Development and validation of an automated and marker-free CT-based spatial analysis method (CTSA) for assessment of femoral hip implant migration: In vitro accuracy and precision comparable to that of radiostereometric analysis (RSA).

BACKGROUND AND PURPOSE: We developed a marker-free automated CT-based spatial analysis (CTSA) method to detect stem-bone migration in consecutive CT datasets and assessed the accuracy and precision in vitro. Our aim was to demonstrate that in vitro accuracy and precision of CTSA is comparable to that of radiostereometric analysis (RSA). MATERIAL AND METHODS: Stem and bone were segmented in 2 CT datasets and both were registered pairwise. The resulting rigid transformations were compared and transferred to an anatomically sound coordinate system, taking the stem as reference. This resulted in 3 translation parameters and 3 rotation parameters describing the relative amount of stem-bone displacement, and it allowed calculation of the point of maximal stem migration. Accuracy was evaluated in 39 comparisons by imposing known stem migration on a stem-bone model. Precision was estimated in 20 comparisons based on a zero-migration model, and in 5 patients without stem loosening. RESULTS: Limits of the 95% tolerance intervals (TIs) for accuracy did not exceed 0.28 mm for translations and 0.20° for rotations (largest standard deviation of the signed error (SD(SE)): 0.081 mm and 0.057°). In vitro, limits of the 95% TI for precision in a clinically relevant setting (8 comparisons) were below 0.09 mm and 0.14° (largest SD(SE): 0.012 mm and 0.020°). In patients, the precision was lower, but acceptable, and dependent on CT scan resolution. INTERPRETATION: CTSA allows detection of stem-bone migration with an accuracy and precision comparable to that of RSA. It could be valuable for evaluation of subtle stem loosening in clinical practice.

High-resolution 3D micro-CT imaging of breast microcalcifications: a preliminary analysis.

BACKGROUND: Detection of microcalcifications on mammograms indicates the presence of breast lesion, and the shapes of the microcalcifications as seen by conventional mammography correlates with the probability of malignancy. This preliminary study evaluated the 3D shape of breast microcalcifications using micro-computed tomography (micro-CT) and compared the findings with those obtained using anatomopathological analysis. METHODS: The study analyzed breast biopsy samples from 11 women with findings of suspicious microcalcifications on routine mammograms. The samples were imaged using a micro-CT (SkyScan 1076) at a resolution of 35 µm. Images were reconstructed using filtered back-projection and analyzed in 3D using surface rendering. The samples were subsequently analyzed by the pathology service. Reconstructed 3D images were compared with the corresponding histological slices. RESULTS: Anatomopathological analysis showed that 5 of 11 patients had ductal breast carcinoma in situ. One patient was diagnosed with invasive ductal carcinoma.Individual object analysis was performed on 597 microcalcifications. Malignant microcalcifications tended to be thinner and to have a smaller volume and surface area, while their surface area-to-volume ratio was greater than that of benign microcalcifications. The structure model index values were the same for malignant and benign microcalcifications. CONCLUSIONS: This is the first study to use micro-CT for quantitative 3D analysis of microcalcifications. This high-resolution imaging technique will be valuable for gaining a greater understanding of the morphologic characteristics of malignant and benign microcalcifications. The presence of many small microcalcifications can be an indication of malignancy. For the larger microcalcifications, 3D parameters confirmed the more irregular shape of malignant microcalcifications.

Phased searching with NEAT in a time-scaled framework: experiments on a computer-aided detection system for lung nodules.

OBJECTIVE: In the field of computer-aided detection (CAD) systems for lung nodules in computed tomography (CT) scans, many image features are presented and many artificial neural network (ANN) classifiers with various structural topologies are analyzed; frequently, the classifier topologies are selected by trial-and-error experiments. To avoid these trial and error approaches, we present a novel classifier that evolves ANNs using genetic algorithms, called "Phased Searching with NEAT in a Time or Generation-Scaled Framework", integrating feature selection with the classification task. METHODS AND MATERIALS: We analyzed our method's performance on 360 CT scans from the public Lung Image Database Consortium database. We compare our method's performance with other more-established classifiers, namely regular NEAT, Feature-Deselective NEAT (FD-NEAT), fixed-topology ANNs, and support vector machines (SVMs) using ten-fold cross-validation experiments of all 360 scans. RESULTS: The results show that the proposed "Phased Searching" method performs better and faster than regular NEAT, better than FD-NEAT, and achieves sensitivities at 3 and 4 false positives (FP) per scan that are comparable with the fixed-topology ANN and SVM classifiers, but with fewer input features. It achieves a detection sensitivity of 83.0±9.7% with an average of 4FP/scan, for nodules with a diameter greater than or equal to 3mm. It also evolves networks with shorter evolution times and with lower complexities than regular NEAT (p=0.026 and p<0.001, respectively). Analysis on the average and best network complexities evolved by regular NEAT and by our approach shows that our approach searches for good solutions in lower dimensional search spaces, and evolves networks without superfluous structure. CONCLUSIONS: We have presented a novel approach that combines feature selection with the evolution of ANN topology and weights. Compared with the original threshold-based Phased Searching method of Green, our method requires fewer parameters and converges to the optimal network complexity required for the classification task at hand. The results of the ten-fold cross-validation experiments also show that our proposed CAD system for lung nodule detection performs well with respect to other methods in the literature.

Assessment of regional adipose tissue depots: a DXA and CT comparison in cadavers of elderly persons.

OBJECTIVE: Since dual energy X-ray absorptiometry (DXA) cannot distinguish between different adipose tissue (AT) deposits, it remains unclear how DXA-derived body composition variables relate to anatomical tissue (sub)compartments. The aim of the present study was to compare and relate regional DXA variables with absolute tissue masses obtained by computer tomography (CT) scanning of the lower limbs in elderly persons. METHODS: Eleven well-preserved white Caucasian adults (seven male and four female cadavers) with a median age of 79.0 years (ranging from 68 to 96 years) were fully scanned with DXA and CT. Separate densities of skin tissue, AT, muscle tissue and bone were obtained by hydrostatic weighing. RESULTS: The leg DXA-variables were significantly related (rho-values between 0.60 and 0.98, P < 0.01) to CT-derived tissue counterparts, but showed significant systematic differences except for subcutaneous AT mass (P = 0.773). After controlling for other AT depots, fat as measured by DXA (fat(DXA)) related only to intermuscular AT (rho = 0.82, P < 0.01) in males and to subcutaneous AT (rho = 0.84, P < 0.05) in females. CONCLUSION: Although significantly interrelated, DXA and CT variables should not be used interchangeably since they have different quantitative and physiological significance. Our results suggest that fat(DXA) represents different parts of AT depots in elderly men and women. Since DXA is not appropriate for assessing tissue variability cautious clinical interpretation is warranted.

ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

A novel computer-aided lung nodule detection system for CT images.

PURPOSE: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. METHODS: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. RESULTS: The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. CONCLUSIONS: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.

Vascular active contour for vessel tree segmentation.

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

Evaluation of the radiation dose in micro-CT with optimization of the scan protocol.

INTRODUCTION: Micro-CT provides non-invasive anatomic evaluation of small animals. Serial micro-CT measurements are, however, hampered by the severity of ionizing radiation doses cumulating over the total period of follow-up. The dose levels may be sufficient to influence experimental outcomes such as animal survival or tumor growth. AIM: This study was designed to evaluate the radiation dose of micro-CT and to optimize the scanning protocol for longitudinal micro-CT scans. METHODS AND MATERIALS: Normal C57Bl/6 mice were euthanized. Radiation exposure was measured using individually calibrated lithium fluoride thermoluminescent dosimeters (TLDs). Thirteen TLDs were placed in the mice at the thyroid, lungs, liver, stomach, colon, bladder and near the spleen. Micro-CT (SkyScan 1178) was performed using two digital X-ray cameras which scanned over 180 degrees at a resolution of 83 microm, a rotation step of 1.08 degrees , 50 kV, 615 microA and 121 s image acquisition time. The TLDs were removed after each scan. CTDI(100) was measured with a 100 mm ionization chamber, centrally positioned in a 2.7 cm diameter water phantom, and rotation steps were increased to reduce both scan time and radiation dose. RESULTS: Internal TLD analysis demonstrated median organ dose of 5.5 +/- 0.6 mGy per mA s, confirmed by CTDI(100) with result of 6.6 mGy per mA s. A rotation step of 2.16 resulted in qualitatively accurate images. At a resolution of 83 microm the scan time is reduced to 63 s with an estimated dose of 2.9 mGy per mA s. At 166 microm resolution, the scan time is limited to 27 s, with a concordant dose of 1.2 mGy per mA s. CONCLUSIONS: The radiation dose of a standard micro-CT scan is relatively high and could influence the experimental outcome. We believe that the presented adaptation of the scan protocol allows for accurate imaging without the risk of interfering with the experimental outcome of the study.

Impact of stem-broach sizing on the cement mantle of Lubinus SP II stems. A CT scan analysis.

Lubinus SP II stems are cemented either line-to-line with the largest broach or one-size undersized. The purpose of this study was to compare both implantation techniques. We used 18 polymeric stem replicas cemented line-to-line and undersized in paired cadaveric femora and analyzed them with CT scan images. Cementing Lubinus stems line-to-line resulted in higher medullary canal-filling indices (28.26 +/- 4.10%), thinner cement mantles (3.29 +/- 0.40 mm), more cement defects (5.12 +/- 1.69%) and more areas of thin cement (23.81 +/- 7.13%) than undersizing (respectively: 23.61 +/- 4.24%, 3.62 +/- 0.43 mm, 1.48 +/- 2.04%, 15.11 +/- 5.93%). In both settings, over 80% of areas of thin or deficient cement were supported by cortex. Using a line-to-line technique, adequate stem alignment was achieved without distal centralizer. Undersizing the stem and using a distal centralizer reduced the incidence of distal cement defects by a factor 10. While stems cemented line-to-line might have mechanical advantages, undersizing and using distal centralizers reduced potential pathways for debris migration to the bone-cement interface.

The cement mantle of femoral hip implants is more influenced by stem-broach sizing than by shape: an in vitro CT analysis of straight Charnley-Kerboul and anatomic Lubinus SPII stems.

INTRODUCTION: Both, the stem shape and the implantation technique could influence the quality of the cement mantle surrounding femoral hip implants. We investigated which of these two factors is most important for the stem-cement-bone construct. METHODS: We compared morphological data of 22 straight Charnley-Kerboul and 18 anatomic Lubinus SPII plastic replicas implanted line-to-line with the broach or one size undersized in paired cadaver femora. Specimens were CT scanned and assessed with validated segmentation software. RESULTS: According to a general linear model, the canal-filling capacity of the stem, the cement thickness, the amount of thin and deficient cement and cortical support to areas of weak cement, were exclusively related to the implantation technique. The anatomic shape of Lubinus stems did not improve centralization or alignment. CONCLUSION: Stem shape was the most obvious difference between Lubinus SPII and Chanrley-Kerboul implants. However, comparing both, stem-broach sizing was the most important determining factor for the cemented stem construct.

Time-course of contrast enhancement in spleen and liver with Exia 160, Fenestra LC, and VC.

OBJECTIVE: The purpose of this study was to compare the time-course of contrast-enhancement in spleen and liver using Exia 160 in comparison with Fenestra LC and VC in healthy mice. PROCEDURES: Healthy C57bl/6 mice were used in this study. Fenestra LC and VC was administered intravenously at a dose of 0.1 ml/20 g or 0.2 ml/20 g. Exia 160 at a dose of 0.05 ml/20 g or 0.1 ml/20 g. Each animal underwent a micro-CT scan before contrast injection (baseline) and immediately after contrast injection. Additional scans were performed at 1, 2, 3, 4, 24, and 48 h after contrast administration. The mice who received Exia 160 were also scanned after 15, 30, and 45 min. RESULTS: The peak enhancement of Exia 160 occurred after 15 min for the spleen and after 30 min for the liver. CONCLUSIONS: Exia 160 allows rapid spleen and liver enhancement. The high iodine content results in small injection volumes.

Region competition based active contour for medical object extraction.

In this paper, a probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour. The algorithms are derived by minimizing a region based probabilistic energy function and implemented in a level set framework. An additional speed-controlling term makes the active contour quickly convergent to the actual contour on strong edges, whereas a probabilistic model makes the active contour performing well for weak edges. Prior knowledge about the initial contour and the probabilistic distribution contributes to more efficient extraction. The developed model has been applied to a variety of medical images, from CTA and MRA of the coronary to rotationally scanned and real-time three-dimensional echocardiography images of the mitral valve. As the results show, the algorithm is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.

3D human pose recognition for home monitoring of elderly.

A toolbox for the automatic monitoring of elderly in a nursing home or in the natural home environment is proposed. Rather than monitoring vital signs or other biomedical parameters, the toolbox is focussed on the monitoring of activity patterns and changes therein. Activity information is derived from visual information using image processing algorithms. The visual information is acquired using 3D camera technology. Besides a traditional visual image, 3D cameras also provide highly accurate depth information. The 3D position of the subject is derived and serves as the primary information source for the different components in the toolbox.

In vitro analysis of the cement mantle of femoral hip implants: development and validation of a CT-scan based measurement tool.

We developed, validated and assessed inter- and intraobserver reliability of a CT-scan based measurement tool to evaluate morphological characteristics of the bone-cement-stem complex of hip implants in cadaver femurs. Two different models were investigated: the stem-cavity model using a double tapered polished femoral-stem that is removed after cement curing and the plastic-replica model using a stereolithographic stem replica that is left in place during CT-scanning. Software was developed to segment and analyze connective CT-images and identify the contours of bone, cement, and stem based on their respective gray values. Volume parameters (whole specimen, cement, stem, air contents of bone and cement), concentricity parameters (distances between centroids of stem and cement, cement and bone, stem and bone), contact surfaces (bone/air and cement/bone) and bone cement mantle thickness parameters were calculated. A three-dimensional protocol was developed to evaluate the minimal mantle thickness out of the CT-plane. The average accuracy for surfaces within CT-images was 7.47 mm2 (1.80%), for bone and cement mantle thickness it was 0.51 mm (9.39%), for distances between centroids it was 0.38 mm (18.5%) and contours: 0.27 mm (2.57%). The intra- and interobserver reliability of air content in bone and cement was sub-optimal (intraclass-correlation coefficient (ICC) as low as 0.54 with an average ICC of 0.85). All other variables were reliable (ICC>0.81, average ICC: 0.96). This in vitro technique can assess characteristics of cement mantles produced by different cementing techniques, stem types or centralizers.