blurring we propose an approach that uses a form filter to simultaneously acquire the data for a typical orthopantomogram and low-dose data with a significantly increased fan-angle, sufficient to perform a low-dose FDK reconstruction. The bow-tie takes care to reduce dose in that extended region to about 1% compared to the dose in the central region. The total dose remains constant. These data will be combined resulting in an orthopantomogram with improved image quality due to reduced intra plane blurring as one is able to subtract the unwanted background structures arising from off focus objects as for example the cervical vertebrae. We conducted simulations to validate our approach. For that we use a volumetric CT data set of a patient's head from which we generated rawdata.
The simulation results show that with our approach panoramic imaging with a flat detector offers the possibility to improve image quality without additional costs in patient dose.