Tibial Tunnel Placement in ACL Reconstruction Using a Novel Grid and Biplanar Stereoradiographic Imaging.

BACKGROUND: Nonanatomic graft placement is a frequent cause of anterior cruciate ligament reconstruction (ACLR) failure, and it can be attributed to either tibial or femoral tunnel malposition. To describe tibial tunnel placement in ACLR, we used EOS, a low-dose biplanar stereoradiographic imaging modality, to create a comprehensive grid that combines anteroposterior (AP) and mediolateral (ML) coordinates. PURPOSE: To (1) validate the automated grid generated from EOS imaging and (2) compare the results with optimal tibial tunnel placement. STUDY DESIGN: Descriptive laboratory study. METHODS: Using EOS, 3-dimensional models were created of the knees of 37 patients who had undergone ACLR. From the most medial, lateral, anterior, and posterior points on the tibial plateau of the EOS 3-dimensional model for each patient, an automated and personalized grid was generated from 2 independent observers' series of reconstructions. To validate this grid, each observer also manually measured the ML and AP distances, the medial proximal tibial angle (MPTA), and the tibial slope for each patient. The ideal tibial tunnel placement, as described in the literature, was compared with the actual tibial tunnel grid coordinates of each patient. RESULTS: The automated grid metrics for observer 1 gave a mean (95% CI) AP depth of 54.7 mm (53.4-55.9), ML width of 75.0 mm (73.3-76.6), MPTA of 84.9° (83.7-86.0), and slope of 7.2° (5.4-9.0). The differences with corresponding manual measurements were means (95% CIs) of 2.4 mm (1.4-3.4 mm), 0.5 mm (-1.3 to 2.2 mm), 1.2° (-0.4° to 2.9°), and -0.4° (-2.1° to 1.2°), respectively. The correlation between automated and manual measurements was r = 0.78 for the AP depth, r = 0.68 for the ML width, r = 0.18 for the MPTA, and r = 0.44 for the slope. The center of the actual tibial aperture on the plateau was a mean of 5.5 mm (95% CI, 4.8-6.1 mm) away from the referenced anatomic position, with a tendency toward more medial placement. CONCLUSION: The automated grid created using biplanar stereoradiographic imaging provided a novel, precise, and reproducible description of the tibial tunnel placement in ACLR. CLINICAL RELEVANCE: This technique can be used during preoperative planning, intraoperative guidance, and postoperative evaluation of tibial tunnel placement in ACLR.

Potential of iterative reconstruction for maxillofacial cone beam CT imaging: technical note.

Iterative reconstruction has been proven to be an effective tool for low-dose computed tomography imaging. However, this technology is currently not available in commercial diagnostic maxillofacial cone beam CT. For this technical note, an iterative reconstruction technique was applied to cone beam CT raw data of two maxillofacial clinical cases to explore its potential for dose reduction and metal artifact reduction. Low-dose imaging was emulated by using only fractions of the clinical projection dataset. The reconstruction algorithms tested were filtered backprojection (FBP) as a reference method, and a total variation minimization (TV) regularized ordered subsets convex (OSC-TV) method as the iterative technique. Upon qualitative examination, the OSC-TV technique was found to conserve most diagnostic information using half the projections. Test images have also shown that at 1/4 of the projections, OSC-TV was more robust than FBP with respect to streaking and metal artifacts.

Femoral Tunnel Placement Analysis in ACL Reconstruction Through Use of a Novel 3-Dimensional Reference With Biplanar Stereoradiographic Imaging.

BACKGROUND: The femoral-sided anatomic footprint of the anterior cruciate ligament (ACL) has been widely studied during the past decades. Nonanatomic placement is an important cause of ACL reconstruction (ACLR) failure. PURPOSE: To describe femoral tunnel placement in ACLR through use of a comprehensive 3-dimensional (3D) cylindrical coordinate system combining both the traditional clockface technique and the quadrant method. Our objective was to validate this technique and evaluate its reproducibility. STUDY DESIGN: Descriptive laboratory study. METHODS: The EOS Imaging System was used to make 3D models of the knee for 37 patients who had undergone ACLR. We designed an automated cylindrical reference software program individualized to the distal femoral morphology of each patient. Cylinder parameters were collected from 2 observers' series of 3D models. Each independent observer also manually measured the corresponding parameters using a lateral view of the 3D contours and a 2-dimensional stereoradiographic image for the corresponding patient. RESULTS: The average cylinder produced from the first observer's EOS 3D models had a 30.0° orientation (95% CI, 28.4°-31.5°), 40.4 mm length (95% CI, 39.3-41.4 mm), and 19.3 mm diameter (95% CI, 18.6-20.0 mm). For the second observer, these measurements were 29.7° (95% CI, 28.1°-31.3°), 40.7 mm (95% CI, 39.7-41.8 mm), and 19.7 mm (95% CI, 18.8-20.6 mm), respectively. Our method showed moderate intertest intraclass correlation among all 3 measuring techniques for both length (r = 0.68) and diameter (r = 0.63) but poor correlation for orientation (r = 0.44). In terms of interobserver reproducibility of the automated EOS 3D method, similar results were obtained: moderate to excellent correlations for length (r = 0.95; P < .001) and diameter (r = 0.66; P < .001) but poor correlation for orientation (r = 0.29; P < .08). With this reference system, we were able to describe the placement of each individual femoral tunnel aperture, averaging a difference of less than 10 mm from the historical anatomic description by Bernard et al. CONCLUSION: This novel 3D cylindrical coordinate system using biplanar, stereoradiographic, low-irradiation imaging showed a precision comparable with standard manual measurements for ACLR femoral tunnel placement. Our results also suggest that automated cylinders issued from EOS 3D models show adequate accuracy and reproducibility. CLINICAL RELEVANCE: This technique will open multiple possibilities in ACLR femoral tunnel placement in terms of preoperative planning, postoperative feedback, and even intraoperative guidance with augmented reality.

Predictive Modeling for Personalized Three-Dimensional Burn Injury Assessments.

For patients with major burn injuries, an accurate burn size estimation is essential to plan appropriate treatment and minimize medical and surgical complications. However, current clinical methods for burn size estimation lack accuracy and reliability. To overcome these limitations, this paper proposes a 3D-based approach-with personalized 3D models from a limited set of anthropometric measurements-to accurately assess the percent TBSA affected by burns. First, a reliability and feasibility study of the anthropometric measuring process was performed to identify clinically relevant measurements. Second, a large representative stratified random sample was generated to output several anthropometric features required for predictive modeling. Machine-learning algorithms assessed the importance and the subsets of anthropometric measurements for predicting the BSA according to specific patient morphological features. Then, the accuracy of both the morphology and BSA of 3D models built from a limited set of measurements was evaluated using error metrics and maximum distances 3D color maps. Results highlighted the height and circumferences of the bust, neck, hips, and waist as the best predictors for BSA. 3D models built from three to four anthropometric measurements showed good accuracy and were geometrically close to gold standard 3D scans. Outcomes of this study aim to decrease medical and surgical complications by decreasing errors in percent TBSA assessments and, therefore, improving patient outcomes by personalizing care.

Iterative reconstruction for image enhancement and dose reduction in diagnostic cone beam CT imaging.

BACKGROUND: Iterative reconstruction is well-established in diagnostic multidetector computed tomography (MDCT) for dose reduction and image quality enhancement. Its application to diagnostic cone beam computed tomography (CBCT) is only emerging and warrants a quantitative evaluation. METHODS: Several phantoms and a canine head specimen were imaged using a commercially available small-field CBCT scanner. Raw projection data were reconstructed using the Feldkamp-Davis-Kress (FDK) method with different filters, including denoising via total variation (TV) minimization (FDK-TV). Iterative reconstruction was carried out using the TV-regularized ordered subsets convex technique (OSC-TV). Signal-to-noise ratio (SNR), noise power spectrum (NPS) and spatial resolution of images were estimated. Dose levels were measured via the weighted computed tomography dose index, while low-dose image quality degradation was estimated via structural similarity (SSIM). RESULTS: OSC-TV and FDK-TV were shown to significantly improve image signal-to-noise ratio (SNR) compared to FDK with a standard filter, 5.8 and 4.0 times, respectively. Spatial resolution attained with different algorithms varied moderately across different experiments. For low-dose acquisitions, image quality decreased dramatically for FDK but not for FDK-TV nor OSC-TV. For low-dose canine head images acquired using about 1/5 of the dose compared to a reference image, SSIM dropped to about 0.3 for FDK, while remaining at 0.92 for FDK-TV and 0.96 for OSC-TV. CONCLUSION: OSC-TV was shown to improve image quality compared to FDK and FDK-TV. Moreover, this iterative approach allowed for significant dose reduction while maintaining image quality.

Quantification of joint alignment and stability during a single leg stance task in a knee osteoarthritis cohort.

BACKGROUND: Knee osteoarthritis alters joint stability but its kinematics during functional weight-bearing tasks remain unclear. We propose and validate an assessment technique for the quantification of knee alignment and stability in patients during a short single leg stance task. METHODS: Three-dimensional knee kinematics were acquired non-invasively from 31 knee osteoarthritis patients (subdivided as moderate or severe) and 15 asymptomatic individuals during six short single-leg stance tasks. Data of participants achieving ≥3 trials were retained. From flexion-extension signals, a data treatment method compared the average between-trial root-mean-square error (RMSE) across trial triplets, and the average within-trial range of movement (RoM) for two data windows. From secondary knee motions (ab/adduction and int/external rotations, anteroposterior and mediolateral translations), we extracted measures characterizing alignments (mean), largest deviations (maximum, minimum), and extent of micro-adjustments (RoM, length of knee excursion). Their sensitivity to disease and severity was determined using an ANOVA, and between-trial repeatability using ICC2,3. RESULTS: Ninety-four percent of patients achieved ≥3 trials. The retained trial triplet and window reduced the RMSE (2.15 to 1.54) and RoM (4.9° to 1.77°) for flexion-extension. Mean, minimum, and maximum measures were sensitive to disease for anteroposterior translations, and to severity for ab/adduction (P < 0.05). High repeatability was found for those measures (ICC ≥0.84). RoM and length of knee excursion, although sensitive to disease for anteroposterior translations, had lower ICC. CONCLUSION: The proposed technique is feasible and exposed measures of knee alignment sensitive to knee osteoarthritis, for instance, an anterior femoral shift and an increased adduction malalignment with greater severity.

An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls.

Three-dimensional (3D) knee kinematic data, measuring flexion/extension, abduction/adduction, and internal/external rotation angle variations during locomotion, provide essential information to diagnose, classify, and treat musculoskeletal knee pathologies. However, and so across genders, the curse of dimensionality, intra-class high variability, and inter-class proximity make this data usually difficult to interpret, particularly in tasks such as knee pathology classification. The purpose of this study is to use data complexity analysis to get some insight into this difficulty. Using 3D knee kinematic measurements recorded from osteoarthritis and asymptomatic subjects, we evaluated both single feature complexity, where each feature is taken individually, and global feature complexity, where features are considered simultaneously. These evaluations afford a characterization of data complexity independent of the used classifier and, therefore, provide information as to the level of classification performance one can expect. Comparative results, using reference databases, reveal that knee kinematic data are highly complex, and thus foretell the difficulty of knee pathology classification.

Liver segmentation: indications, techniques and future directions.

OBJECTIVES: Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals. METHODS: Using images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice. RESULTS: Liver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the "gold standard" in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers. CONCLUSIONS: Liver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice. TEACHING POINTS: • Liver volume is assessed via organ segmentation on CT and MRI examinations. • Liver segmentation is used for volume assessment prior to major hepatic procedures. • Segmentation approaches may be categorised according to the amount of user input involved. • Emerging applications include surgical planning and integration with MRI-based biomarkers.

Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase.

OBJECTIVE: To investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers, as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) (Altman et al., 1986). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording. METHODS: 100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity. RESULTS: Diagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes. CONCLUSION: The results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial compartment knee OA.

Liver Segmentation on CT and MR Using Laplacian Mesh Optimization.

OBJECTIVE: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. METHODS: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient's liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. RESULTS: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. CONCLUSION: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. SIGNIFICANCE: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study.

PURPOSE: To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume. METHODS: This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland-Altman analysis. Total interaction time was recorded. RESULTS: Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: -187 to 247 ml) for MRI and -10 ± 143 ml (-153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was -14 ± 136 ml (-150 to 122 ml) for MRI and 50 ± 226 ml (-176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (-37 to 57 ml) to 2 ± 214 ml (-212 to 216 ml) for MRI and 9 ± 45 ml (-36 to 54 ml) to -46 ± 183 ml (-229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min, p < 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min, p < 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min). CONCLUSION: MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.

A multi-criteria evaluation platform for segmentation algorithms.

The purpose of this paper is to present a platform for evaluating segmentation algorithms that detect anatomical structures in medical images. Structure detection being subject to human interpretation, we first describe a method to define a ground truth model, i.e. a generated bronze standard, that will be the reference for subsequent analysis. This bronze standard will be characterized in order to retrieve its confidence level that will later be used to normalize the algorithm evaluation. We then describe how the developed platform helps in evaluating algorithm performances described using five evaluation criteria: accuracy, reliability, robustness, under/over segmentation sensitivity and outlier sensitivity. First, we explain how to extract those evaluation criteria using specific normalized metrics commonly found in the literature, then we present how to combine all the information in order to get a global evaluation of segmentation algorithms. Lastly, a radar-style graph analysis is presented for easy multi-criteria interpretation.

Data correction to determine a representative pattern of a set of 3D knee kinematic measurements.

The purpose of this study is to determine a representative pattern of a set of three dimensional (3D) knee kinematic measurement curves recorded throughout several trials with a patient walking on a treadmill. The measurements are knee angles, (namely joint angles) with respect to the sagittal, frontal, and transverse planes, as a function of time during a gait cycle. Two serious difficulties met while extracting a representative pattern from the trials are that the curves possess phase variability and there are outliers. We propose a scheme which first removes outliers using the modified band depth index method, and follows with phase variability reduction by curve registration. This scheme leads to retaining the mean curve of the corrected set of curves, as the most representative.

Assessment of femur geometrical parameters using EOS™ imaging technology in patients with atypical femur fractures; preliminary results.

Atypical femur fractures (AFF) arise in the subtrochanteric and diaphyseal regions. Because of this unique distribution, we hypothesized that patients with AFF demonstrate specific geometrical variations of their lower limb whereby baseline tensile forces applied to the lateral cortex are higher and might favor the appearance of these rare stress fractures, when exposed to bisphosphonates. Using the low irradiation 2D-3D X-ray scanner EOS™ imaging technology we aimed to characterize and compare femur geometric parameters between women who sustained bisphosphonate-associated AFF and those who had experienced similar duration of exposure to bisphosphonates but did not sustain fractures. Conditional logistic regression models were constructed to estimate the association between selected geometric parameters and the occurrence of AFF. We identified 16 Caucasian women with AFF and recruited 16 ethnicity-, sex-, age-, height- and cumulative bisphosphonate exposure-matched controls from local osteoporosis clinics. Compared to controls, those with AFF had more lateral femur bowing (-3.2° SD [3.4] versus -0.8° SD [1.9] p=0.02). In regression analysis, lateral femur bowing was associated with the risk of AFF (aOR 1.54; 95% CI 1.04-2.28, p=0.03). Women who sustained a subtrochanteric AFF demonstrated a lesser femoral neck shaft angle (varus geometry) than those with a fracture at a diaphyseal site (121.9 [3.6]° versus 127.6 [7.2]°, p=0.07), whereas femur bowing was more prominent in those with a diaphyseal fracture compared to those with a subtrochanteric fracture (-4.3 [3.2]° versus -0.9 [2.7]°, p=0.07). Our analyses support that subjects with AFF exhibit femoral geometry parameters that result in higher tensile mechanical load on the lateral femur. This may play a critical role in the pathogenesis of AFF and requires further evaluation in a larger size population.

Investigation of 3D glenohumeral displacements from 3D reconstruction using biplane X-ray images: Accuracy and reproducibility of the technique and preliminary analysis in rotator cuff tear patients.

Rotator cuff (RC) tears may be associated with increased glenohumeral instability; however, this instability is difficult to quantify using currently available diagnostic tools. Recently, the three-dimensional (3D) reconstruction and registration method of the scapula and humeral head, based on sequences of low-dose biplane X-ray images, has been proposed for glenohumeral displacement assessment. This research aimed to evaluate the accuracy and reproducibility of this technique and to investigate its potential with a preliminary application comparing RC tear patients and asymptomatic volunteers. Accuracy was assessed using CT scan model registration on biplane X-ray images for five cadaveric shoulder specimens and showed differences ranging from 0.6 to 1.4mm depending on the direction of interest. Intra- and interobserver reproducibility was assessed through two operators who repeated the reconstruction of five subjects three times, allowing defining 95% confidence interval ranging from ±1.8 to ±3.6mm. Intraclass correlation coefficient varied between 0.84 and 0.98. Comparison between RC tear patients and asymptomatic volunteers showed differences of glenohumeral displacements, especially in the superoinferior direction when shoulder was abducted at 20° and 45°. This study thus assessed the accuracy of the low-dose 3D biplane X-ray reconstruction technique for glenohumeral displacement assessment and showed potential in biomechanical and clinical research.

A perceptual map for gait symmetry quantification and pathology detection.

BACKGROUND: The gait movement is an essential process of the human activity and the result of collaborative interactions between the neurological, articular and musculoskeletal systems, working efficiently together. This explains why gait analysis is important and increasingly used nowadays for the diagnosis of many different types (neurological, muscular, orthopedic, etc.) of diseases. This paper introduces a novel method to quickly visualize the different parts of the body related to an asymmetric movement in the human gait of a patient for daily clinical usage. The proposed gait analysis algorithm relies on the fact that the healthy walk has (temporally shift-invariant) symmetry properties in the coronal plane. The goal is to provide an inexpensive and easy-to-use method, exploiting an affordable consumer depth sensor, the Kinect, to measure the gait asymmetry and display results in a perceptual way. METHOD: We propose a multi-dimensional scaling mapping using a temporally shift invariant distance, allowing us to efficiently visualize (in terms of perceptual color difference) the asymmetric body parts of the gait cycle of a subject. We also propose an index computed from this map and which quantifies locally and globally the degree of asymmetry. RESULTS: The proposed index is proved to be statistically significant and this new, inexpensive, marker-less, non-invasive, easy to set up, gait analysis system offers a readable and flexible tool for clinicians to analyze gait characteristics and to provide a fast diagnostic. CONCLUSION: This system, which estimates a perceptual color map providing a quick overview of asymmetry existing in the gait cycle of a subject, can be easily exploited for disease progression, recovery cues from post-operative surgery (e.g., to check the healing process or the effect of a treatment or a prosthesis) or might be used for other pathologies where gait asymmetry might be a symptom.

Automatic detection of selective arterial devices for advanced visualization during abdominal aortic aneurysm endovascular repair.

Here we address the automatic segmentation of endovascular devices used in the endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) that deform vascular tissues. Using this approach, the vascular structure is automatically reshaped solving the issue of misregistration observed on 2D/3D image fusion for EVAR guidance. The endovascular devices we considered are the graduated pigtail catheter (PC) used for contrast injection and the stent-graft delivery device (DD). The segmentation of the DD was enhanced using an asymmetric Frangi filter. The segmented geometries were then analysed using their specific features to remove artefacts. The radiopaque markers of the PC were enhanced using a fusion of Hessian and newly introduced gradient norm shift filters. Extensive experiments were performed using a database of images taken during 28 AAA-EVAR interventions. This dataset was divided into two parts: the first half was used to optimize parameters and the second to compile performances using optimal values obtained. The radiopaque markers of the PC were detected with a sensitivity of 88.3% and a positive predictive value (PPV) of 96%. The PC can therefore be positioned with a majority of its markers localized while the artefacts were all located inside the vessel lumen. The major parts of the DD, the dilatator tip and the pusher surfaces, were detected accurately with a sensitivity of 85.9% and a PPV of 88.7%. The less visible part of the DD, the stent enclosed within the sheath, was segmented with a sensitivity of 63.4% because the radiopacity of this region is low and uneven. The centreline of the DD in this stent region was alternatively traced within a 0.74 mm mean error. The automatic segmentation of endovascular devices during EVAR is feasible and accurate; it could be useful to perform elastic registration of the vascular lumen during endovascular repair.

[(18)F]-fluorodeoxyglucose positron emission tomography of the cat brain: A feasibility study to investigate osteoarthritis-associated pain.

The objective of this pilot study was to investigate central nervous system (CNS) changes related to osteoarthritis (OA)-associated chronic pain in cats using [(18)F]-fluorodeoxyglucose ((18)FDG) positron emission tomography (PET) imaging. The brains of five normal, healthy (non-OA) cats and seven cats with pain associated with naturally occurring OA were imaged using (18)FDG-PET during a standardized mild anesthesia protocol. The PET images were co-registered over a magnetic resonance image of a cat brain segmented into several regions of interest. Brain metabolism was assessed in these regions using standardized uptake values. The brain metabolism in the secondary somatosensory cortex, thalamus and periaqueductal gray matter was increased significantly (P ≤ 0.005) in OA cats compared with non-OA cats. This study indicates that (18)FDG-PET brain imaging in cats is feasible to investigate CNS changes related to chronic pain. The results also suggest that OA is associated with sustained nociceptive inputs and increased activity of the descending modulatory pathways.

Validation of a semiautomated liver segmentation method using CT for accurate volumetry.

RATIONALE AND OBJECTIVES: To compare the repeatability and agreement of a semiautomated liver segmentation method with manual segmentation for assessment of total liver volume on CT (computed tomography). MATERIALS AND METHODS: This retrospective, institutional review board-approved study was conducted in 41 subjects who underwent liver CT for preoperative planning. The major pathologies encountered were colorectal cancer metastases, benign liver lesions and hepatocellular carcinoma. This semiautomated segmentation method is based on variational interpolation and 3D minimal path-surface segmentation. Total and subsegmental liver volumes were segmented from contrast-enhanced CT images in venous phase. Two image analysts independently performed semiautomated segmentations and two other image analysts performed manual segmentations. Repeatability and agreement of both methods were evaluated with intraclass correlation coefficients (ICC) and Bland-Altman analysis. Interaction time was recorded for both methods. RESULTS: Bland-Altman analysis revealed an intrareader agreement of -1 ± 27 mL (mean ± 1.96 standard deviation) with ICC of 0.999 (P < .001) for manual segmentation and 12 ± 97 mL with ICC of 0.991 (P < .001) for semiautomated segmentation. Bland-Altman analysis revealed an interreader agreement of -4 ± 22 mL with ICC of 0.999 (P < .001) for manual segmentation and 5 ± 98 mL with ICC of 0.991 (P < .001) for semiautomated segmentation. Intermethod agreement was found to be 3 ± 120 mL with ICC of 0.988 (P < .001). Mean interaction time was 34.3 ± 16.7 minutes for the manual method and 8.0 ± 1.2 minutes for the semiautomated method (P < .001). CONCLUSIONS: A semiautomated segmentation method can substantially shorten interaction time while preserving a high repeatability and agreement with manual segmentation.

A rule-based algorithm can output valid surgical strategies in the treatment of AIS.

BACKGROUND: Variability in surgical strategies for the treatment of adolescent idiopathic scoliosis (AIS) has been demonstrated despite the existence of classifications to guide selection of AIS curves to include in fusion. Decision trees and rule-based algorithms have demonstrated their potential to improve reliability of AIS classification because of their systematic approach and they have also been proposed in algorithms for selection of instrumentation levels in scoliosis. Our working hypothesis is that a rule-based algorithm with a knowledge base extracted from the literature can efficiently output surgical strategies alternatives for a given AIS case. Our objective is to develop a rule-based algorithm based on peer-reviewed literature to output alternative surgical strategies for approach and levels of fusion. METHODS: A literature search of all English Manuscripts published between 2000 and December 2009 with Pubmed and Google scholar electronic search using the following keywords: "adolescent idiopathic scoliosis" and "surgery" alternatively with "levels of fusion" or "approach". All returned abstracts were screened for contents that could contain rules to include in the knowledge base. A dataset of 1,556 AIS cases treated surgically was used to test the surgical strategy rule-based algorithm (SSRBA) and evaluate how many surgical treatments are covered by the algorithm. The SSRBA was programmed using Matlab. Descriptive statistic was used to evaluate the ability of the rule-based algorithm to cover all treatment alternatives. RESULTS: A SSRBA was successfully developed following Lenke classification's concept that the spine is divided into three curve segments [proximal thoracic (PT), main thoracic (MT) and thoracolumbar/lumbar (TL)]. Each of the 1,556 AIS patients in the dataset was ran through the SSRBA. It proposed an average of 3.78 (±2.06) surgical strategies per case. Overall, the SSRBA is able to match the treatment offered by the surgeon in approach and level of fusion 70 % of the time (with one vertebral level leeway). CONCLUSION: This study is to the author's knowledge the first attempt at proposing an algorithm to output all surgical alternatives for a given AIS case. It uses a rule-based algorithm with a knowledge base extracted from peer-reviewed literature in an area with great variability. When tested against a database of AIS patients treated surgically, the SSRBA developed has the ability to propose a surgical plan with respect to approach and levels of fusion that match the surgeon's plan in a great majority of cases. Since this SSRBA seems to output multiple valid surgical strategies, it could allow the comparisons of various strategies and the outcomes achieved in similar cases in large databases for a given case and guide surgical treatment.