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In radiography and fluoroscopy, the dose-area product (DAP) is used for dose documentation and the evaluation, whether the applied dose is too high, adequate or too low. In dose management systems (applied in fluoroscopy and radiography) a mean value of the DAP of a number of consecutive examinations is calculated and compared to the diagnostic reference levels of the different examination types. This shows, if on average the dose level is too high. However, on an individual this would not work. To achieve a radiograph of adequate image quality the required DAP for a slender patient is significantly lower than for a standard patient and vice versa for obese patients. Thereby, without knowledge about patient thickness, there is no way to judge, if the dose level for an individual would be appropriate. To overcome this problem, an estimate of the patient size was calculated from information of the dicom header of the images. By extracting the dose at the detector, the DAP, exam type, information about the beam quality of the used radiation (spectrum) and the exposed area of the detector an estimate of the water equivalent patient thickness can be determined. Monte Carlo simulations and measurements with varying thicknesses of a water phantom were in excellent agreement. The accuracy of the estimate was better than 1 cm. Further clinical experiments with patients undergoing an examination of the lumbar spine showed, that an accuracy better than 20% and a standard derivation of 10% is achievable. Therefore an automatic estimate of the patient thickness in fluoroscopy and radioscopy is feasible and facilitates a computer-based judgement if the dose for an individual patient is adequate.

In particle therapy sub-millimeter sized heterogeneities like lung tissue cause a Bragg peak degradation, which should be considered in treatment planning to ensure an optimal dose distribution in tumor tissue. To determine the magnitude of this degradation extensive experiments could be carried out. More convenient and reproducible is the use of our mathematical model to describe the degradation properties of lung tissue and to design 3D-printable substitutes based on high-resolution CT images of human lung samples. High-resolution CT images of human lung samples (resolution: 4 μm) were used to create binary cubic datasets with voxels corresponding to either air or lung tissue. The number of tissue voxels is calculated along the z-axis for every lateral position. This represents the “tissue length” for all particle paths through the dataset of a parallel beam. The square based lung substitute is divided into columns with different heights corresponding to the occurring tissue lengths. The columns lateral extend complies with the quantity of the corresponding tissue lengths present in the dataset. The lung substitutes were validated by Monte Carlo simulations with the Monte Carlo toolkit TOPAS. The Monte Carlo simulations proved that the depth dose distributions and hence the Bragg peak degradations of the lung substitutes mimics the degradation of the corresponding lung tissue sample.

Flat panel detectors have become the standard technology in projection radiography. Further progress in detector technology will result in an improvement of MTF and DQE. The new detector (DX-D45C; Agfa; Mortsel/Belgium) is based on cesium-iodine crystals and has a change in the detector material and the readout electronics. The detector has a size of 30 cm x 24 cm and a pixel matrix of 2560 x 2048 with a pixel pitch of 124 μm. The system includes an automatic exposure detector, which enables the use of the detector without a connection to the x-ray generator. The physical properties of the detector were determined following IEC 62220-1-1 in a laboratory setting. The MTF showed an improvement compared to the previous version of cesium-iodine based flat-panel detectors. Thereby the DQE is also improved especially for the higher frequencies. The new detector showed an improvement in the physical properties compared to the previous versions. This enables a potential for further dose reductions in clinical imaging.

In CT, the magnitude of enhancement is proportional to the amount of contrast medium (CM) injected. However, high doses of iodinated CM pose health risks, ranging from mild side effects to serious complications such as contrast-induced nephropathy (CIN). This work presents a method that enables the reduction of CM dosage, without affecting the diagnostic image quality. The technique proposed takes advantage of the additional spectral information provided by photon-counting CT systems. In the first step, we apply a material decomposition technique on the projection data to discriminate iodine from other materials. Then, we estimated the noise of the decomposed image by calculating the Cramer-Rao lower bound of the parameter estimator. Next, we iteratively reconstruct the iodine-only image by using the decomposed image and the estimation of noise as an input into a maximum-likelihood iterative reconstruction algorithm. Finally, we combine the iodine-only image with the original image to enhance the contrast of low iodine concentrations. The resulting reconstructions show a notably improved contrast in the final images. Quantitatively, the combined image has a significantly improved CNR, while the measured concentrations are closer to the actual concentrations of the iodine. The preliminary results from our technique show the possibility of reducing the clinical dosage of iodine, without affecting the diagnostic image quality.

Photon-counting detectors (PCD) not only have the advantage of providing spectral information but also offer high quantum efficiencies, producing high image quality in combination with a minimal amount of radiation dose. Due to the clinical unavailability of photon-counting CT, the need to evaluate different CT simulation tools for researching different applications for photon-counting systems is essential. In this work, we investigate two different methods to simulate PCD data: Monte-Carlo based simulation (MCS) and analytical based simulation (AS). The MCS is a general-purpose photon transport simulation based on EGSnrc C++ class library. The AS uses analytical forward-projection in combination with additional acquisition parameters. MCS takes into account all physical effects, but is computationally expensive (several days per CT acquisition). AS is fast (several minutes), but lacks the accurateness of MCS with regard to physical interactions. To evaluate both techniques an entrance spectra of 100kvp, a modified CTP515 module of the CatPhan 600 phantom, and a detector system with six thresholds was simulated. For evaluation the simulated projection data are decomposed via a maximum likelihood technique, and reconstructed via standard filtered-back projection (FBP). Image quality from both methods is subjectively and objectively assessed. Visually, the difference in the image quality was not significant. When further evaluated, the relative difference was below 4%. As a conclusion, both techniques offer different advantages, while at different stages of development the accelerated calculations via AS can make a significant difference. For the future one could foresee a combined method to join accuracy and speed.

Flat panel detectors have become the standard technology in projection radiography. Further progress in detector technology will result in an improvement of MTF and DQE. The new detector (FDR D-Evo plus C24i, Fuji, Japan) is based on cesium-iodine crystals and has a change in the detector layout. The read-out electrodes are moved to the irradiated side of the detector. The physical properties of the detector were determined following IEC 62220-1-1 as close as possible. The MTF showed a significant improvement compared to other cesium-iodine based flat-panel detectors. Thereby the DQE is improved to other cesium-iodine based detectors especially for the higher frequencies. The average distance between the point of interaction of the x-rays in the detector and the light collector is shorter, due to the exponential absorption law in the detector. Thereby there is a reduction in light scatter and light absorption in the cesium-iodine needle crystals. This might explain the improvement of the MTF and DQE results in our measurements. The new detector design results in an improvement in the physical properties of flat-panel detectors. This enables a potential for further dose reductions in clinical imaging.

The recent advancement in detector technology contributed towards the development of photon counting detectors with the ability to discriminate photons according to their energy on reaching the detector. This provides spectral information about the acquired object; thus, giving additional data on the type of material as well as its density. In this paper, we investigate possible reduction of dental artifacts in cone-beam CT (CBCT) via integration of spectral information into a penalized maximum log-likelihood algorithm. For this investigation we simulated (with Monte-Carlo CT simulator) a virtual jaw phantom, which replicates components of a real jaw such as soft-tissue, bone, teeth and gold crowns. A maximum-likelihood basis-component decomposition technique was used to calculate sinograms of the individual materials. The decomposition revealed the spatial as well as material density of the dental implant. This information was passed on as prior information into the penalized maximum log-likelihood algorithm. The resulting reconstructions showed significant reduced streaking artifacts. The overall image quality is improved such that the contrast-to-noise ratio increased compared to the conventional FBP reconstruction. In this work we presented a new algorithm that makes use of spectral information to provide a prior for a penalized maximum log-likelihood algorithm.

Flat panel cone-beam computed tomography (CBCT) is developing to the state-of-the-art technique in several medical disciplines such as dental and otorhinolaryngological imaging. Dental and otorhinolaryngological CBCT systems offer a variety of different field-of-view sizes from 6.0 to 17.0 cm. Standard phantoms are only designed for the use in multi-slices CT (MSCT) and there is no phantom which provides detail structures for all common characteristic values and Hounsfield calibration. In this study we present a new phantom specially designed for use with MSCT and CBCT systems providing detail structures for MTF, 3D MTF, NPS, SNR, geometric distortion and HU calibration. With this phantom you'll only need one acquisition for image quality investigation and assurance. Materials and methods: The phantom design is shown in figure 1. To investigate the practicability, the phantom was scanned using dedicated MSCT-scanners, 3D C-arms und digital volume tomographs. The acquired axial image stacks were analyzed using a dedicated computer program, which is provided as an ImageJ plugin. The MTF was compared to other methodologies such as a thin wire, a sphere or noise response [10, 13, 14]. The HU values were also computed using other common methods. Results: These results are similar to the results of others studies [10, 13, 14]. The method has proven to be stable and delivers comparable results to other methodologies such as using a thin wire. The NPS was calculated for all materials. Furthermore, CT numbers for all materials were computed and compared to the desired values. The measurement of geometric deformation has proven to be accurate. Conclusion: A unique feature of this phantom is to compute the geometric deformation of the 3D-volume image. This offers the chance to improve accuracy, e.g. in dental implant planning. Another convenient feature is that the phantom needs to be scanned only once with otorhinolaryngological volume tomographs to be fully displayed. It is shown, that this new phantom provides amazing potential for image quality assurance in MSCT as well as in CBCT. It contains detail structures for all characteristic image quality values. In combination with a software tool it is the silver bullet for image quality assurance.

We present a spectrally resolved confocal imaging approach to qualitatively asses the overall uptake and the penetration depth of fluorescent dyes into biological tissue. We use a confocal microscope with a spectral resolution of 5 nm to measure porcine skin tissue after performing a Franz-Diffusion experiment with a submicron emulsion enriched with the fluorescent dye Nile Red. The evaluation uses linear unmixing of the dye and the tissue autofluorescence spectra. The results are combined with a manual segmentation of the skin's epidermis and dermis layers to assess the penetration behavior additionally to the overall uptake. The diffusion experiments, performed for 3h and 24h, show a 3-fold increased dye uptake in the epidermis and dermis for the 24h samples. As the method is based on spectral information it does not face the problem of superimposed dye and tissue spectra and therefore is more precise compared to intensity based evaluation methods.

Fluorescent nanodiamonds (ND) provide advantageous properties as a fluorescent biomarker for in vitro and in vivo studies. The maximum fluorescence occurs around 700 nm, they do not show photobleaching or blinking and seem to be nontoxic. After a pretreatment with strong acid fluorescent ND can be functionalized and coupled to endotoxin. Endotoxin is a decay product of bacteria and causes strong immune reactions. Therefore endotoxin has to be removed for most applications. An effective removal procedure is membrane filtration. The endotoxin, coupled to fluorescent ND can be visualized by using confocal microscopy which allows the investigation of the separation mechanisms of the filtration process within the membranes.

When confocal depth stacks are taken, the collected signal (normally the fluorescence signal), decays dependent of the depth of the confocal slice in the turbid medium. This decay is caused by scattering and absorption of the exciting light and of the fluorescence light. As the attenuation parameters, i.e. scattering and absorption coefficients, are normally unknown when observing a new sample, a method is proposed to compensate for the attenuation of the involved light by correcting the fluorescence signal using the attenuation behavior of the sample measured directly on the spot where the fluorescence stack is taken. The method works without any a priori knowledge about the optical properties of the sample. Using this self-reference technique, a confocal fluorescence depth stack can be created where the signal intensity is not dependent on the scattering and absorption caused intensity decay. The proposed method is tested on fluorescent beads embedded in scattering and absorbing hydrogel phantoms.

Optical imaging (OI) is a relatively new method in detecting active inflammation of hand joints of patients suffering from rheumatoid arthritis (RA). With the high number of people affected by this disease especially in western countries, the availability of OI as an early diagnostic imaging method is clinically highly relevant. In this paper, we present a newly in-house developed OI analyzing tool and a clinical evaluation study. Our analyzing tool extends the capability of existing OI tools. We include many features in the tool, such as region-based image analysis, hyper perfusion curve analysis, and multi-modality image fusion to aid clinicians in localizing and determining the intensity of inflammation in joints. Additionally, image data management options, such as the full integration of PACS/RIS, are included. In our clinical study we demonstrate how OI facilitates the detection of active inflammation in rheumatoid arthritis. The preliminary clinical results indicate a sensitivity of 43.5%, a specificity of 80.3%, an accuracy of 65.7%, a positive predictive value of 76.6%, and a negative predictive value of 64.9% in relation to clinical results from MRI. The accuracy of inflammation detection serves as evidence to the potential of OI as a useful imaging modality for early detection of active inflammation in patients with rheumatoid arthritis. With our in-house developed tool we extend the usefulness of OI imaging in the clinical arena. Overall, we show that OI is a fast, inexpensive, non-invasive and nonionizing yet highly sensitive and accurate imaging modality.

The use of Cone Beam Computed Tomography (CBCT) for in-room image guided interventions has gained more and more popularity over the last decade. In this study, we compared a low dose and a standard dose Multi Detector Computed Tomography (MDCT) protocol for abdominal imaging with a CBCT system in terms of image quality and radiation dose. Both systems used in this study are latest generation, so both offer high radiation dose efficiency. To determine the dose distribution of both systems, a Rando-Alderson-Phantom in combination with 41 thermoluminescence dosimeters (TLDs) were used. The equivalent dose for the whole body was calculated after ICRP. To determine the image quality of the reconstructed slices, the Catphan600 phantom was used. In terms of quality we determined the spatial resolution, contrast-to-noise ratio (CNR), and visual inspection. The dose could be reduced by 46.3% when using the low-dose MDCT protocol (120kV 50mAs) compared to the CBCT system (89kV 153mAs). CNR and image noise are improved for the MDCT, in some cases the CNR up to 74.4%. However, the spatial resolution of the CBCT system was superior, even after reconstructing the MDCT data with a small field-of-view and a relatively hard filter. Visually, the MDCT reconstructions are of higher diagnostic quality. In conclusion, the MDCT provides better dose efficiency in relation to the image quality. For example, in cases such as the chemoembolization, the CBCT system is more convenient because of the possibility to be used during interventions.

The aim of this project was to develop a skin phantom that resembles the epidermis including the lipid matrix of the stratum corneum and the dermis. The main intent was to achieve optical properties similar to skin tissue. Therefore, two compartments of the skin, dermis and epidermis, were examined regarding their optical properties. Based on these results, the skin phantom was designed using relevant skin components. The scattering coefficient was measured by using Reflectance-based Confocal Microscopy (RCM) and the fluorescence spectrum was detected via confocal laser-scanning microscopy (CLSM). Prospective, the skin phantom can be used to incorporate various fluorescing chemicals, such as fluorescent dyes and fluorescent-labeled drugs to perform calibration measurements in wide-field and laser-scanning microscopes to provide a basis for the quantification of skin penetration studies.

Skin penetration studies are an important part for the development of dermal drug carrier systems. As a novel approach a 7-tesla Magnetic Resonance Imaging (MRI) Scanner was used to obtain information about the penetration of agents into the skin. The main advantage of this method is, that the properties of the skin does not influence the signals. Compared to optical assessments the MRI method is not limited to imaging depth. Furthermore, it is possible to analyze fat and water components of the skin separately. The aim of this work was to evaluate, if this method is a promising analysis tool for the visualization of the transport of substances across the skin. Gadobutrol (Gadovist®1.0), respresenting a coventional contrast agent in MRI, was used as a model drug for the visualization of the skin penetration. These first promising results showed that Gadobutrol, incorporated in an oil-in-water emulsion, could be detected across the skin tissue compared to an aqueous solution. After 24 hours, the pixel intensity value was increased about 4-fold compared to an untreated tissue.

A new hardware device called Microemulsion Analyzer (MEA), which facilitates the preparation and evaluation of microemulsions, was developed. Microemulsions, consisting of three phases (oil, surfactant and water) and prepared on deep well plates according to the PDMPD method can be automatically evaluated by means of the optical properties. The ratio of ingredients to form a microemulsion strongly depends on the properties and the amounts of the used ingredients. A microemulsion assay is set up on deep well plates to determine these ratios. The optical properties of the ingredients change from turbid to transparent as soon as a microemulsion is formed. The MEA contains a frame and an imageprocessing and analysis algorithm. The frame itself consists of aluminum, an electro luminescent foil (ELF) and a camera. As the frame keeps the well plate at the correct position and angle, the ELF provides constant illumination of the plate from below. The camera provides an image that is processed by the algorithm to automatically evaluate the turbidity in the wells. Using the determined parameters, a phase diagram is created that visualizes the information. This build-up can be used to analyze microemulsion assays and to get results in a standardized way. In addition, it is possible to perform stability tests of the assay by creating special differential stability diagrams after a period of time.

A computer-aided detection (CAD) system for lung nodules in CT scans was developed. For the detection of lung nodules two different methods were applied and only pixels which were detected by both methods are marked as true positives. The first method uses a multi-threshold algorithm, which detect connected regions within the lung that have an intensity between specified threshold values. The second is a multi-scale detection method. The data are searched for points located in spherical objects. The image data were smoothed with a 3D Gaussian filter and computed the Hessian matrix and eigenvectors and eigenvalues for all pixels detected by the first algorithm. By analyzing the eigenvalues points that lie within a spherical structure can be located. For segmentation of the detected nodules an active contour model was used. A two-dimensional active contour with four energy terms describing form and position of the contour in the image data was implemented. In addition balloon energy to get the active contour was used growing out from one point. The result of our detection part is used as input for the segmentation part. To test the detection algorithms we used 19 CT volume data sets from a low-dose CT studies. Our CAD system detected 58% of the nodules with a falsepositive rate of 1.38. Additionally we take part at the ANODE09 study whose results will be presented at the SPIE meeting in 2009.

Computed tomography of the chest can be used as a screening method for lung cancer in a high-risk population. However, the detection of lung nodules is a difficult and time-consuming task for radiologists. The developed technique should improve the sensitivity of the detection of lung nodules without showing too many false positive nodules. In the first step the CAD technique for nodule detection in CT examinations of the lung eliminates all air outside the patient, then soft tissue and bony structures are removed. In the remaining lung fields a three-dimensional region detection is performed and rule-based analysis is used to detect possible lung nodules. In a study, which should evaluate the feasibility of screening lung cancer, about 2000 thoracic examinations were performed. The CAD system was used for reporting in a consecutive subset (n=100) of those studies. Computation time is about 5 min on an Silicon Graphics O2 workstation. Of the total number of found nodules >= 5 mm (n=68) 26 were found by the CAD scheme, 59 were detected by the radiologist. The CAD workstation helped the radiologist to identify 9 additional nodules. The false positive rate was less than 0.1 per image. The nodules missed by the CAD scheme were analyzed and the reasons for failure categorized into the density of the nodule is too low, nodules is connected to chest wall, segmentation error, and misclassification. Possible solutions for those problems are presented. We have developed a technique, which increased the detection rate of the radiologist in the detection of pulmonary nodules in CT exams of the chest. Correction of the CAD scheme using the analysis of the missed nodules will further enhance the performance of this method.

The purpose of the study was to evaluate a computer aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Two radiologists in consensus reported 88 consecutive spiral CT examinations. All examinations were reviewed using a UNIX-based CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm was designed to detect nodules with at least 5 mm diameter. The results of automatic nodule detection were compared to the consensus reporting of two radiologists as gold standard. Additional CAD findings were regarded as nodules initially missed by the radiologists or as false positive results. A total of 153 nodules were detected with all modalities (diameter: 85 nodules = 10 mm). Reasons for failure of automatic nodule detection were assessed. Sensitivity of radiologists for nodules >=5 mm was 85%, sensitivity of CAD was 38%. For nodules >=5 mm without pleural contact sensitivity was 84% for radiologists at 45% for CAD. CAD detected 15 (10%) nodules not mentioned in the radiologist's report but representing real nodules, among them 10 (15%) nodules with a diameter $GREW5 mm. Reasons for nodules missed by CAD include: exclusion because of morphological features during region analysis (33%), nodule density below the detection threshold (26%), pleural contact (33%), segmentation errors (5%) and other reasons (2%). CAD improves detection of pulmonary nodules at spiral CT significantly and is a valuable second opinion in a clinical setting for lung cancer screening. Optimization of region analysis and an appropriate density threshold have a potential for further improvement of automatic nodule detection.

We evaluated the practical application of a Computer-Aided Diagnosis (CAD) system for viewing spiral computed tomography (CT) of the chest low-dose screening examinations which includes an automatic detection of pulmonary nodules. A UNIX- based CAD system was developed including a detection algorithm for pulmonary nodules and a user interface providing an original axial image, the same image with nodules highlighted, a thin-slab MIP, and a cine mode. As yet, 26 CT examinations with 1625 images were reviewed in a clinical setting and reported by an experienced radiologist using both the CAD system and hardcopies. The CT studies exhibited 19 nodules found on the hardcopies in consensus reporting of 2 experienced radiologists. Viewing with the CAD system was more time consuming than using hardcopies (4.16 vs. 2.92 min) due to analyzing MIP and cine mode. The algorithm detected 49% (18/37) pulmonary nodules larger than 5 mm and 30% (21/70) of all nodules. It produced an average of 6.3 false positive findings per CT study. Most of the missed nodules were adjacent to the pleura. However, the program detected 6 nodules missed by the radiologists. Automatic nodule detection increases the radiologists's awareness of pulmonary lesions. Simultaneous display of axial image and thin-slab MIP makes the radiologist more confident in diagnosis of smaller pulmonary nodules. The CAD system improves the detection of pulmonary nodules at spiral CT. Lack of sensitivity and specificity is still an issue to be addressed but does not prevent practical use.

Computed tomography of the chest can be used as a screening method for lung cancer in a high-risk population. However, the detection of lung nodules is a difficult and time-consuming task for radiologists. The developed technique should improve the sensitivity of the detection of lung nodules without showing too many false positive nodules. In a study, which should evaluate the feasibility of screening lung cancer, about 1400 thoracic studies were acquired. Scanning parameters were 120 kVp, 5 mm collimation pitch of 2, and a reconstruction index of 5 mm. This results in a data set of about 60 to 70 images per exam. In the images the detection technique first eliminates all air outside the patient, then soft tissue and bony structures are removed. In the remaining lung fields a three-dimensional region detection is performed and rule-based analysis is used to detect possible lung nodules. This technique was applied to a small subset (n equals 17) of above studies. Computation time is about 5 min on an O2 workstation. The use of low-dose exams proved not be a hindrance in the detection of lung nodules. All of the nodules (n equals 23), except one with a size of 3 mm, were detected. The false positive rate was less than 0.3 per image. We have developed a technique, which might help the radiologist in the detection of pulmonary nodules in CT exams of the chest.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Early detection of colorectal polyps can improve morbidity and mortality due to cancer of the colon. The colon centerline can be used to expedite examination of the endoluminal surface for colorectal polyps. An automated technique has been developed that calculates the colon centerline from rectum to cecum from helical computed tomography slices of fully insufflated colons. Volume growing is initiated by indicating a seed point in the rectum, air voxels are grown and tagged with growth step numbers. The centers of mass of grown voxels with similar growth step numbers are used as a `forward' centerline. This procedure is repeated by growing from the cecum to the rectum to generate a `backward centerline'. The forward and backward centerlines are averaged to produce the calculated centerline. The technique was evaluated on a clinical colon case by comparing the calculated centerline with points indicated by 2 radiologists. Root mean square differences between the computed and indicated points were small (4 - 5 mm) and comparable to inter-observer differences. Results indicate that with this technique the centerline of the colon can be accurately and quickly calculated.

We have developed a technique for fast and reliable, computer-assisted segmentation of the vessels, thereby obviating time-consuming manual segmentation of intracranial vessels for creation of a 3D model. The high quality of the bone segmentation greatly facilitates the segmentation of the vascular structures. As a result, computer tomography angiography examinations may be a viable alternative to a more invasive and expensive conventional angiography techniques used in the diagnosis of the pathology of intracranial vessels, especially in the cerebrovascular emergencies.

The purpose of this study was to develop methods for automatic 3D-segmentation and automatic quantification of vascular structures in CT angiographic studies, e.g., abdominal aortic aneurysms. Methods for segmentation were developed based on thresholding, maximum gradient, and second derivative techniques. All parameters for the segmentation are generated automatically, i.e. no user interaction is necessary for this process. Median filtering of all images is initially performed to reduce the image noise. The algorithm then automatically identifies the starting point inside the aorta for the volume growing. The segmentation of the vascular tree is achieved in two steps. First, only the aorta and small parts of branch vessels are segmented by using strong restrictions in the parameters for threshold and gradient. A description of the aorta is generated by fitting the detected outer border of the aorta with an ellipse. This description includes centerline, direction, contour, eccentricity, and area. In the second step, segmentation parameters are changed automatically for segmentation of branch vessels. A shaded surface display of the segmented structures is then generated. The segmentation of the aorta appears accurate, is fast, and the 3D display can be manipulated in real time. The quantitative description of the aorta is reliable giving reproducible information. Total CPU time for the segmentation and description is less than five minutes on a standard workstation. Time-consuming manual segmentation and parameterization of vascular structures are obviated, with 3D visualization and quantitative results available in minutes instead of hours. This technique for segmentation and description of the aorta and renal arteries shows the feasibility of computer assisted diagnosis in CT angiographic studies without user interaction. Besides the description, a rapid 3D view of the vessels is generated, often needed by the physician and normally only achievable by time consuming manual segmentation.

In vivo analyses of pacemaker lead motion during the cardiac cycle have become important due to incidences of failure of some of the components. For the calculation and evaluation of in vivo stresses in pacemaker leads, the 3D motion of the lead must be determined. To accomplish this, we have developed a technique for calculation of the overall and relative 3D position, and thereby the 3D motion, of in vivo pacemaker leads through the cardiac cycle.Biplane image sequences of patients with pacemakers were acquired for at least two cardiac cycles. After the patient acquisitions, biplane images of a calibration phantom were obtained. The biplane imaging geometries were calculated from the images of the calibration phantom. Points on the electrodes and the lead centerlines were indicated manually in all acquired images. The indicated points along the leads were then fit using a cubic spline. In each projection, the cumulative arclength along the centerlines in two temporally adjacent images was used to identify corresponding points along the centerlines. To overcome the non-synchronicity of the biplane image acquisition, temporal interpolation was performed using these corresponding points based on a linear scheme. For each time point, corresponding points along the lead centerlines in the pairs of biplane images were identified using epipolar lines. The 3D lead centerlines were calculated from the calculated imaging geometries and the corresponding image points along the lead centerlines. From these data, 3D lead motion and the variations of the lead position with time were calculated and evaluated throughout the cardiac cycle. The reproducibility of the indicated lead centerlines was approximately 0.3 mm. The precision of the calculated rotation matrix and translation vector defining image geometry were approximately 2 mm. 3D positions were reproducible to within 2 mm. Relative positional errors were less than 0.3 mm. Lead motion correlated strongly with phases of the cardiac cycle. Our results indicate that complex motions of in vivo pacemaker leads can be precisely determined. Thus, we believe that this technique will provide precise 3D motion and shapes on which to base subsequent stress analysis of pacemaker lead components.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.