Dynamic Reconstruction with Statistical Ray Weighting for C-Arm CT Perfusion Imaging

Abstract—Tissue perfusion measurement using C-arm angiography systems is a novel technique with potential high benefit for catheter-guided treatment of stroke in the interventional suite. However, perfusion C-arm CT (PCCT) is challenging: the slow C-arm rotation speed only allows measuring samples of contrast time attenuation curves (TACs) every 5 – 6 s if reconstruction algorithms for static data are used. Furthermore, the peaks of the tissue TACs typically lie in a range of 5 – 30 HU, thus perfusion imaging is very sensitive to noise. Recently we presented a dynamic, iterative reconstruction (DIR) approach to reconstruct TACs described by a weighted sum of linear spline functions with a regularization based on joint bilateral filtering (JBF). In this work we incorporate statistical ray weighting into the algorithm and show how this helps to improve the reconstructed cerebral blood flow (CBF) maps in a simulation study with a realistic dynamic brain phantom. The Pearson correlation of the CBF maps to ground truth maps increases from 0.85 (FDK), 0.87 (FDK with JBF), and 0.90 (DIR with JBF) to 0.92 (DIR with JBF and ray weighting). The results suggest that the statistical ray weighting approach improves the diagnostic accuracy of PCCT based on DIR. I

A dynamic reconstruction approach for cerebral blood flow quantification with an interventional C-arm CT

Tomographic perfusion imaging is a well accepted method for stroke diagnosis that is available with current CT and MRI scanners. A challenging new method, which is currently not available, is perfusion imaging with an interventional C-arm CT that can acquire 4-D images using a C-arm angiography system. This method may help to optimize the workflow du-ring catheter-guided stroke treatment. The main challenge in perfusion C-arm CT is the comparably slow rotational speed of the C-arm (approximately 5 seconds) which decreases the overall temporal resolution. In this work we present a dyna-mic reconstruction approach optimized for perfusion C-arm CT based on temporal estimation of partially backprojected volumes. We use numerical simulations to validate the algo-rithm: For a typical configuration the relative error in estima-ted arterial peak enhancement decreases from 14.6 % to 10.5% using the dynamic reconstruction. Furthermore we present in-itial results obtained with a clinical C-arm CT in a pig model. 1

Coronary Computed Tomography Angiography

Coronary computed tomographic angiography (cCTA) as a noninvasive approach underlies a rapid technological development with an impressive improvement of spatial and temporal resolution of the images. Therefore, it has become an accurate and cost-effective method to detect or exclude obstructive coronary artery disease (CAD) in patients with low to medium cardiovascular risk profile, as recommended by the ESC/AHA/ACC guidelines. The results show an excellent sensitivity, but still with a lack of specificity compared with invasive measurement. Several novel techniques like myocardial perfusion, plaque characterization or CT-based measurement of the fractional flow reserve have been developed to improve the positive predictive value and create more accurate results in detecting hemodynamically relevant stenoses. Moreover, during the last decade, the need to reduce radiation dose has become a central issue in clinical use, while the current generation of CT scanners has drastically lowered radiation dose. In conclusion, cCTA has become a promising alternative to invasive cardiac catheterization with still existing limitations. Thus, an appropriate patient selection is mandatory to utilize the advantages of this technique

My future and I:cardiovascular risk stratification of asymptomatic individuals

In the coming decades, a continuing increase in the number of cases of coronary heart disease (CHD) is expected. This is caused by, amongst others, the increasing prevalence of obesity and diabetes, and the rising numbers of elderly citizens. The morbidity and mortality toll of CHD is high. In many cases, a coronary event occurs acutely, without earlier signs suggesting CHD. So how can we identify individuals in the asymptomatic population at high risk of CHD, and prevent coronary events? Cardiovas- cular risk estimation in the general population is based on determining risk factors such as hypertension and smoking. Risk factor levels can be used to calculate a risk-scoring algorithm, like the European SCORE, and guide medical therapy. Unfortunately, risk factor based algorithms are neither highly sensitive nor specific. Accurate identification of asymptomatic indi- viduals who will develop a coronary event is challenging