Motion compensation for cone-beam CT using Fourier consistency conditions.

In cone-beam CT, involuntary patient motion and inaccurate or irreproducible scanner motion substantially degrades image quality. To avoid artifacts this motion needs to be estimated and compensated during image reconstruction. In previous work we showed that Fourier consistency conditions (FCC) can be used in fan-beam CT to estimate motion in the sinogram domain. This work extends the FCC to [Formula: see text] cone-beam CT. We derive an efficient cost function to compensate for [Formula: see text] motion using [Formula: see text] detector translations. The extended FCC method have been tested with five translational motion patterns, using a challenging numerical phantom. We evaluated the root-mean-square-error and the structural-similarity-index between motion corrected and motion-free reconstructions. Additionally, we computed the mean-absolute-difference (MAD) between the estimated and the ground-truth motion. The practical applicability of the method is demonstrated by application to respiratory motion estimation in rotational angiography, but also to motion correction for weight-bearing imaging of knees. Where the latter makes use of a specifically modified FCC version which is robust to axial truncation. The results show a great reduction of motion artifacts. Accurate estimation results were achieved with a maximum MAD value of 708 µm and 1184 µm for motion along the vertical and horizontal detector direction, respectively. The image quality of reconstructions obtained with the proposed method is close to that of motion corrected reconstructions based on the ground-truth motion. Simulations using noise-free and noisy data demonstrate that FCC are robust to noise. Even high-frequency motion was accurately estimated leading to a considerable reduction of streaking artifacts. The method is purely image-based and therefore independent of any auxiliary data.

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling.

Time-resolved tomographic cardiac imaging using an angiographic C-arm device may support clinicians during minimally invasive therapy by enabling a thorough analysis of the heart function directly in the catheter laboratory. However, clinically feasible acquisition protocols entail a highly challenging reconstruction problem which suffers from sparse angular sampling of the trajectory. Compressed sensing theory promises that useful images can be recovered despite massive undersampling by means of sparsity-based regularization. For a multitude of reasons-most notably the desired reduction of scan time, dose and contrast agent required-it is of great interest to know just how little data is actually sufficient for a certain task. In this work, we apply a convex optimization approach based on primal-dual splitting to 4D cardiac C-arm computed tomography. We examine how the quality of spatially and temporally total-variation-regularized reconstruction degrades when using as few as [Formula: see text] projection views per heart phase. First, feasible regularization weights are determined in a numerical phantom study, demonstrating the individual benefits of both regularizers. Secondly, a task-based evaluation is performed in eight clinical patients. Semi-automatic segmentation-based volume measurements of the left ventricular blood pool performed on strongly undersampled images show a correlation of close to 99% with measurements obtained from less sparsely sampled data.

Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT.

PURPOSE: Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. METHODS: A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anterior-posterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. RESULTS: The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. CONCLUSIONS: The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.

Quantitative SPECT/CT Imaging of (177)Lu with In Vivo Validation in Patients Undergoing Peptide Receptor Radionuclide Therapy.

PURPOSE: The purpose of this study is to extend an established SPECT/CT quantitation protocol to (177)Lu and validate it in vivo using urine samples, thus providing a basis for 3D dosimetry of (177)Lu radiotherapy and improvement over current planar methods which improperly account for anatomical variations, attenuation, and overlapping organs. PROCEDURES: In our quantitation protocol, counts in images reconstructed using an ordered subset-expectation maximization algorithm are converted to kilobecquerels per milliliter using a calibration factor derived from a phantom experiment. While varying reconstruction parameters, we tracked the ratio of image to true activity concentration (recovery coefficient, RC) in hot spheres and a noise measure in a homogeneous region. The optimal parameter set was selected as the point where recovery in the largest three spheres (16, 8, and 4 ml) stagnated, while the noise continued to increase. Urine samples were collected following 12 SPECT/CT acquisitions of patients undergoing [(177)Lu]DOTATATE therapy, and activity concentrations were measured in a well counter. Data was reconstructed using parameters chosen in the phantom experiment, and estimated activity concentration from the images was compared to the urine values to derive RCs. RESULTS: In phantom data, our chosen parameter set yielded RCs in 16, 8, and 4 ml spheres of 80.0, 74.1, and 64.5 %, respectively. For patients, the mean bladder RC was 96.1 ± 13.2% (range, 80.6-122.4 %), with a 95 % confidence interval between 88.6 and 103.6 %. The mean error of SPECT/CT concentrations was 10.1 ± 8.3% (range, -19.4-22.4 %). CONCLUSIONS: Our results show that quantitative (177)Lu SPECT/CT in vivo is feasible but could benefit from improved reconstruction methods. Quantifying bladder activity is analogous to determining the amount of activity in the kidneys, an important task in dosimetry, and our results provide a useful benchmark for future efforts.

CoroEval: a multi-platform, multi-modality tool for the evaluation of 3D coronary vessel reconstructions.

We present a software, called CoroEval, for the evaluation of 3D coronary vessel reconstructions from clinical data. It runs on multiple operating systems and is designed to be independent of the imaging modality used. At this point, its purpose is the comparison of reconstruction algorithms or acquisition protocols, not the clinical diagnosis. Implemented metrics are vessel sharpness and diameter. All measurements are taken from the raw intensity data to be independent of display windowing functions. The user can either import a vessel centreline segmentation from other software, or perform a manual segmentation in CoroEval. An automated segmentation correction algorithm is provided to improve non-perfect centrelines. With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 ± 0.03 mm was found. The software is available in binary and source code form at http://www5.cs.fau.de/CoroEval/.

Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction.

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper.

Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT.

The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3D/3D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. In conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm.

Influence of the phase effect on gradient-based and statistics-based focus measures in bright field microscopy.

Autofocusing is essential to high throughput microscopy and live cell imaging and requires reliable focus measures. Phase objects such as separated single Chinese hamster ovary cells are almost invisible at the optical focus position in bright field microscopy images. Because of the phase effect, defocused images of phase objects have more contrast. In this paper, we show that widely used focus measures exhibit an untypical behaviour for such images. In the case of homogeneous cells, that is, when most cells tend to lie in the same focal plane, both gradient-based and statistics-based focus measures tend to have a local minimum instead of a global maximum at the optical focus position. On the other hand, if images show inhomogeneous cells, gradient-based focus measures tend to yield typical focus curves, whereas statistics-based focus measures deliver curves similar to the case of homogeneous cells. These results were interpreted using the equation describing the phase effect and patch-wise analysis of the focus curves. Bioprocess engineering experts are also influenced by the phase effect. Forty-four focus positions selected by them led to the conclusion that they prefer to look at defocused images instead of those at the optical focus.

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction.

Three-dimensional reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-the-art approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts. We present an algorithm to correct for residual motion. Our approach is based on a deformable 2D-2D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available CAVAREV platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the CAVAREV ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13 ± 6%. Vessel sharpness was improved by 25 ± 12% on average.

Robust vessel segmentation in fundus images.

One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

In vitro evaluation of the imaging accuracy of C-arm conebeam CT in cerebral perfusion imaging.

PURPOSE: The authors have developed a method to enable cerebral perfusion CT imaging using C-arm based conebeam CT (CBCT). This allows intraprocedural monitoring of brain perfusion during treatment of stroke. Briefly, the technique consists of acquiring multiple scans (each scan comprised of six sweeps) acquired at different time delays with respect to the start of the x-ray contrast agent injection. The projections are then reconstructed into angular blocks and interpolated at desired time points. The authors have previously demonstrated its feasibility in vivo using an animal model. In this paper, the authors describe an in vitro technique to evaluate the accuracy of their method for measuring the relevant temporal signals. METHODS: The authors' evaluation method is based on the concept that any temporal signal can be represented by a Fourier series of weighted sinusoids. A sinusoidal phantom was developed by varying the concentration of iodine as successive steps of a sine wave. Each step corresponding to a different dilution of iodine contrast solution contained in partitions along a cylinder. By translating the phantom along the axis at different velocities, sinusoidal signals at different frequencies were generated. Using their image acquisition and reconstruction algorithm, these sinusoidal signals were imaged with a C-arm system and the 3D volumes were reconstructed. The average value in a slice was plotted as a function of time. The phantom was also imaged using a clinical CT system with 0.5 s rotation. C-arm CBCT results using 6, 3, 2, and 1 scan sequences were compared to those obtained using CT. Data were compared for linear velocities of the phantom ranging from 0.6 to 1 cm∕s. This covers the temporal frequencies up to 0.16 Hz corresponding to a frequency range within which 99% of the spectral energy for all temporal signals in cerebral perfusion imaging is contained. RESULTS: The errors in measurement of temporal frequencies are mostly below 2% for all multiscan sequences. For single scan sequences, the errors increase sharply beyond 0.10 Hz. The amplitude errors increase with frequency and with decrease in the number of scans used. CONCLUSIONS: Our multiscan perfusion CT approach allows low errors in signal frequency measurement. Increasing the number of scans reduces the amplitude errors. A two-scan sequence appears to offer the best compromise between accuracy and the associated total x-ray and iodine dose.

Toward automatic detection of vessel stenoses in cerebral 3D DSA volumes.

Vessel diseases are a very common reason for permanent organ damage, disability and death. This fact necessitates further research for extracting meaningful and reliable medical information from the 3D DSA volumes. Murray's law states that at each branch point of a lumen-based system, the sum of the minor branch diameters each raised to the power x, is equal to the main branch diameter raised to the power x. The principle of minimum work and other factors like the vessel type, impose typical values for the junction exponent x. Therefore, deviations from these typical values may signal pathological cases. In this paper, we state the necessary and the sufficient conditions for the existence and the uniqueness of the solution for x. The second contribution is a scale- and orientation- independent set of features for stenosis classification. A support vector machine classifier was trained in the space of these features. Only one branch was misclassified in a cross validation on 23 branches. The two contributions fit into a pipeline for the automatic detection of the cerebral vessel stenoses.

[Integrity/demyelination of the optic radiation, morphology of the papilla, and contrast sensitivity in glaucoma patients]. / Integrität/Demyelinisierung der Radiatio optica, Morphologie der Papille und Kontrastsensitivität bei Glaukompatienten.

BACKGROUND: In primary open angle glaucoma (POAG) and its non-barotraumatic subgroup, normal tension glaucoma (NTG), the pathophysiological differences are not clear. A participation of the 4th neuron of the visual pathway (optic radiation) appears possible on the basis of related experimental studies. The goal of the present study was the evaluation of the optic radiation by diffusion tensor imaging (DTI), which is based on the magnetic resonance imaging. The diffusion and anisotropy parameters of the optic radiation as a marker of axonal integrity and demyelination/damage of glial cells, respectively, were used to investigate the relation between the morphology of the papilla (BLDF, linear discriminant function of Burk) and the contrast sensitivity (FDT, frequency doubling test). PATIENTS AND METHODS: In this prospective observational study 13 POAG patients, 13 NTG patients, and 7 control patients of the same mean age were included. For segmentation of the optic radiation a semi-automated algorithm was applied and the diffusion and anisotropy parameters were calculated. The importance of the covariates age, BLDF, and FDT for the DTI parameters was determined using partial correlation analysis. RESULTS: Analysis of the covariates partially showed a clear autocorrelation. The correlations between the DTI parameters and BLDF were significant in all groups after correction of the measurement values for the covariates. FDT correlated with DTI parameters in controls and POAG. The NTG group did not show this correlation due to a strong spreading of the FDT values. CONCLUSION: After statistical elimination of the autocorrelation of the covariates age, BLDF, and FDT the morphology of the papilla correlated with the axonal integrity and demyelination/glia cell impairment of the optic radiation in controls and glaucoma. In NTG the impaired contrast sensitivity is highly variable and is not associated with the condition of the 3rd or 4th neuron, respectively, as compared to POAG. The autocorrelation between individual covariates represents an important element for the judgement of the visual pathway.

A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data.

Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.

[Mobile biosensor-based gait analysis: a diagnostic and therapeutic tool in Parkinson's disease]. / Mobile biometrische Ganganalyse : Potenzial für Diagnose und Therapiemonitoring beim Parkinson-Syndrom.

Parkinson's disease (PD) is characterized by progressive motor and non-motor symptoms, leading to distinct diagnostic and therapeutic challenges in all stages of the disease. This study investigated a mobile biosensor-based gait analysis system for patients in early and intermediate stages of PD compared to controls. Subjects wearing a motion sensor-equipped shoe performed a standardized gait exercise. Accelerometer- and gyroscope-based signals were analysed using a complex set of pattern recognition algorithms. The analysis was able (1) to distinguish between PD patients and controls, (2) to identify patients at an early stage of the disease and (3) to distinguish between early and intermediate stage patients. Thus, using this mobile biosensor-based analysis system we were able to obtain objective classifications of gait characteristics in PD. Future studies will show that mobile biosensor-based movement detection technology will support identification of early PD stages and allow objective characterization of motor fluctuations in advanced stages of the disease. This will provide an important and supportive tool for patients, caregivers and therapists.

Anatomical accuracy of abdominal lesion localization. Retrospective automatic rigid image registration between FDG-PET and MRI.

UNLABELLED: Software-based image registration can improve the diagnostic value of imaging procedures and is an alternative to hybrid scanners. The aim of this study was to evaluate the anatomical accuracy of automatic rigid image registration of independently acquired datasets of positron emission tomography with 18F-deoxyglucose and abdominal magnetic resonance imaging. PATIENTS, METHODS: Analyses were performed on 28 abdominal lesions from 20 patients. The PET data were obtained using a stand-alone PET camera in 14 cases and a hybrid PET/CT scanner in 9 cases. The abdominal T1- and T2-weighted MRI scans were acquired on 1.5 T MRI scanners. The mean time interval between MRI and PET was 7.3 days (0-28 days). Automatic rigid registration was carried out using a self-developed registration tool integrated into commercial available software (InSpace for Siemens Syngo). Distances between the centres of gravity of 28 manually delineated neoplastic lesions represented in PET and MRI were measured in X-, Y-, and Z-direction. The intra- (intraclass correlation 0.94) and inter- (intraclass correlation 0.86) observer repeatability were high. RESULTS: The average distance in all MRI sequences was 5.2±7.6 mm in X-direction, 4.0±3.7 mm in Y-direction and 6.1±5.1 mm in Z-direction. There was a significantly higher misalignment in Z-direction (p<0.05). The misalignment was not significantly different for the registration of T1- and T2- weighted sequences (p=0.7). CONCLUSION: The misalignment between FDG-PET and abdominal MRI registered using an automated rigid registration tool was comparable to data reported for software-based fusion between PET and CT. Although this imprecision may not affect diagnostic accuracy, it is not sufficient to allow for pixel-wise integration of MRI and PET information.

Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms.

X-ray-based 2D digital subtraction angiography (DSA) plays a major role in the diagnosis, treatment planning and assessment of cerebrovascular disease, i.e. aneurysms, arteriovenous malformations and intracranial stenosis. DSA information is increasingly used for secondary image post-processing such as vessel segmentation, registration and comparison to hemodynamic calculation using computational fluid dynamics. Depending on the amount of injected contrast agent and the duration of injection, these DSA series may not exhibit one single DSA image showing the entire vessel tree. The interesting information for these algorithms, however, is usually depicted within a few images. If these images would be combined into one image the complexity of segmentation or registration methods using DSA series would drastically decrease. In this paper, we propose a novel method automatically splitting a DSA series into three parts, i.e. mask, arterial and parenchymal phase, to provide one final image showing all important vessels with less noise and moving artifacts. This final image covers all arterial phase images, either by image summation or by taking the minimum intensities. The phase classification is done by a two-step approach. The mask/arterial phase border is determined by a Perceptron-based method trained from a set of DSA series. The arterial/parenchymal phase border is specified by a threshold-based method. The evaluation of the proposed method is two-sided: (1) comparison between automatic and medical expert-based phase selection and (2) the quality of the final image is measured by gradient magnitudes inside the vessels and signal-to-noise (SNR) outside. Experimental results show a match between expert and automatic phase separation of 93%/50% and an average SNR increase of up to 182% compared to summing up the entire series.

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.

Respiratory motion compensation by model-based catheter tracking during EP procedures.

In many cases, radio-frequency catheter ablation of the pulmonary veins attached to the left atrium still involves fluoroscopic image guidance. Two-dimensional X-ray navigation may also take advantage of overlay images derived from static pre-operative 3D volumetric data to add anatomical details otherwise not visible under X-ray. Unfortunately, respiratory motion may impair the utility of static overlay images for catheter navigation. We developed a novel approach for image-based 3D motion estimation and compensation as a solution to this problem. It is based on 3D catheter tracking which, in turn, relies on 2D/3D registration. To this end, a bi-plane C-arm system is used to take X-ray images of a special circumferential mapping catheter from two directions. In the first step of the method, a 3D model of the device is reconstructed. Three-dimensional respiratory motion at the site of ablation is then estimated by tracking the reconstructed catheter model in 3D based on bi-plane fluoroscopy. Phantom data and clinical data were used to assess model-based catheter tracking. Our phantom experiments yielded an average 2D tracking error of 1.4mm and an average 3D tracking error of 1.1mm. Our evaluation of clinical data sets comprised 469 bi-plane fluoroscopy frames (938 monoplane fluoroscopy frames). We observed an average 2D tracking error of 1.0 + or - 0.4mm and an average 3D tracking error of 0.8 + or - 0.5mm. These results demonstrate that model-based motion-compensation based on 2D/3D registration is both feasible and accurate.

Technical note: RabbitCT--an open platform for benchmarking 3D cone-beam reconstruction algorithms.

PURPOSE: Fast 3D cone beam reconstruction is mandatory for many clinical workflows. For that reason, researchers and industry work hard on hardware-optimized 3D reconstruction. Backprojection is a major component of many reconstruction algorithms that require a projection of each voxel onto the projection data, including data interpolation, before updating the voxel value. This step is the bottleneck of most reconstruction algorithms and the focus of optimization in recent publications. A crucial limitation, however, of these publications is that the presented results are not comparable to each other. This is mainly due to variations in data acquisitions, preprocessing, and chosen geometries and the lack of a common publicly available test dataset. The authors provide such a standardized dataset that allows for substantial comparison of hardware accelerated backprojection methods. METHODS: They developed an open platform RabbitCT (www.rabbitCT.com) for worldwide comparison in backprojection performance and ranking on different architectures using a specific high resolution C-arm CT dataset of a rabbit. This includes a sophisticated benchmark interface, a prototype implementation in C++, and image quality measures. RESULTS: At the time of writing, six backprojection implementations are already listed on the website. Optimizations include multithreading using Intel threading building blocks and OpenMP, vectorization using SSE, and computation on the GPU using CUDA 2.0. CONCLUSIONS: There is a need for objectively comparing backprojection implementations for reconstruction algorithms. RabbitCT aims to provide a solution to this problem by offering an open platform with fair chances for all participants. The authors are looking forward to a growing community and await feedback regarding future evaluations of novel software- and hardware-based acceleration schemes.