Erdogan Cesmeli

Gauging effective spatial resolution in multirow helical cardiac computed tomography with a dynamic phantom

PurposeTo devise a numerical indicator of image quality for multirow helical cardiac computed tomography (CT) and its relation to temporal resolution. Materials and MethodsA pulsatile cardiac assist device was used to simulate cardiac wall motion by mechanically transmitting the device dynamics to a piece of tungsten wire. Wire motion induced by different device rates was captured with an 8-row subsecond helical CT scanner operating with various scanning parameters. Image artifacts were visually assessed and compared with the image point spread function (PSF) using the full width at half maximum (FWHM) area as a numerical estimate of spatial accuracy. ResultsAt rest, the FWHM area was determined as 1.3 mm2. At a device rate of 60 bpm, the FWHM area ranged from 1.51 mm2 to 21.62 mm2, depending on the time of image reconstruction. Mean reproducibility of the FWHM area measurements was determined as 0.05, whereas visual estimates of motion artifact were highly variable between different readers (kappa = 0.19). Visually determined image quality correlated closely with the FWHM area metric (Spearman’s rank correlation, P = 0.0001, rho = 0.841). At a device rate of 100 bpm, the minimum FWHM area was 2.00 mm2 using a single-sector algorithm, 1.41 mm2 using a 2-segment algorithm, and 1.37 mm2 using a 4-segment algorithm. ConclusionsUse of a pulsatile cardiac assist device could serve as an in vitro test bed for cardiac CT imaging methods. Area FWHM of the PSF correlates well with visually determined image quality of a dynamic phantom, but provides better reproducibility than visual analysis.

Cardiac-state-driven CT image reconstruction algorithm for cardiac imaging

Multi-slice CT scanners use EKG gating to predict the cardiac phase during slice reconstruction from projection data. Cardiac phase is generally defined with respect to the RR interval. The implicit assumption made is that the duration of events in a RR interval scales linearly when the heart rate changes. Using a more detailed EKG analysis, we evaluate the impact of relaxing this assumption on image quality. We developed a reconstruction algorithm that analyzes the associated EKG waveform to extract the natural cardiac states. A wavelet transform was used to decompose each RR-interval into P, QRS, and T waves. Subsequently, cardiac phase was defined with respect to these waves instead of a percentage or time delay from the beginning or the end of RR intervals. The projection data was then tagged with the cardiac phase and processed using temporal weights that are function of their cardiac phases. Finally, the tagged projection data were combined from multiple cardiac cycles using a multi-sector algorithm to reconstruct images. The new algorithm was applied to clinical data, collected on a 4-slice (GE LightSpeed Qx/i) and 8-slice CT scanner (GE LightSpeed Plus), with heart rates of 40 to 80 bpm. The quality of reconstruction is assessed by the visualization of the major arteries, e.g. RCA, LAD, LC in the reformat 3D images. Preliminary results indicate that Cardiac State Driven reconstruction algorithm offers better image quality than their RR-based counterparts.

Novel reconstruction algorithm for multiphasic cardiac imaging using multislice helical CT

Cardiac imaging is still a challenge to CT reconstruction algorithms due to the dynamic nature of the heart. We have developed a new reconstruction technique, called the Flexible Algorithm, which achieves high temporal resolution while it is robust to heart-rate variations. The Flexible Algorithm, first, retrospectively tags helical CT views with corresponding cardiac phases obtained from associated EKG. Next, it determines a set of views for each slice, a stack of which covers the entire heart. Subsequently, the algorithm selects an optimum subset of views to achieve the highest temporal resolution for the desired cardiac phase. Finally, it spatiotemporally filters the views in the selected subsets to reconstruct slices. We tested the performance of our algorithm using both a dynamic analytical phantom and clinical data. Preliminary results indicate that the Flexible Algorithm obtains improved spatiotemporal resolution for a large range of heart rates and variations than standard algorithms do. By providing improved image quality at any desired cardiac phase, and robustness to heart rate variations, the Flexible Algorithm enables cardiac applications in CT, including those that benefit from multiphase information.

Four-dimensional x-ray attenuation model of the human heart and the coronary vasculature and its use for the assessment of cardiac CT imaging technology

Using helical, multi-detector computed tomography (CT) imaging technology operating at sub-second scanning speeds, clinicians are investigating the capabilities of CT for cardiac imaging. In this paper, we describe the application of novel modeling tools to assess CT system capability. These tools allow us to quantify the capabilities of both hardware and software algorithms for cardiac imaging. The model consists of a human thorax, a dynamic model of a human heart, and a complete physics-based, CT system model. The use of the model to predict image quality is demonstrated by varying both the reconstruction algorithm (half-scan, sector-based) and CT system parameters (axial detector resolution). The mathematical tools described provide a means to rapidly evaluate new reconstruction algorithms and CT system designs for cardiac imaging.

Four-dimensional x-ray attenuation model of the human heart and the coronary vasculature for assessment of CT system capability

With the introduction of helical, multi-detector computed tomography (CT) scanners having sub-second scanning speeds, clinicians are currently investigating the role of CT in cardiac imaging. In this paper, we describe a four-dimensional (4D) x-ray attenuation model of a human heart and the use of this model to assess the capabilities of both hardware and software algorithms for cardiac imaging. We developed a model of the human thorax, composed of several analytical structures, and a model of the human heart, constructed from several elliptical surfaces. A model for each coronary vessel consists of a torus placed at a suitable location on the hearts surface. The motion of the heart during the cardiac cycle was implemented by applying transformational operators to each surface composing the heart. We used the 4D model of the heart to generate forward projection data, which then became input into a model of a CT imaging system. The use of the model to predict image quality is demonstrated by varying both the reconstruction algorithm (sector-based, half-scan) and CT system parameters (gantry speed, spatial resolution). The mathematical model of the human heart, while having limitations, provides a means to rapidly evaluate new reconstruction algorithms and CT system designs for cardiac imaging.