Validation study of the CT-based cross-sectional evaluation of muscular atrophy and fatty degeneration around the pelvis and the femur.

BACKGROUND: Muscle atrophy and degeneration around hip joint have been evaluated using computer tomography (CT) or magnetic resonance imaging two-dimensionally (2D). However, it is unclear how these 2D measurements of muscle atrophy and degeneration reflect their three-dimensional (3D) measurements. The purpose of this study is to examine the validity of the 2D evaluation of muscular atrophy and fatty degeneration compared to 3D evaluation. METHODS: The study included computed tomography (CT) images of 20 patients with unilateral hip joint disorders. We manually segmented the edges of 14 muscles around the pelvis and the femur. The correlation coefficient between the 3D and 2D measurements at the anatomic landmarks was calculated. Furthermore, the 2D evaluation was performed at 1 cm interval from each anatomic landmark to determine the cross-section that correlated most strongly with the 3D measurements. A strong correlation in Spearman's rank correlation coefficient (r) was defined as r > 0.7. RESULTS: Nine (64%) and 10 (71%) muscles had a strong correlation coefficient between the 3D and 2D anatomic landmark measurements of muscular atrophy and degeneration, respectively. The maximum distance between the cross-section with the highest correlation coefficient and the anatomical landmark was 11 cm. CONCLUSIONS: More than half of muscles exhibited a strong correlation between the 3D and 2D anatomic landmark measurements of muscular atrophy and degeneration. To improve the correlation coefficient, the position of the cross-section was needed to be changed.

Reproducibility of pelvic sagittal inclination while acquiring radiographs in supine and standing postures.

PURPOSE: Pelvic position on the sagittal plane is usually evaluated with the pelvic sagittal inclination (PSI) angle from a single radiograph. However, the reproducibility of pelvic positioning has not been investigated, and thus, the validity of measuring the PSI from a single film/time point is not understood. Herein, the reproducibility of a patient's pelvic positions in supine and standing postures was analyzed. METHODS: A total of 34 patients who underwent either a pelvic osteotomy or total hip arthroplasty were enrolled in this study. Preoperative radiographs in both supine and standing postures were acquired twice (first X-ray and second X-ray) within 6 months; preoperative computed tomography (CT) images of the full pelvis were also acquired in a supine posture (preop-CT). To eliminate measurement variability, each PSI was automatically measured from radiographs and CT images through the use of CT segmentation and landmark localization followed by intensity-based 2D-3D registration. The absolute difference of PSI among each image was calculated and the intra-class correlation coefficient (ICC) in each posture was also analyzed. RESULTS: The median absolute differences of PSI in the supine posture were 1.3° between the first and second X-rays, 1.2° between the first X-ray and preop-CT, and 1.3° between the second X-ray and preop-CT. The median absolute difference of PSI in the standing posture was 1.5°. The ICC was 0.965 (95% CI: 0.939-0.981) in supine and 0.977 (95% CI: 0.954-0.988) during standing. CONCLUSIONS: Pelvic positions in supine and standing postures are reproducible. Thus, measuring the PSI from a single radiograph is reliable.

Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method.

PURPOSE: Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh. METHOD: We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures. RESULTS: The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm). CONCLUSION: We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.

Can Anatomic Measurements of Stem Anteversion Angle Be Considered as the Functional Anteversion Angle?

BACKGROUND: Stem anteversion angle is important in the combined anteversion theory to avoid implant impingement after total hip arthroplasty (THA). However, anatomic measurements of stem anteversion angle may not represent functional anteversion of the femur if the femur undergoes axial rotation. Herein, the femoral rotational angle (FRA) was measured in supine and standing positions before and after THA to evaluate the difference between anatomic and functional measurements. METHODS: A total of 191 hips (174 patients) treated with THA for osteoarthritis were analyzed in this retrospective, case-controlled study. The FRA was measured as the angle between the posterior condylar line and the line through the bilateral anterior superior iliac spines (positive for external rotation) and was measured preoperatively and postoperatively in supine and standing positions with computed tomography segmentation and landmark localization of the pelvis and the femur followed by intensity-based 2D-3D registration. The number of cases in which the absolute FRA remained <15° in both positions was also calculated. RESULTS: The average ± standard deviation preoperative FRA was 0.3° ± 8.3° in the supine position and -4.5° ± 8.8° during standing; the postoperative FRA was -3.8° ± 9.0° in supine and -14.3° ± 8.3° during standing. There were 134 cases (70%) in which the preoperative absolute FRA remained <15° in both positions while only 85 hips (45%) remained <15°, postoperatively. CONCLUSION: Substantial variability was seen in the FRA, especially during the postoperative period. These results suggest that the anatomic stem anteversion angle may not represent the functional anteversion of the femur.

Change in Pelvic Sagittal Inclination From Supine to Standing Position Before Hip Arthroplasty.

BACKGROUND: Cup anteversion and inclination are important for avoiding implant impingement and dislocation in total hip arthroplasty. However, functional cup anteversion and cup inclination also change as the pelvic sagittal inclination (PSI) changes. Therefore, PSI in both supine and standing positions was measured in a large cohort in this study. METHODS: A total of 422 patients (median age, 61; range, 15-87) who underwent total hip arthroplasty were the subjects of this study. There were 83 patients with primary osteoarthritis (OA), 274 patients with developmental dysplasia-derived secondary OA, 48 patients with osteonecrosis, and 17 patients with rapidly destructive coxopathy (RDC). Preoperative PSI in supine and standing positions was measured by automated computed topography segmentation and landmark localization of the pelvis followed by intensity-based 2D-3D registration, and the number of cases in which PSI changed more than 10° posteriorly was calculated. Hip disease, sex, and age were analyzed if they were related to a PSI change of more than 10°. RESULTS: The median PSI was 5.1° (interquartile range, 0.4°-9.4°) in supine and -1.3° (interquartile range, -6.5° to 4.2°) in standing position. There were 79 cases (19%) in which the PSI changed more than 10° posteriorly from supine to standing. Elder age and patients with primary OA and RDC were revealed to be the related factors. CONCLUSION: PSI changed more than 10° posteriorly from supine to standing in 19% of cases. Age and diagnosis of primary OA and RDC were the significant factors for the posterior rotation.

Prediction of forearm bone shape based on partial least squares regression from partial shape.

BACKGROUND: Computer-assisted corrective osteotomy using a mirror image of the normal contralateral shape as reference is increasingly used. Instead, we propose to use the shape predicted by statistical learning to deal with cases demonstrating bilateral abnormality, such as bilateral trauma, congenital disease, and metabolic disease. METHODS: Computed tomography (CT) scans of 100 normal forearms were used in this study. The whole bone shape was predicted from its partial shape based on statistical learning of the other 99 bones. Accuracy was evaluated by average symmetric surface distance (ASD), and translational and rotational errors. RESULTS: ASDs for predicted shapes were 0.71-1.03 mm. Mean absolute translational and rotational errors were 0.48-1.76 mm and 0.99-6.08°, respectively. CONCLUSION: Normal bone shape was predicted with an acceptable accuracy from its partial shape using statistical learning. Predicted shape can be an alternative to a mirror image, which may enable reduced radiation exposure and examination costs.

Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers.

PURPOSE: Patient-specific musculoskeletal biomechanical simulation is useful in preoperative surgical planning and postoperative assessment in orthopedic surgery and rehabilitation medicine. A difficulty in application of the patient-specific musculoskeletal modeling comes from the fact that the muscle attachment regions are typically invisible in CT and MRI. Our purpose is to develop a method for estimating patient-specific muscle attachment regions from 3D medical images and to validate with cadaver experiments. METHODS: Eight fresh cadaver specimens of the lower extremity were used in the experiments. Before dissection, CT images of all the specimens were acquired and the bone regions in CT images were extracted using an automated segmentation method to reconstruct the bone shape models. During dissection, ten different muscle attachment regions were recorded with an optical motion tracker. Then, these regions obtained from eight cadavers were integrated on an average bone surface via non-rigid registration, and muscle attachment probabilistic atlases (PAs) were constructed. An average muscle attachment region derived from the PA was non-rigidly mapped to the patients bone surface to estimate the patient-specific muscle attachment region. RESULTS: Average Dice similarity coefficient between the true and estimated attachment areas computed by the proposed method was more than 10% higher than the one computed by a previous method in most cases and the average boundary distance error of the proposed method was 1.1 mm smaller than the previous method on average. CONCLUSION: We conducted cadaver experiments to measure the attachment regions of the hip muscles and constructed PAs of the muscle attachment regions. The muscle attachment PA clarified the variations of the location of the muscle attachments and allowed us to estimate the patient-specific attachment area more accurately based on the patient bone shape derived from CT.

Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching.

PURPOSE: Accurate segmentation of the mandibular canal in cone beam CT data is a prerequisite for implant surgical planning. In this article, a new segmentation method based on the combination of anatomical and statistical information is presented to segment mandibular canal in CBCT scans. METHODS: Generally, embedding shape information in segmentation models is challenging. The proposed approach consists of three main steps as follows: At first, a method based on low-rank decomposition is proposed for preprocessing. Then, a conditional statistical shape model is trained, and mandibular bone is segmented with high accuracy. In the final stage, fast marching with a new speed function is utilized to find the optimal path between mandibular and mental foramen. Fast marching tries to find the darkest tunnel close to the initial segmentation of the canal, which was obtained with conditional SSM model. In this regard, localization of mandibular canal is performed more accurately. RESULTS: The method is applied to the identification of mandibular canal in 120 sets of CBCT images. Conditional statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. The capability of the proposed model is evaluated in the segmentation of mandibular bone and canal. The framework is effective in noisy scans and is able to detect canal in cases with mild bone resorption. CONCLUSION: Quantitative analysis of the results shows that the method performed better than two other recent methods in the literature. Experimental results demonstrate that the proposed framework is effective and can be used in computer-guided dental implant surgery.

CT-based automated planning of acetabular cup for total hip arthroplasty (THA) based on hybrid use of two statistical atlases.

PURPOSE: This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness. METHODS: From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon's expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration. RESULTS: The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritis patients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation). CONCLUSION: We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon's preference during cup planning.

Shape-based acetabular cartilage segmentation: application to CT and MRI datasets.

PURPOSE: A new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested. METHODS: The new segmentation method is based on the combination of shape and intensity information. Shape information is acquired according to the predictable nonlinear relationship between the U-shaped acetabulum region and acetabular cartilage. Intensity information is obtained from the acetabular cartilage region automatically to complete the segmentation procedures. This method is evaluated using 54 CT arthrography datasets with two different radiation doses and 20 MRI datasets. Additionally, the performance of this method in identifying acetabular cartilage is compared with four other acetabular cartilage segmentation methods. RESULTS: This method performed better than the comparison methods. Indeed, this method maintained good accuracy level for 74 datasets independent of the cartilage modality and with minimum user interaction in the bone segmentation procedures. In addition, this method was efficient in noisy conditions and in detection of the damaged cartilages with zero thickness, which confirmed its potential clinical usefulness. CONCLUSIONS: Our new method proposes acetabular cartilage segmentation in three different datasets based on the combination of the shape and intensity information. This method executes well in situations where there are clear boundaries between the acetabular and femoral cartilages. However, the acetabular cartilage and pelvic bone information should be obtained from one dataset such as CT arthrography or MRI datasets with leg traction.

Cup Implant Planning Based on 2-D/3-D Radiographic Pelvis Reconstruction-First Clinical Results.

GOAL: In the following, we will present a newly developed X-ray calibration phantom and its integration for 2-D/3-D pelvis reconstruction and subsequent automatic cup planning. Two different planning strategies were applied and evaluated with clinical data. METHODS: Two different cup planning methods were investigated: The first planning strategy is based on a combined pelvis and cup statistical atlas. Thereby, the pelvis part of the combined atlas is matched to the reconstructed pelvis model, resulting in an optimized cup planning. The second planning strategy analyzes the morphology of the reconstructed pelvis model to determine the best fitting cup implant. RESULTS: The first planning strategy was compared to 3-D CT-based planning. Digitally reconstructed radiographs of THA patients with differently severe pathologies were used to evaluate the accuracy of predicting the cup size and position. Within a discrepancy of one cup size, the size was correctly identified in 100% of the cases for Crowe type I datasets and in 77.8% of the cases for Crowe type II, III, and IV datasets. The second planning strategy was analyzed with respect to the eventually implanted cup size. In seven patients, the estimated cup diameter was correct within one cup size, while the estimation for the remaining five patients differed by two cup sizes. CONCLUSION: While both planning strategies showed the same prediction rate with a discrepancy of one cup size (87.5%), the prediction of the exact cup size was increased for the statistical atlas-based strategy (56%) in contrast to the anatomically driven approach (37.5%). SIGNIFICANCE: The proposed approach demonstrated the clinical validity of using 2-D/3-D reconstruction technique for cup planning.

Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas.

PURPOSE: Determination of acetabular cartilage loss in the hip joint is a clinically significant metric that requires image segmentation. A new semiautomatic method to segment acetabular cartilage in computed tomography (CT) arthrography scans was developed and tested. METHODS: A semiautomatic segmentation method was developed based on the combination of anatomical and statistical information. Anatomical information is identified using the pelvic bone position and the contact area between cartilage and bone. Statistical information is acquired from CT intensity modeling of acetabular cartilage and adjacent tissue structures. This method was applied to the identification of acetabular cartilages in 37 intra-articular CT arthrography scans. RESULTS: The semiautomatic anatomical-statistical method performed better than other segmentation methods. The semiautomatic method was effective in noisy scans and was able to detect damaged cartilage. CONCLUSIONS: The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.

Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models.

Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure.

Segmentation of the femur and pelvis from 3D data is prerequisite of patient specific planning and simulation for hip surgery. Separation of the femoral head and acetabulum is one of main difficulties in the diseased hip joint due to deformed shapes and extreme narrowness of the joint space. In this paper, we develop a hierarchical multi-object statistical shape model representing joint structure for automated segmentation of the diseased hip from 3D CT images. In order to represent shape variations as well as pose variations of the femur against the pelvis, both shape and pose variations are embedded in a combined pelvis and femur statistical shape model (SSM). Further, the whole combined SSM is divided into individual pelvis and femur SSMs and a partial combined SSM only including the acetabulum and proximal femur. The partial combined SSM maintains the consistency of the two bones by imposing the constraint that the shapes of the overlapped portions of the individual and partial combined SSMs are identical. The experimental results show that segmentation and separation accuracy of the femur and pelvis was improved using the proposed method compared with independent use of the pelvis and femur SSMs.