Accuracy of a Mitral Valve Segmentation Method Using J-Splines for Real-Time 3D Echocardiography Data

Patient-specific models of the heart’s mitral valve (MV) exhibit potential for surgical planning. While advances in 3D echocardiography (3DE) have provided adequate resolution to extract MV leaflet geometry, no study has quantitatively assessed the accuracy of their modeled leaflets vs. a ground-truth standard for temporal frames beyond systolic closure or for differing valvular dysfunctions. The accuracy of a 3DE-based segmentation methodology based on J-splines was assessed for porcine MVs with known 4D leaflet coordinates within a pulsatile simulator during closure, peak closure, and opening for a control, prolapsed, and billowing MV model. For all time points, the mean distance error between the segmented models and ground-truth data were 0.40 ± 0.32 mm, 0.52 ± 0.51 mm, and 0.74 ± 0.69 mm for the control, flail, and billowing models. For all models and temporal frames, 95% of the distance errors were below 1.64 mm. When applied to a patient data set, segmentation was able to confirm a regurgitant orifice and post-operative improvements in coaptation. This study provides an experimental platform for assessing the accuracy of an MV segmentation methodology at phases beyond systolic closure and for differing MV dysfunctions. Results demonstrate the accuracy of a MV segmentation methodology for the development of future surgical planning tools.     

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm’s dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.     

Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation

Transcatheter aortic valve implantation is a minimal-invasive intervention for implanting prosthetic valves in patients with aortic stenosis. Accurate automated sizing for planning and patient selection is expected to reduce adverse effects such as paravalvular leakage and stroke. Segmentation of the aortic root in CTA is pivotal to enable automated sizing and planning. We present a fully automated segmentation algorithm to extract the aortic root from CTA volumes consisting of a number of steps: first, the volume of interest is automatically detected, and the centerline through the ascending aorta and aortic root centerline are determined. Subsequently, high intensities due to calcifications are masked. Next, the aortic root is represented in cylindrical coordinates. Finally, the aortic root is segmented using 3D normalized cuts. The method was validated against manual delineations by calculating Dice coefficients and average distance error in 20 patients. The method successfully segmented the aortic root in all 20 cases. The mean Dice coefficient was 0.95 ± 0.03, and the mean radial absolute error was 0.74 ± 0.39 mm, where the interobserver Dice coefficient was 0.95 ± 0.03 and the mean error was 0.68 ± 0.34?mm. The proposed algorithm showed accurate results compared to manual segmentations.     

Knee cartilage segmentation and thickness computation from ultrasound images

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen’s κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.     

Method for Segmentation of Knee Articular Cartilages Based on Contrast-Enhanced CT Images

Segmentation of contrast-enhanced computed tomography (CECT) images enables quantitative evaluation of morphology of articular cartilage as well as the significance of the lesions. Unfortunately, automatic segmentation methods for CECT images are currently lacking. Here, we introduce a semiautomated technique to segment articular cartilage from in vivo CECT images of human knee. The segmented cartilage geometries of nine knee joints, imaged using a clinical CT-scanner with an intra-articular contrast agent, were compared with manual segmentations from CT and magnetic resonance (MR) images. The Dice similarity coefficients (DSCs) between semiautomatic and manual CT segmentations were 0.79–0.83 and sensitivity and specificity values were also high (0.76–0.86). When comparing semiautomatic and manual CT segmentations, mean cartilage thicknesses agreed well (intraclass correlation coefficient?=?0.85–0.93); the difference in thickness (mean?±?SD) was 0.27?±?0.03 mm. Differences in DSC, when MR segmentations were compared with manual and semiautomated CT segmentations, were statistically insignificant. Similarly, differences in volume were not statistically significant between manual and semiautomatic CT segmentations. Semiautomation decreased the segmentation time from 450?±?190 to 42?±?10 min per joint. The results reveal that the proposed technique is fast and reliable for segmentation of cartilage. Importantly, this is the first study presenting semiautomated segmentation of cartilage from CECT images of human knee joint with minimal user interaction.     

Marker-controlled watershed for lymphoma segmentation in sequential CT images

Segmentation of lymphoma containing lymph nodes is a difficult task because of multiple variables associated with the tumor's location, intensity distribution, and contrast to its surrounding tissues. In this paper, we present a reliable and practical marker-controlled watershed algorithm for semi-automated segmentation of lymphoma in sequential CT images. Robust determination of internal and external markers is the key to successful use of the marker-controlled watershed transform in the segmentation of lymphoma and is the focus of this work. The external marker in our algorithm is the circle enclosing the lymphoma in a single slice. The internal marker, however, is determined automatically by combining techniques including Canny edge detection, thresholding, morphological operation, and distance map estimation. To obtain tumor volume, the segmented lymphoma in the current slice needs to be propagated to the adjacent slice to help determine the external and internal markers for delineation of the lymphoma in that slice. The algorithm was applied to 29 lymphomas (size range, 9-53 mm in diameter; mean, 23 mm) in nine patients. A blinded radiologist manually delineated all lymphomas on all slices. The manual result served as the "gold standard" for comparison. Several quantitative methods were applied to objectively evaluate the performance of the segmentation algorithm. The algorithm received a mean overlap, overestimation, and underestimation ratios of 83.2%, 13.5%, and 5.5%, respectively. The mean average boundary distance and Hausdorff boundary distance were 0.7 and 3.7 mm. Preliminary results have shown the potential of this computer algorithm to allow reliable segmentation and quantification of lymphomas on sequential CT images.     

Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging

Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ～60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm³ in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task.     

Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.     

Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours

Rheumatoid arthritis (RA) is a chronic multisystemic autoimmune disease, with an unclear etiopathogenesis. Its early diagnosis and activity assessment are essential to adjust the proper therapy. Among the different imaging techniques, ultrasonography (US) allows direct visualization of early inflammatory joint changes as synovitis, being also rapidly performed and easily accepted by patients. We propose an algorithm to semi-automatically detect synovial boundaries on US images, requiring minimal user interaction. In order to identify the synovia-bone and the synovia-soft tissues interfaces, and to tackle the morphological variability of diseased joints, a cascade of two different active contours is developed, whose composition corresponds to the whole synovial boundary.The algorithm was tested on US images acquired from proximal interphalangeal (PIP) and metacarpophalangeal (MCP) finger joints of 34 subjects. The results have been compared with a consensus manual segmentation. We obtained an overall mean sensitivity of 85 ± 13%, and a mean Dice's similarity index of 80 ± 8%, with a mean Hausdorff distance from the manual segmentation of 28 ± 10 pixels (approximately 1.4 ± 0.5 mm), that are a better performance than those obtained by the raters with respect to the consensus.     

Toward Improving Safety in Neurosurgery with an Active Handheld Instrument

Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons’ unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep-learning-based segmentation strategy in microscopy images, and its integration with a feature-based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual-fixture-based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron’s tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 μm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures.     

A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis,density assessment and lesion localization

Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07).     

A fully automated human knee 3D MRI bone segmentation using the ray casting technique

This study aimed at developing a fully automated bone segmentation method for the human knee (femur and tibia) from magnetic resonance (MR) images. MR imaging was acquired on a whole body 1.5T scanner with a gradient echo fat suppressed sequence using an extremity coil. The method was based on the Ray Casting technique which relies on the decomposition of the MR images into multiple surface layers to localize the boundaries of the bones and several partial segmentation objects being automatically merged to obtain the final complete segmentation of the bones. Validation analyses were performed on 161 MR images from knee osteoarthritis patients, comparing the developed fully automated to a validated semi-automated segmentation method, using the average surface distance (ASD), volume correlation coefficient, and Dice similarity coefficient (DSC). For both femur and tibia, respectively, data showed excellent bone surface ASD (0.50 ± 0.12 mm; 0.37 ± 0.09 mm), average oriented distance between bone surfaces within the cartilage domain (0.02 ± 0.07 mm; −0.05 ± 0.10 mm), and bone volume DSC (0.94 ± 0.05; 0.92 ± 0.07). This newly developed fully automated bone segmentation method will enable large scale studies to be conducted within shorter time durations, as well as increase stability in the reading of pathological bone.     

The anatomy of the clavicle

The clavicle has a complex osteologic structure that makes morphological analysis extremely difficult. A three‐dimensional study was conducted to examine the anatomical variations and characteristics of the bone. Sixty‐eight human cadaver clavicles were dissected, CAT‐scanned, and reconstructed. An automated parameterization and correspondence shape analysis system was developed. A new length, designated as centerline (CL) length, was defined and measured. This length represents the true length of the clavicle. The endpoint length was measured as the distance between two endpoints. The width and curvature were measured in the axial (AX) and frontal (FR) plane and defined along the CL. Next gender and side characteristics and variations were examined. The mean CL length was 159.0 ± 11.0 mm. The mean endpoint length was 149.4 ± 10.3 mm, which was statistically significantly shorter than the CL. The male clavicle was significantly longer (166.8 ± 7.3 mm vs. 151.0 ± 8.2 mm), wider (14.6 ± 1.5 mm vs. 12.7 ± 1.3 mm lateral FR plane, 25.9 ± 4.1 mm vs. 23.5 ± 3.0 mm lateral AX plane and 24.7 ± 2.8 mm vs. 22.8 ± 2.8 mm medial AX plane), and more curved (10.8 ± 2.8 mm vs. 8.6 ± 2.3 mm medial and 10.5 ± 3.3 mm vs. 9.1 ± 2.5 mm lateral) than the female one. Left clavicles were significant longer (159.8 ± 10.9 mm vs. 158.0 ± 11.2 mm) than right clavicles. A novel three‐dimensional system was developed, used and tested in order to explore the anatomical variations and characteristics of the human clavicle. This information, together with the automated system, can be applied to future clavicle populations and to the design of fixation plates for clavicle fractures. Clin. Anat. 27:712–723, 2014. © 2013 Wiley Periodicals, Inc.     

A Numerical Preoperative Planning Model to Predict Arterial Deformations in Endovascular Aortic Aneurysm Repair

Endovascular aneurysm repair is rapidly emerging as the primary preferred method for treating abdominal aortic aneurysm. In this image-guided interventional procedure, to obtain the roadmap and decrease contrast injections, preoperative CT images are overlaid onto live fluoroscopy images using various 2D/3D image fusion techniques. However, the structural changes due to the insertion of stiff tools degrade the fusion accuracy. To correct the mismatch and quantify the intraoperative deformations, we present a patient-specific biomechanical model of the aorto-iliac structure and its surrounding tissues. The predictive capability of the model was evaluated against intraoperative data for a group of four patients. Incorporating the perivascular tissues into the model significantly improved the results and the mean distance between the real and simulated endovascular tools was 2.99?±?1.78 mm on the ipsilateral side and 4.59?±?3.25 mm on the contralateral side. Moreover, the distance between the deformed iliac ostia and their corresponding landmarks on intraoperative images was 2.99?±?2.48 mm.     

Anatomical landmarks for locating the sphenoid ostium during endoscopic endonasal approach: a cadaveric study

Purpose

The sphenoid ostium (SO) provides a natural portal for entering the sphenoid sinus and beyond up to the skull base. It is not always easy to locate the ostium during the endoscopic approach. The present study was designed to establish readily identifiable anatomical landmarks for locating the sphenoid ostium.

Methods

Cadaveric dissection was performed in 30 hemisections of head and neck and various measurements were taken from fixed anatomical landmarks in the nasal cavity to the sphenoid ostium. The size, shape and position of sphenoid ostium were determined in relation to the anterior wall of the sphenoid sinus and the superior turbinate.

Results

The mean distance from the supero-lateral angle of the posterior choana to the SO was found to be 21.21 ± 6.02 mm. The mean distance of the SO from the midline was 4.85 ± 2.89 mm. In all the specimens, the SO was situated within 1 cm of the midline. The mean distance between the inferior end of the SO and the postero-inferior edge of the superior turbinate was 8.03 ± 3.52 mm. The SO was present on an average distance of 55.1 ± 3.54 mm from the limen nasi. In 93.3 % of the specimens, the SO was situated between 5 and 6 cm of the inferior end of the limen nasi. The angle between the anterior nasal spine and the SO was found to be remarkably constant. In 93.3 % of the specimens, it was from 25° to 30°.

Conclusions

The sphenoid ostium could be localized medial to the superior turbinate between 1.5 and 3 cm above the supero-lateral angle of the posterior choana, within 1 cm of the midline and within 1 cm of the postero-inferior edge of the superior turbinate.     

Surgical and radiologic anatomy of a cochleostomy produced via posterior tympanotomy for cochlear implantation based on three-dimensional reconstructed temporal bone CT images

Purpose

In this study, we evaluated the surgical and radiologic anatomy of a cochleostomy produced via posterior tympanotomy for cochlear implantation (CI).

Materials and methods

Twenty computed tomography (CT) images of the temporal bone from patients aged between 20 and 60 years were selected. The inclusion criterion was a radiologically normal temporal bone CT scan. Three-dimensional (3D) reconstructed images were obtained using high-resolution axial temporal bone CT scans. Eight points were used to evaluate the surgical anatomy of the posterior tympanotomy and cochleostomy. The length of lines between the points and the angles between the lines were measured.

Results

The mean length of line AB (superior-inferior length of the posterior tympanotomy for CI) was 6.48 ± 0.26 mm, while line AC (width of the chorda tympani and facial nerves) was 3.60 ± 0.2 mm. The mean angle of ABC (angle at which the chorda tympani nerve branched from the facial nerve) was 18.40° ± 1.05°. The mean length of line AD (distance from the facial ridge to the point of cochleostomy) was 9.58 ± 0.47 mm.

Conclusions

3D imaging of the facial recess and round window can be used to identify the facial recess before surgery. This may help to avoid injury to the chorda tympani nerve during posterior tympanotomy, and make it easier to insert the electrode array during CI by creating a large enough posterior tympanotomy to avoid injury to the facial nerve, which can cause immediate or delayed facial palsy.     

Automatic string generation for estimating in vivo length changes of the medial patellofemoral ligament during knee flexion

Modeling ligaments as three-dimensional strings is a popular method for in vivo estimation of ligament length. The purpose of this study was to develop an algorithm for automated generation of non-penetrating strings between insertion points and to evaluate its feasibility for estimating length changes of the medial patellofemoral ligament during normal knee flexion. Three-dimensional knee models were generated from computed tomography (CT) scans of 10 healthy subjects. The knee joint under weight-bearing was acquired in four flexion positions (0°–120°). The path between insertion points was computed in each position to quantify string length and isometry. The average string length was maximal in 0° of flexion (64.5 ± 3.9 mm between femoral and proximal patellar point; 62.8 ± 4.0 mm between femoral and distal patellar point). It was minimal in 30° (60.0 ± 2.6 mm) for the proximal patellar string and in 120° (58.7 ± 4.3 mm) for the distal patellar string. The insertion points were considered to be isometric in 4 of the 10 subjects. The proposed algorithm appears to be feasible for estimating string lengths between insertion points in an automatic fashion. The length measurements based on CT images acquired under physiological loading conditions may give further insights into knee kinematics.     

Morphometric analysis of the infraorbital groove, canal, and foramen on three-dimensional reconstruction of computed tomography scans

Purpose

This study aimed to investigate the anatomy of the infraorbital foramen (IOF), infraorbital canal (IOC), and infraorbital groove (IOG) with regard to surgical and invasive procedures using three-dimensional reconstruction of CT scans.

Methods

The CT scans of 100 patients were evaluated retrospectively. The morphology of the IOF, IOC, and IOG as well as their relationships to different anatomic landmarks was assessed in a three-dimensional model.

Results

The mean length of the IOC and IOG and the angle of the IOC relative to IOG were 11.7 ± 1.9, 16.7 ± 2.4 mm, and 145.5° ± 8.5°, respectively. The mean angles of the IOC relative to vertical and horizontal planes were 13.2° ± 6.4° and 46.7° ± 7.6°, respectively. In the relationships between the IOF and different anatomic landmarks, the mean distances from the IOF to supraorbital notch/foramen, facial midline, and infraorbital rim were 5.6 ± 3.1 mm laterally, 26.5 ± 1.9 mm laterally, and 9.6 ± 1.7 mm inferiorly, respectively. The mean distance from the IOF to anterior nasal spine (ANS) was 35.0 ± 2.6 mm, and the mean angle of the axis that passed the IOF and ANS relative to horizontal plane was 28.8° ± 4.1°. In addition, the mean soft tissue thickness overlying the IOF was 11.4 ± 1.9 mm.

Conclusions

These results provide detailed knowledge of the anatomical characteristics and clinical importance of the IOF. Such knowledge is of paramount importance for surgeons when performing maxillofacial surgery and regional block anesthesia.     

The anatomical and clinical details of the pterygoid canal: a three-dimensional reconstructive virtual anatomic evaluation based on CT

Purpose

To delineate the pterygoid canal (PC) configuration and its position in relation to surrounding important anatomical landmarks using three-dimensional reconstructive technology based on CT for the Chinese.

Methods

The computerized tomography arteriography (CTA) data of 137 patients were retrospectively evaluated using neuroimaging three-dimensional reconstructive software. The morphological parameters of the PC as well as the spatial relationship and distance between the PC relative to internal carotid artery (ICA) and the foramen rotundum were evaluated.

Results

83.9 % of the PC can be identified by our neuroimaging three-dimensional reconstructive software. The mean distance from the PC to the ICA was 2.6 ± 1.2 mm. The mean distance between medial aspects of bilateral ICA was 19.6 ± 2.7 mm. The distal vertical and horizontal distances between the PC and foramen rotundum were 5.2 ± 3.2 and 6.1 ± 2.8 mm, respectively. All the proximal end of the PC were inferior-lateral to the ICA. The PC mainly (92.9 %) ran posteriorly with a medial to lateral direction. The distance from the PC to ICA was positively correlated with the distance between bilateral ICA and the distal diameter of the PC. The vertical distance between the PC and foramen rotundum was positively correlated with the length of the PC and the horizontal distance between the PC and foramen rotundum.

Conclusions

Understanding the configuration and spatial relationship of the PC may be helpful to improve the accuracy and safety of operation during the expanded transnasal endoscopic approaches to skull base. The three-dimensional reconstructive virtual anatomic technology may be a useful tool to delineate the PC configuration and its position to surrounding important anatomical landmarks.     

Three-dimensional Quantification of Femoral Head Shape in Controls and Patients with Cam-type Femoroacetabular Impingement

An objective measurement technique to quantify 3D femoral head shape was developed and applied to normal subjects and patients with cam-type femoroacetabular impingement (FAI). 3D reconstructions were made from high-resolution CT images of 15 cam and 15 control femurs. Femoral heads were fit to ideal geometries consisting of rotational conchoids and spheres. Geometric similarity between native femoral heads and ideal shapes was quantified. The maximum distance native femoral heads protruded above ideal shapes and the protrusion area were measured. Conchoids provided a significantly better fit to native femoral head geometry than spheres for both groups. Cam-type FAI femurs had significantly greater maximum deviations (4.99 ± 0.39 mm and 4.08 ± 0.37 mm) than controls (2.41 ± 0.31 mm and 1.75 ± 0.30 mm) when fit to spheres or conchoids, respectively. The area of native femoral heads protruding above ideal shapes was significantly larger in controls when a lower threshold of 0.1 mm (for spheres) and 0.01 mm (for conchoids) was used to define a protrusion. The 3D measurement technique described herein could supplement measurements of radiographs in the diagnosis of cam-type FAI. Deviations up to 2.5 mm from ideal shapes can be expected in normal femurs while deviations of 4–5 mm are characteristic of cam-type FAI.