Jan Boese

Towards cardiac C-arm computed tomography

Cardiac interventional procedures would benefit tremendously from sophisticated three-dimensional image guidance. Such procedures are typically performed with C-arm angiography systems, and tomographic imaging is currently available only by using preprocedural computed tomography (CT) or magnetic resonance imaging (MRI) scans. Recent developments in C-arm CT (Angiographic CT) allow three-dimensional (3-D) imaging of low contrast details with angiography imaging systems for noncardiac applications. We propose a new approach for cardiac imaging that takes advantage of this improved contrast resolution and is based on intravenous contrast injection. The method is an analogue to multisegment reconstruction in cardiac CT adapted to the much slower rotational speed of C-arm CT. Motion of the heart is considered in the reconstruction process by retrospective electrocardiogram (ECG)-gating, using only projections acquired at a similar heart phase. A series of N almost identical rotational acquisitions is performed at different heart phases to obtain a complete data set at a minimum temporal resolution of 1/N of the heart cycle time. First results in simulation, using an experimental phantom, and in preclinical in vivo studies showed that excellent image quality can be achieved

Weighted FBP-a simple approximate 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch

A new 3D reconstruction scheme, weighted filtered backprojection (WFBP) for multirow spiral CT based on an extension of the two-dimensional SMPR algorithm is described and results are presented. In contrast to other 3D algorithms available, the algorithm makes use of all available data for all pitch values. The algorithm is a FBP algorithm: linear convolution of the parallel data along the row direction followed by a 3D backprojection. Data usage for arbitrary pitch values is maintained through a weighting scheme which takes into account redundant data. If proper row weighting is applied, the image quality is superior to the image quality of the SMPR algorithm.

Estimation of aortic compliance using magnetic resonance pulse wave velocity measurement

A method for compliance estimation employing magnetic resonance pulse wave velocity measurement is presented. Time-resolved flow waves are recorded at several positions along the vessel using a phase contrast sequence, and pulse wave velocity is calculated from the delay of the wave onsets. Using retrospective cardiac gating in combination with an optically decoupled electrocardiogram acquisition, a high temporal resolution of 3 ms can be achieved. A phantom set-up for the simulation of pulsatile flow in a compliant vessel is described. In the phantom, relative errors of pulse wave velocity estimation were found to be about 15%, whereas in a volunteer, larger errors were found that might be caused by vessel branches. Results of pulse wave velocity estimation agree with direct aortic distension measurements which rely on a peripheral estimate of aortic pressure and are therefore less accurate. Studies in 12 volunteers show values of pulse wave velocity consistent with the literature; in particular the well-known increase in pulse wave velocity with age was observed. Preliminary results show that the method can be applied to aortic aneurysms.

Automatic aorta segmentation and valve landmark detection in C-Arm CT: application to aortic valve implantation

C-arm CT is an emerging imaging technique in transcatheter aortic valve implantation (TAVI) surgery. Automatic aorta segmentation and valve landmark detection in a C-arm CT volume has important applications in TAVI by providing valuable 3D measurements for surgery planning. Overlaying 3D segmentation onto 2D real time fluoroscopic images also provides critical visual guidance during the surgery. In this paper, we present a part-based aorta segmentation approach, which can handle aorta structure variation in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three aortic hinge points, three commissure points, and two coronary ostia) are also detected automatically in our system. Under the guidance of the detected landmarks, the physicians can deploy the prosthetic valve properly. Our approach is robust under variations of contrast agent. Taking about 1.4 seconds to process one volume, it is also computationally efficient.

3D Imaging with Flat-Detector C-Arm Systems

Three-dimensional (3D) C-arm computed tomography is a new and innovative imaging technique. It uses two-dimensional (2D) X-ray projections acquired with a flat-panel detector C-arm angiography system to generate CT-like images. To this end, the C-arm system performs a sweep around the patient, acquiring up to several hundred 2D views. They serve as input for 3D cone-beam reconstruction. Resulting voxel data sets can be visualized either as cross-sectional images or as 3D data sets using different volume rendering techniques. Initially targeted at 3D high-contrast neurovascular applications, 3D C-arm imaging has been continuously improved over the years and is now capable of providing CT-like soft-tissue image quality. In combination with 2D fluoroscopic or radiographic imaging, information provided by 3D C-arm imaging can be valuable for therapy planning, guidance, and outcome assessment all in the interventional suite.

Automatic Aorta Segmentation and Valve Landmark Detection in C-Arm CT for Transcatheter Aortic Valve Implantation

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm computed tomography (CT) plays a more and more important role in TAVI on both pre-operative surgical planning (e.g., providing 3-D valve measurements) and intra-operative guidance (e.g., determining a proper C-arm angulation). Automatic aorta segmentation and aortic valve landmark detection in a C-arm CT volume facilitate the seamless integration of C-arm CT into the TAVI workflow and improve the patient care. In this paper, we present a part-based aorta segmentation approach, which can handle structural variation of the aorta in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three hinges, three commissures, and two coronary ostia) are also detected automatically with an efficient hierarchical approach. Our approach is robust under all kinds of variations observed in a real clinical setting, including changes in the field-of-view, contrast agent injection, scan timing, and aortic valve regurgitation. Taking about 1.1 s to process a volume, it is also computationally efficient. Under the guidance of the automatically extracted patient-specific aorta model, the physicians can properly determine the C-arm angulation and deploy the prosthetic valve. Promising outcomes have been achieved in real clinical applications.

System to guide transcatheter aortic valve implantations based on interventional c-arm CT imaging

Transcatheter aortic valve implantation is an emerging technique to be applied in patients with aortic valve defects. Angiographic and fluoroscopic X-ray imaging with a C-arm system is crucial in these minimally invasive procedures. We describe a prototypical system based on the ability to acquire a 3D C-arm CT image during transcatheter aortic valve implantations. It supports the physician in measuring critical anatomical parameters, finding an optimum C-arm angulation, and guiding the positioning and deployment of the prosthesis by 3D overlay with fluoroscopic images. To yield high acceptance by the physicians in the operating room, our approach is fast, fully integrated into an angiographic C-arm system, and designed to minimize the necessary user interaction. We evaluate the accuracy of our system on 20 clinical cases.

Interventional 4D motion estimation and reconstruction of cardiac vasculature without motion periodicity assumption

Anatomical and functional information of cardiac vasculature is a key component in the field of interventional cardiology. With the technology of C-arm CT it is possible to reconstruct static intraprocedural 3D images from angiographic projection data. Current approaches attempt to add the temporal dimension (4D). In the assumption of periodic heart motion, ECG-gating techniques can be used. However, arrhythmic heart signals and slight breathing motion are degrading image quality frequently. To overcome those problems, we present a reconstruction method based on a 4D time-continuous B-spline motion field. The temporal component of the motion field is parameterized by the acquisition time and does not assume a periodic heart motion. The analytic dynamic FDK-reconstruction formula is used directly for the motion estimation and image reconstruction. In a physical phantom experiment two vessels of size 3.1mm and 2.3mm were reconstructed using the proposed method and an algorithm with periodicity assumption. For a periodic motion both methods obtained an error of 0.1mm. For a non-periodic motion the proposed method was superior, obtaining an error of 0.3mm/0.2mm in comparison to 1.2mm/1.0mm for the algorithm with periodicity assumption. For a clinical test case of a left coronary artery it could be further shown that the method is capable to produce diameter measurements with an absolute error of 0.1mm compared to state-of-the-art measurement tools from orthogonal coronary angiography. Further, it is shown for three different clinical cases (left/right coronary artery, coronary sinus) that the proposed method is able to handle a large variability of vascular structures and motion patterns. The complete algorithm is hardware-accelerated using the GPU requiring a computation time of less than 3min for typical clinical scenarios.

Determination of aortic compliance from magnetic resonance images using an automatic active contour model.

The possibility of monitoring changes in aortic elasticity in humans has important applications for clinical trials because it estimates the efficacy of plaque-reducing therapies. The elasticity is usually quantified by compliance measurements. Therefore, the relative temporal change in the vessel cross-sectional area throughout the cardiac cycle has to be determined. In this work we determined and compared the compliance between three magnetic resonance (MR) methods (FLASH, TrueFISP and pulse-wave). Since manual outlining of the aortic wall area is a very time-consuming process and depends on an operators variability, an algorithm for the automatic segmentation of the artery wall from MR images through the entire heart cycle is presented. The reliable detection of the artery cross-sectional area over the whole heart cycle was possible with a relative error of about 1%. Optimizing the temporal resolution to 60 ms we obtained a relative error in compliance of about 7% from TrueFISP (1.0 x 1.0 x 10 mm3, signal-to-noise ratio (SNR) > 12) and FLASH (0.7 x 0.7 x 10 mm3, SNR > 12) measurements in volunteers. Pulse-wave measurements yielded an error of more than 9%. In a study of ten volunteers, a compliance between C = 3 x 10(-5) Pa(-1) and C = 8 x 10(-5) Pa(-1) was determined, depending on age. The results of the TrueFISP and the pulse-wave measurements agreed very well with one another (confidence interval of 1 x 10(-5) Pa(-1)) while the results of the FLASH method more clearly deviated from the TrueFISP and pulse-wave (confidence interval of more than 2 x 10(-5) Pa(-1)).

Interventional 4-D C-Arm CT Perfusion Imaging Using Interleaved Scanning and Partial Reconstruction Interpolation

Tissue perfusion measurement during catheter-guided stroke treatment in the interventional suite is currently not possible. In this work, we present a novel approach that uses a C-arm angiography system capable of computed tomography (CT)-like imaging (C-arm CT) for this purpose. With C-arm CT one reconstructed volume can be obtained every 4-6 s which makes it challenging to measure the flow of an injected contrast bolus. We have developed an interleaved scanning (IS) protocol that uses several scan sequences to increase temporal sampling. Using a dedicated 4-D reconstruction approach based on partial reconstruction interpolation (PRI) we can optimally process our data. We evaluated our combined approach (IS-PRI) with simulations and a study in five healthy pigs. In our simulations, the cerebral blood flow values (unit: ml/100 g/min) were 60 (healthy tissue) and 20 (pathological tissue). For one scan sequence the values were estimated with standard deviations of 14.3 and 2.9, respectively. For two interleaved sequences the standard deviations decreased to 3.6 and 1.5, respectively. We used perfusion CT to validate the in vivo results. With two interleaved sequences we achieved promising correlations ranging from r=0.63 to r=0.94. The results suggest that C-arm CT tissue perfusion imaging is feasible with two interleaved scan sequences.