In most clinical image-guided procedures like stenting and needle based biopsies one is only interested to visualize a specific part of the patient’s body. Therefore, only the relevant parts should be irradiated in order to minimise the patient’s dosage. Utilizing such volume-of-interest imaging has the potential to save a lot of dose but causes artifacts to appear in the resulting image, stemming from the reconstruction from incomplete projections. Modern bi-plane C-arm CT systems have the potential to acquire some projections complete and other projections limited to the region of interest. Here, we investigate how extrapolation methods can be used to replace the missing data and minimize the resulting artifacts. A new extrapolation method is proposed and compared to an existing extrapolation method. Subsequently, both the image quality in the recorded region of interest as well as the extrapolated outer area are examined. It is shown, that blockwise contiguous truncated/non-truncated bi-plane projections are an advantageous compromise between fully acquired data and completely truncated data. Significant amounts of dosage can be saved, whilst largely maintaining the relevant image quality.
KEYWORDS: Image registration, Data acquisition, Image quality, Image restoration, 3D acquisition, Radon, Matrices, Data modeling, Computed tomography, Medical image reconstruction, 3D image reconstruction
In many interventional settings it would be beneficial to perform a final CBCT acquisition for outcome control after the intervention is done. However, due to high patient dose this is often omitted. Volume-of-interest acquisitions offer considerable dose reduction, but image reconstruction typically suffers from cupping artifacts and offsets in radiodensity due to the truncated projection data. In a previous work we presented a method which allows to incorporate available prior volume data into the reconstruction of volume-of-interest acquisitions in CBCT. The method works by making use of the fluoroscopic positioning images typically acquired before CBCT acquisitions in a 3D Radon space-based registration method registering the prior volume to the volumeof-interest scenario. Here, we demonstrate the application of this method on real clinical data or the first time.
Dynamic dose modulation techniques are applied in CT acquisitions to ensure optimal working conditions of the detector unit and to reduce overall radiation exposure of the patient. In C-arm CT systems, large variations in the desired irradiation may require tube voltage modulation (TVM). Recent studies showed that TVM does not affect the quality of perfusion images obtained by clinical CT. Here, we investigate the impact of TVM in a C-arm cone beam CT perfusion imaging setting. We conduct a simulation study based on a real perfusion acquisition (incl. tube modulation) to directly compare results from acquisitions with and without modulation. Using two different reconstruction techniques, we analyze the influence of TVM on the extracted perfusion parameters and quantify the similarity by their correlation coefficients. Our results demonstrate that high correlation (r < 0.99) between the results with and without TVM are achieved for all perfusion parameters using a straightforward and model-based reconstruction technique. These findings suggest that dose modulation techniques, incl. TVM, can be used in C-arm CT perfusion scans without the need for additional correction methods to retain image quality of constant voltage scans.
CT perfusion imaging (CTP) plays an important role in decision making for the treatment of acute ischemic stroke with large vessel occlusion. Since the CT perfusion scan time is approximately one minute, the patient is exposed to a non-negligible dose of ionizing radiation. However, further dose reduction increases the level of noise in the data and the resulting perfusion maps. We present a method for reducing noise in perfusion data based on dimension reduction of time attenuation curves. For dimension reduction, we use either the fit of the first five terms of the trigonometric polynomial or the first five terms of the SVD decomposition of the time attenuation profiles. CTP data from four patients with large vessel occlusion and three control subjects were studied. To compare the noise level in the perfusion maps, we use the wavelet estimation of the noise standard deviation implemented in the scikit-image package. We show that both methods significantly reduce noise in the data while preserving important information about the perfusion deficits. These methods can be used to further reduce the dose in CT perfusion protocols or in perfusion studies using C-arm CT, which are burdened by high noise levels.
The use of C-Arm-based cone-beam computed tomography (CBCT) plays an increasing role in interventions, especially for guidance and therapy control. The slow speed and the high dose limit the use of CBCT to research and prevent its widespread application in clinical routine. Acquiring less data using greater angular step or limited angular range is an obvious way to overcome these issues. However, images reconstructed from such datasets using standard reconstruction algorithms are deteriorated with severe artifacts. In this work, we investigate the use of a new nullspace-constrained modification scheme for sparse-view and limited-angle intra-operative CT image reconstruction. This scheme allows to perform fast unconstrained ART reconstruction, and, based on prior knowledge regarding the object to be reconstructed, some modifications restricted to the nullspace of the system can be easily applied as a post-processing step. Within this scheme, we enforce sparsity by integrating geometric prior information regarding the interventional tool itself, besides a high-quality pre-operative CT image. The presented method was compared to the compressed sensing-based algorithms NIHT and PrIDICT. Performance was evaluated qualitatively and quantitatively. This new scheme is shown to be promising for low-dose intra-operative image reconstruction. Compared to PrIDICT and NIHT, it shows higher reconstruction accuracy and demonstrates the ability to precisely visualize the instrument’s position even when only 15 projection views are acquired over a full angular range. It demonstrates an accurate reconstruction with a high degree of robustness against data incompleteness and sparsity level over-estimation.
Optical flow-based methods are commonly used to detect and correct patient motion in modalities such as cone-beam computed tomography. With such methods, the rotational motion deriving from the acquisition trajectory itself is obscuring the patient motion and therefore considered a perturbation. In this work, the question commonly posed in motion estimation is reversed. Instead of considering the rotational motion as obscuring the patient motion, it can be used to derive shape information about the patient. This is done by computing the optical flow from projection images in order to find point correspondences in different frames of the acquisition. Finally, projective geometry is used to localize a given pair of corresponding 2D image points in 3D object space.
The advantage of this method is that it allows for the localization of structures which are not contained within the scan field of view of truncated acquisitions but within an extended projection field of view. Therefore, it is shown that for certain high contrast structures, e.g. the skull, this localization method can be used to estimate the maximum extent of a patient from truncated projection data, which is an important information for extrapolation methods, or to localize highly absorbing structures outside of the scan field of view which contribute to the severity of the typically observed truncation artifacts.
A typical incomplete data problem arising in cone-beam computed tomography (CBCT) occurs when an object is either too large to be projected onto the detector or is deliberately only projected in parts. This problem is called truncation. Tomographic images reconstructed from truncated projection data can be severely impaired by image artifacts depending on the degree of truncation. A typical strategy to counter this is to extend the projection data by some smooth extrapolation. In order to accurately approximate the shape of the scanned object outside of the volume of interest (VOI), we previously presented a method which fits an extrapolation model to the truncated data by minimizing an error function based on the Grangeat consistency condition (GCC). In this work we propose a method of reducing the complexity of the extrapolation by making use of the 0th image moments of the truncated projection data.
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